K. Bruninx
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1
Network Representation on Hydrogen Production Siting
A controlled comparison of electricity grid and hydrogen network modelling in the North Sea region
The North Sea is central to Europe's offshore wind and hydrogen ambitions, yet studies on where hydrogen production should be sited, onshore or offshore, reach conflicting conclusions. This disagreement is patterned: studies differ simultaneously in system boundary, cost assumptions, spatial resolution, and how they represent the electricity grid and hydrogen network, making it impossible to isolate what the network representation alone contributes to the siting answer. This thesis addresses that gap by holding all other assumptions fixed and varying only the network representation, within the Tulipa Energy Model applied to an integrated electricity and hydrogen system covering eight North Sea countries at NUTS-2 resolution.
Four scenarios cross two representation choices: the electricity grid as a transport model versus DC optimal power flow (DC-OPF), and the hydrogen network with no linepacking versus a four-hour linepacking approximation, for milestone years 2030 and 2040. A two-step design first sizes the onshore network under the transport formulation and holds it fixed across all four scenarios, isolating the effect of representation from differences in network capacity.
Electrolyser investment is predominantly onshore in every scenario, exceeding 80% of capacity throughout, with offshore electrolysis appearing only as a targeted addition at specific hubs, reaching at most 18.3% of the fleet. Moving from the transport model to DC-OPF shifts capacity toward offshore hubs and northern nodes, brings investment forward to 2030, and raises total capacity by roughly 15%. This shift follows directly from locational electricity prices: DC-OPF enforces flow physics that separate the network into distinct price areas, while the transport model prices nearly every node alike. Linepacking, by contrast, changes only how electrolysers operate within the day, leaving siting largely unchanged. Sensitivity analysis confirms the onshore-dominant result is robust to demand and network assumptions, but conditional on the offshore cost premium, the parameter most able to overturn it.
These findings show that the electricity grid representation, not the hydrogen network representation, is the decisive modelling choice for hydrogen siting studies in the North Sea, and that part of the disagreement in the existing literature may stem from this largely untested structural choice rather than from genuine disagreement about the underlying system.
...
Four scenarios cross two representation choices: the electricity grid as a transport model versus DC optimal power flow (DC-OPF), and the hydrogen network with no linepacking versus a four-hour linepacking approximation, for milestone years 2030 and 2040. A two-step design first sizes the onshore network under the transport formulation and holds it fixed across all four scenarios, isolating the effect of representation from differences in network capacity.
Electrolyser investment is predominantly onshore in every scenario, exceeding 80% of capacity throughout, with offshore electrolysis appearing only as a targeted addition at specific hubs, reaching at most 18.3% of the fleet. Moving from the transport model to DC-OPF shifts capacity toward offshore hubs and northern nodes, brings investment forward to 2030, and raises total capacity by roughly 15%. This shift follows directly from locational electricity prices: DC-OPF enforces flow physics that separate the network into distinct price areas, while the transport model prices nearly every node alike. Linepacking, by contrast, changes only how electrolysers operate within the day, leaving siting largely unchanged. Sensitivity analysis confirms the onshore-dominant result is robust to demand and network assumptions, but conditional on the offshore cost premium, the parameter most able to overturn it.
These findings show that the electricity grid representation, not the hydrogen network representation, is the decisive modelling choice for hydrogen siting studies in the North Sea, and that part of the disagreement in the existing literature may stem from this largely untested structural choice rather than from genuine disagreement about the underlying system.
...
The North Sea is central to Europe's offshore wind and hydrogen ambitions, yet studies on where hydrogen production should be sited, onshore or offshore, reach conflicting conclusions. This disagreement is patterned: studies differ simultaneously in system boundary, cost assumptions, spatial resolution, and how they represent the electricity grid and hydrogen network, making it impossible to isolate what the network representation alone contributes to the siting answer. This thesis addresses that gap by holding all other assumptions fixed and varying only the network representation, within the Tulipa Energy Model applied to an integrated electricity and hydrogen system covering eight North Sea countries at NUTS-2 resolution.
Four scenarios cross two representation choices: the electricity grid as a transport model versus DC optimal power flow (DC-OPF), and the hydrogen network with no linepacking versus a four-hour linepacking approximation, for milestone years 2030 and 2040. A two-step design first sizes the onshore network under the transport formulation and holds it fixed across all four scenarios, isolating the effect of representation from differences in network capacity.
Electrolyser investment is predominantly onshore in every scenario, exceeding 80% of capacity throughout, with offshore electrolysis appearing only as a targeted addition at specific hubs, reaching at most 18.3% of the fleet. Moving from the transport model to DC-OPF shifts capacity toward offshore hubs and northern nodes, brings investment forward to 2030, and raises total capacity by roughly 15%. This shift follows directly from locational electricity prices: DC-OPF enforces flow physics that separate the network into distinct price areas, while the transport model prices nearly every node alike. Linepacking, by contrast, changes only how electrolysers operate within the day, leaving siting largely unchanged. Sensitivity analysis confirms the onshore-dominant result is robust to demand and network assumptions, but conditional on the offshore cost premium, the parameter most able to overturn it.
These findings show that the electricity grid representation, not the hydrogen network representation, is the decisive modelling choice for hydrogen siting studies in the North Sea, and that part of the disagreement in the existing literature may stem from this largely untested structural choice rather than from genuine disagreement about the underlying system.
Four scenarios cross two representation choices: the electricity grid as a transport model versus DC optimal power flow (DC-OPF), and the hydrogen network with no linepacking versus a four-hour linepacking approximation, for milestone years 2030 and 2040. A two-step design first sizes the onshore network under the transport formulation and holds it fixed across all four scenarios, isolating the effect of representation from differences in network capacity.
Electrolyser investment is predominantly onshore in every scenario, exceeding 80% of capacity throughout, with offshore electrolysis appearing only as a targeted addition at specific hubs, reaching at most 18.3% of the fleet. Moving from the transport model to DC-OPF shifts capacity toward offshore hubs and northern nodes, brings investment forward to 2030, and raises total capacity by roughly 15%. This shift follows directly from locational electricity prices: DC-OPF enforces flow physics that separate the network into distinct price areas, while the transport model prices nearly every node alike. Linepacking, by contrast, changes only how electrolysers operate within the day, leaving siting largely unchanged. Sensitivity analysis confirms the onshore-dominant result is robust to demand and network assumptions, but conditional on the offshore cost premium, the parameter most able to overturn it.
These findings show that the electricity grid representation, not the hydrogen network representation, is the decisive modelling choice for hydrogen siting studies in the North Sea, and that part of the disagreement in the existing literature may stem from this largely untested structural choice rather than from genuine disagreement about the underlying system.
On the Need for Locational Signals in a Capacity Market
Procurement Costs, Redispatch, and Adequacy Under Network Constraints
The growing share of renewable generation increasingly pushes controllable generators out of the market. At the same time, the need for controllable capacity during periods of low renewable output remains. Capacity mechanisms have therefore become a common policy instrument to secure resource adequacy. However, procuring enough capacity in total does not ensure that it is located where the transmission network can deliver it during scarcity. This is especially relevant in zonal electricity markets, where internal network constraints are ignored during market clearing and corrected afterwards through redispatch. A locational signal in the capacity mechanism could improve the deliverability of procured capacity but may also increase capacity market expenditure. This thesis investigates how locational signals shape the trade-off between capacity procurement costs and congestion outcomes in a zonal electricity market.
The thesis develops a sequential two-stage model consisting of a market-equilibrium stage followed by a cost-based redispatch stage. The first stage combines a zonal energy market with a capacity mechanism, while the second stage restores network feasibility through cost-based redispatch. Because several market-equivalent investment patterns can lead to different congestion outcomes, the analysis does not rely on a single solver-selected equilibrium. Instead, PTDF-based exploration weights are used to identify spatially different siting outcomes with the same first-stage market result. The model is first verified on a small proof-of-concept system and then applied to a 34-node representation of the Dutch transmission network using eight representative days. Two case studies are considered: one in which most controllable capacity must still be built, and one in which the existing generation fleet already covers most of the system-wide requirement.
The results show that the locational signal changes where capacity is built more clearly than how much capacity is procured. In both case studies, zonal procurement redirects investment towards locally deficient areas and improves the ability of the system to restore network feasibility during scarcity. The effect on redispatch volume and cost is less consistent, while grid-related load shedding responds more clearly. The impact on capacity market expenditure depends strongly on the inherited generation fleet. When most capacity is newly built, the increase in expenditure remains limited. When only a small residual deficit remains, the locational design becomes considerably more expensive because procurement must take place in the deficient zone and accepted legacy capacity also receives the zonal clearing price. Cross-zonal participation can reduce these costs, but at the same time weakens the locational signal and increases reliance on the transmission network.
The findings show that aggregate resource adequacy is not sufficient when internal network constraints limit the delivery of procured capacity. Locational capacity procurement can improve deliverability, but it does not remove congestion and should not be treated as a substitute for grid expansion, bidding-zone reform, or other congestion-management measures. The design problem is therefore not only how much capacity should be procured, but how much of it must be local to remain useful during scarcity.
...
The thesis develops a sequential two-stage model consisting of a market-equilibrium stage followed by a cost-based redispatch stage. The first stage combines a zonal energy market with a capacity mechanism, while the second stage restores network feasibility through cost-based redispatch. Because several market-equivalent investment patterns can lead to different congestion outcomes, the analysis does not rely on a single solver-selected equilibrium. Instead, PTDF-based exploration weights are used to identify spatially different siting outcomes with the same first-stage market result. The model is first verified on a small proof-of-concept system and then applied to a 34-node representation of the Dutch transmission network using eight representative days. Two case studies are considered: one in which most controllable capacity must still be built, and one in which the existing generation fleet already covers most of the system-wide requirement.
The results show that the locational signal changes where capacity is built more clearly than how much capacity is procured. In both case studies, zonal procurement redirects investment towards locally deficient areas and improves the ability of the system to restore network feasibility during scarcity. The effect on redispatch volume and cost is less consistent, while grid-related load shedding responds more clearly. The impact on capacity market expenditure depends strongly on the inherited generation fleet. When most capacity is newly built, the increase in expenditure remains limited. When only a small residual deficit remains, the locational design becomes considerably more expensive because procurement must take place in the deficient zone and accepted legacy capacity also receives the zonal clearing price. Cross-zonal participation can reduce these costs, but at the same time weakens the locational signal and increases reliance on the transmission network.
The findings show that aggregate resource adequacy is not sufficient when internal network constraints limit the delivery of procured capacity. Locational capacity procurement can improve deliverability, but it does not remove congestion and should not be treated as a substitute for grid expansion, bidding-zone reform, or other congestion-management measures. The design problem is therefore not only how much capacity should be procured, but how much of it must be local to remain useful during scarcity.
...
The growing share of renewable generation increasingly pushes controllable generators out of the market. At the same time, the need for controllable capacity during periods of low renewable output remains. Capacity mechanisms have therefore become a common policy instrument to secure resource adequacy. However, procuring enough capacity in total does not ensure that it is located where the transmission network can deliver it during scarcity. This is especially relevant in zonal electricity markets, where internal network constraints are ignored during market clearing and corrected afterwards through redispatch. A locational signal in the capacity mechanism could improve the deliverability of procured capacity but may also increase capacity market expenditure. This thesis investigates how locational signals shape the trade-off between capacity procurement costs and congestion outcomes in a zonal electricity market.
The thesis develops a sequential two-stage model consisting of a market-equilibrium stage followed by a cost-based redispatch stage. The first stage combines a zonal energy market with a capacity mechanism, while the second stage restores network feasibility through cost-based redispatch. Because several market-equivalent investment patterns can lead to different congestion outcomes, the analysis does not rely on a single solver-selected equilibrium. Instead, PTDF-based exploration weights are used to identify spatially different siting outcomes with the same first-stage market result. The model is first verified on a small proof-of-concept system and then applied to a 34-node representation of the Dutch transmission network using eight representative days. Two case studies are considered: one in which most controllable capacity must still be built, and one in which the existing generation fleet already covers most of the system-wide requirement.
The results show that the locational signal changes where capacity is built more clearly than how much capacity is procured. In both case studies, zonal procurement redirects investment towards locally deficient areas and improves the ability of the system to restore network feasibility during scarcity. The effect on redispatch volume and cost is less consistent, while grid-related load shedding responds more clearly. The impact on capacity market expenditure depends strongly on the inherited generation fleet. When most capacity is newly built, the increase in expenditure remains limited. When only a small residual deficit remains, the locational design becomes considerably more expensive because procurement must take place in the deficient zone and accepted legacy capacity also receives the zonal clearing price. Cross-zonal participation can reduce these costs, but at the same time weakens the locational signal and increases reliance on the transmission network.
The findings show that aggregate resource adequacy is not sufficient when internal network constraints limit the delivery of procured capacity. Locational capacity procurement can improve deliverability, but it does not remove congestion and should not be treated as a substitute for grid expansion, bidding-zone reform, or other congestion-management measures. The design problem is therefore not only how much capacity should be procured, but how much of it must be local to remain useful during scarcity.
The thesis develops a sequential two-stage model consisting of a market-equilibrium stage followed by a cost-based redispatch stage. The first stage combines a zonal energy market with a capacity mechanism, while the second stage restores network feasibility through cost-based redispatch. Because several market-equivalent investment patterns can lead to different congestion outcomes, the analysis does not rely on a single solver-selected equilibrium. Instead, PTDF-based exploration weights are used to identify spatially different siting outcomes with the same first-stage market result. The model is first verified on a small proof-of-concept system and then applied to a 34-node representation of the Dutch transmission network using eight representative days. Two case studies are considered: one in which most controllable capacity must still be built, and one in which the existing generation fleet already covers most of the system-wide requirement.
The results show that the locational signal changes where capacity is built more clearly than how much capacity is procured. In both case studies, zonal procurement redirects investment towards locally deficient areas and improves the ability of the system to restore network feasibility during scarcity. The effect on redispatch volume and cost is less consistent, while grid-related load shedding responds more clearly. The impact on capacity market expenditure depends strongly on the inherited generation fleet. When most capacity is newly built, the increase in expenditure remains limited. When only a small residual deficit remains, the locational design becomes considerably more expensive because procurement must take place in the deficient zone and accepted legacy capacity also receives the zonal clearing price. Cross-zonal participation can reduce these costs, but at the same time weakens the locational signal and increases reliance on the transmission network.
The findings show that aggregate resource adequacy is not sufficient when internal network constraints limit the delivery of procured capacity. Locational capacity procurement can improve deliverability, but it does not remove congestion and should not be treated as a substitute for grid expansion, bidding-zone reform, or other congestion-management measures. The design problem is therefore not only how much capacity should be procured, but how much of it must be local to remain useful during scarcity.
From Firm to Flexible
A Sector-Comparative Assessment of TDTR Participation for Large Electricity Consumers in the Netherlands
Electricity grid congestion increasingly limits the ability of large electricity consumers to expand, electrify, or connect new assets. While grid reinforcement remains necessary, it is often too slow to resolve local capacity constraints in the short term. This thesis evaluates the time-duration-based transport right (TDTR) as a complementary instrument for using existing grid capacity more efficiently. TDTR provides firm capacity for part of a customer’s demand and conditional capacity for the remaining share, which may be restricted during predefined congestion-related call-hours.
A customer-level techno-economic optimisation model is developed and applied to three flexibility archetypes: logistics with an electric-vehicle fleet, cold storage, and dairy processing with an industrial heat pump. For each case, the model determines the cost-optimal firm-to-TDTR ratio and evaluates both customer-side savings and grid-side effects. Grid impact is assessed through peak reduction, weighted grid-pressure reduction, and changes in consumption during local call-hours. The cases are tested across four substation call-hour profiles to examine when customer incentives and local grid needs align or diverge.
The results show that TDTR can create a positive business case for all three archetypes, but that the magnitude of savings depends primarily on the share of load that can be shifted in time. Logistics achieves the largest savings, followed by cold storage and dairy processing. However, a positive customer business case does not automatically imply local grid relief. TDTR delivers the strongest grid value when congestion call-hours coincide with hours in which the customer already has an economic incentive to reduce demand. When call-hours coincide with low electricity prices, customer cost minimisation may work against the DSO’s congestion objective.
The main conclusion is that TDTR can align customer value and grid value, but only under specific conditions. For DSOs, this implies that TDTR should be targeted, monitored, and adjusted based on customer flexibility, local call-hour profiles, and realised load reduction rather than contracted TDTR share alone. ...
Electricity grid congestion increasingly limits the ability of large electricity consumers to expand, electrify, or connect new assets. While grid reinforcement remains necessary, it is often too slow to resolve local capacity constraints in the short term. This thesis evaluates the time-duration-based transport right (TDTR) as a complementary instrument for using existing grid capacity more efficiently. TDTR provides firm capacity for part of a customer’s demand and conditional capacity for the remaining share, which may be restricted during predefined congestion-related call-hours.
A customer-level techno-economic optimisation model is developed and applied to three flexibility archetypes: logistics with an electric-vehicle fleet, cold storage, and dairy processing with an industrial heat pump. For each case, the model determines the cost-optimal firm-to-TDTR ratio and evaluates both customer-side savings and grid-side effects. Grid impact is assessed through peak reduction, weighted grid-pressure reduction, and changes in consumption during local call-hours. The cases are tested across four substation call-hour profiles to examine when customer incentives and local grid needs align or diverge.
The results show that TDTR can create a positive business case for all three archetypes, but that the magnitude of savings depends primarily on the share of load that can be shifted in time. Logistics achieves the largest savings, followed by cold storage and dairy processing. However, a positive customer business case does not automatically imply local grid relief. TDTR delivers the strongest grid value when congestion call-hours coincide with hours in which the customer already has an economic incentive to reduce demand. When call-hours coincide with low electricity prices, customer cost minimisation may work against the DSO’s congestion objective.
The main conclusion is that TDTR can align customer value and grid value, but only under specific conditions. For DSOs, this implies that TDTR should be targeted, monitored, and adjusted based on customer flexibility, local call-hour profiles, and realised load reduction rather than contracted TDTR share alone.
The increasing penetration of renewable energy sources makes power system expansion planning with storage strongly dependent on chronological operational conditions. Time-series aggregation (TSA) can reduce the computational burden of full-year planning models, but it distorts the temporal information that determines storage operation and investment decisions. This thesis investigates how SOC-based diagnostic information can improve representative-day-based expansion planning for storage-embedded power systems. The planning model jointly considers transmission expansion, wind investment, and energy storage sizing. Representative days are selected through hierarchical clustering with preserved extreme days, while sequentially linked days are used to reconstruct inter-day chronology. The framework aims to find investment decisions that achieve lower total cost under full chronological evaluation.
Reduced models with different numbers of representative days are evaluated, and their reconstructed SOC trajectories are analyzed. SOC-based diagnostic metrics are developed to characterize the components of the trajectory gap. These metrics are used as diagnostic signals for identifying where the current temporal representation is insufficient for storage-related operation.
The results show that daily-cycling storage is mainly affected by intra-day shape mismatch, while long-duration storage is more sensitive to accumulated drift and inventory-level bias. Natural days with large intra-day shape mismatch are useful feedback candidates because they reveal inadequacy in the current representative-day set for describing storage charging and discharging patterns. Preserving these day-shape-critical days improves storage-relevant temporal representation and can lead to better investment decisions. ...
Reduced models with different numbers of representative days are evaluated, and their reconstructed SOC trajectories are analyzed. SOC-based diagnostic metrics are developed to characterize the components of the trajectory gap. These metrics are used as diagnostic signals for identifying where the current temporal representation is insufficient for storage-related operation.
The results show that daily-cycling storage is mainly affected by intra-day shape mismatch, while long-duration storage is more sensitive to accumulated drift and inventory-level bias. Natural days with large intra-day shape mismatch are useful feedback candidates because they reveal inadequacy in the current representative-day set for describing storage charging and discharging patterns. Preserving these day-shape-critical days improves storage-relevant temporal representation and can lead to better investment decisions. ...
The increasing penetration of renewable energy sources makes power system expansion planning with storage strongly dependent on chronological operational conditions. Time-series aggregation (TSA) can reduce the computational burden of full-year planning models, but it distorts the temporal information that determines storage operation and investment decisions. This thesis investigates how SOC-based diagnostic information can improve representative-day-based expansion planning for storage-embedded power systems. The planning model jointly considers transmission expansion, wind investment, and energy storage sizing. Representative days are selected through hierarchical clustering with preserved extreme days, while sequentially linked days are used to reconstruct inter-day chronology. The framework aims to find investment decisions that achieve lower total cost under full chronological evaluation.
Reduced models with different numbers of representative days are evaluated, and their reconstructed SOC trajectories are analyzed. SOC-based diagnostic metrics are developed to characterize the components of the trajectory gap. These metrics are used as diagnostic signals for identifying where the current temporal representation is insufficient for storage-related operation.
The results show that daily-cycling storage is mainly affected by intra-day shape mismatch, while long-duration storage is more sensitive to accumulated drift and inventory-level bias. Natural days with large intra-day shape mismatch are useful feedback candidates because they reveal inadequacy in the current representative-day set for describing storage charging and discharging patterns. Preserving these day-shape-critical days improves storage-relevant temporal representation and can lead to better investment decisions.
Reduced models with different numbers of representative days are evaluated, and their reconstructed SOC trajectories are analyzed. SOC-based diagnostic metrics are developed to characterize the components of the trajectory gap. These metrics are used as diagnostic signals for identifying where the current temporal representation is insufficient for storage-related operation.
The results show that daily-cycling storage is mainly affected by intra-day shape mismatch, while long-duration storage is more sensitive to accumulated drift and inventory-level bias. Natural days with large intra-day shape mismatch are useful feedback candidates because they reveal inadequacy in the current representative-day set for describing storage charging and discharging patterns. Preserving these day-shape-critical days improves storage-relevant temporal representation and can lead to better investment decisions.
Master thesis
(2025)
-
A.A. Mulder, Simon H. Tindemans, Irina Oleinikova, Shiromani Goerdin, K. Bruninx
Due to the rapid electrification, congestion has become a critical challenge in modern power systems, particularly in the Dutch grid. Capacity limitations are negatively affecting existing and new grid connections. Flexibility, represented by the ability to adjust power injections and withdrawals, offers a potential solution.
This thesis investigated the optimal distribution of congestion-relieving power injections in terms of time, location and quantity for energy system planning purposes. A methodology was developed to assess congestion and determine optimal power injection locations using a DC Security-Constrained Optimal Power Flow (SC-OPF) formulation. Where congestion was defined as a state in which an increase in power flow on one or more network branches would lead to the violation of operational security limits. The approach was based on Power Transfer Distribution Factors (PTDFs) and Line Outage Distribution Factors (LODFs) to model the effects of power injections, which represent flexibility. A 73-bus system was used to test with a year of hourly timesteps, considering both N-0 and N-1 security constraints.
According to the results, buses close to overloaded lines and at the end of radial lines are optimal to manage the congestion under N-1 constraints. The radial lines appear to be optimal due to the reduction of system losses. Under N-0 constraints, only buses close to overloaded lines were found to be optimal. The system experienced overloading for 67.4\% of the hours and needed 2,995,225 MWh to manage the congestion under N-1. By contrast, under N-0 constraints, overloading occurred in only 17.1% of the hours, requiring 279,264 MWh. Limiting optimal injection locations under N-0 increased energy needs by 58.2%, while N-1 constraints allowed multiple near-optimal solutions. Scenario comparisons also indicated that increasing branch limits reduced the required corrective energy, whereas imposing additional constraints on injections increased it.
These findings provide insights into the role of flexibility in congestion management for system planning and emphasize the location sensitivity of optimal solutions under different constraints. Future work should explore the sensitivity, uniqueness, and feasibility of solutions, as well as apply the methodology to real system data and location-dependent costs to enhance practical applicability. ...
This thesis investigated the optimal distribution of congestion-relieving power injections in terms of time, location and quantity for energy system planning purposes. A methodology was developed to assess congestion and determine optimal power injection locations using a DC Security-Constrained Optimal Power Flow (SC-OPF) formulation. Where congestion was defined as a state in which an increase in power flow on one or more network branches would lead to the violation of operational security limits. The approach was based on Power Transfer Distribution Factors (PTDFs) and Line Outage Distribution Factors (LODFs) to model the effects of power injections, which represent flexibility. A 73-bus system was used to test with a year of hourly timesteps, considering both N-0 and N-1 security constraints.
According to the results, buses close to overloaded lines and at the end of radial lines are optimal to manage the congestion under N-1 constraints. The radial lines appear to be optimal due to the reduction of system losses. Under N-0 constraints, only buses close to overloaded lines were found to be optimal. The system experienced overloading for 67.4\% of the hours and needed 2,995,225 MWh to manage the congestion under N-1. By contrast, under N-0 constraints, overloading occurred in only 17.1% of the hours, requiring 279,264 MWh. Limiting optimal injection locations under N-0 increased energy needs by 58.2%, while N-1 constraints allowed multiple near-optimal solutions. Scenario comparisons also indicated that increasing branch limits reduced the required corrective energy, whereas imposing additional constraints on injections increased it.
These findings provide insights into the role of flexibility in congestion management for system planning and emphasize the location sensitivity of optimal solutions under different constraints. Future work should explore the sensitivity, uniqueness, and feasibility of solutions, as well as apply the methodology to real system data and location-dependent costs to enhance practical applicability. ...
Due to the rapid electrification, congestion has become a critical challenge in modern power systems, particularly in the Dutch grid. Capacity limitations are negatively affecting existing and new grid connections. Flexibility, represented by the ability to adjust power injections and withdrawals, offers a potential solution.
This thesis investigated the optimal distribution of congestion-relieving power injections in terms of time, location and quantity for energy system planning purposes. A methodology was developed to assess congestion and determine optimal power injection locations using a DC Security-Constrained Optimal Power Flow (SC-OPF) formulation. Where congestion was defined as a state in which an increase in power flow on one or more network branches would lead to the violation of operational security limits. The approach was based on Power Transfer Distribution Factors (PTDFs) and Line Outage Distribution Factors (LODFs) to model the effects of power injections, which represent flexibility. A 73-bus system was used to test with a year of hourly timesteps, considering both N-0 and N-1 security constraints.
According to the results, buses close to overloaded lines and at the end of radial lines are optimal to manage the congestion under N-1 constraints. The radial lines appear to be optimal due to the reduction of system losses. Under N-0 constraints, only buses close to overloaded lines were found to be optimal. The system experienced overloading for 67.4\% of the hours and needed 2,995,225 MWh to manage the congestion under N-1. By contrast, under N-0 constraints, overloading occurred in only 17.1% of the hours, requiring 279,264 MWh. Limiting optimal injection locations under N-0 increased energy needs by 58.2%, while N-1 constraints allowed multiple near-optimal solutions. Scenario comparisons also indicated that increasing branch limits reduced the required corrective energy, whereas imposing additional constraints on injections increased it.
These findings provide insights into the role of flexibility in congestion management for system planning and emphasize the location sensitivity of optimal solutions under different constraints. Future work should explore the sensitivity, uniqueness, and feasibility of solutions, as well as apply the methodology to real system data and location-dependent costs to enhance practical applicability.
This thesis investigated the optimal distribution of congestion-relieving power injections in terms of time, location and quantity for energy system planning purposes. A methodology was developed to assess congestion and determine optimal power injection locations using a DC Security-Constrained Optimal Power Flow (SC-OPF) formulation. Where congestion was defined as a state in which an increase in power flow on one or more network branches would lead to the violation of operational security limits. The approach was based on Power Transfer Distribution Factors (PTDFs) and Line Outage Distribution Factors (LODFs) to model the effects of power injections, which represent flexibility. A 73-bus system was used to test with a year of hourly timesteps, considering both N-0 and N-1 security constraints.
According to the results, buses close to overloaded lines and at the end of radial lines are optimal to manage the congestion under N-1 constraints. The radial lines appear to be optimal due to the reduction of system losses. Under N-0 constraints, only buses close to overloaded lines were found to be optimal. The system experienced overloading for 67.4\% of the hours and needed 2,995,225 MWh to manage the congestion under N-1. By contrast, under N-0 constraints, overloading occurred in only 17.1% of the hours, requiring 279,264 MWh. Limiting optimal injection locations under N-0 increased energy needs by 58.2%, while N-1 constraints allowed multiple near-optimal solutions. Scenario comparisons also indicated that increasing branch limits reduced the required corrective energy, whereas imposing additional constraints on injections increased it.
These findings provide insights into the role of flexibility in congestion management for system planning and emphasize the location sensitivity of optimal solutions under different constraints. Future work should explore the sensitivity, uniqueness, and feasibility of solutions, as well as apply the methodology to real system data and location-dependent costs to enhance practical applicability.
Dispatch Optimization of Hybrid Power Plants for Short-Term Electricity Markets
A comparative analysis between stand-alone and co-located assets
The transition to a sustainable energy system has accelerated the integration of renewable energy sources (RES), such as wind and solar power, into electricity markets and infrastructures. While these sources contribute significantly to the decarbonization of the energy sector, their inherent variability and limited predictability introduce operational challenges for maintaining system balance and ensuring cost-effective grid utilization. Additionally, the geographic distribution of RES, often located in remote areas, has exacerbated issues of grid congestion, leading to limitations in grid access, rising curtailment levels, and deferred renewable projects. In response to these challenges, Hybrid Power Plants (HPPs), which co-locate RES with Battery Energy Storage Systems (BESS), have emerged as a viable solution offering increased operational flexibility, improved forecast error mitigation, and enhanced economic performance, while utilizing one grid connection point.
This study evaluates the added economic and energetic value of co-locating a BESS with a RES under the Dutch electricity market structure. A multi-stage stochastic optimization framework is developed to simulate HPP participation across the day-ahead, intraday, and imbalance markets. The model incorporates power forecast uncertainty through scenario generation and reduction techniques, and captures the physical constraints of energy systems, including battery operations and capacity, RES capacity, and restricted grid connection capacity. Optimization is conducted on a rolling horizon to reflect the sequential nature of market decision-making.
Three configurations are analyzed: a standalone RES system, a standalone BESS, and a co-located HPP. These systems are evaluated based on simulated operations over four weeks each representing a season using real market prices and wind power data. The co-located HPP demonstrates superior performance in terms of both economic return and renewable energy utilization. By enabling time-shifting of generation, reducing curtailment, and participating more effectively in short-term markets, the HPP captures additional value that standalone systems cannot access. Moreover, the ability to operate flexibly within a fixed grid export limit allows the HPP to relieve grid congestion, using storage to shift energy dispatch in line with the needs of the electricity system, indicated by price signals, thereby reducing strain on network infrastructure.
A comprehensive analysis investigates the impact of key assumptions regarding price and power forecasts and operational parameters, such as battery size, technology characteristics, grid connection capacity, and reoptimization frequency. Results indicate that system performance is highly dependent on the quality of imbalance price forecasts and the ability to respond dynamically to power forecast updates and market prices. This has been demonstrated by a comparison between no foresight, perfect foresight and using the day-ahead clearing price as imbalance forecast. Moreover, allowing the HPP to withdraw energy from the grid, results in signficant economic gains, however, this comes at the cost of renewable energy utilization. Although the study excludes capital and degradation costs, the findings underscore the operational advantages of co-locating RES and BESS under uncertainty and grid limitations.
Overall, this research contributes to a deeper understanding of how flexible, market-responsive HPPs can support the transition to a resilient and economically efficient low-carbon power system. It highlights the importance of integrated modeling approaches for optimizing renewable dispatch strategies in evolving electricity markets, while also demonstrating how HPPs can contribute to better utilizing grid connection capacity and enabling greater renewable integration.
...
This study evaluates the added economic and energetic value of co-locating a BESS with a RES under the Dutch electricity market structure. A multi-stage stochastic optimization framework is developed to simulate HPP participation across the day-ahead, intraday, and imbalance markets. The model incorporates power forecast uncertainty through scenario generation and reduction techniques, and captures the physical constraints of energy systems, including battery operations and capacity, RES capacity, and restricted grid connection capacity. Optimization is conducted on a rolling horizon to reflect the sequential nature of market decision-making.
Three configurations are analyzed: a standalone RES system, a standalone BESS, and a co-located HPP. These systems are evaluated based on simulated operations over four weeks each representing a season using real market prices and wind power data. The co-located HPP demonstrates superior performance in terms of both economic return and renewable energy utilization. By enabling time-shifting of generation, reducing curtailment, and participating more effectively in short-term markets, the HPP captures additional value that standalone systems cannot access. Moreover, the ability to operate flexibly within a fixed grid export limit allows the HPP to relieve grid congestion, using storage to shift energy dispatch in line with the needs of the electricity system, indicated by price signals, thereby reducing strain on network infrastructure.
A comprehensive analysis investigates the impact of key assumptions regarding price and power forecasts and operational parameters, such as battery size, technology characteristics, grid connection capacity, and reoptimization frequency. Results indicate that system performance is highly dependent on the quality of imbalance price forecasts and the ability to respond dynamically to power forecast updates and market prices. This has been demonstrated by a comparison between no foresight, perfect foresight and using the day-ahead clearing price as imbalance forecast. Moreover, allowing the HPP to withdraw energy from the grid, results in signficant economic gains, however, this comes at the cost of renewable energy utilization. Although the study excludes capital and degradation costs, the findings underscore the operational advantages of co-locating RES and BESS under uncertainty and grid limitations.
Overall, this research contributes to a deeper understanding of how flexible, market-responsive HPPs can support the transition to a resilient and economically efficient low-carbon power system. It highlights the importance of integrated modeling approaches for optimizing renewable dispatch strategies in evolving electricity markets, while also demonstrating how HPPs can contribute to better utilizing grid connection capacity and enabling greater renewable integration.
...
The transition to a sustainable energy system has accelerated the integration of renewable energy sources (RES), such as wind and solar power, into electricity markets and infrastructures. While these sources contribute significantly to the decarbonization of the energy sector, their inherent variability and limited predictability introduce operational challenges for maintaining system balance and ensuring cost-effective grid utilization. Additionally, the geographic distribution of RES, often located in remote areas, has exacerbated issues of grid congestion, leading to limitations in grid access, rising curtailment levels, and deferred renewable projects. In response to these challenges, Hybrid Power Plants (HPPs), which co-locate RES with Battery Energy Storage Systems (BESS), have emerged as a viable solution offering increased operational flexibility, improved forecast error mitigation, and enhanced economic performance, while utilizing one grid connection point.
This study evaluates the added economic and energetic value of co-locating a BESS with a RES under the Dutch electricity market structure. A multi-stage stochastic optimization framework is developed to simulate HPP participation across the day-ahead, intraday, and imbalance markets. The model incorporates power forecast uncertainty through scenario generation and reduction techniques, and captures the physical constraints of energy systems, including battery operations and capacity, RES capacity, and restricted grid connection capacity. Optimization is conducted on a rolling horizon to reflect the sequential nature of market decision-making.
Three configurations are analyzed: a standalone RES system, a standalone BESS, and a co-located HPP. These systems are evaluated based on simulated operations over four weeks each representing a season using real market prices and wind power data. The co-located HPP demonstrates superior performance in terms of both economic return and renewable energy utilization. By enabling time-shifting of generation, reducing curtailment, and participating more effectively in short-term markets, the HPP captures additional value that standalone systems cannot access. Moreover, the ability to operate flexibly within a fixed grid export limit allows the HPP to relieve grid congestion, using storage to shift energy dispatch in line with the needs of the electricity system, indicated by price signals, thereby reducing strain on network infrastructure.
A comprehensive analysis investigates the impact of key assumptions regarding price and power forecasts and operational parameters, such as battery size, technology characteristics, grid connection capacity, and reoptimization frequency. Results indicate that system performance is highly dependent on the quality of imbalance price forecasts and the ability to respond dynamically to power forecast updates and market prices. This has been demonstrated by a comparison between no foresight, perfect foresight and using the day-ahead clearing price as imbalance forecast. Moreover, allowing the HPP to withdraw energy from the grid, results in signficant economic gains, however, this comes at the cost of renewable energy utilization. Although the study excludes capital and degradation costs, the findings underscore the operational advantages of co-locating RES and BESS under uncertainty and grid limitations.
Overall, this research contributes to a deeper understanding of how flexible, market-responsive HPPs can support the transition to a resilient and economically efficient low-carbon power system. It highlights the importance of integrated modeling approaches for optimizing renewable dispatch strategies in evolving electricity markets, while also demonstrating how HPPs can contribute to better utilizing grid connection capacity and enabling greater renewable integration.
This study evaluates the added economic and energetic value of co-locating a BESS with a RES under the Dutch electricity market structure. A multi-stage stochastic optimization framework is developed to simulate HPP participation across the day-ahead, intraday, and imbalance markets. The model incorporates power forecast uncertainty through scenario generation and reduction techniques, and captures the physical constraints of energy systems, including battery operations and capacity, RES capacity, and restricted grid connection capacity. Optimization is conducted on a rolling horizon to reflect the sequential nature of market decision-making.
Three configurations are analyzed: a standalone RES system, a standalone BESS, and a co-located HPP. These systems are evaluated based on simulated operations over four weeks each representing a season using real market prices and wind power data. The co-located HPP demonstrates superior performance in terms of both economic return and renewable energy utilization. By enabling time-shifting of generation, reducing curtailment, and participating more effectively in short-term markets, the HPP captures additional value that standalone systems cannot access. Moreover, the ability to operate flexibly within a fixed grid export limit allows the HPP to relieve grid congestion, using storage to shift energy dispatch in line with the needs of the electricity system, indicated by price signals, thereby reducing strain on network infrastructure.
A comprehensive analysis investigates the impact of key assumptions regarding price and power forecasts and operational parameters, such as battery size, technology characteristics, grid connection capacity, and reoptimization frequency. Results indicate that system performance is highly dependent on the quality of imbalance price forecasts and the ability to respond dynamically to power forecast updates and market prices. This has been demonstrated by a comparison between no foresight, perfect foresight and using the day-ahead clearing price as imbalance forecast. Moreover, allowing the HPP to withdraw energy from the grid, results in signficant economic gains, however, this comes at the cost of renewable energy utilization. Although the study excludes capital and degradation costs, the findings underscore the operational advantages of co-locating RES and BESS under uncertainty and grid limitations.
Overall, this research contributes to a deeper understanding of how flexible, market-responsive HPPs can support the transition to a resilient and economically efficient low-carbon power system. It highlights the importance of integrated modeling approaches for optimizing renewable dispatch strategies in evolving electricity markets, while also demonstrating how HPPs can contribute to better utilizing grid connection capacity and enabling greater renewable integration.
Battery Sizing for PV-based Hybrid Parks in the Netherlands
A Multi-Stage Stochastic Optimisation Approach
As grid congestion continues to limit the expansion of renewable energy in the Netherlands, co-locating battery storage systems with existing PV farms is gaining attention as a practical and cost-effective solution. These hybrid parks make better use of existing grid connections and enhance system flexibility. However, the capital intensity of batteries, combined with grid constraints and associated fees, makes it challenging to determine the optimal battery size within a hybrid park.
The aim of this thesis is to determine the optimal battery size for a co-located PV and storage system behind a shared grid connection, referred to as a hybrid park, in a computationally efficient manner. A multi-stage stochastic optimisation model is developed to maximise the Net Present Value of the hybrid park. The model includes participation on the Day-Ahead, Frequency Containment Reserve and Intraday markets. To manage complexity, representative days are carefully selected using k-medoids clustering and uncertainty in solar generation and Intraday prices is introduced through scenario sampling.
Results show that the hybrid park improves financial performance compared to stand-alone configurations. This advantage is primarily driven by cost savings from shared infrastructure and reduced grid fees. Battery sizing is strongly influenced by the choice of market participation, the presence of grid fees and the level of uncertainty included in the model. FCR participation is the main driver for the required power capacity, while energy capacity is primarily shaped by participation in the Intraday market. Including uncertainty in solar generation and Intraday prices typically leads to larger energy capacity, enabling the battery to respond more effectively to variability. Grid fees, modelled as peak offtake charges, have a notable impact by discouraging high grid offtake and reducing the need for large offtake grid connections. Allowing combinations of multiple C-rates adds design flexibility but also increases model complexity, which can affect convergence and runtime.
In conclusion, this study shows that optimal sizing depends on the strategy of the hybrid park, including market participation and regulatory context. The developed model provides a practical tool for optimising battery size within a reasonable computation time. Future research could focus on improving the representation of uncertainty and market dynamics to better reflect real-world operation and enable more robust sizing decisions. ...
The aim of this thesis is to determine the optimal battery size for a co-located PV and storage system behind a shared grid connection, referred to as a hybrid park, in a computationally efficient manner. A multi-stage stochastic optimisation model is developed to maximise the Net Present Value of the hybrid park. The model includes participation on the Day-Ahead, Frequency Containment Reserve and Intraday markets. To manage complexity, representative days are carefully selected using k-medoids clustering and uncertainty in solar generation and Intraday prices is introduced through scenario sampling.
Results show that the hybrid park improves financial performance compared to stand-alone configurations. This advantage is primarily driven by cost savings from shared infrastructure and reduced grid fees. Battery sizing is strongly influenced by the choice of market participation, the presence of grid fees and the level of uncertainty included in the model. FCR participation is the main driver for the required power capacity, while energy capacity is primarily shaped by participation in the Intraday market. Including uncertainty in solar generation and Intraday prices typically leads to larger energy capacity, enabling the battery to respond more effectively to variability. Grid fees, modelled as peak offtake charges, have a notable impact by discouraging high grid offtake and reducing the need for large offtake grid connections. Allowing combinations of multiple C-rates adds design flexibility but also increases model complexity, which can affect convergence and runtime.
In conclusion, this study shows that optimal sizing depends on the strategy of the hybrid park, including market participation and regulatory context. The developed model provides a practical tool for optimising battery size within a reasonable computation time. Future research could focus on improving the representation of uncertainty and market dynamics to better reflect real-world operation and enable more robust sizing decisions. ...
As grid congestion continues to limit the expansion of renewable energy in the Netherlands, co-locating battery storage systems with existing PV farms is gaining attention as a practical and cost-effective solution. These hybrid parks make better use of existing grid connections and enhance system flexibility. However, the capital intensity of batteries, combined with grid constraints and associated fees, makes it challenging to determine the optimal battery size within a hybrid park.
The aim of this thesis is to determine the optimal battery size for a co-located PV and storage system behind a shared grid connection, referred to as a hybrid park, in a computationally efficient manner. A multi-stage stochastic optimisation model is developed to maximise the Net Present Value of the hybrid park. The model includes participation on the Day-Ahead, Frequency Containment Reserve and Intraday markets. To manage complexity, representative days are carefully selected using k-medoids clustering and uncertainty in solar generation and Intraday prices is introduced through scenario sampling.
Results show that the hybrid park improves financial performance compared to stand-alone configurations. This advantage is primarily driven by cost savings from shared infrastructure and reduced grid fees. Battery sizing is strongly influenced by the choice of market participation, the presence of grid fees and the level of uncertainty included in the model. FCR participation is the main driver for the required power capacity, while energy capacity is primarily shaped by participation in the Intraday market. Including uncertainty in solar generation and Intraday prices typically leads to larger energy capacity, enabling the battery to respond more effectively to variability. Grid fees, modelled as peak offtake charges, have a notable impact by discouraging high grid offtake and reducing the need for large offtake grid connections. Allowing combinations of multiple C-rates adds design flexibility but also increases model complexity, which can affect convergence and runtime.
In conclusion, this study shows that optimal sizing depends on the strategy of the hybrid park, including market participation and regulatory context. The developed model provides a practical tool for optimising battery size within a reasonable computation time. Future research could focus on improving the representation of uncertainty and market dynamics to better reflect real-world operation and enable more robust sizing decisions.
The aim of this thesis is to determine the optimal battery size for a co-located PV and storage system behind a shared grid connection, referred to as a hybrid park, in a computationally efficient manner. A multi-stage stochastic optimisation model is developed to maximise the Net Present Value of the hybrid park. The model includes participation on the Day-Ahead, Frequency Containment Reserve and Intraday markets. To manage complexity, representative days are carefully selected using k-medoids clustering and uncertainty in solar generation and Intraday prices is introduced through scenario sampling.
Results show that the hybrid park improves financial performance compared to stand-alone configurations. This advantage is primarily driven by cost savings from shared infrastructure and reduced grid fees. Battery sizing is strongly influenced by the choice of market participation, the presence of grid fees and the level of uncertainty included in the model. FCR participation is the main driver for the required power capacity, while energy capacity is primarily shaped by participation in the Intraday market. Including uncertainty in solar generation and Intraday prices typically leads to larger energy capacity, enabling the battery to respond more effectively to variability. Grid fees, modelled as peak offtake charges, have a notable impact by discouraging high grid offtake and reducing the need for large offtake grid connections. Allowing combinations of multiple C-rates adds design flexibility but also increases model complexity, which can affect convergence and runtime.
In conclusion, this study shows that optimal sizing depends on the strategy of the hybrid park, including market participation and regulatory context. The developed model provides a practical tool for optimising battery size within a reasonable computation time. Future research could focus on improving the representation of uncertainty and market dynamics to better reflect real-world operation and enable more robust sizing decisions.
Exploring the Role of Long Duration Electricity Storage in Ensuring Adequacy
Amid High Renewable Penetration and Future Climate Uncertainties
In the context of increasing renewable energy integration and the push for decarbonization, this research addresses the need for long-duration electricity storage to ensure the reliability and adequacy of the future European power system. The variability of renewable energy sources such as wind and solar, coupled with the challenges posed by climate change, requires advanced storage solutions to balance periods of low energy generation and high demand.
This thesis explores how long-duration energy storage (LDES) and short-duration energy storage (SDES) can effectively contribute to resource adequacy by assessing various operational strategies, storage capacities, and system conditions. Using the Calliope energy system modeling framework, the research simulates a future interconnected European grid in 2050, considering different weather conditions and storage configurations to evaluate metrics such as Expected Energy Not Served and Loss of Load Expectation.
The results demonstrate that increasing the energy storage capacity and discharge duration of LDES significantly reduces LOLE, particularly during extended periods of low renewable output. However, SDES plays a complementary role, addressing short-term reliability challenges by reducing EENS during shorter disruption events. This combination highlights the need for a balanced deployment of both SDES and LDES to optimize grid performance and ensure resource adequacy. Through these findings, the thesis contributes valuable insights for energy planners and policymakers, aiming to foster a resilient, low-carbon electricity grid capable of meeting the demands of a renewable-dominated future. ...
This thesis explores how long-duration energy storage (LDES) and short-duration energy storage (SDES) can effectively contribute to resource adequacy by assessing various operational strategies, storage capacities, and system conditions. Using the Calliope energy system modeling framework, the research simulates a future interconnected European grid in 2050, considering different weather conditions and storage configurations to evaluate metrics such as Expected Energy Not Served and Loss of Load Expectation.
The results demonstrate that increasing the energy storage capacity and discharge duration of LDES significantly reduces LOLE, particularly during extended periods of low renewable output. However, SDES plays a complementary role, addressing short-term reliability challenges by reducing EENS during shorter disruption events. This combination highlights the need for a balanced deployment of both SDES and LDES to optimize grid performance and ensure resource adequacy. Through these findings, the thesis contributes valuable insights for energy planners and policymakers, aiming to foster a resilient, low-carbon electricity grid capable of meeting the demands of a renewable-dominated future. ...
In the context of increasing renewable energy integration and the push for decarbonization, this research addresses the need for long-duration electricity storage to ensure the reliability and adequacy of the future European power system. The variability of renewable energy sources such as wind and solar, coupled with the challenges posed by climate change, requires advanced storage solutions to balance periods of low energy generation and high demand.
This thesis explores how long-duration energy storage (LDES) and short-duration energy storage (SDES) can effectively contribute to resource adequacy by assessing various operational strategies, storage capacities, and system conditions. Using the Calliope energy system modeling framework, the research simulates a future interconnected European grid in 2050, considering different weather conditions and storage configurations to evaluate metrics such as Expected Energy Not Served and Loss of Load Expectation.
The results demonstrate that increasing the energy storage capacity and discharge duration of LDES significantly reduces LOLE, particularly during extended periods of low renewable output. However, SDES plays a complementary role, addressing short-term reliability challenges by reducing EENS during shorter disruption events. This combination highlights the need for a balanced deployment of both SDES and LDES to optimize grid performance and ensure resource adequacy. Through these findings, the thesis contributes valuable insights for energy planners and policymakers, aiming to foster a resilient, low-carbon electricity grid capable of meeting the demands of a renewable-dominated future.
This thesis explores how long-duration energy storage (LDES) and short-duration energy storage (SDES) can effectively contribute to resource adequacy by assessing various operational strategies, storage capacities, and system conditions. Using the Calliope energy system modeling framework, the research simulates a future interconnected European grid in 2050, considering different weather conditions and storage configurations to evaluate metrics such as Expected Energy Not Served and Loss of Load Expectation.
The results demonstrate that increasing the energy storage capacity and discharge duration of LDES significantly reduces LOLE, particularly during extended periods of low renewable output. However, SDES plays a complementary role, addressing short-term reliability challenges by reducing EENS during shorter disruption events. This combination highlights the need for a balanced deployment of both SDES and LDES to optimize grid performance and ensure resource adequacy. Through these findings, the thesis contributes valuable insights for energy planners and policymakers, aiming to foster a resilient, low-carbon electricity grid capable of meeting the demands of a renewable-dominated future.
Master thesis
(2024)
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M. van Dalsum, P.P. Vergara Barrios, W. Zomerdijk, K. Bruninx, J.L. Rueda Torres, A. van de Schootbrugge, I. Papaioannou
With the energy transition already underway in the Netherlands, one of the largest obstacles currently facing this process is the widespread grid congestion.
To deal with this grid congestion, many different flexibility solutions have been proposed. One of these solutions is the use of non-firm grid connections, which dynamically allocate grid capacity to connected parties based on the free space available on their specific part of the grid.
A key design parameter of this flexibility options is the manner in which the available capacity is allocated across the non-firm connections.
In this work, a comparison is made between five methodologies for allocating capacity among non-firm grid connections: a contract age based methodology (and a variation upon it which also takes into account the location of a connection in the grid), a rotating priority list, a pure mathematical optimisation of total allocated capacity as well as a proportional allocation methodology which tries to allocate equally between loads.
To compare these different methodologies, a set of metrics is devised, which attempt to capture the three key dimensions of relevance in this space, each representing a stakeholder: the customer/connection side, the grid operator side and the societal/wider grid user side.
It is found from this comparison across these metrics that each of the methodologies excels at different metrics, making each of them viable in their own respect. ...
To deal with this grid congestion, many different flexibility solutions have been proposed. One of these solutions is the use of non-firm grid connections, which dynamically allocate grid capacity to connected parties based on the free space available on their specific part of the grid.
A key design parameter of this flexibility options is the manner in which the available capacity is allocated across the non-firm connections.
In this work, a comparison is made between five methodologies for allocating capacity among non-firm grid connections: a contract age based methodology (and a variation upon it which also takes into account the location of a connection in the grid), a rotating priority list, a pure mathematical optimisation of total allocated capacity as well as a proportional allocation methodology which tries to allocate equally between loads.
To compare these different methodologies, a set of metrics is devised, which attempt to capture the three key dimensions of relevance in this space, each representing a stakeholder: the customer/connection side, the grid operator side and the societal/wider grid user side.
It is found from this comparison across these metrics that each of the methodologies excels at different metrics, making each of them viable in their own respect. ...
With the energy transition already underway in the Netherlands, one of the largest obstacles currently facing this process is the widespread grid congestion.
To deal with this grid congestion, many different flexibility solutions have been proposed. One of these solutions is the use of non-firm grid connections, which dynamically allocate grid capacity to connected parties based on the free space available on their specific part of the grid.
A key design parameter of this flexibility options is the manner in which the available capacity is allocated across the non-firm connections.
In this work, a comparison is made between five methodologies for allocating capacity among non-firm grid connections: a contract age based methodology (and a variation upon it which also takes into account the location of a connection in the grid), a rotating priority list, a pure mathematical optimisation of total allocated capacity as well as a proportional allocation methodology which tries to allocate equally between loads.
To compare these different methodologies, a set of metrics is devised, which attempt to capture the three key dimensions of relevance in this space, each representing a stakeholder: the customer/connection side, the grid operator side and the societal/wider grid user side.
It is found from this comparison across these metrics that each of the methodologies excels at different metrics, making each of them viable in their own respect.
To deal with this grid congestion, many different flexibility solutions have been proposed. One of these solutions is the use of non-firm grid connections, which dynamically allocate grid capacity to connected parties based on the free space available on their specific part of the grid.
A key design parameter of this flexibility options is the manner in which the available capacity is allocated across the non-firm connections.
In this work, a comparison is made between five methodologies for allocating capacity among non-firm grid connections: a contract age based methodology (and a variation upon it which also takes into account the location of a connection in the grid), a rotating priority list, a pure mathematical optimisation of total allocated capacity as well as a proportional allocation methodology which tries to allocate equally between loads.
To compare these different methodologies, a set of metrics is devised, which attempt to capture the three key dimensions of relevance in this space, each representing a stakeholder: the customer/connection side, the grid operator side and the societal/wider grid user side.
It is found from this comparison across these metrics that each of the methodologies excels at different metrics, making each of them viable in their own respect.
Unlocking Offshore Hybrid Projects
Modelling Price and Volume Risks and their Mitigation Measures for Offshore Hybrid Projects in Offshore Bidding Zones under Flow-Based Market Coupling
The European Commission (EC) has set ambitious targets of achieving 60 GW and 300 GW of offshore wind capacity by 2030 and 2050, respectively, to meet its energy and climate objectives. This push towards renewable energy necessitates the integration of hybrid offshore wind farms (OWFs) connected to multiple countries and markets, facilitating cross-border electricity connections, security of supply, and increased renewable energy integration. Hybrid projects, combining OWFs and interconnector transmission cables, play a crucial role in this transition, aiming to create a meshed offshore energy network in the North Sea.
The development of the European Target Model, incorporating Flow-Based Market Coupling (FBMC), Advanced Hybrid Coupling (AHC), and the Offshore Bidding Zone (OBZ), aims to address challenges in hybrid projects but introduces new price and volume risks. These risks, stemming from the unique market mechanisms of the OBZ and the increased dependency on interconnectors, lead to revenue uncertainties and potential curtailment for offshore wind farms (OWFs), complicating investment climates and hindering the achievement of renewable energy targets.
The main objective of this thesis is to identify the key factors leading to price and volume risks for hybrid projects and assess the effectiveness of mitigation measures. This is achieved by answering the main research question in this thesis: How do the offshore grid topology, onshore grid attenuations and the integration of renewable energy
sources influence price and volume risk and to what extend do regulatory and technological measures
mitigate these risks?
Aiding in answering this question, four subquestions have been introduced, aimed at identifying specific price and volume risks, exploring regulatory and technological mitigation measures, determining the most impactful risk factors on OWFs' economic viability, and evaluating the potential mitigation measures' effectiveness.
The research approach consists of two phases: qualitative desk research and quantitative modelling. The first phase involves a literature review to identify and categorize price and volume risks and mitigation measures, forming a risk framework. The second phase addresses the Risk Framework from phase 1 and uses it to extend the model from Kenis et al. (2023) to quantify the frequency and severity of the risks and ultimately determine the key factors leading to these risks. The primary research method applied in this thesis is a linear optimisation model mimicking the FBMC process of TSOs deployed in Julia. The methodology encompasses a four-key steps process. In the first step, the Case Determination, indicators are established to define case groups and systematically vary variables and isolate the primary considered variables, i.e. offshore grid topology, onshore grid attenuations, and renewable energy integration. In the second step, the Case Simulation, the base case (D-2), day-ahead market clearing (D-1) and redispatch (D-0) modelling steps of the FBMC process are simulated, followed by the added modelling step to distinguish between the capacity calculation and allocation volume risk and the calculation of the risk indicators. In the third step, the Case Group Analysis, case-specific results are collected and analysed per case group and an ex-post analysis of the FTR and TAG compensations is conducted. Th final step, the Cross-Case Group Analysis, involves aggregating and analysing all results with the aid of standardisation methods to create Risk Matrices.
The study identifies two key factors leading to price and volume risks: the transmission grid’s physical characteristic, i.e. the OBZ’s export capacity or FB domain as influenced by the offshore grid topology and onshore outages, and the market characterises of the bidding zones connected via the hybrid interconnector, i.e. the level of competition between the OBZ and onshore (renewable) generators for the allocation of scarce transmission capacity.
Changes in offshore grid topology primarily impact price and volume risks by altering the OBZ's export capacity, with increased transmission capacity generally reducing curtailment by capacity calculation but potentially affecting price collapses and curtailment by capacity allocation depending on onshore grid restrictions and market dynamics. Onshore grid attenuations similarly influence these risks, with high-priced zone outages typically increasing curtailment by capacity allocation and price collapses,
while low-priced zone outages increase curtailment by capacity calculation and positive non-intuitive price formation. The integration of renewable energy sources in onshore markets exacerbates competition for transmission capacity, leading to more frequent curtailment by capacity allocation and price collapses during high-wind hours.
Technological mitigation measures, i.e. flexible demand agents (e.g. offshore and onshore electrolysers), mitigate price and volume risks by increasing local demand in the OBZ, setting a floor price and raising electricity prices, and decreasing the need for wind exports, which reduces curtailment by the capacity allocation and calculation risks. FTRs effectively cover price spreads, prevent price collapses and non intuitive price formation. The TAG moderately compensates for curtailment by the capacity calculation volume risk.
Policy makers should actively promote the deployment of flexible demand technologies to balance supply-demand mismatches and support renewable energy generation. They should also decide on a support strategy for hybrid projects, either a merchant-based approach focusing on flexible demand deployment or a regulatory approach implementing FTRs and TAG, potentially including 2-sided capability-based CfDs for initial projects to address supply/demand mismatches. TSOs should strategically select inland landing points and prioritize grid enhancements to mitigate structural congestion and reduce hybrid projects' exposure to price and volume risks, while timely communicating potential delays in intra-zonal and cross-border transmission developments to developers. Developers should pro-actively invest in flexible demand assets to mitigate price and volume risk and increase OWF revenues, optionally focussing on strategic locations of onshore assets near landing points. Finally, uniform decision-making and alignment in hybrid project design and support instrument deployment across North Sea countries are crucial, potentially facilitated by establishing an independent Offshore Transmission System Operator and an Offshore Investment Bank to manage transmission assets and reallocate costs and benefits among stakeholders. ...
The development of the European Target Model, incorporating Flow-Based Market Coupling (FBMC), Advanced Hybrid Coupling (AHC), and the Offshore Bidding Zone (OBZ), aims to address challenges in hybrid projects but introduces new price and volume risks. These risks, stemming from the unique market mechanisms of the OBZ and the increased dependency on interconnectors, lead to revenue uncertainties and potential curtailment for offshore wind farms (OWFs), complicating investment climates and hindering the achievement of renewable energy targets.
The main objective of this thesis is to identify the key factors leading to price and volume risks for hybrid projects and assess the effectiveness of mitigation measures. This is achieved by answering the main research question in this thesis: How do the offshore grid topology, onshore grid attenuations and the integration of renewable energy
sources influence price and volume risk and to what extend do regulatory and technological measures
mitigate these risks?
Aiding in answering this question, four subquestions have been introduced, aimed at identifying specific price and volume risks, exploring regulatory and technological mitigation measures, determining the most impactful risk factors on OWFs' economic viability, and evaluating the potential mitigation measures' effectiveness.
The research approach consists of two phases: qualitative desk research and quantitative modelling. The first phase involves a literature review to identify and categorize price and volume risks and mitigation measures, forming a risk framework. The second phase addresses the Risk Framework from phase 1 and uses it to extend the model from Kenis et al. (2023) to quantify the frequency and severity of the risks and ultimately determine the key factors leading to these risks. The primary research method applied in this thesis is a linear optimisation model mimicking the FBMC process of TSOs deployed in Julia. The methodology encompasses a four-key steps process. In the first step, the Case Determination, indicators are established to define case groups and systematically vary variables and isolate the primary considered variables, i.e. offshore grid topology, onshore grid attenuations, and renewable energy integration. In the second step, the Case Simulation, the base case (D-2), day-ahead market clearing (D-1) and redispatch (D-0) modelling steps of the FBMC process are simulated, followed by the added modelling step to distinguish between the capacity calculation and allocation volume risk and the calculation of the risk indicators. In the third step, the Case Group Analysis, case-specific results are collected and analysed per case group and an ex-post analysis of the FTR and TAG compensations is conducted. Th final step, the Cross-Case Group Analysis, involves aggregating and analysing all results with the aid of standardisation methods to create Risk Matrices.
The study identifies two key factors leading to price and volume risks: the transmission grid’s physical characteristic, i.e. the OBZ’s export capacity or FB domain as influenced by the offshore grid topology and onshore outages, and the market characterises of the bidding zones connected via the hybrid interconnector, i.e. the level of competition between the OBZ and onshore (renewable) generators for the allocation of scarce transmission capacity.
Changes in offshore grid topology primarily impact price and volume risks by altering the OBZ's export capacity, with increased transmission capacity generally reducing curtailment by capacity calculation but potentially affecting price collapses and curtailment by capacity allocation depending on onshore grid restrictions and market dynamics. Onshore grid attenuations similarly influence these risks, with high-priced zone outages typically increasing curtailment by capacity allocation and price collapses,
while low-priced zone outages increase curtailment by capacity calculation and positive non-intuitive price formation. The integration of renewable energy sources in onshore markets exacerbates competition for transmission capacity, leading to more frequent curtailment by capacity allocation and price collapses during high-wind hours.
Technological mitigation measures, i.e. flexible demand agents (e.g. offshore and onshore electrolysers), mitigate price and volume risks by increasing local demand in the OBZ, setting a floor price and raising electricity prices, and decreasing the need for wind exports, which reduces curtailment by the capacity allocation and calculation risks. FTRs effectively cover price spreads, prevent price collapses and non intuitive price formation. The TAG moderately compensates for curtailment by the capacity calculation volume risk.
Policy makers should actively promote the deployment of flexible demand technologies to balance supply-demand mismatches and support renewable energy generation. They should also decide on a support strategy for hybrid projects, either a merchant-based approach focusing on flexible demand deployment or a regulatory approach implementing FTRs and TAG, potentially including 2-sided capability-based CfDs for initial projects to address supply/demand mismatches. TSOs should strategically select inland landing points and prioritize grid enhancements to mitigate structural congestion and reduce hybrid projects' exposure to price and volume risks, while timely communicating potential delays in intra-zonal and cross-border transmission developments to developers. Developers should pro-actively invest in flexible demand assets to mitigate price and volume risk and increase OWF revenues, optionally focussing on strategic locations of onshore assets near landing points. Finally, uniform decision-making and alignment in hybrid project design and support instrument deployment across North Sea countries are crucial, potentially facilitated by establishing an independent Offshore Transmission System Operator and an Offshore Investment Bank to manage transmission assets and reallocate costs and benefits among stakeholders. ...
The European Commission (EC) has set ambitious targets of achieving 60 GW and 300 GW of offshore wind capacity by 2030 and 2050, respectively, to meet its energy and climate objectives. This push towards renewable energy necessitates the integration of hybrid offshore wind farms (OWFs) connected to multiple countries and markets, facilitating cross-border electricity connections, security of supply, and increased renewable energy integration. Hybrid projects, combining OWFs and interconnector transmission cables, play a crucial role in this transition, aiming to create a meshed offshore energy network in the North Sea.
The development of the European Target Model, incorporating Flow-Based Market Coupling (FBMC), Advanced Hybrid Coupling (AHC), and the Offshore Bidding Zone (OBZ), aims to address challenges in hybrid projects but introduces new price and volume risks. These risks, stemming from the unique market mechanisms of the OBZ and the increased dependency on interconnectors, lead to revenue uncertainties and potential curtailment for offshore wind farms (OWFs), complicating investment climates and hindering the achievement of renewable energy targets.
The main objective of this thesis is to identify the key factors leading to price and volume risks for hybrid projects and assess the effectiveness of mitigation measures. This is achieved by answering the main research question in this thesis: How do the offshore grid topology, onshore grid attenuations and the integration of renewable energy
sources influence price and volume risk and to what extend do regulatory and technological measures
mitigate these risks?
Aiding in answering this question, four subquestions have been introduced, aimed at identifying specific price and volume risks, exploring regulatory and technological mitigation measures, determining the most impactful risk factors on OWFs' economic viability, and evaluating the potential mitigation measures' effectiveness.
The research approach consists of two phases: qualitative desk research and quantitative modelling. The first phase involves a literature review to identify and categorize price and volume risks and mitigation measures, forming a risk framework. The second phase addresses the Risk Framework from phase 1 and uses it to extend the model from Kenis et al. (2023) to quantify the frequency and severity of the risks and ultimately determine the key factors leading to these risks. The primary research method applied in this thesis is a linear optimisation model mimicking the FBMC process of TSOs deployed in Julia. The methodology encompasses a four-key steps process. In the first step, the Case Determination, indicators are established to define case groups and systematically vary variables and isolate the primary considered variables, i.e. offshore grid topology, onshore grid attenuations, and renewable energy integration. In the second step, the Case Simulation, the base case (D-2), day-ahead market clearing (D-1) and redispatch (D-0) modelling steps of the FBMC process are simulated, followed by the added modelling step to distinguish between the capacity calculation and allocation volume risk and the calculation of the risk indicators. In the third step, the Case Group Analysis, case-specific results are collected and analysed per case group and an ex-post analysis of the FTR and TAG compensations is conducted. Th final step, the Cross-Case Group Analysis, involves aggregating and analysing all results with the aid of standardisation methods to create Risk Matrices.
The study identifies two key factors leading to price and volume risks: the transmission grid’s physical characteristic, i.e. the OBZ’s export capacity or FB domain as influenced by the offshore grid topology and onshore outages, and the market characterises of the bidding zones connected via the hybrid interconnector, i.e. the level of competition between the OBZ and onshore (renewable) generators for the allocation of scarce transmission capacity.
Changes in offshore grid topology primarily impact price and volume risks by altering the OBZ's export capacity, with increased transmission capacity generally reducing curtailment by capacity calculation but potentially affecting price collapses and curtailment by capacity allocation depending on onshore grid restrictions and market dynamics. Onshore grid attenuations similarly influence these risks, with high-priced zone outages typically increasing curtailment by capacity allocation and price collapses,
while low-priced zone outages increase curtailment by capacity calculation and positive non-intuitive price formation. The integration of renewable energy sources in onshore markets exacerbates competition for transmission capacity, leading to more frequent curtailment by capacity allocation and price collapses during high-wind hours.
Technological mitigation measures, i.e. flexible demand agents (e.g. offshore and onshore electrolysers), mitigate price and volume risks by increasing local demand in the OBZ, setting a floor price and raising electricity prices, and decreasing the need for wind exports, which reduces curtailment by the capacity allocation and calculation risks. FTRs effectively cover price spreads, prevent price collapses and non intuitive price formation. The TAG moderately compensates for curtailment by the capacity calculation volume risk.
Policy makers should actively promote the deployment of flexible demand technologies to balance supply-demand mismatches and support renewable energy generation. They should also decide on a support strategy for hybrid projects, either a merchant-based approach focusing on flexible demand deployment or a regulatory approach implementing FTRs and TAG, potentially including 2-sided capability-based CfDs for initial projects to address supply/demand mismatches. TSOs should strategically select inland landing points and prioritize grid enhancements to mitigate structural congestion and reduce hybrid projects' exposure to price and volume risks, while timely communicating potential delays in intra-zonal and cross-border transmission developments to developers. Developers should pro-actively invest in flexible demand assets to mitigate price and volume risk and increase OWF revenues, optionally focussing on strategic locations of onshore assets near landing points. Finally, uniform decision-making and alignment in hybrid project design and support instrument deployment across North Sea countries are crucial, potentially facilitated by establishing an independent Offshore Transmission System Operator and an Offshore Investment Bank to manage transmission assets and reallocate costs and benefits among stakeholders.
The development of the European Target Model, incorporating Flow-Based Market Coupling (FBMC), Advanced Hybrid Coupling (AHC), and the Offshore Bidding Zone (OBZ), aims to address challenges in hybrid projects but introduces new price and volume risks. These risks, stemming from the unique market mechanisms of the OBZ and the increased dependency on interconnectors, lead to revenue uncertainties and potential curtailment for offshore wind farms (OWFs), complicating investment climates and hindering the achievement of renewable energy targets.
The main objective of this thesis is to identify the key factors leading to price and volume risks for hybrid projects and assess the effectiveness of mitigation measures. This is achieved by answering the main research question in this thesis: How do the offshore grid topology, onshore grid attenuations and the integration of renewable energy
sources influence price and volume risk and to what extend do regulatory and technological measures
mitigate these risks?
Aiding in answering this question, four subquestions have been introduced, aimed at identifying specific price and volume risks, exploring regulatory and technological mitigation measures, determining the most impactful risk factors on OWFs' economic viability, and evaluating the potential mitigation measures' effectiveness.
The research approach consists of two phases: qualitative desk research and quantitative modelling. The first phase involves a literature review to identify and categorize price and volume risks and mitigation measures, forming a risk framework. The second phase addresses the Risk Framework from phase 1 and uses it to extend the model from Kenis et al. (2023) to quantify the frequency and severity of the risks and ultimately determine the key factors leading to these risks. The primary research method applied in this thesis is a linear optimisation model mimicking the FBMC process of TSOs deployed in Julia. The methodology encompasses a four-key steps process. In the first step, the Case Determination, indicators are established to define case groups and systematically vary variables and isolate the primary considered variables, i.e. offshore grid topology, onshore grid attenuations, and renewable energy integration. In the second step, the Case Simulation, the base case (D-2), day-ahead market clearing (D-1) and redispatch (D-0) modelling steps of the FBMC process are simulated, followed by the added modelling step to distinguish between the capacity calculation and allocation volume risk and the calculation of the risk indicators. In the third step, the Case Group Analysis, case-specific results are collected and analysed per case group and an ex-post analysis of the FTR and TAG compensations is conducted. Th final step, the Cross-Case Group Analysis, involves aggregating and analysing all results with the aid of standardisation methods to create Risk Matrices.
The study identifies two key factors leading to price and volume risks: the transmission grid’s physical characteristic, i.e. the OBZ’s export capacity or FB domain as influenced by the offshore grid topology and onshore outages, and the market characterises of the bidding zones connected via the hybrid interconnector, i.e. the level of competition between the OBZ and onshore (renewable) generators for the allocation of scarce transmission capacity.
Changes in offshore grid topology primarily impact price and volume risks by altering the OBZ's export capacity, with increased transmission capacity generally reducing curtailment by capacity calculation but potentially affecting price collapses and curtailment by capacity allocation depending on onshore grid restrictions and market dynamics. Onshore grid attenuations similarly influence these risks, with high-priced zone outages typically increasing curtailment by capacity allocation and price collapses,
while low-priced zone outages increase curtailment by capacity calculation and positive non-intuitive price formation. The integration of renewable energy sources in onshore markets exacerbates competition for transmission capacity, leading to more frequent curtailment by capacity allocation and price collapses during high-wind hours.
Technological mitigation measures, i.e. flexible demand agents (e.g. offshore and onshore electrolysers), mitigate price and volume risks by increasing local demand in the OBZ, setting a floor price and raising electricity prices, and decreasing the need for wind exports, which reduces curtailment by the capacity allocation and calculation risks. FTRs effectively cover price spreads, prevent price collapses and non intuitive price formation. The TAG moderately compensates for curtailment by the capacity calculation volume risk.
Policy makers should actively promote the deployment of flexible demand technologies to balance supply-demand mismatches and support renewable energy generation. They should also decide on a support strategy for hybrid projects, either a merchant-based approach focusing on flexible demand deployment or a regulatory approach implementing FTRs and TAG, potentially including 2-sided capability-based CfDs for initial projects to address supply/demand mismatches. TSOs should strategically select inland landing points and prioritize grid enhancements to mitigate structural congestion and reduce hybrid projects' exposure to price and volume risks, while timely communicating potential delays in intra-zonal and cross-border transmission developments to developers. Developers should pro-actively invest in flexible demand assets to mitigate price and volume risk and increase OWF revenues, optionally focussing on strategic locations of onshore assets near landing points. Finally, uniform decision-making and alignment in hybrid project design and support instrument deployment across North Sea countries are crucial, potentially facilitated by establishing an independent Offshore Transmission System Operator and an Offshore Investment Bank to manage transmission assets and reallocate costs and benefits among stakeholders.
Impacts of the Hydrogen Integration Process in the 2030 Iberian Peninsula Energy System's Energy Transition Process
A Case Study Model-Based Approach
The study delves into integrating green hydrogen technologies into the Iberian Peninsula's energy transition toward 2030, focusing on impacts and uncertainties. Both Spain and Portugal have national plans aiming to decarbonize critical sectors using renewable energies and green hydrogen.
The research investigates hydrogen's role in buffering excess renewable electricity and substituting fossil fuels in industry and power generation. It aims to understand how Spain and Portugal can achieve decarbonization goals through energy storage and hydrogen integration.
Using an optimization model and Exploratory Modeling and Analysis (EMA), the study addresses the complexity and uncertainty of the Iberian energy system. Data from various sources including the IEA and transmission operators were used.
Findings reveal sensitivity to electrolyzer capacity, influencing green hydrogen production. Despite cost uncertainties, the system aims to minimize costs by installing comparable renewable energy capacity. Renewable source selection is influenced by costs, favoring wind over solar when costs decrease.
Results suggest a tendency to rely more on natural gas than nuclear energy for cost minimization. Robust policy recommendations emphasize adaptive policies to overcome barriers in the energy transition. Green hydrogen promotion is crucial, acting as an energy storage medium and linking electricity and gas markets.
Conclusions highlight the technical and economic viability of hydrogen integration, contingent on appropriate policies and innovation. Continued research and development in hydrogen and energy storage are crucial for a successful transition. ...
The research investigates hydrogen's role in buffering excess renewable electricity and substituting fossil fuels in industry and power generation. It aims to understand how Spain and Portugal can achieve decarbonization goals through energy storage and hydrogen integration.
Using an optimization model and Exploratory Modeling and Analysis (EMA), the study addresses the complexity and uncertainty of the Iberian energy system. Data from various sources including the IEA and transmission operators were used.
Findings reveal sensitivity to electrolyzer capacity, influencing green hydrogen production. Despite cost uncertainties, the system aims to minimize costs by installing comparable renewable energy capacity. Renewable source selection is influenced by costs, favoring wind over solar when costs decrease.
Results suggest a tendency to rely more on natural gas than nuclear energy for cost minimization. Robust policy recommendations emphasize adaptive policies to overcome barriers in the energy transition. Green hydrogen promotion is crucial, acting as an energy storage medium and linking electricity and gas markets.
Conclusions highlight the technical and economic viability of hydrogen integration, contingent on appropriate policies and innovation. Continued research and development in hydrogen and energy storage are crucial for a successful transition. ...
The study delves into integrating green hydrogen technologies into the Iberian Peninsula's energy transition toward 2030, focusing on impacts and uncertainties. Both Spain and Portugal have national plans aiming to decarbonize critical sectors using renewable energies and green hydrogen.
The research investigates hydrogen's role in buffering excess renewable electricity and substituting fossil fuels in industry and power generation. It aims to understand how Spain and Portugal can achieve decarbonization goals through energy storage and hydrogen integration.
Using an optimization model and Exploratory Modeling and Analysis (EMA), the study addresses the complexity and uncertainty of the Iberian energy system. Data from various sources including the IEA and transmission operators were used.
Findings reveal sensitivity to electrolyzer capacity, influencing green hydrogen production. Despite cost uncertainties, the system aims to minimize costs by installing comparable renewable energy capacity. Renewable source selection is influenced by costs, favoring wind over solar when costs decrease.
Results suggest a tendency to rely more on natural gas than nuclear energy for cost minimization. Robust policy recommendations emphasize adaptive policies to overcome barriers in the energy transition. Green hydrogen promotion is crucial, acting as an energy storage medium and linking electricity and gas markets.
Conclusions highlight the technical and economic viability of hydrogen integration, contingent on appropriate policies and innovation. Continued research and development in hydrogen and energy storage are crucial for a successful transition.
The research investigates hydrogen's role in buffering excess renewable electricity and substituting fossil fuels in industry and power generation. It aims to understand how Spain and Portugal can achieve decarbonization goals through energy storage and hydrogen integration.
Using an optimization model and Exploratory Modeling and Analysis (EMA), the study addresses the complexity and uncertainty of the Iberian energy system. Data from various sources including the IEA and transmission operators were used.
Findings reveal sensitivity to electrolyzer capacity, influencing green hydrogen production. Despite cost uncertainties, the system aims to minimize costs by installing comparable renewable energy capacity. Renewable source selection is influenced by costs, favoring wind over solar when costs decrease.
Results suggest a tendency to rely more on natural gas than nuclear energy for cost minimization. Robust policy recommendations emphasize adaptive policies to overcome barriers in the energy transition. Green hydrogen promotion is crucial, acting as an energy storage medium and linking electricity and gas markets.
Conclusions highlight the technical and economic viability of hydrogen integration, contingent on appropriate policies and innovation. Continued research and development in hydrogen and energy storage are crucial for a successful transition.
The Netherlands aims to have a carbon-neutral energy system by 2050. Hydrogen is seen as an important energy carrier that can contribute to this transition. Not only can hydrogen potentially decarbonise industry, transportation, and possibly agriculture and the built environment, but it can also be used to store electricity for longer periods. As more green hydrogen is produced over the years by electrolysis, the hydrogen demand and supply will increasingly diverge. Underground hydrogen storage can provide a solution for the imbalance between hydrogen demand and supply in both the short and long term.
For this study, a model has been constructed to simulate the Dutch electricity and hydrogen market in 2040. Four different energy systems have been modelled, differing mainly in the extent to which the Netherlands is self-sufficient in their electricity and hydrogen demand. The model is constructed in Linny-R: a graphical modelling tool specifically designed for the formulation of Mixed Integer Linear Programming (MILP) problems, particularly for Unit Commitment (UC) problems.
Firstly, using the model, the required underground hydrogen storage capacity in 2040 in the four different energy systems has been determined. The performance of different storage configurations, consisting of a certain number of short-cyclic salt caverns in addition to a certain policy for seasonal storage in gas fields, has been compared, and it has been concluded that only when a large installed capacity of solar and wind energy is placed in the Netherlands, the preference is for a mid-seasonal storage policy. In the other energy systems, the preference was for a low-seasonal storage policy.
The second part of the research focuses on the financial feasibility of underground hydrogen storage facilities. The average system costs and profits have been mapped out. For the most plausible energy system, it has been determined that the storage facilities in 2040, when they have to generate their income purely based on market prices, are not profitable. To recoup the investment in salt caverns, the government will need to subsidise 0.26 €/kg of hydrogen.
In addition to the insight that subsidisation will be needed to kickstart investments in underground hydrogen storage, some other important insights for policymakers have emerged. The installed capacity of hydrogen turbines has a significant effect on both the operational system costs of the entire energy system and the economic feasibility of the storage facilities. Furthermore, it has been found that in an energy system already dominated by green electricity and green hydrogen, adding additional storage facilities does not result in further CO2 reduction. As long as the carbon capture rate is limited to 90%, approximately 10,000 kton of CO2 will still be emitted annually. To become fully carbon-neutral, the Dutch government must therefore consider negative emission measures such as reforestation and ecosystem restoration.
...
For this study, a model has been constructed to simulate the Dutch electricity and hydrogen market in 2040. Four different energy systems have been modelled, differing mainly in the extent to which the Netherlands is self-sufficient in their electricity and hydrogen demand. The model is constructed in Linny-R: a graphical modelling tool specifically designed for the formulation of Mixed Integer Linear Programming (MILP) problems, particularly for Unit Commitment (UC) problems.
Firstly, using the model, the required underground hydrogen storage capacity in 2040 in the four different energy systems has been determined. The performance of different storage configurations, consisting of a certain number of short-cyclic salt caverns in addition to a certain policy for seasonal storage in gas fields, has been compared, and it has been concluded that only when a large installed capacity of solar and wind energy is placed in the Netherlands, the preference is for a mid-seasonal storage policy. In the other energy systems, the preference was for a low-seasonal storage policy.
The second part of the research focuses on the financial feasibility of underground hydrogen storage facilities. The average system costs and profits have been mapped out. For the most plausible energy system, it has been determined that the storage facilities in 2040, when they have to generate their income purely based on market prices, are not profitable. To recoup the investment in salt caverns, the government will need to subsidise 0.26 €/kg of hydrogen.
In addition to the insight that subsidisation will be needed to kickstart investments in underground hydrogen storage, some other important insights for policymakers have emerged. The installed capacity of hydrogen turbines has a significant effect on both the operational system costs of the entire energy system and the economic feasibility of the storage facilities. Furthermore, it has been found that in an energy system already dominated by green electricity and green hydrogen, adding additional storage facilities does not result in further CO2 reduction. As long as the carbon capture rate is limited to 90%, approximately 10,000 kton of CO2 will still be emitted annually. To become fully carbon-neutral, the Dutch government must therefore consider negative emission measures such as reforestation and ecosystem restoration.
...
The Netherlands aims to have a carbon-neutral energy system by 2050. Hydrogen is seen as an important energy carrier that can contribute to this transition. Not only can hydrogen potentially decarbonise industry, transportation, and possibly agriculture and the built environment, but it can also be used to store electricity for longer periods. As more green hydrogen is produced over the years by electrolysis, the hydrogen demand and supply will increasingly diverge. Underground hydrogen storage can provide a solution for the imbalance between hydrogen demand and supply in both the short and long term.
For this study, a model has been constructed to simulate the Dutch electricity and hydrogen market in 2040. Four different energy systems have been modelled, differing mainly in the extent to which the Netherlands is self-sufficient in their electricity and hydrogen demand. The model is constructed in Linny-R: a graphical modelling tool specifically designed for the formulation of Mixed Integer Linear Programming (MILP) problems, particularly for Unit Commitment (UC) problems.
Firstly, using the model, the required underground hydrogen storage capacity in 2040 in the four different energy systems has been determined. The performance of different storage configurations, consisting of a certain number of short-cyclic salt caverns in addition to a certain policy for seasonal storage in gas fields, has been compared, and it has been concluded that only when a large installed capacity of solar and wind energy is placed in the Netherlands, the preference is for a mid-seasonal storage policy. In the other energy systems, the preference was for a low-seasonal storage policy.
The second part of the research focuses on the financial feasibility of underground hydrogen storage facilities. The average system costs and profits have been mapped out. For the most plausible energy system, it has been determined that the storage facilities in 2040, when they have to generate their income purely based on market prices, are not profitable. To recoup the investment in salt caverns, the government will need to subsidise 0.26 €/kg of hydrogen.
In addition to the insight that subsidisation will be needed to kickstart investments in underground hydrogen storage, some other important insights for policymakers have emerged. The installed capacity of hydrogen turbines has a significant effect on both the operational system costs of the entire energy system and the economic feasibility of the storage facilities. Furthermore, it has been found that in an energy system already dominated by green electricity and green hydrogen, adding additional storage facilities does not result in further CO2 reduction. As long as the carbon capture rate is limited to 90%, approximately 10,000 kton of CO2 will still be emitted annually. To become fully carbon-neutral, the Dutch government must therefore consider negative emission measures such as reforestation and ecosystem restoration.
For this study, a model has been constructed to simulate the Dutch electricity and hydrogen market in 2040. Four different energy systems have been modelled, differing mainly in the extent to which the Netherlands is self-sufficient in their electricity and hydrogen demand. The model is constructed in Linny-R: a graphical modelling tool specifically designed for the formulation of Mixed Integer Linear Programming (MILP) problems, particularly for Unit Commitment (UC) problems.
Firstly, using the model, the required underground hydrogen storage capacity in 2040 in the four different energy systems has been determined. The performance of different storage configurations, consisting of a certain number of short-cyclic salt caverns in addition to a certain policy for seasonal storage in gas fields, has been compared, and it has been concluded that only when a large installed capacity of solar and wind energy is placed in the Netherlands, the preference is for a mid-seasonal storage policy. In the other energy systems, the preference was for a low-seasonal storage policy.
The second part of the research focuses on the financial feasibility of underground hydrogen storage facilities. The average system costs and profits have been mapped out. For the most plausible energy system, it has been determined that the storage facilities in 2040, when they have to generate their income purely based on market prices, are not profitable. To recoup the investment in salt caverns, the government will need to subsidise 0.26 €/kg of hydrogen.
In addition to the insight that subsidisation will be needed to kickstart investments in underground hydrogen storage, some other important insights for policymakers have emerged. The installed capacity of hydrogen turbines has a significant effect on both the operational system costs of the entire energy system and the economic feasibility of the storage facilities. Furthermore, it has been found that in an energy system already dominated by green electricity and green hydrogen, adding additional storage facilities does not result in further CO2 reduction. As long as the carbon capture rate is limited to 90%, approximately 10,000 kton of CO2 will still be emitted annually. To become fully carbon-neutral, the Dutch government must therefore consider negative emission measures such as reforestation and ecosystem restoration.
Take-as-Produced, or Pay the Premium for a Baseload Profile?
Evaluating the Costs Implications of Delivery Requirements in Bilateral Offtake Agreements of Electrolytic Hydrogen Production
The absence of a liquid market for renewable hydrogen causes uncertainty in the revenue stream for early water electrolysis facilities in the Netherlands, and a highly volatile day-ahead market causes uncertainty in production costs.
The EU is promoting renewable hydrogen projects to engage in bilateral contracts to mitigate these uncertainties. Hydrogen Purchase Agreements (HPA) can be used to secure an offtaker and a price, reducing demand and price uncertainty. While a Power Purchase Agreement (PPA) secures a renewable power supply and mitigates price uncertainty, the inherent volume uncertainty of renewable energy sources (RES) remains a significant challenge.
This challenge is exacerbated by the requirement of most offtakers to be supplied with a consistent baseload volume. The hydrogen producer is thus tasked with creating a consistent supply of hydrogen out of an intermittent supply of renewable power and takes on the volume risk of renewable power. It is not yet clear how this and other offtake requirements affect the production cost of hydrogen.
The main question of this thesis was: "How are the hydrogen production costs under a long-term hydrogen purchase agreement affected by the offtake volume, offtake profile, the availability of hydrogen storage, and the type and size of the RES portfolio?"
This thesis utilizes mathematical optimization to quantify the impact of these offtake requirements on the levelized cost of hydrogen (LCOH). The dispatch of the electrolyzer and the operation of the storage is the central decision in the model. Through a case study, the sensitivity of the LCOH with respect to the offtake volume, the size of the RES portfolio, and the size of the storage is determined.
The results show that the LCOH increases by 11–28% if a baseload profile is required and no hydrogen storage is available. Access to an optimal amount of hydrogen storage reduces the extra costs to 2–8%. To achieve the maximum benefit from hydrogen storage, the outflow capacity should be larger than the baseload volume so that the storage can accommodate the entire demand at times when power prices are high. Our results also show that with the current investment costs, the utilization factor plays a significant role in the final LCOH, with a utilization factor of 90% leading to the lowest overall LCOH, even though the average power price at this point is much higher than at lower utilization factors.
Future research could explore different project ownership structures for electrolyzer facilities and quantify the difference in costs. Alternatively, a complete risk analysis using Monte Carlo simulations could be performed. The addition of a more dynamic market model would also enhance the value of the analysis, as would the inclusion of more markets, such as intraday and balancing. Future studies could also assess the impact of linepack flexibility services in hydrogen pipelines on the need for storage. ...
The EU is promoting renewable hydrogen projects to engage in bilateral contracts to mitigate these uncertainties. Hydrogen Purchase Agreements (HPA) can be used to secure an offtaker and a price, reducing demand and price uncertainty. While a Power Purchase Agreement (PPA) secures a renewable power supply and mitigates price uncertainty, the inherent volume uncertainty of renewable energy sources (RES) remains a significant challenge.
This challenge is exacerbated by the requirement of most offtakers to be supplied with a consistent baseload volume. The hydrogen producer is thus tasked with creating a consistent supply of hydrogen out of an intermittent supply of renewable power and takes on the volume risk of renewable power. It is not yet clear how this and other offtake requirements affect the production cost of hydrogen.
The main question of this thesis was: "How are the hydrogen production costs under a long-term hydrogen purchase agreement affected by the offtake volume, offtake profile, the availability of hydrogen storage, and the type and size of the RES portfolio?"
This thesis utilizes mathematical optimization to quantify the impact of these offtake requirements on the levelized cost of hydrogen (LCOH). The dispatch of the electrolyzer and the operation of the storage is the central decision in the model. Through a case study, the sensitivity of the LCOH with respect to the offtake volume, the size of the RES portfolio, and the size of the storage is determined.
The results show that the LCOH increases by 11–28% if a baseload profile is required and no hydrogen storage is available. Access to an optimal amount of hydrogen storage reduces the extra costs to 2–8%. To achieve the maximum benefit from hydrogen storage, the outflow capacity should be larger than the baseload volume so that the storage can accommodate the entire demand at times when power prices are high. Our results also show that with the current investment costs, the utilization factor plays a significant role in the final LCOH, with a utilization factor of 90% leading to the lowest overall LCOH, even though the average power price at this point is much higher than at lower utilization factors.
Future research could explore different project ownership structures for electrolyzer facilities and quantify the difference in costs. Alternatively, a complete risk analysis using Monte Carlo simulations could be performed. The addition of a more dynamic market model would also enhance the value of the analysis, as would the inclusion of more markets, such as intraday and balancing. Future studies could also assess the impact of linepack flexibility services in hydrogen pipelines on the need for storage. ...
The absence of a liquid market for renewable hydrogen causes uncertainty in the revenue stream for early water electrolysis facilities in the Netherlands, and a highly volatile day-ahead market causes uncertainty in production costs.
The EU is promoting renewable hydrogen projects to engage in bilateral contracts to mitigate these uncertainties. Hydrogen Purchase Agreements (HPA) can be used to secure an offtaker and a price, reducing demand and price uncertainty. While a Power Purchase Agreement (PPA) secures a renewable power supply and mitigates price uncertainty, the inherent volume uncertainty of renewable energy sources (RES) remains a significant challenge.
This challenge is exacerbated by the requirement of most offtakers to be supplied with a consistent baseload volume. The hydrogen producer is thus tasked with creating a consistent supply of hydrogen out of an intermittent supply of renewable power and takes on the volume risk of renewable power. It is not yet clear how this and other offtake requirements affect the production cost of hydrogen.
The main question of this thesis was: "How are the hydrogen production costs under a long-term hydrogen purchase agreement affected by the offtake volume, offtake profile, the availability of hydrogen storage, and the type and size of the RES portfolio?"
This thesis utilizes mathematical optimization to quantify the impact of these offtake requirements on the levelized cost of hydrogen (LCOH). The dispatch of the electrolyzer and the operation of the storage is the central decision in the model. Through a case study, the sensitivity of the LCOH with respect to the offtake volume, the size of the RES portfolio, and the size of the storage is determined.
The results show that the LCOH increases by 11–28% if a baseload profile is required and no hydrogen storage is available. Access to an optimal amount of hydrogen storage reduces the extra costs to 2–8%. To achieve the maximum benefit from hydrogen storage, the outflow capacity should be larger than the baseload volume so that the storage can accommodate the entire demand at times when power prices are high. Our results also show that with the current investment costs, the utilization factor plays a significant role in the final LCOH, with a utilization factor of 90% leading to the lowest overall LCOH, even though the average power price at this point is much higher than at lower utilization factors.
Future research could explore different project ownership structures for electrolyzer facilities and quantify the difference in costs. Alternatively, a complete risk analysis using Monte Carlo simulations could be performed. The addition of a more dynamic market model would also enhance the value of the analysis, as would the inclusion of more markets, such as intraday and balancing. Future studies could also assess the impact of linepack flexibility services in hydrogen pipelines on the need for storage.
The EU is promoting renewable hydrogen projects to engage in bilateral contracts to mitigate these uncertainties. Hydrogen Purchase Agreements (HPA) can be used to secure an offtaker and a price, reducing demand and price uncertainty. While a Power Purchase Agreement (PPA) secures a renewable power supply and mitigates price uncertainty, the inherent volume uncertainty of renewable energy sources (RES) remains a significant challenge.
This challenge is exacerbated by the requirement of most offtakers to be supplied with a consistent baseload volume. The hydrogen producer is thus tasked with creating a consistent supply of hydrogen out of an intermittent supply of renewable power and takes on the volume risk of renewable power. It is not yet clear how this and other offtake requirements affect the production cost of hydrogen.
The main question of this thesis was: "How are the hydrogen production costs under a long-term hydrogen purchase agreement affected by the offtake volume, offtake profile, the availability of hydrogen storage, and the type and size of the RES portfolio?"
This thesis utilizes mathematical optimization to quantify the impact of these offtake requirements on the levelized cost of hydrogen (LCOH). The dispatch of the electrolyzer and the operation of the storage is the central decision in the model. Through a case study, the sensitivity of the LCOH with respect to the offtake volume, the size of the RES portfolio, and the size of the storage is determined.
The results show that the LCOH increases by 11–28% if a baseload profile is required and no hydrogen storage is available. Access to an optimal amount of hydrogen storage reduces the extra costs to 2–8%. To achieve the maximum benefit from hydrogen storage, the outflow capacity should be larger than the baseload volume so that the storage can accommodate the entire demand at times when power prices are high. Our results also show that with the current investment costs, the utilization factor plays a significant role in the final LCOH, with a utilization factor of 90% leading to the lowest overall LCOH, even though the average power price at this point is much higher than at lower utilization factors.
Future research could explore different project ownership structures for electrolyzer facilities and quantify the difference in costs. Alternatively, a complete risk analysis using Monte Carlo simulations could be performed. The addition of a more dynamic market model would also enhance the value of the analysis, as would the inclusion of more markets, such as intraday and balancing. Future studies could also assess the impact of linepack flexibility services in hydrogen pipelines on the need for storage.
Sustainable energy transition is the need of the hour, more so, because of the accelerated effects of climate change. This necessitates rapid, continuous, and persuasive political and technological approaches to enable an ecosystem of green alternatives for countries to support their net-zero ambitions. One such emerging technology approach is green hydrogen, which is hydrogen produced from renewable sources. Green hydrogen is considered as a versatile energy carrier to support transitioning of industries, energy systems and transport, towards sustainability. As such, globally, many countries have increasingly considered green hydrogen as a part of their decarbonisation plans. Today, at least 26 countries have adopted hydrogen policy strategies and supporting policy instruments. However, mere adoption is not enough – a test of policy effectiveness is required to evaluate whether policies will be executed in keeping with their stated objectives, whether the various policy instruments/tools introduced to support overall strategies will be collaborative or may conflict with one another, and whether the policies will address issues systematically or ad hoc. It becomes important to understand whether policy strategies and instruments, the underlying processes, and their characteristics - collectively called a policy mix- could actually support policy goals, objectives, and ambitions of various countries.
...
Sustainable energy transition is the need of the hour, more so, because of the accelerated effects of climate change. This necessitates rapid, continuous, and persuasive political and technological approaches to enable an ecosystem of green alternatives for countries to support their net-zero ambitions. One such emerging technology approach is green hydrogen, which is hydrogen produced from renewable sources. Green hydrogen is considered as a versatile energy carrier to support transitioning of industries, energy systems and transport, towards sustainability. As such, globally, many countries have increasingly considered green hydrogen as a part of their decarbonisation plans. Today, at least 26 countries have adopted hydrogen policy strategies and supporting policy instruments. However, mere adoption is not enough – a test of policy effectiveness is required to evaluate whether policies will be executed in keeping with their stated objectives, whether the various policy instruments/tools introduced to support overall strategies will be collaborative or may conflict with one another, and whether the policies will address issues systematically or ad hoc. It becomes important to understand whether policy strategies and instruments, the underlying processes, and their characteristics - collectively called a policy mix- could actually support policy goals, objectives, and ambitions of various countries.
The Potential of Cable Pooling in the Dutch Context
Identifying and Optimizing the Potential of a Shared Grid Connection
Grid congestion, which results from a larger demand for electricity transport than the available transport capacity, is a challenge in the Dutch energy transition. The growing pace of decentralized renewable energy development in less densely populated areas and the electrification of demand leads to congestion, which potentially hinders the connection of new renewable projects to the grid and slows down the energy transition.
Due to the intermittent behaviour of renewable resources, the grid connection capacity is not used to its full capacity at all times. Cable pooling is introduced as a possible solution to congestion, allowing an existing and a new renewable resource to share a grid connection and improving the utlilisation of the current grid infrastructure.
This thesis’ main objective is to develop a calculation framework to assist developers to evaluate the economic potential of a cable pooling location by optimizing the Net Present Value (”NPV”) of a shared grid connection, considering technical, regulatory, legal, and financial aspects. It aims to provide developers with a tool for making a preliminary decision on whether to continue development for a potential cable pooling location.
The Dutch context is used to identify different grid connecting possibilities, different combinations of wind and solar to form a hybrid farm and different revenue streams. These are used in a methodology to find the optimal installed capacity of the added resource and the impact of on-site storage to one of the hybrid farms. The approach considers the influence of market prices on the technology specific cable pooling business case at an hourly level and accounts for long-term market developments. Other factors accounted for in the tool are the hourly export capacity, defined as the residual space after the export of the existing farm, land size and costs of installation.
A case study of a solar farm oriented to the east-west is used to test the tool and draw conclusions on the potential of cable pooling for this case study. The additions of a wind and solar resource with either south or east-west generation all yield positive NPV values with the highest values seen for the wind addition.
Sensitivity tests for technical and economic inputs show that the results are sensitive to weather data and various economic inputs, but the results of this case study are robust. The case study however considers a relatively large grid connection. Generalizing the results by considering a smaller grid connection shows the value of complementary production patterns.
The impact on the cost-benefit framework by adding a battery is considered. The addition of a battery adds value by peak-shifting the produced energy, but not enough to cover the costs of the battery without any subsidy or alternative revenue streams.
In conclusion, cable pooling shows potential for this case study, for different revenue streams and additions. The tool used to obtain the results can be tailored to different case studies and input scenarios and shows the economical attractiveness of a cable pooling location, based on the Dutch context. The Dutch context shows a promising potential for cable pooling as a method to deal with congestion, but not many projects are present yet. Considering different parties sharing one grid connection, coming to an agreement on the terms can form a hurdle. Therefore, the introduction of more transparent cable capacity calculations by DSOs and a separate subsidy for cable pooling projects could help incentivize the development of more cable pooling projects. The impact of developments such as the ”Use-itor-lose-it” on the cable pooling potential should be monitored closely. Other solutions to dealing with congestion, such as using the fault reserves, should not be disregarded. ...
Due to the intermittent behaviour of renewable resources, the grid connection capacity is not used to its full capacity at all times. Cable pooling is introduced as a possible solution to congestion, allowing an existing and a new renewable resource to share a grid connection and improving the utlilisation of the current grid infrastructure.
This thesis’ main objective is to develop a calculation framework to assist developers to evaluate the economic potential of a cable pooling location by optimizing the Net Present Value (”NPV”) of a shared grid connection, considering technical, regulatory, legal, and financial aspects. It aims to provide developers with a tool for making a preliminary decision on whether to continue development for a potential cable pooling location.
The Dutch context is used to identify different grid connecting possibilities, different combinations of wind and solar to form a hybrid farm and different revenue streams. These are used in a methodology to find the optimal installed capacity of the added resource and the impact of on-site storage to one of the hybrid farms. The approach considers the influence of market prices on the technology specific cable pooling business case at an hourly level and accounts for long-term market developments. Other factors accounted for in the tool are the hourly export capacity, defined as the residual space after the export of the existing farm, land size and costs of installation.
A case study of a solar farm oriented to the east-west is used to test the tool and draw conclusions on the potential of cable pooling for this case study. The additions of a wind and solar resource with either south or east-west generation all yield positive NPV values with the highest values seen for the wind addition.
Sensitivity tests for technical and economic inputs show that the results are sensitive to weather data and various economic inputs, but the results of this case study are robust. The case study however considers a relatively large grid connection. Generalizing the results by considering a smaller grid connection shows the value of complementary production patterns.
The impact on the cost-benefit framework by adding a battery is considered. The addition of a battery adds value by peak-shifting the produced energy, but not enough to cover the costs of the battery without any subsidy or alternative revenue streams.
In conclusion, cable pooling shows potential for this case study, for different revenue streams and additions. The tool used to obtain the results can be tailored to different case studies and input scenarios and shows the economical attractiveness of a cable pooling location, based on the Dutch context. The Dutch context shows a promising potential for cable pooling as a method to deal with congestion, but not many projects are present yet. Considering different parties sharing one grid connection, coming to an agreement on the terms can form a hurdle. Therefore, the introduction of more transparent cable capacity calculations by DSOs and a separate subsidy for cable pooling projects could help incentivize the development of more cable pooling projects. The impact of developments such as the ”Use-itor-lose-it” on the cable pooling potential should be monitored closely. Other solutions to dealing with congestion, such as using the fault reserves, should not be disregarded. ...
Grid congestion, which results from a larger demand for electricity transport than the available transport capacity, is a challenge in the Dutch energy transition. The growing pace of decentralized renewable energy development in less densely populated areas and the electrification of demand leads to congestion, which potentially hinders the connection of new renewable projects to the grid and slows down the energy transition.
Due to the intermittent behaviour of renewable resources, the grid connection capacity is not used to its full capacity at all times. Cable pooling is introduced as a possible solution to congestion, allowing an existing and a new renewable resource to share a grid connection and improving the utlilisation of the current grid infrastructure.
This thesis’ main objective is to develop a calculation framework to assist developers to evaluate the economic potential of a cable pooling location by optimizing the Net Present Value (”NPV”) of a shared grid connection, considering technical, regulatory, legal, and financial aspects. It aims to provide developers with a tool for making a preliminary decision on whether to continue development for a potential cable pooling location.
The Dutch context is used to identify different grid connecting possibilities, different combinations of wind and solar to form a hybrid farm and different revenue streams. These are used in a methodology to find the optimal installed capacity of the added resource and the impact of on-site storage to one of the hybrid farms. The approach considers the influence of market prices on the technology specific cable pooling business case at an hourly level and accounts for long-term market developments. Other factors accounted for in the tool are the hourly export capacity, defined as the residual space after the export of the existing farm, land size and costs of installation.
A case study of a solar farm oriented to the east-west is used to test the tool and draw conclusions on the potential of cable pooling for this case study. The additions of a wind and solar resource with either south or east-west generation all yield positive NPV values with the highest values seen for the wind addition.
Sensitivity tests for technical and economic inputs show that the results are sensitive to weather data and various economic inputs, but the results of this case study are robust. The case study however considers a relatively large grid connection. Generalizing the results by considering a smaller grid connection shows the value of complementary production patterns.
The impact on the cost-benefit framework by adding a battery is considered. The addition of a battery adds value by peak-shifting the produced energy, but not enough to cover the costs of the battery without any subsidy or alternative revenue streams.
In conclusion, cable pooling shows potential for this case study, for different revenue streams and additions. The tool used to obtain the results can be tailored to different case studies and input scenarios and shows the economical attractiveness of a cable pooling location, based on the Dutch context. The Dutch context shows a promising potential for cable pooling as a method to deal with congestion, but not many projects are present yet. Considering different parties sharing one grid connection, coming to an agreement on the terms can form a hurdle. Therefore, the introduction of more transparent cable capacity calculations by DSOs and a separate subsidy for cable pooling projects could help incentivize the development of more cable pooling projects. The impact of developments such as the ”Use-itor-lose-it” on the cable pooling potential should be monitored closely. Other solutions to dealing with congestion, such as using the fault reserves, should not be disregarded.
Due to the intermittent behaviour of renewable resources, the grid connection capacity is not used to its full capacity at all times. Cable pooling is introduced as a possible solution to congestion, allowing an existing and a new renewable resource to share a grid connection and improving the utlilisation of the current grid infrastructure.
This thesis’ main objective is to develop a calculation framework to assist developers to evaluate the economic potential of a cable pooling location by optimizing the Net Present Value (”NPV”) of a shared grid connection, considering technical, regulatory, legal, and financial aspects. It aims to provide developers with a tool for making a preliminary decision on whether to continue development for a potential cable pooling location.
The Dutch context is used to identify different grid connecting possibilities, different combinations of wind and solar to form a hybrid farm and different revenue streams. These are used in a methodology to find the optimal installed capacity of the added resource and the impact of on-site storage to one of the hybrid farms. The approach considers the influence of market prices on the technology specific cable pooling business case at an hourly level and accounts for long-term market developments. Other factors accounted for in the tool are the hourly export capacity, defined as the residual space after the export of the existing farm, land size and costs of installation.
A case study of a solar farm oriented to the east-west is used to test the tool and draw conclusions on the potential of cable pooling for this case study. The additions of a wind and solar resource with either south or east-west generation all yield positive NPV values with the highest values seen for the wind addition.
Sensitivity tests for technical and economic inputs show that the results are sensitive to weather data and various economic inputs, but the results of this case study are robust. The case study however considers a relatively large grid connection. Generalizing the results by considering a smaller grid connection shows the value of complementary production patterns.
The impact on the cost-benefit framework by adding a battery is considered. The addition of a battery adds value by peak-shifting the produced energy, but not enough to cover the costs of the battery without any subsidy or alternative revenue streams.
In conclusion, cable pooling shows potential for this case study, for different revenue streams and additions. The tool used to obtain the results can be tailored to different case studies and input scenarios and shows the economical attractiveness of a cable pooling location, based on the Dutch context. The Dutch context shows a promising potential for cable pooling as a method to deal with congestion, but not many projects are present yet. Considering different parties sharing one grid connection, coming to an agreement on the terms can form a hurdle. Therefore, the introduction of more transparent cable capacity calculations by DSOs and a separate subsidy for cable pooling projects could help incentivize the development of more cable pooling projects. The impact of developments such as the ”Use-itor-lose-it” on the cable pooling potential should be monitored closely. Other solutions to dealing with congestion, such as using the fault reserves, should not be disregarded.
Master thesis
(2023)
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O. Düzgün, S. Renes, K. Bruninx, N.G. van den Berg, G. van de Kaa, J.M. Bergsma
Alternative fuels can help the maritime shipping industry to lower its emissions. However, there are commercial risks that surround the adoption of these cleaner fuels. A book & claim based carbon insetting initiative could reduce these commercial risks and accelerate the decarbonization of the maritime shipping industry. Carbon insetting is the process of funding an emission reduction within the sector where the emissions that are being compensated originate from, through the purchase of certificates.
Book & claim facilitates the generation and trade of certificates for certain products & their properties. Combining carbon insetting with the book & claim chain of custody concept enables the generation and trade of scope 3 CO2 certificates for shippers when vessels bunker alternative fuels. Effectively enabling shippers that want their goods to be transported sustainably to pay the price premium for using alternative fuels, thereby facilitating change and sustainability in maritime shipping, without a change in logistics.
The purpose of this master’s thesis is to understand how the concept of book & claim based carbon insetting can enhance the competitive position of the port of Rotterdam for the bunkering of alternative fuels. By simultaneously understanding what potentially the most effective way is to implement this concept at the port. This is achieved through a literature review, together with a workshop conducted for Port of Rotterdam Authority (PoRA) employees.
The workshop results show that the participants have different views on how a book & claim based carbon insetting initiative should be approached. The participants’ answers covered a range, lying on two different ends of the spectrum for potential starting points.
A book & claim based carbon insetting initiative at the port of Rotterdam can potentially enhance the port’s competitive bunkering position for alternative fuels. Implementing this concept in the port would create a clear demand stream for alternative fuels, which will enable the scaling of the supply of alternative fuels within the port, improving their affordability.
Three frameworks were identified that each have their own approach to implementing book & claim based carbon insetting in the maritime shipping industry. These frameworks were evaluated based on six criteria, which were formulated from the results of the workshop. All three frameworks were evaluated for two scenarios based on the two different starting points addressed by the workshop participants.
Based on the above research results, a business strategy has been formulated for the PoRA regarding book & claim based carbon insetting. This proposed business strategy consists of general recommendations and a range of possible approaches the PoRA could take to support the concept.
Book & claim based carbon insetting for the maritime shipping industry is still in its infancy. It has the potential to lower the commercial risks surrounding the adoption of alternative fuels, accelerating the development of a market for sustainable shipping. In turn, accelerating the decarbonization of the maritime shipping industry while potentially enhancing the competitive bunkering position of the port of Rotterdam for alternative fuels if such an initiative is set up in the port.
...
Book & claim facilitates the generation and trade of certificates for certain products & their properties. Combining carbon insetting with the book & claim chain of custody concept enables the generation and trade of scope 3 CO2 certificates for shippers when vessels bunker alternative fuels. Effectively enabling shippers that want their goods to be transported sustainably to pay the price premium for using alternative fuels, thereby facilitating change and sustainability in maritime shipping, without a change in logistics.
The purpose of this master’s thesis is to understand how the concept of book & claim based carbon insetting can enhance the competitive position of the port of Rotterdam for the bunkering of alternative fuels. By simultaneously understanding what potentially the most effective way is to implement this concept at the port. This is achieved through a literature review, together with a workshop conducted for Port of Rotterdam Authority (PoRA) employees.
The workshop results show that the participants have different views on how a book & claim based carbon insetting initiative should be approached. The participants’ answers covered a range, lying on two different ends of the spectrum for potential starting points.
A book & claim based carbon insetting initiative at the port of Rotterdam can potentially enhance the port’s competitive bunkering position for alternative fuels. Implementing this concept in the port would create a clear demand stream for alternative fuels, which will enable the scaling of the supply of alternative fuels within the port, improving their affordability.
Three frameworks were identified that each have their own approach to implementing book & claim based carbon insetting in the maritime shipping industry. These frameworks were evaluated based on six criteria, which were formulated from the results of the workshop. All three frameworks were evaluated for two scenarios based on the two different starting points addressed by the workshop participants.
Based on the above research results, a business strategy has been formulated for the PoRA regarding book & claim based carbon insetting. This proposed business strategy consists of general recommendations and a range of possible approaches the PoRA could take to support the concept.
Book & claim based carbon insetting for the maritime shipping industry is still in its infancy. It has the potential to lower the commercial risks surrounding the adoption of alternative fuels, accelerating the development of a market for sustainable shipping. In turn, accelerating the decarbonization of the maritime shipping industry while potentially enhancing the competitive bunkering position of the port of Rotterdam for alternative fuels if such an initiative is set up in the port.
...
Alternative fuels can help the maritime shipping industry to lower its emissions. However, there are commercial risks that surround the adoption of these cleaner fuels. A book & claim based carbon insetting initiative could reduce these commercial risks and accelerate the decarbonization of the maritime shipping industry. Carbon insetting is the process of funding an emission reduction within the sector where the emissions that are being compensated originate from, through the purchase of certificates.
Book & claim facilitates the generation and trade of certificates for certain products & their properties. Combining carbon insetting with the book & claim chain of custody concept enables the generation and trade of scope 3 CO2 certificates for shippers when vessels bunker alternative fuels. Effectively enabling shippers that want their goods to be transported sustainably to pay the price premium for using alternative fuels, thereby facilitating change and sustainability in maritime shipping, without a change in logistics.
The purpose of this master’s thesis is to understand how the concept of book & claim based carbon insetting can enhance the competitive position of the port of Rotterdam for the bunkering of alternative fuels. By simultaneously understanding what potentially the most effective way is to implement this concept at the port. This is achieved through a literature review, together with a workshop conducted for Port of Rotterdam Authority (PoRA) employees.
The workshop results show that the participants have different views on how a book & claim based carbon insetting initiative should be approached. The participants’ answers covered a range, lying on two different ends of the spectrum for potential starting points.
A book & claim based carbon insetting initiative at the port of Rotterdam can potentially enhance the port’s competitive bunkering position for alternative fuels. Implementing this concept in the port would create a clear demand stream for alternative fuels, which will enable the scaling of the supply of alternative fuels within the port, improving their affordability.
Three frameworks were identified that each have their own approach to implementing book & claim based carbon insetting in the maritime shipping industry. These frameworks were evaluated based on six criteria, which were formulated from the results of the workshop. All three frameworks were evaluated for two scenarios based on the two different starting points addressed by the workshop participants.
Based on the above research results, a business strategy has been formulated for the PoRA regarding book & claim based carbon insetting. This proposed business strategy consists of general recommendations and a range of possible approaches the PoRA could take to support the concept.
Book & claim based carbon insetting for the maritime shipping industry is still in its infancy. It has the potential to lower the commercial risks surrounding the adoption of alternative fuels, accelerating the development of a market for sustainable shipping. In turn, accelerating the decarbonization of the maritime shipping industry while potentially enhancing the competitive bunkering position of the port of Rotterdam for alternative fuels if such an initiative is set up in the port.
Book & claim facilitates the generation and trade of certificates for certain products & their properties. Combining carbon insetting with the book & claim chain of custody concept enables the generation and trade of scope 3 CO2 certificates for shippers when vessels bunker alternative fuels. Effectively enabling shippers that want their goods to be transported sustainably to pay the price premium for using alternative fuels, thereby facilitating change and sustainability in maritime shipping, without a change in logistics.
The purpose of this master’s thesis is to understand how the concept of book & claim based carbon insetting can enhance the competitive position of the port of Rotterdam for the bunkering of alternative fuels. By simultaneously understanding what potentially the most effective way is to implement this concept at the port. This is achieved through a literature review, together with a workshop conducted for Port of Rotterdam Authority (PoRA) employees.
The workshop results show that the participants have different views on how a book & claim based carbon insetting initiative should be approached. The participants’ answers covered a range, lying on two different ends of the spectrum for potential starting points.
A book & claim based carbon insetting initiative at the port of Rotterdam can potentially enhance the port’s competitive bunkering position for alternative fuels. Implementing this concept in the port would create a clear demand stream for alternative fuels, which will enable the scaling of the supply of alternative fuels within the port, improving their affordability.
Three frameworks were identified that each have their own approach to implementing book & claim based carbon insetting in the maritime shipping industry. These frameworks were evaluated based on six criteria, which were formulated from the results of the workshop. All three frameworks were evaluated for two scenarios based on the two different starting points addressed by the workshop participants.
Based on the above research results, a business strategy has been formulated for the PoRA regarding book & claim based carbon insetting. This proposed business strategy consists of general recommendations and a range of possible approaches the PoRA could take to support the concept.
Book & claim based carbon insetting for the maritime shipping industry is still in its infancy. It has the potential to lower the commercial risks surrounding the adoption of alternative fuels, accelerating the development of a market for sustainable shipping. In turn, accelerating the decarbonization of the maritime shipping industry while potentially enhancing the competitive bunkering position of the port of Rotterdam for alternative fuels if such an initiative is set up in the port.
Master thesis
(2022)
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M.J.H. Brouwers, P.P. Vergara Barrios, N.K. Panda, J.L. Rueda Torres, K. Bruninx
The electrification of modern-day society keeps increasing and the demand for electricity grows along with it. Technologies like EVs and heat pumps are both part of the new types of demand, while PV systems and wind farms are meant to be our main new sources of generation. Contrary to traditional resources connected to the electricity network, most of these resources tend to be placed and operated in a distributed manner, increasing the demand for transport capacity within the LV and MV grid. This demand cannot always be met however, which leads to situations of congestion.
This thesis seeks to reduce this congestion by means of optimising the grid topology in line with the seasonal variations in the supply and demand of electricity. This is done by means of implementing a two-stage reconfiguration algorithm. The benefit of network reconfiguration is that it is a short-term and low-cost solution which can be implemented by the DSO without relying on other external parties.
In the first stage of the reconfiguration algorithm, the positions of the normally open switches within the network are optimised in order to adjust the power flow therein. These optimised positions are subsequently used in the second stage to calculate the network variables. The first stage is implemented as a MILP optimisation in Python, while the second stage consists of a Newton-Raphson calculation in the commercial software PowerFactory. This distinction is made to enhance the accuracy of the final network parameters, while still being able to optimise the switch positions in a deterministic manner.
Rather than day-ahead or real-time reconfiguration, as is often considered in most literature, this thesis focuses on seasonal reconfiguration to accommodate for the manual operation of the switches present within the MV grid in the Netherlands. These manually operated switches severely reduce the frequency by which reconfiguration actions can be performed, but that does not mean that the topology of the grid cannot be enhanced.
The presented reconfiguration algorithm is able to consistently reduce congestion within the analysed network, completely removing it or reducing its severity. It also outperforms two optimisation options in PowerFactory with regards to the objective function value. Those being an iterative exploration of meshes and a genetic algorithm.
...
This thesis seeks to reduce this congestion by means of optimising the grid topology in line with the seasonal variations in the supply and demand of electricity. This is done by means of implementing a two-stage reconfiguration algorithm. The benefit of network reconfiguration is that it is a short-term and low-cost solution which can be implemented by the DSO without relying on other external parties.
In the first stage of the reconfiguration algorithm, the positions of the normally open switches within the network are optimised in order to adjust the power flow therein. These optimised positions are subsequently used in the second stage to calculate the network variables. The first stage is implemented as a MILP optimisation in Python, while the second stage consists of a Newton-Raphson calculation in the commercial software PowerFactory. This distinction is made to enhance the accuracy of the final network parameters, while still being able to optimise the switch positions in a deterministic manner.
Rather than day-ahead or real-time reconfiguration, as is often considered in most literature, this thesis focuses on seasonal reconfiguration to accommodate for the manual operation of the switches present within the MV grid in the Netherlands. These manually operated switches severely reduce the frequency by which reconfiguration actions can be performed, but that does not mean that the topology of the grid cannot be enhanced.
The presented reconfiguration algorithm is able to consistently reduce congestion within the analysed network, completely removing it or reducing its severity. It also outperforms two optimisation options in PowerFactory with regards to the objective function value. Those being an iterative exploration of meshes and a genetic algorithm.
...
The electrification of modern-day society keeps increasing and the demand for electricity grows along with it. Technologies like EVs and heat pumps are both part of the new types of demand, while PV systems and wind farms are meant to be our main new sources of generation. Contrary to traditional resources connected to the electricity network, most of these resources tend to be placed and operated in a distributed manner, increasing the demand for transport capacity within the LV and MV grid. This demand cannot always be met however, which leads to situations of congestion.
This thesis seeks to reduce this congestion by means of optimising the grid topology in line with the seasonal variations in the supply and demand of electricity. This is done by means of implementing a two-stage reconfiguration algorithm. The benefit of network reconfiguration is that it is a short-term and low-cost solution which can be implemented by the DSO without relying on other external parties.
In the first stage of the reconfiguration algorithm, the positions of the normally open switches within the network are optimised in order to adjust the power flow therein. These optimised positions are subsequently used in the second stage to calculate the network variables. The first stage is implemented as a MILP optimisation in Python, while the second stage consists of a Newton-Raphson calculation in the commercial software PowerFactory. This distinction is made to enhance the accuracy of the final network parameters, while still being able to optimise the switch positions in a deterministic manner.
Rather than day-ahead or real-time reconfiguration, as is often considered in most literature, this thesis focuses on seasonal reconfiguration to accommodate for the manual operation of the switches present within the MV grid in the Netherlands. These manually operated switches severely reduce the frequency by which reconfiguration actions can be performed, but that does not mean that the topology of the grid cannot be enhanced.
The presented reconfiguration algorithm is able to consistently reduce congestion within the analysed network, completely removing it or reducing its severity. It also outperforms two optimisation options in PowerFactory with regards to the objective function value. Those being an iterative exploration of meshes and a genetic algorithm.
This thesis seeks to reduce this congestion by means of optimising the grid topology in line with the seasonal variations in the supply and demand of electricity. This is done by means of implementing a two-stage reconfiguration algorithm. The benefit of network reconfiguration is that it is a short-term and low-cost solution which can be implemented by the DSO without relying on other external parties.
In the first stage of the reconfiguration algorithm, the positions of the normally open switches within the network are optimised in order to adjust the power flow therein. These optimised positions are subsequently used in the second stage to calculate the network variables. The first stage is implemented as a MILP optimisation in Python, while the second stage consists of a Newton-Raphson calculation in the commercial software PowerFactory. This distinction is made to enhance the accuracy of the final network parameters, while still being able to optimise the switch positions in a deterministic manner.
Rather than day-ahead or real-time reconfiguration, as is often considered in most literature, this thesis focuses on seasonal reconfiguration to accommodate for the manual operation of the switches present within the MV grid in the Netherlands. These manually operated switches severely reduce the frequency by which reconfiguration actions can be performed, but that does not mean that the topology of the grid cannot be enhanced.
The presented reconfiguration algorithm is able to consistently reduce congestion within the analysed network, completely removing it or reducing its severity. It also outperforms two optimisation options in PowerFactory with regards to the objective function value. Those being an iterative exploration of meshes and a genetic algorithm.