Ö. Okur
Please Note
16 records found
1
Modelling Future Power Systems
Integrating offshore wind energy and electrolysers in a PyPSA-based model and discovering policy trade-offs
A PyPSA model of the Dutch electricity system was developed iteratively and validated against TenneT’s reference models. First, the Netherlands was represented as a single node to reproduce dispatch results comparable to PLEXOS. Second, the transmission network was added to incorporate power flows and grid constraints, with results compared to PowerFactory/PSSE load-flow outcomes. Finally, offshore wind farms and electrolysers were included to assess their system impacts. After validation, the EMA Workbench was used to explore investment cost uncertainty by varying key technology costs between −20% and +40%, identifying when cost-driven trade-offs between technologies occur.
Results show that large solar capacity is consistently cost-effective but requires flexibility options due to variability. In the single-node model, PyPSA favors batteries and gas plants for flexibility, whereas PLEXOS indicates a stronger role for offshore wind, likely due to more detailed cost representations. When transmission constraints are included, offshore wind becomes more attractive because its coastal location reduces congestion and long-distance transport to major industrial demand centers. Load-flow comparisons indicate that PyPSA captures overall flow patterns well: approximately 70–75% of flow directions match TenneT models, and congestion hotspots align with planned reinforcements.
Introducing electrolysers increases annual electricity consumption but does not raise peak demand, as they primarily operate during surplus, low-price periods. Consequently, total optimal generation capacity remains largely unchanged. Sensitivity analyses confirm that PyPSA responds to cost variations similarly to PLEXOS, increasing confidence in its use for scenario analysis. The EMA Workbench results reveal that the future system does not converge to a single optimal configuration; instead, multiple viable configurations balance costs, emissions, and flexibility differently. Gas plants reduce investment costs and peak prices but increase emissions, offshore wind lowers emissions but raises upfront costs, solar-battery systems require large capacities, and nuclear becomes competitive only under substantially lower costs.
Computation time is significantly reduced compared to detailed models. The most detailed PyPSA setup simulates a full year in about 1.5 hours, down from more than a day, and time-aggregation techniques can reduce runtime to minutes. This enables rapid exploratory analysis.
Overall, the study concludes that PyPSA is suitable for supporting early-stage planning of the Dutch transmission system, especially under uncertainty. The future electricity system appears robust but highly sensitive to technology costs, implying that policymakers should prepare flexible strategies that remain effective across multiple possible development pathways rather than relying on a single optimal scenario. ...
A PyPSA model of the Dutch electricity system was developed iteratively and validated against TenneT’s reference models. First, the Netherlands was represented as a single node to reproduce dispatch results comparable to PLEXOS. Second, the transmission network was added to incorporate power flows and grid constraints, with results compared to PowerFactory/PSSE load-flow outcomes. Finally, offshore wind farms and electrolysers were included to assess their system impacts. After validation, the EMA Workbench was used to explore investment cost uncertainty by varying key technology costs between −20% and +40%, identifying when cost-driven trade-offs between technologies occur.
Results show that large solar capacity is consistently cost-effective but requires flexibility options due to variability. In the single-node model, PyPSA favors batteries and gas plants for flexibility, whereas PLEXOS indicates a stronger role for offshore wind, likely due to more detailed cost representations. When transmission constraints are included, offshore wind becomes more attractive because its coastal location reduces congestion and long-distance transport to major industrial demand centers. Load-flow comparisons indicate that PyPSA captures overall flow patterns well: approximately 70–75% of flow directions match TenneT models, and congestion hotspots align with planned reinforcements.
Introducing electrolysers increases annual electricity consumption but does not raise peak demand, as they primarily operate during surplus, low-price periods. Consequently, total optimal generation capacity remains largely unchanged. Sensitivity analyses confirm that PyPSA responds to cost variations similarly to PLEXOS, increasing confidence in its use for scenario analysis. The EMA Workbench results reveal that the future system does not converge to a single optimal configuration; instead, multiple viable configurations balance costs, emissions, and flexibility differently. Gas plants reduce investment costs and peak prices but increase emissions, offshore wind lowers emissions but raises upfront costs, solar-battery systems require large capacities, and nuclear becomes competitive only under substantially lower costs.
Computation time is significantly reduced compared to detailed models. The most detailed PyPSA setup simulates a full year in about 1.5 hours, down from more than a day, and time-aggregation techniques can reduce runtime to minutes. This enables rapid exploratory analysis.
Overall, the study concludes that PyPSA is suitable for supporting early-stage planning of the Dutch transmission system, especially under uncertainty. The future electricity system appears robust but highly sensitive to technology costs, implying that policymakers should prepare flexible strategies that remain effective across multiple possible development pathways rather than relying on a single optimal scenario.
Governing CCS Networks
A Techno-Economic Analysis of Governance and Risks in the Developing Dutch CCS Market
Adopting a mixed-method approach, this research combines a qualitative system analysis and stakeholder interviews with a quantitative techno-economic optimization model (PyPSA). The methodology integrates insights from policymakers and infrastructure operators with a multi-period model that simulates the cost-optimal rollout of transport and storage infrastructure from 2030 to 2050.
The analysis identifies distinct risks hindering market development. Qualitatively, interviews reveal that coordination risks create a "chicken-and-egg" problem in the start-up phase, preventing Final Investment Decisions (FID). Quantitatively, the model identifies utilization risk as the most critical factor for economic viability. Notably, the system is highly sensitive to cross-border flows, where a lack of import volume creates significant stranded asset risks. Furthermore, the model confirms the natural monopoly characteristics of the pipeline backbone, demonstrating that ship transport remains approximately 38% more expensive than fully utilized pipeline transport, limiting its viability as a competitive alternative in a mature market.
To address these challenges, a governance options framework is developed based on Transaction Cost Economics and Network Regulation Theory. The findings demonstrate that no single mechanism is sufficient and that governance must be adaptive to specific phases of development. To resolve initial coordination failures, the thesis recommends financial incentives such as state guarantees and structural coordination through Public-Private Partnerships (PPPs). As the system matures and natural monopolies solidify, the governance structure must transition toward regulatory oversight, specifically through legal unbundling and Regulated Third-Party Access (rTPA), to mitigate market power risks and ensure fair market entry. The research concludes that policymakers must explicitly weigh the trade-offs between short-term investment certainty and long-term market efficiency to successfully leverage CCS in the climate transition. In the end, decision-making comes down to weighing public values against the identified governance solutions. ...
Adopting a mixed-method approach, this research combines a qualitative system analysis and stakeholder interviews with a quantitative techno-economic optimization model (PyPSA). The methodology integrates insights from policymakers and infrastructure operators with a multi-period model that simulates the cost-optimal rollout of transport and storage infrastructure from 2030 to 2050.
The analysis identifies distinct risks hindering market development. Qualitatively, interviews reveal that coordination risks create a "chicken-and-egg" problem in the start-up phase, preventing Final Investment Decisions (FID). Quantitatively, the model identifies utilization risk as the most critical factor for economic viability. Notably, the system is highly sensitive to cross-border flows, where a lack of import volume creates significant stranded asset risks. Furthermore, the model confirms the natural monopoly characteristics of the pipeline backbone, demonstrating that ship transport remains approximately 38% more expensive than fully utilized pipeline transport, limiting its viability as a competitive alternative in a mature market.
To address these challenges, a governance options framework is developed based on Transaction Cost Economics and Network Regulation Theory. The findings demonstrate that no single mechanism is sufficient and that governance must be adaptive to specific phases of development. To resolve initial coordination failures, the thesis recommends financial incentives such as state guarantees and structural coordination through Public-Private Partnerships (PPPs). As the system matures and natural monopolies solidify, the governance structure must transition toward regulatory oversight, specifically through legal unbundling and Regulated Third-Party Access (rTPA), to mitigate market power risks and ensure fair market entry. The research concludes that policymakers must explicitly weigh the trade-offs between short-term investment certainty and long-term market efficiency to successfully leverage CCS in the climate transition. In the end, decision-making comes down to weighing public values against the identified governance solutions.
aFRR Bidding Strategies for Home Battery Aggregators
A comparison between Passive Balancing and Automatic Frequency Restoration Reserve Bidding Strategies
The Dutch electricity system is undergoing a rapid transformation driven by the increasing share of variable renewable energy sources like wind and solar power. This transition, coupled with the electrification of various sectors, is leading to growing imbalances between electricity supply and demand, challenging the stability of the grid. To maintain balance, the Transmission System Operator (TSO), TenneT, relies on balancing services such as the automatic Frequency Restoration Reserve (aFRR). Traditionally provided by conventional power plants, there is a growing need for new flexible resources like Battery Energy Storage Systems (BESS) to participate in these markets.
Currently, the business model for home battery aggregators like Zonneplan is based on "passive balancing," where batteries strategically take out-of-balance positions to profit from imbalance price fluctuations. However, the sudden widespread and simultaneous application of this strategy has led to unintended consequences, including system oscillations and a high frequency of "Regulation State 2" (RS2) quarters, where the system swings between shortage and surplus. This development not only complicates grid management for TenneT but also reduces the profitability of passive balancing for market participants.
In response, TenneT is working to make active participation in the aFRR market more attractive for fast-responding assets like batteries. This research addresses the critical question of whether a shift from passive balancing to active aFRR participation is financially viable for aggregators of home batteries, using Zonneplan as a case study.
This thesis compares the performance of Zonneplan's passive balancing strategy with three simulated aFRR bidding strategies. The research is guided by the following main research question: How does the performance of aFRR bidding strategies compare to Zonneplan’s currently used passive balancing strategy?
To answer this, the study is structured around three sub-questions:
1. How does the currently used passive balancing strategy of Zonneplan work and how does it perform under the growing frequency of Regulation State 2 ISPs?
2. What are the current trends in the aFRR bidding ladder, prices, and volumes?
3. What are possible bidding strategies for Zonneplan to participate in the Dutch aFRR market and how do they perform?
To answer these research questions, the study applies a mixed-methods approach combining qualitative case analysis, quantitative data analysis, and simulation modeling. Zonneplan's passive balancing strategy was examined through internal documentation and discussions with employees. A backcast of the period January 2024–March 2025 was used to analyze the passive balancing strategy performance using Python, while considering Ex-Post trading and both internal and external portfolio advantages. The aFRR market analysis drew on publicly available TenneT datasets to identify trends in prices, volumes, and bidding ladder. Finally, three aFRR bidding strategies were simulated using a custom-built Python model that replicates bidding, activation, and financial settlement processes at one-minute resolution, enabling a robust comparison with Zonneplan’s passive balancing strategy. The model incorporates real-world constraints such as bid timing and state-of-charge limitations.
The analysis of Zonneplan's passive balancing backcast data from January 2024 to March 2025 reveals that while the strategy initially showed growing revenue, its revenue has been declining since August 2024. This is mainly due to increasing charging costs and partly due to the increased frequency of RS2, which, despite price risk mitigation through Ex-Post trading and Portfolio advantages, limits revenue opportunities.
The aFRR market analysis identifies key trends, including the impact of the European PICASSO platform, which has led to higher activation volumes. While extreme price peaks for upward regulation have become less frequent, average prices remain attractive. A clear daily pattern in aFRR prices, often correlated with Day-Ahead market prices, presents opportunities for dynamic bidding.
Three aFRR bidding strategies were simulated: a constant bid strategy, a Day-Ahead price-based strategy, and an intraday price-based strategy. The Day-Ahead price-based strategy (Bid Strategy 2) yielded the highest net revenue, consistently outperforming the others.
The final comparison shows that the simulated aFRR bid strategy (Bid Strategy 2) consistently generates higher net revenue than the passive balancing strategy throughout the entire analysis period. The aFRR strategy achieves higher activation volumes and benefits from being able to capture revenue during RS2 periods, where passive balancing is ineffective. Furthermore, aFRR participation aligns directly with the TSO's need for system stability, offering a more robust and system-supportive business model.
The discussion highlights key limitations of this research, including limited data availability on passive balancing, simplifications in the bidding simulations, and the rapidly evolving nature of the aFRR market. It also outlines the main implications for the broader context and key stakeholders. For home battery aggregators, the findings demonstrate a clear financial incentive to transition from passive balancing to active aFRR participation, offering not only more stable and higher revenues but also enabling batteries to contribute more effectively to grid stability. For TenneT, it is encouraging that there is a clear financial incentive for aggregators to shift from passive balancing to active aFRR participation. This shift is expected to reduce the frequency oscillations that are currently amplified by uncoordinated passive responses, while enabling batteries to actively support grid stability by following TenneT's aFRR setpoints, effectively using their fast response capabilities to prevent oscillations rather than cause them. Finally, for home battery owners, the study suggests that while passive balancing revenues are declining, participating in aFRR through aggregators can maintain a reasonable payback period.
The research concludes with several suggestions for future work. Future studies should examine how an aFRR-focused strategy can be integrated into broader multi-market value stacking approaches, including intraday arbitrage and selective passive balancing, to reduce revenue volatility and improve overall profitability. More detailed econometric and scenario-based analyses are also needed to better understand aFRR market dynamics, especially in light of regulatory changes and increasing cross-border integration via PICASSO. As battery participation grows, future research should explore potential market saturation effects and their impact on price levels, activation frequency, and aggregator returns. Lastly, the gradual phase-out of gas-fired power plants from balancing markets introduces new uncertainties, warranting investigation into how this shift could affect price volatility, scarcity pricing, and the strategic positioning of new flexible assets like batteries. ...
The Dutch electricity system is undergoing a rapid transformation driven by the increasing share of variable renewable energy sources like wind and solar power. This transition, coupled with the electrification of various sectors, is leading to growing imbalances between electricity supply and demand, challenging the stability of the grid. To maintain balance, the Transmission System Operator (TSO), TenneT, relies on balancing services such as the automatic Frequency Restoration Reserve (aFRR). Traditionally provided by conventional power plants, there is a growing need for new flexible resources like Battery Energy Storage Systems (BESS) to participate in these markets.
Currently, the business model for home battery aggregators like Zonneplan is based on "passive balancing," where batteries strategically take out-of-balance positions to profit from imbalance price fluctuations. However, the sudden widespread and simultaneous application of this strategy has led to unintended consequences, including system oscillations and a high frequency of "Regulation State 2" (RS2) quarters, where the system swings between shortage and surplus. This development not only complicates grid management for TenneT but also reduces the profitability of passive balancing for market participants.
In response, TenneT is working to make active participation in the aFRR market more attractive for fast-responding assets like batteries. This research addresses the critical question of whether a shift from passive balancing to active aFRR participation is financially viable for aggregators of home batteries, using Zonneplan as a case study.
This thesis compares the performance of Zonneplan's passive balancing strategy with three simulated aFRR bidding strategies. The research is guided by the following main research question: How does the performance of aFRR bidding strategies compare to Zonneplan’s currently used passive balancing strategy?
To answer this, the study is structured around three sub-questions:
1. How does the currently used passive balancing strategy of Zonneplan work and how does it perform under the growing frequency of Regulation State 2 ISPs?
2. What are the current trends in the aFRR bidding ladder, prices, and volumes?
3. What are possible bidding strategies for Zonneplan to participate in the Dutch aFRR market and how do they perform?
To answer these research questions, the study applies a mixed-methods approach combining qualitative case analysis, quantitative data analysis, and simulation modeling. Zonneplan's passive balancing strategy was examined through internal documentation and discussions with employees. A backcast of the period January 2024–March 2025 was used to analyze the passive balancing strategy performance using Python, while considering Ex-Post trading and both internal and external portfolio advantages. The aFRR market analysis drew on publicly available TenneT datasets to identify trends in prices, volumes, and bidding ladder. Finally, three aFRR bidding strategies were simulated using a custom-built Python model that replicates bidding, activation, and financial settlement processes at one-minute resolution, enabling a robust comparison with Zonneplan’s passive balancing strategy. The model incorporates real-world constraints such as bid timing and state-of-charge limitations.
The analysis of Zonneplan's passive balancing backcast data from January 2024 to March 2025 reveals that while the strategy initially showed growing revenue, its revenue has been declining since August 2024. This is mainly due to increasing charging costs and partly due to the increased frequency of RS2, which, despite price risk mitigation through Ex-Post trading and Portfolio advantages, limits revenue opportunities.
The aFRR market analysis identifies key trends, including the impact of the European PICASSO platform, which has led to higher activation volumes. While extreme price peaks for upward regulation have become less frequent, average prices remain attractive. A clear daily pattern in aFRR prices, often correlated with Day-Ahead market prices, presents opportunities for dynamic bidding.
Three aFRR bidding strategies were simulated: a constant bid strategy, a Day-Ahead price-based strategy, and an intraday price-based strategy. The Day-Ahead price-based strategy (Bid Strategy 2) yielded the highest net revenue, consistently outperforming the others.
The final comparison shows that the simulated aFRR bid strategy (Bid Strategy 2) consistently generates higher net revenue than the passive balancing strategy throughout the entire analysis period. The aFRR strategy achieves higher activation volumes and benefits from being able to capture revenue during RS2 periods, where passive balancing is ineffective. Furthermore, aFRR participation aligns directly with the TSO's need for system stability, offering a more robust and system-supportive business model.
The discussion highlights key limitations of this research, including limited data availability on passive balancing, simplifications in the bidding simulations, and the rapidly evolving nature of the aFRR market. It also outlines the main implications for the broader context and key stakeholders. For home battery aggregators, the findings demonstrate a clear financial incentive to transition from passive balancing to active aFRR participation, offering not only more stable and higher revenues but also enabling batteries to contribute more effectively to grid stability. For TenneT, it is encouraging that there is a clear financial incentive for aggregators to shift from passive balancing to active aFRR participation. This shift is expected to reduce the frequency oscillations that are currently amplified by uncoordinated passive responses, while enabling batteries to actively support grid stability by following TenneT's aFRR setpoints, effectively using their fast response capabilities to prevent oscillations rather than cause them. Finally, for home battery owners, the study suggests that while passive balancing revenues are declining, participating in aFRR through aggregators can maintain a reasonable payback period.
The research concludes with several suggestions for future work. Future studies should examine how an aFRR-focused strategy can be integrated into broader multi-market value stacking approaches, including intraday arbitrage and selective passive balancing, to reduce revenue volatility and improve overall profitability. More detailed econometric and scenario-based analyses are also needed to better understand aFRR market dynamics, especially in light of regulatory changes and increasing cross-border integration via PICASSO. As battery participation grows, future research should explore potential market saturation effects and their impact on price levels, activation frequency, and aggregator returns. Lastly, the gradual phase-out of gas-fired power plants from balancing markets introduces new uncertainties, warranting investigation into how this shift could affect price volatility, scarcity pricing, and the strategic positioning of new flexible assets like batteries.
Impact of Alternative Transport Tariffs on Battery Performance
An Optimization and Distribution Network Model
This research examines how alternative transport tariffs impact battery behavior and grid stability in the Dutch distribution network. Specifically, it evaluates the effects of two newly introduced tariff structures: Time-of-Use (TOU) tariffs, which warrant flexible participation on the grid, and Time-Block (TB) tariffs, which provide discounts for energy usage within predefined time windows. The study assesses battery behavior, congestion relief, and financial viability under these tariffs, comparing their effects to a baseline scenario without alternative transport tariffs and without a battery. A key objective is to determine whether non-market-based mechanisms such as alternative transport tariffs can enhance congestion management and whether the TOU tariff should be extended to the distribution grid.
To analyze these effects, a quantitative modeling approach is used, combining a Mixed Integer Linear Programming (MILP) model, which optimizes battery operation in the day-ahead and intraday electricity markets, with a PyPSA distribution network model, which simulates battery interactions within the grid. The study evaluates three scenarios: no tariff, TOU tariffs, and TB tariffs. A sensitivity analysis is conducted to examine the robustness of results under different price fluctuations and seasonal variations.
The results show that battery storage significantly improves congestion management by reducing line overloading, renewable energy curtailment, and peak loads. However, the extent of these benefits depends on the tariff design. The Time-of-Use tariff proves to be an effective mechanism, providing a structured yet flexible approach that allows batteries to optimize charging and discharging based on real-time grid conditions. This improves both their financial viability and their role in congestion relief. In contrast, the Time-Block tariff imposes rigid constraints that limit battery owners’ ability to adapt to market signals, significantly reducing both the financial attractiveness and technical effectiveness of batteries for congestion management. Seasonal variations also affect battery performance, with winter periods exhibiting higher volatility due to fluctuating energy demand and supply conditions. While some peak shaving occurs under all tariff scenarios, its effectiveness is reduced under the TB tariff because of its restrictive design... ...
This research examines how alternative transport tariffs impact battery behavior and grid stability in the Dutch distribution network. Specifically, it evaluates the effects of two newly introduced tariff structures: Time-of-Use (TOU) tariffs, which warrant flexible participation on the grid, and Time-Block (TB) tariffs, which provide discounts for energy usage within predefined time windows. The study assesses battery behavior, congestion relief, and financial viability under these tariffs, comparing their effects to a baseline scenario without alternative transport tariffs and without a battery. A key objective is to determine whether non-market-based mechanisms such as alternative transport tariffs can enhance congestion management and whether the TOU tariff should be extended to the distribution grid.
To analyze these effects, a quantitative modeling approach is used, combining a Mixed Integer Linear Programming (MILP) model, which optimizes battery operation in the day-ahead and intraday electricity markets, with a PyPSA distribution network model, which simulates battery interactions within the grid. The study evaluates three scenarios: no tariff, TOU tariffs, and TB tariffs. A sensitivity analysis is conducted to examine the robustness of results under different price fluctuations and seasonal variations.
The results show that battery storage significantly improves congestion management by reducing line overloading, renewable energy curtailment, and peak loads. However, the extent of these benefits depends on the tariff design. The Time-of-Use tariff proves to be an effective mechanism, providing a structured yet flexible approach that allows batteries to optimize charging and discharging based on real-time grid conditions. This improves both their financial viability and their role in congestion relief. In contrast, the Time-Block tariff imposes rigid constraints that limit battery owners’ ability to adapt to market signals, significantly reducing both the financial attractiveness and technical effectiveness of batteries for congestion management. Seasonal variations also affect battery performance, with winter periods exhibiting higher volatility due to fluctuating energy demand and supply conditions. While some peak shaving occurs under all tariff scenarios, its effectiveness is reduced under the TB tariff because of its restrictive design...
Bridging barriers to energy efficiency: the challenge for Dutch office buildings
An investigation into corporate adaptation to climate policy, the energy transition, and strategic responses in office buildings
Three main regulations are included and investigated in this study: the minimum energy label C for office buildings, the energy savings obligation (including the recognised measures list), and the Energy Efficiency Directive (EED) audit obligation. The study provides detailed insights into how involved companies navigate the technical and organisational complexities of complying with these policies, including negotiating with landlords for rented office spaces and balancing compliance costs with financial viability. Additionally, this research highlights the extent to which companies’ sustainability practices are driven by regulatory compliance or by a deeper commitment to social corporate responsibility in sustainability. Long term strategies and culture will be included in this research to create a more complete view of companies’ stance and strategies.
By presenting a nuanced analysis of the effects of Dutch climate policy on the business sector, this research contributes to the broader understanding of how companies are responding to the evolving sustainability landscape and energy efficiency goals in the business sector. The findings inform both policymakers and companies on effective strategies for enhancing energy efficiency in office buildings while addressing the practical and organisational barriers to implementation.
...
Three main regulations are included and investigated in this study: the minimum energy label C for office buildings, the energy savings obligation (including the recognised measures list), and the Energy Efficiency Directive (EED) audit obligation. The study provides detailed insights into how involved companies navigate the technical and organisational complexities of complying with these policies, including negotiating with landlords for rented office spaces and balancing compliance costs with financial viability. Additionally, this research highlights the extent to which companies’ sustainability practices are driven by regulatory compliance or by a deeper commitment to social corporate responsibility in sustainability. Long term strategies and culture will be included in this research to create a more complete view of companies’ stance and strategies.
By presenting a nuanced analysis of the effects of Dutch climate policy on the business sector, this research contributes to the broader understanding of how companies are responding to the evolving sustainability landscape and energy efficiency goals in the business sector. The findings inform both policymakers and companies on effective strategies for enhancing energy efficiency in office buildings while addressing the practical and organisational barriers to implementation.
Building a Sustainable Future of Education
An Investigation into the Sustainability of Digital Education Technologies in European Higher Education Institutions
Sustainability assessment can be a useful model to evaluate an institution’s DET selection process as it provides a holistic evaluation through a multidimensional perspective to develop a more responsible and future-proof approach to digital education infrastructure. However, a multidimensional sustainability analysis has not been applied in the context of DET selection. Therefore, it is unclear to decision-makers what sustainable DET looks like and what role sustainability plays in the DET selection process. This study addressed this gap by answering the following question: How are European higher education institutions incorporating sustainability into selecting digital education technologies?
The sustainability dimensions of DETs were formulated by conducting a literature review of contemporary models, encompassing the environmental, social, and technological aspects. A more sustainable DET increases the positive impact along each of these dimensions. An environmentally sustain- able DET preserves and protects natural resources by reducing the environmental impact through its hardware and software. A socially sustainable DET increases equal access to education for all learners, regardless of socioeconomic status, disabilities, or geographic location while preserving individual privacy. A technologically sustainable DET is long-lasting, possesses the necessary functionalities, and balances a tool’s simplicity, openness, and ownership. While most sustainability models include the economic dimension, due to the university’s non-profit nature and the common prioritization of economic factors above other criteria in decision-making, this study excluded the economic dimension to examine the other dimensions more closely. Furthermore, the pedagogical dimension was omitted due to its sustainability considerations typically arising after the implementation of a DET, rather than during its selection stage and therefore is beyond the scope of this research.
Four key actors involved in the DET selection process were identified through an actor analysis. These include the university’s Head of IT who oversees the institution’s infrastructure system and their IT tool specialists who provide technical expertise, service providers whose products comprise the DET market, and education associations who help universities procure DETs. Ten semi-structured interviews were conducted with European university Heads of IT to gather data on the current DET selection process and the challenges institutions face when incorporating sustainability into DET selection.
The sustainability dimensions were used in conjunction with grounded theory open and axial coding analysis to evaluate the sustainability of current DET selection processes. The results showed that decision-makers predominantly utilize the EU-regulated tendering process to select DETs, which comprises minimal sustainability criteria while assigning significant importance to the economic factor (i.e., DET price). Additionally, interviewees shared they prioritize social and technological sustainability, specifically the privacy, data security, and functionality of DETs over other sustainability criteria. On the other hand, environmental sustainability is underrepresented in DET selection criteria. This is primarily due to the lack of available data and initiatives collecting DET environmental impact metrics, making it difficult for decision-makers to create relevant requirements and kickout criteria to compare DET options based on environmental sustainability. Finally, the analysis illustrated the three most common challenges that hinder sustainable DET selection are the limited financial and human resources, the insignificant or lack of sustainability criterion weighting, and the long and inflexible tender process.
Overall, this study contributes to filling the knowledge gap in understanding the sustainability of current European universities’ DET selection process and highlights key challenges decision-makers and researchers should focus on to improve the sustainability of digital education technologies. Future research can build on this work by expanding the scope beyond Northwestern European institutions, interviewing other decision-maker actors, and developing a standardized selection process for sustainable DET selection.
Additionally, recommendations were made to the four actor groups as well as general advice for universities to increase DET sustainability. The Head of IT should prioritize the environmental aspect in DET criteria and collaborate with service providers to address environmental impact metrics. They should also encourage the development of new tools by teachers and students. The IT tool specialist should engage in co-development with service providers for better tool support and to ensure a secure and functional digital infrastructure. Service providers need to align their products with sustainability criteria, propose pilot projects to universities, and share environmental impact metrics with relevant stakeholders. Education associations should organize collective efforts to enhance the sustainability of the DET tendering process and offer streamlined services like joint procurement and model contracts to simplify the selection process. Universities could transition to renewable energy to reduce DET’s carbon footprint, implement e-waste recycling and disposal programs, and support research into sustainable DET. ...
Sustainability assessment can be a useful model to evaluate an institution’s DET selection process as it provides a holistic evaluation through a multidimensional perspective to develop a more responsible and future-proof approach to digital education infrastructure. However, a multidimensional sustainability analysis has not been applied in the context of DET selection. Therefore, it is unclear to decision-makers what sustainable DET looks like and what role sustainability plays in the DET selection process. This study addressed this gap by answering the following question: How are European higher education institutions incorporating sustainability into selecting digital education technologies?
The sustainability dimensions of DETs were formulated by conducting a literature review of contemporary models, encompassing the environmental, social, and technological aspects. A more sustainable DET increases the positive impact along each of these dimensions. An environmentally sustain- able DET preserves and protects natural resources by reducing the environmental impact through its hardware and software. A socially sustainable DET increases equal access to education for all learners, regardless of socioeconomic status, disabilities, or geographic location while preserving individual privacy. A technologically sustainable DET is long-lasting, possesses the necessary functionalities, and balances a tool’s simplicity, openness, and ownership. While most sustainability models include the economic dimension, due to the university’s non-profit nature and the common prioritization of economic factors above other criteria in decision-making, this study excluded the economic dimension to examine the other dimensions more closely. Furthermore, the pedagogical dimension was omitted due to its sustainability considerations typically arising after the implementation of a DET, rather than during its selection stage and therefore is beyond the scope of this research.
Four key actors involved in the DET selection process were identified through an actor analysis. These include the university’s Head of IT who oversees the institution’s infrastructure system and their IT tool specialists who provide technical expertise, service providers whose products comprise the DET market, and education associations who help universities procure DETs. Ten semi-structured interviews were conducted with European university Heads of IT to gather data on the current DET selection process and the challenges institutions face when incorporating sustainability into DET selection.
The sustainability dimensions were used in conjunction with grounded theory open and axial coding analysis to evaluate the sustainability of current DET selection processes. The results showed that decision-makers predominantly utilize the EU-regulated tendering process to select DETs, which comprises minimal sustainability criteria while assigning significant importance to the economic factor (i.e., DET price). Additionally, interviewees shared they prioritize social and technological sustainability, specifically the privacy, data security, and functionality of DETs over other sustainability criteria. On the other hand, environmental sustainability is underrepresented in DET selection criteria. This is primarily due to the lack of available data and initiatives collecting DET environmental impact metrics, making it difficult for decision-makers to create relevant requirements and kickout criteria to compare DET options based on environmental sustainability. Finally, the analysis illustrated the three most common challenges that hinder sustainable DET selection are the limited financial and human resources, the insignificant or lack of sustainability criterion weighting, and the long and inflexible tender process.
Overall, this study contributes to filling the knowledge gap in understanding the sustainability of current European universities’ DET selection process and highlights key challenges decision-makers and researchers should focus on to improve the sustainability of digital education technologies. Future research can build on this work by expanding the scope beyond Northwestern European institutions, interviewing other decision-maker actors, and developing a standardized selection process for sustainable DET selection.
Additionally, recommendations were made to the four actor groups as well as general advice for universities to increase DET sustainability. The Head of IT should prioritize the environmental aspect in DET criteria and collaborate with service providers to address environmental impact metrics. They should also encourage the development of new tools by teachers and students. The IT tool specialist should engage in co-development with service providers for better tool support and to ensure a secure and functional digital infrastructure. Service providers need to align their products with sustainability criteria, propose pilot projects to universities, and share environmental impact metrics with relevant stakeholders. Education associations should organize collective efforts to enhance the sustainability of the DET tendering process and offer streamlined services like joint procurement and model contracts to simplify the selection process. Universities could transition to renewable energy to reduce DET’s carbon footprint, implement e-waste recycling and disposal programs, and support research into sustainable DET.
Transitioning from natural gas to carbon-free households in the Netherlands
The influence of behavioral characteristics and policy conditions in the heat transition
The importance of behavioral characteristics and policy conditions that surround households regarding this transition has not been studied in depth in the documented literature and therefore stands as an opportunity area that this thesis covers. For achieving this, an agent-based model was developed using NetLogo, taking into account the characteristics of the Dutch population, being represented in the form of a sample neighborhood. The technical settings available in the model represent mature technologies that are currently in use in the Netherlands, and that can be used collectively or individually. Complementing and supporting these technologies, the Dutch government has made available a series of policy instruments, namely subsidies and credits, that financially support households in acquiring them. The availability of these subsidies is mainly dependent on the type of household ownership and additional factors, which are included in the model.
A behavioral theory known as the theory of planned behavior was implemented to define the households’ behavior. This theory links three elements, namely attitude, subjective norms, and perceived behavioral control, to the final intention of households to execute an action. Regarding this specific topic, the study found out that the main beliefs that influence attitude are environmental friendliness, awareness of gas-saving measures, energy independence and economic drive. The subjective norms, which relate to social influence, are represented through the concept of belief dynamics, which develops the idea that social connections can shape a household’s set of beliefs. Finally, perceived behavioral control, which is a measure of the apparent facility to execute a certain action, is reflected in four main external elements, namely the availability of subsidies, the municipality efforts, time availability, and financial capability of households.
The results of the model showed that with the current conditions, the Netherlands would be able to achieve a total of 55% of gas-free households by 2050. This, in turn, would represent a reduction of 45% of the current natural gas being used. To evaluate the extent to which behavioral characteristics and policy conditions influence the heat transition, additional scenarios were developed, where random behavioral attributes were assigned. Following the same line, additional scenarios were defined where the number of available subsidies and the amount awarded per subsidy varied, in addition to a scenario with a higher gas price. These scenarios showed that behavioral characteristics are a very relevant factor and can shape the extent to which the heat transition can be achieved. The policy conditions showed an influence, both in the extent of the heat transition and on the uptake of specific technologies. However, the amount awarded per subsidy and the price of gas showed no relevant differences in gas consumption or technology choice.
The results provide insights into the possibilities for policymakers to ensure that this transition is fulfilled. Policies should target the beliefs of society, either by inducing people into them or by reinforcing them. Besides this, technology choice seems to be directly influenced by the number of subsidies available for each specific technology, which could be useful to target specific technologies that might result more promising than others. Finally, considering that varying the amount awarded per subsidy did not generate a substantial difference, there is a possibility to redefine the subsidy schemes and reallocate this financial means to support the tenants, which is currently the group with the most restricted access to subsidies. ...
The importance of behavioral characteristics and policy conditions that surround households regarding this transition has not been studied in depth in the documented literature and therefore stands as an opportunity area that this thesis covers. For achieving this, an agent-based model was developed using NetLogo, taking into account the characteristics of the Dutch population, being represented in the form of a sample neighborhood. The technical settings available in the model represent mature technologies that are currently in use in the Netherlands, and that can be used collectively or individually. Complementing and supporting these technologies, the Dutch government has made available a series of policy instruments, namely subsidies and credits, that financially support households in acquiring them. The availability of these subsidies is mainly dependent on the type of household ownership and additional factors, which are included in the model.
A behavioral theory known as the theory of planned behavior was implemented to define the households’ behavior. This theory links three elements, namely attitude, subjective norms, and perceived behavioral control, to the final intention of households to execute an action. Regarding this specific topic, the study found out that the main beliefs that influence attitude are environmental friendliness, awareness of gas-saving measures, energy independence and economic drive. The subjective norms, which relate to social influence, are represented through the concept of belief dynamics, which develops the idea that social connections can shape a household’s set of beliefs. Finally, perceived behavioral control, which is a measure of the apparent facility to execute a certain action, is reflected in four main external elements, namely the availability of subsidies, the municipality efforts, time availability, and financial capability of households.
The results of the model showed that with the current conditions, the Netherlands would be able to achieve a total of 55% of gas-free households by 2050. This, in turn, would represent a reduction of 45% of the current natural gas being used. To evaluate the extent to which behavioral characteristics and policy conditions influence the heat transition, additional scenarios were developed, where random behavioral attributes were assigned. Following the same line, additional scenarios were defined where the number of available subsidies and the amount awarded per subsidy varied, in addition to a scenario with a higher gas price. These scenarios showed that behavioral characteristics are a very relevant factor and can shape the extent to which the heat transition can be achieved. The policy conditions showed an influence, both in the extent of the heat transition and on the uptake of specific technologies. However, the amount awarded per subsidy and the price of gas showed no relevant differences in gas consumption or technology choice.
The results provide insights into the possibilities for policymakers to ensure that this transition is fulfilled. Policies should target the beliefs of society, either by inducing people into them or by reinforcing them. Besides this, technology choice seems to be directly influenced by the number of subsidies available for each specific technology, which could be useful to target specific technologies that might result more promising than others. Finally, considering that varying the amount awarded per subsidy did not generate a substantial difference, there is a possibility to redefine the subsidy schemes and reallocate this financial means to support the tenants, which is currently the group with the most restricted access to subsidies.
Policy platforms as support tools for climate change mitigation and adaptation policymaking
A case study of policymakers and policy advisors’ perceptions of policy platforms as support tools and how to improve their design and use
Multiple challenges affect the effectiveness of climate change mitigation and adaptation measures, such as accountability, intergenerational justice and developing countries’ increased participation in greenhouse gas emissions. The high complexity of information surrounding models’ assumptions, results, and scenarios related to climate change also presents a challenge, especially when communicating with policymakers. In this context, support tools such as policy platforms can help bridge the science-policy gap by allowing policymakers to understand scenarios and available policy levers, enabling a better understanding of relevant concepts and models or serving as hubs for disseminating best practices and success stories. Available literature often evaluates support tools within the context of use by regular citizens, making it unclear how policymakers perceive support tools and how well they meet their needs, pointing to an important knowledge gap. This thesis explores policymakers’ and policy advisors’ perceptions of the usefulness of climate change mitigation and adaptation (CCMA) policy platforms and the characteristics of such policy platforms they prefer in order to use them as support tools. A collective case study was conducted with ten CCMA policy platforms within the context of the EU-funded Horizon 2020 programme. In addition, interviews (11) and surveys (9) with policymakers and policymakers’ advisors in the Netherlands and seven other countries within and beyond the EU were conducted.
Nine characteristics of CCMA policy platforms were identified: Transparency & Credibility of information, Ease of use, Flexibility of use, Accessibility & Portability, Education & Awareness, Communication of complex information, Data visualisation & interactivity, Actively maintained and supported, and Security & privacy. Interviews and surveys show that accessibility, relevance, applicability, and credibility were identified as the primary factors driving the perception of policymakers about the usefulness of CCMA policy platforms and four groups of characteristics were identified with decreasing levels of priority for policymakers: mandatory or must-have (formed by communication of complex information, free and open access to the tool, transparency regarding data sources and limitations, and high level of detail for spatial and temporal data); highly desirable or should-have (formed by availability of training and learning functionalities, availability of detailed documentation on concepts and models, interactive and easy-to-navigate elements, and availability of a web-based platform); ‘nice to have’ or could-have (user stories from policymakers or communities, availability of very recent data, ability to import user data/export results, and ability to modify parameters and run custom analyses), and indifferent (availability of languages beyond English and the ability to use the tool in mobile phones or tablets).
Four main recommendations are made to improve the design and use of CCMA policy platforms: Incorporating systematic reviews of existing CCMA policy platforms as part of projects developing such platforms, involving boundary organisations in the development and use of CCMA policy platforms, developing CCMA policy platforms with longer life expectancies and developing CCMA policy platforms flexible for different needs and preferences of policymakers.
With these results, new CCMA policy platforms can be developed with a better understanding of how useful policymakers perceive them and what they want from these support tools. ...
Multiple challenges affect the effectiveness of climate change mitigation and adaptation measures, such as accountability, intergenerational justice and developing countries’ increased participation in greenhouse gas emissions. The high complexity of information surrounding models’ assumptions, results, and scenarios related to climate change also presents a challenge, especially when communicating with policymakers. In this context, support tools such as policy platforms can help bridge the science-policy gap by allowing policymakers to understand scenarios and available policy levers, enabling a better understanding of relevant concepts and models or serving as hubs for disseminating best practices and success stories. Available literature often evaluates support tools within the context of use by regular citizens, making it unclear how policymakers perceive support tools and how well they meet their needs, pointing to an important knowledge gap. This thesis explores policymakers’ and policy advisors’ perceptions of the usefulness of climate change mitigation and adaptation (CCMA) policy platforms and the characteristics of such policy platforms they prefer in order to use them as support tools. A collective case study was conducted with ten CCMA policy platforms within the context of the EU-funded Horizon 2020 programme. In addition, interviews (11) and surveys (9) with policymakers and policymakers’ advisors in the Netherlands and seven other countries within and beyond the EU were conducted.
Nine characteristics of CCMA policy platforms were identified: Transparency & Credibility of information, Ease of use, Flexibility of use, Accessibility & Portability, Education & Awareness, Communication of complex information, Data visualisation & interactivity, Actively maintained and supported, and Security & privacy. Interviews and surveys show that accessibility, relevance, applicability, and credibility were identified as the primary factors driving the perception of policymakers about the usefulness of CCMA policy platforms and four groups of characteristics were identified with decreasing levels of priority for policymakers: mandatory or must-have (formed by communication of complex information, free and open access to the tool, transparency regarding data sources and limitations, and high level of detail for spatial and temporal data); highly desirable or should-have (formed by availability of training and learning functionalities, availability of detailed documentation on concepts and models, interactive and easy-to-navigate elements, and availability of a web-based platform); ‘nice to have’ or could-have (user stories from policymakers or communities, availability of very recent data, ability to import user data/export results, and ability to modify parameters and run custom analyses), and indifferent (availability of languages beyond English and the ability to use the tool in mobile phones or tablets).
Four main recommendations are made to improve the design and use of CCMA policy platforms: Incorporating systematic reviews of existing CCMA policy platforms as part of projects developing such platforms, involving boundary organisations in the development and use of CCMA policy platforms, developing CCMA policy platforms with longer life expectancies and developing CCMA policy platforms flexible for different needs and preferences of policymakers.
With these results, new CCMA policy platforms can be developed with a better understanding of how useful policymakers perceive them and what they want from these support tools.
Understanding energy injustices experienced by stakeholders in the Rotterdam-The Hague energy region
Examining the decision-making process on a local and regional level and its impact on a just energy transition
Climate justice, an emerging theoretical concept, recognizes the unevenly distributed burdens and benefits of carbon emissions and calls for effective mitigation strategies. One of the most polluting sectors is the energy sector, which still heavily relies on fossil fuels. To achieve climate neutrality, a transition to sustainable forms of energy is necessary. Energy justice, a theoretical framework advocating for equitable energy distribution, representative energy decision-making, and a balanced cost-benefit distribution among citizens, has gained traction in recent academic literature.
In the Netherlands, the energy transition is governed at regional and local levels through regional energy strategies. However, understanding of local and regional governance in this context is somewhat limited. This research combines the concepts of regional governance and the energy justice framework and applies them to the Rotterdam-The Hague energy region.
The research objectives of this thesis are to comprehend regional energy decision-making, identify stakeholders experiencing energy injustices, examine how these injustices are currently addressed, and propose potential improvements.
Utilizing a mixed-methods approach involving interviews and media analysis, the research reveals instances of energy injustices experienced by citizens in this region, including energy poverty. While the regional and local governments are presently unaware of this framework, they acknowledge the importance of a just energy transition and are actively developing strategies to mitigate these injustices.
Policy recommendations encompass various levels: providing structural financial support to local governments at the national level, promoting knowledge exchange and cross-border collaboration at the regional level, and enhancing citizen participation, ownership in energy projects, and insulation programs at the local level.
In conclusion, this thesis offers valuable insights into local and regional energy transition governance, focusing on energy justice, energy poverty, and citizen participation. ...
Climate justice, an emerging theoretical concept, recognizes the unevenly distributed burdens and benefits of carbon emissions and calls for effective mitigation strategies. One of the most polluting sectors is the energy sector, which still heavily relies on fossil fuels. To achieve climate neutrality, a transition to sustainable forms of energy is necessary. Energy justice, a theoretical framework advocating for equitable energy distribution, representative energy decision-making, and a balanced cost-benefit distribution among citizens, has gained traction in recent academic literature.
In the Netherlands, the energy transition is governed at regional and local levels through regional energy strategies. However, understanding of local and regional governance in this context is somewhat limited. This research combines the concepts of regional governance and the energy justice framework and applies them to the Rotterdam-The Hague energy region.
The research objectives of this thesis are to comprehend regional energy decision-making, identify stakeholders experiencing energy injustices, examine how these injustices are currently addressed, and propose potential improvements.
Utilizing a mixed-methods approach involving interviews and media analysis, the research reveals instances of energy injustices experienced by citizens in this region, including energy poverty. While the regional and local governments are presently unaware of this framework, they acknowledge the importance of a just energy transition and are actively developing strategies to mitigate these injustices.
Policy recommendations encompass various levels: providing structural financial support to local governments at the national level, promoting knowledge exchange and cross-border collaboration at the regional level, and enhancing citizen participation, ownership in energy projects, and insulation programs at the local level.
In conclusion, this thesis offers valuable insights into local and regional energy transition governance, focusing on energy justice, energy poverty, and citizen participation.
Multi-Objective Optimization of a Grid-Connected PV-Battery-Electrolyzer Fuel Cell Energy System
A Case Study at The Green Village
Previous research has highlighted how an off-grid configuration would result in inconveniently high costs for the community's users, if compared to the average cost of energy in The Netherlands. The aim of this thesis is to study the system in a grid-connected configuration, and in particular to find the optimal sizes of the components in order to achieve the best trade off between three conflicting objectives : minimizing total costs, maximizing self- sufficiency and maximizing reliability. After modeling the system's components and their mutual interactions, the optimization was carried out on MATLAB using a variant of the NSGA-II algorithm, which provides a Pareto Set of equally optimal solutions for the problem. The solutions were then ranked with a Technique for Order Preference based on Similarity to the Ideal Solution (TOPSIS), to assist the decision-making process.
The simulations have determined that an installed capacity of 85.41 kWp (composed of 234 panels of 365 Wp each) results in the most effective choice for the solar energy generation, irrespective of the external conditions imposed. The optimal storage capacity, however, results significantly more influenced by factors such as grid imports limitations and price uncertainties. Under the conditions of limited imports from the grid, an optimal capacity of 75 kWh in the form of batteries was found. In general, the study confirms that the adoption of an hydrogen storage system is far from being convenient on a small scale residential level, regardless of the pricing conditions. The research has also posed an accent on the incremented costs incurred to reach full reliability of the system with low values of dependence from the grid, due to the high costs of the necessary storage equipment. Additionally, despite the best solutions found represent the optimal compromises balancing the conflicting objectives, reasonable solutions in terms of costs faced by the Community's users are usually not among the first choices of the ranking algorithm, mainly because they necessitate of at least 50% of the load to be supplied through grid imports. ...
Previous research has highlighted how an off-grid configuration would result in inconveniently high costs for the community's users, if compared to the average cost of energy in The Netherlands. The aim of this thesis is to study the system in a grid-connected configuration, and in particular to find the optimal sizes of the components in order to achieve the best trade off between three conflicting objectives : minimizing total costs, maximizing self- sufficiency and maximizing reliability. After modeling the system's components and their mutual interactions, the optimization was carried out on MATLAB using a variant of the NSGA-II algorithm, which provides a Pareto Set of equally optimal solutions for the problem. The solutions were then ranked with a Technique for Order Preference based on Similarity to the Ideal Solution (TOPSIS), to assist the decision-making process.
The simulations have determined that an installed capacity of 85.41 kWp (composed of 234 panels of 365 Wp each) results in the most effective choice for the solar energy generation, irrespective of the external conditions imposed. The optimal storage capacity, however, results significantly more influenced by factors such as grid imports limitations and price uncertainties. Under the conditions of limited imports from the grid, an optimal capacity of 75 kWh in the form of batteries was found. In general, the study confirms that the adoption of an hydrogen storage system is far from being convenient on a small scale residential level, regardless of the pricing conditions. The research has also posed an accent on the incremented costs incurred to reach full reliability of the system with low values of dependence from the grid, due to the high costs of the necessary storage equipment. Additionally, despite the best solutions found represent the optimal compromises balancing the conflicting objectives, reasonable solutions in terms of costs faced by the Community's users are usually not among the first choices of the ranking algorithm, mainly because they necessitate of at least 50% of the load to be supplied through grid imports.
Analyzing the spatial relationship between energy and the built environment
A cluster-based approach to spatial planning
Aggregated Flexibility to support Congestion Management
A case study at Eneco CrowdNett
Aggregator Eneco CrowdNett is planning to commission a pool of households which will have a combined system of solar-PV and residential Battery Energy Storage (BES) units. Next to optimizing the household’s self-consumption, these systems will jointly be able to deliver power to the grid. Therefore, the system as designed by Eneco CrowdNett is used as a case-study during this research.
This research aims to provide a comprehensive understanding of how the value of an Aggregator’s electric flexibility services can be determined for DSOs, without jeopardizing the business case for the Aggregator and the CrowdNett participants themselves. A tool that simulates the behavior of residential BES units connected to the LV distribution grid is developed and tested, followed by a reflection on its usability for other cases.
The system under review in this study is framed as a Unit Commitment Problem (UCP), in which the generation units are represented by CrowdNett’s residential BES units that primarily deliver local service, but can also deliver congestion management services through load shifting. Linear Programming (LP) is considered a useful method to solve the UCP, which is implemented in the modelling software Linny-R.
It can be concluded the value of Eneco CrowdNett’s electric flexibility services for DSOs can not be determined by exclusive using a Unit Commitment model with a LP solver to replicate the behavior of residential BES units located at the LV distribution grid.
The model rather provides a tool to generally assess whether an Aggregator is able to reduce congestions in a given distribution network without jeopardizing her own business case and that of the end-users owning the BES units. The model should be supplied with input data regarding residential demand profiles, PV production profiles and the power capacity of the LV substation before the model output can be compared with the expected costs of grid reinforcements on a case-by-case basis to determine the value for DSOs.
...
Aggregator Eneco CrowdNett is planning to commission a pool of households which will have a combined system of solar-PV and residential Battery Energy Storage (BES) units. Next to optimizing the household’s self-consumption, these systems will jointly be able to deliver power to the grid. Therefore, the system as designed by Eneco CrowdNett is used as a case-study during this research.
This research aims to provide a comprehensive understanding of how the value of an Aggregator’s electric flexibility services can be determined for DSOs, without jeopardizing the business case for the Aggregator and the CrowdNett participants themselves. A tool that simulates the behavior of residential BES units connected to the LV distribution grid is developed and tested, followed by a reflection on its usability for other cases.
The system under review in this study is framed as a Unit Commitment Problem (UCP), in which the generation units are represented by CrowdNett’s residential BES units that primarily deliver local service, but can also deliver congestion management services through load shifting. Linear Programming (LP) is considered a useful method to solve the UCP, which is implemented in the modelling software Linny-R.
It can be concluded the value of Eneco CrowdNett’s electric flexibility services for DSOs can not be determined by exclusive using a Unit Commitment model with a LP solver to replicate the behavior of residential BES units located at the LV distribution grid.
The model rather provides a tool to generally assess whether an Aggregator is able to reduce congestions in a given distribution network without jeopardizing her own business case and that of the end-users owning the BES units. The model should be supplied with input data regarding residential demand profiles, PV production profiles and the power capacity of the LV substation before the model output can be compared with the expected costs of grid reinforcements on a case-by-case basis to determine the value for DSOs.