The Dutch electricity grid is increasingly facing challenges due to grid congestion. If left unaddressed, this could eventually lead to electricity supply disruptions, significant economic losses, and a decline in living standards. In response, the Netherlands Authority for Consumers and Markets (ACM) has proposed the introduction of injection charges for large-scale electricity producers as a potential solution to incentivize more efficient use of the existing grid infrastructure.

This study evaluates the effects of different injection charge designs on grid load and the resulting need for network reinforcement. The results were obtained by constructing a network model based on the electricity network topology of the municipality of Reimerswaal in the Netherlands. The model incorporated generation technologies such as onshore wind and solar PV, and applied a priority dispatch policy that favors local renewable electricity generation over imports from elsewhere.

In total, four injection charge variants were assessed. These include a uniform tariff based on the kWcontract and kWmax charge components, a time-dependent tariff on the same components, a uniform tariff based on kWh, and a combined variant incorporating time-dependent kWcontract and kWmax charges along with a uniform kWh tariff.

Results show that peak grid loads can be reduced by up to 35.55% when using time-dependent tariffs based on the kWcontract and kWmax components. These variants also proved more effective at reducing peak occurrences than uniform, non-time-based tariffs. While the uniform kWh-based variant marginally decreased import-related peak loads by up to 0.99%, it had no effect on injection-related peaks, making its overall impact rather limited. The combined variant yielded results similar to those of its individual components, combining injection-related peak reduction with a marginal decrease in import-related peaks.

These findings indicate that well-designed injection charges can effectively reduce peak grid load and mitigate the need for network reinforcement. However, their effectiveness depends on the specific charge design and local grid conditions. In addition, the design and implementation of such charges require careful consideration of factors beyond technical efficacy. Legitimacy, feasibility, stakeholder support, and potential economic and environmental implications must also be taken into account.

The global shift toward renewable energy is increasing the share of intermittent electricity sources like wind and solar, creating new challenges for electricity system stability. Demand-side management (DSM), adjusting electricity use to better align with variable supply, offers a key solution. This thesis examines the potential for DSM in the Dutch food industry, a sector well-suited for electrification due to its low-temperature processes.

The research evaluates the economic and operational impacts of fully electric DSM strategies under various 2030 grid tariff designs proposed for The Netherlands. Four DSM strategies are modelled using the Calliope optimisation framework. These strategies combine different electricity procurement methods, electricity market purchases or Power Purchase Agreements (PPAs), with flexibility options: flexible production machinery or thermal energy storage (TES).

Key findings include:

- Economic Performance: Strategies using flexible machinery combined with PPAs result in the lowest total costs. TES-based flexibility also lowers costs relative to constant production but is less cost-effective than machinery flexibility. PPAs offer additional benefits by hedging against electricity price volatility, though their effectiveness depends on the contract price.

- Grid Tariff Effects: All proposed 2030 grid tariffs lead to higher grid costs than current designs. However, they do not alter the relative cost-effectiveness of the DSM strategies, flexible machinery with PPAs remains the most economical.

- Operational Behaviour: Flexible strategies shift production to low-price periods (spring and summer) and reduce it during high-price hours. TES allows constant production by shifting heat generation instead. DSM strategies without PPAs show more adaptive behaviour to price and tariff signals, while PPA-based strategies are constrained by fixed electricity volumes.

In conclusion, DSM can improve the economic viability of electrifying the food industry and contribute to grid flexibility. Grid tariff design plays a significant role in shaping industrial electricity use and can incentivize load shifting, though it may limit flexibility in responding to real-time market prices.

The decarbonisation of Dutch non-residential service buildings is vital for achieving national climate goals. Yet current policies have primarily focused on new constructions, leaving the existing building stock—responsible for a significant share of emissions—underregulated. Financial, organisational, and behavioural barriers delay or prevent timely investments in sustainable heating. Since major heating system replacements typically occur only once or twice before 2050, failure to act during these moments risks locking in carbon-intensive technologies for decades. Targeted policy measures are therefore needed to steer building owners toward low-carbon alternatives at these critical decision points.

This study investigates how selected policy interventions can accelerate CO₂ emission reductions in the Dutch non-residential service sector, assessed through their effectiveness, cost-effectiveness, and distributional impacts. The CEKER techno-economic model, developed by CE Delft and originally tailored to the residential sector, was adapted to reflect the heterogeneity of the non-residential building stock in terms of function, size, and energy demand. The model simulates rational, cost-minimising investment decisions made at natural heating system replacement moments. Twelve policy scenarios were analysed, including increases in the energy tax on natural gas, mandatory energy label standards, and a ban on gas-based heating systems, both as stand-alone measures and in combined scenarios. All scenarios were tested across three electricity price futures to assess policy robustness.

Results show that combined interventions—particularly higher gas taxes paired with mandatory insulation standards—achieve the highest CO₂ reductions (up to 1.2 Mt). Economic measures alone are more cost-effective per tonne of CO₂, but less effective overall. Smaller buildings (≤500 m²) and assembly buildings exhibit high abatement potential but face relatively high retrofit costs.

Policy recommendations include gradually increasing the energy tax on natural gas, mandating a minimum energy label (e.g., Label D by 2030–2035), and offering targeted financial support for small buildings (≤500 m²) and assembly buildings. These results represent a techno-economic upper bound, assuming fully rational investment behaviour without non-financial barriers. As such, they provide a benchmark for evaluating the environmental impact, economic efficiency, and fairness of alternative policy strategies.

The Dutch energy system is rapidly transforming due to electrification, decentralization, and renewable integration, causing widespread grid congestion. While grid reinforcement is slow and costly, Multi-Carrier Energy Hubs (MCEHs) are increasingly promoted as flexible, localized alternatives. These hubs, situated on industrial estates, coordinate electricity, heat, and potentially hydrogen to reduce reliance on transmission infrastructure. However, current evaluation methods of MCEHs are focused on short term feasibility and stakeholder-driven, often ignoring long-term societal impacts.
This research introduces the concept of structural societal value—the enduring economic, environmental, and social benefits of an energy hub beyond short-term congestion relief. Using a societal cost-benefit approach (MKBA) integrated with expert interviews and a custom-built energy flow model, the study evaluates MCEHs not only against grid reinforcement but also in terms of broader system outcomes. A case study of the Tholen industrial estate illustrates this approach in practice.
Results show that MCEHs offer temporary relief when congestion costs are high and reinforcements delayed. However, their structural value depends on local load profiles, flexibility needs, and renewable integration potential. By evaluating MCEHs through a consistent, criteria-based framework, decision-makers can determine whether a hub should remain temporary or evolve into a long-term solution. The findings stress the need for transparent trade-offs between hubs and traditional infrastructure, supporting more informed, adaptive energy planning.

The Dutch heating sector, responsible for 41% of national energy use, remains heavily reliant on fossil fuels. In light of climate goals, this thesis explores how integrating district heating systems with the electricity grid, particularly using geothermal energy and high-temperature aquifer thermal energy storage (HT-ATES), can reduce operational costs and improve flexibility and sustainability. Using a techno-economic optimization model, a real-world case study of Delft was analyzed across four scenarios: fixed and dynamic electricity pricing, grid constraints, and grid reinforcement. The results show that dynamic pricing enhances system responsiveness and storage value, while thermal storage proves value in shifting demand, reducing gas reliance, and improving grid stability. Grid limitations significantly impact electric heating, but storage offers a viable short-term mitigation. A trade-off is evident between capital costs, system resilience, and emissions: larger systems reduce CO₂ but require higher investment.

The steel industry is one of the most energy-intensive and carbon-emitting sectors, accounting for nearly 10% of global greenhouse gas (GHG) emissions. Tata Steel IJmuiden faces growing pressure to decarbonize due to rising CO₂ costs and increasing demand for green steel. This study develops an optimization model to evaluate Tata Steel’s current operations and identify feasible transition pathways towards carbon-neutral steel production. The model simulates energy flows, material usage, and economic impacts under different decarbonization scenarios, using real operational data and future energy price projections.

Three progressive transition phases from 2030 to 2050 are proposed: (1) a shift from the traditional blast furnace–basic oxygen furnace (BF-BOF) route to a hybrid model incorporating direct reduced iron (DRI) and electric arc furnace (EAF) technologies, (2) increased reliance on hydrogen-based steelmaking, and (3) full adoption of hydrogen-powered DRI plants. Results indicate that while green steel production is achievable, it will lead to higher costs due to electricity price volatility, hydrogen dependency, and network constraints. Key recommendations include optimizing capital investments, enhancing energy flexibility through EAFs, accelerating hydrogen infrastructure development, and advocating for policy reforms.

This study provides critical insights for Tata Steel and the broader industrial sector, demonstrating that strategic planning and investment in energy-efficient technologies are essential for a sustainable and competitive steel industry.

Greenhouse horticulture is a key sector in the Netherlands, significantly contributing to the economy and society but also heavily reliant on natural gas, leading to substantial CO2 emissions. The sector, in partnership with the government, aims to achieve climate neutrality by 2040 as part of the Energy Transition Covenant for Greenhouse Horticulture 2022-2030. To discourage natural gas use, particularly in combined heat and power (CHP) systems, policymakers have restructured energy taxation and implemented a flat CO2 levy. Additionally, increased network charges are anticipated due to infrastructure expansion, while electricity and gas prices are expected to decline over the next decade. The transition also depends on factors like Dutch sustainable energy system advancements, third-party heat and electricity supply, and consumer preference for climate-neutral products.

In this master's thesis, part of the TU Delft DEMOSES project, a unit commitment (UC) model was developed to analyse the evolving energy landscape of the Dutch horticultural sector and its impact on energy management practices, emissions, and expenses until 2030. The research addresses four sub-questions focusing on trends in energy utilisation, the effects of regulations and costs, the evolution of emissions and financial dynamics, and the impact of energy price developments on the sector. Using multi-integer linear programming, the UC model simulates the dynamics of electricity, heat, and CO2 within the greenhouse industry, capturing system intricacies and exploring various scenarios. Inputs include demand, generation asset specifications, and national energy prices.

Key findings indicate that by 2030, revised energy taxes will shift incentives away from self-generated electricity from CHP systems, resulting in higher costs and only modest emission reductions without renewable energy sources. With renewable availability, substantial emission reductions are possible but at significantly increased costs. The most cost-effective scenario combines geothermal and waste heat with CHP generation, balancing costs and emissions reductions. However, high network charges could undermine regulatory efforts to promote sustainable thermal generation, emphasising the need for effective capacity management.

Geothermal energy is predicted to become the primary sustainable thermal source in the horticultural sector by 2030, though its economic advantage over CHP systems remains limited under current fiscal policies. The research suggests horticulturists will transition from electricity suppliers to balanced electricity traders, integrating both renewable and conventional thermal sources. This transition enhances sustainability but increases net costs due to reduced revenue from less frequent CHP operation.

Recommendations highlight the necessity of policy intervention to manage network charges and incentivise sustainable energy adoption. Revising pricing mechanisms and incorporating subsidies can mitigate costs and promote electric technologies for thermal generation. Future research should explore diverse scenarios for geothermal and waste heat availability, refine models for optimal inclusion, and investigate various tax regimes to enhance economic viability. Horticulturists should invest in waste heat and geothermal energy while maintaining CHP systems for load-following needs and managing heat pump capacity within contracted capacities to mitigate costs.

This study addresses the revenue losses from network congestion of an Offshore Wind Farm (OWF) resulting from the implementation of an Offshore Bidding Zone (OBZ) and explores potential measures to mitigate these effects. The quantitative analysis involves a model that simulates a hypothetical OWF located in the North Sea between Norway and the Netherlands, operating with historical electricity prices and wind data. The OBZ approach is compared with the traditional home market approach to analyse revenues and explore the magnitude of the losses. The implementation of mitigation measures provides insights into their impact on revenues, congestion rents, and compensation. Additionally, the role of long-term contracts is examined to clarify how they influence the effectiveness of these measures. This is followed by an exploration of the different perspectives of the wind farm and the government to understand how these could shape the OBZ market design. Finally, an analysis of different future alternative market connections is conducted to understand their implications and the trade-offs that must be made for designing the OBZ market. In the end, the following research question is answered: How can revenue losses from network congestion of wind farms be mitigated in an offshore bidding zone in the North Sea?

In the European Union, heating and cooling account for 50% of the total energy consumption and half of the carbon emissions, as 75% of the energy is still supplied by fossil fuels. In the Netherlands, energy transition decision-makers are sailing in the dark in an attempt to realize the heat transition in high-density areas. How can market parties optimize the mix of heat supply technologies, while responding to policies and network requirements? To adequately manage renewable energy on a large scale, flexibility measures need to be in place across all components of the system, from generation to distribution, to supply and demand. This master thesis is part of the TU Delft research project "DEMOSES" and is done in collaboration with the energy company Eneco. The project aims to study to what extent the integration of heat storage and additional heat sources can enhance the flexibility and long-term sustainability of the South-Holland district heating network. This is done by developing and analyzing a district heating network model of South-Holland, and expanding the network with additional heat sources and heat storage. Additional heat sources lead to increased flexibility and sustainability, while additional heat storage primarily increases only the flexibility. Looking ahead to 2030, to realize the heat transition in South-Holland, many additional sources and storage should be added in the following years to realize a flexible, sustainable, and affordable district heating network. However, with the proposal of the Collective Heat System Act, the question is whether investments in additional heat sources and storage are attractive for energy companies when ownership of these assets in the following years cannot be guaranteed.

The potential of households to adapt their energy use to the conditions of the energy system remains largely untapped due to shortcomings in consumer governance (i.e., the organization of household energy use). A lack of price signals and services leads to uncoordinated household energy use. Various proposals exist for updating consumer governance (e.g., virtual power plants, variable tariffs, energy communities). A research gap arises from the fact that a single governance design cannot meet all household needs and that the priorities of household needs are ambiguous.

The empirical research in this dissertation demonstrates that a governance design should focus on enabling households to achieve energy cost savings, convincing them to participate by safeguarding their control needs and keeping them involved by limiting their operational burden. These priorities speak for virtual power plants as consumer governance design. If intermediaries could anticipate latent, upcoming household needs in the design, make tradeoffs transparent for households, and create dedicated points for decision-making, then they would support households in making more informed decisions and taking on an active role in the energy system.

The decarbonization of the energy system is driven by the electrification process and the expansion of variable renewable energy capacity. Green hydrogen, produced through electrolysis powered with renewable electricity, represents a major complementary vector that can support this transition. By enabling long-term storage, it addresses the challenges arising from the unpredictability of a renewable energy-based electricity system. It represents a backup and increases the reliability of the electricity system.

In an interconnected decarbonized system where energy carriers are interdependent, new sector-coupling dynamics emerge. Energy-only markets, in theory, yield socially optimal levels of installed capacity across both sectors assuming rational participants and perfect markets. However, risk-averse behaviour among market participants might lead to underinvestment in generation capacity, undermining the efficacy of hydrogen backup.

The main objective of this research is to assess the adequacy of market designs in attracting socially optimal levels of investment in electrolyzers and storage capacity within a decarbonized and integrated system with risk-averse agents.

The stylized stochastic equilibrium model formulated is solved by using the Alternating Direction Method of Multipliers algorithm. Participants formulate their strategy based only on market prices, which are influenced by 18 scenarios that differ in terms of electricity demand, hydrogen demand and variable renewable energy sources availability. The demand for electricity and hydrogen is considered price-elastic.

An energy-only market, an energy market supported by a capacity market for electricity generators, and an energy market supported by capacity markets for both electricity and hydrogen generators are tested under different degrees of risk aversion.

The analysis confirms how risk aversion, by increasing the risk premium required, entails a reduction in installed capacity. Underinvestment is combined with a general reduction in the served energy and an increase in energy prices and frequency of periods of high prices. These trends are more considerable in the hydrogen market, where agents are exposed to risk from the uncertainties in both hydrogen demand and electricity prices.

The energy-only market is more sensitive to the impact of risk aversion due to the absence of risk trading opportunities. In this case, the increase in energy prices is the only solution to recover the risk premium.
Introducing a capacity market for dispatchable electricity generators mitigates the impact of risk aversion in the electricity sector. However, this exacerbates performance issues in the hydrogen market during scarcity periods by amplifying hydrogen price fluctuations. On the other hand, incorporating a capacity market for hydrogen counteracts the effects of risk aversion on the hydrogen sector. This dual approach not only benefits the hydrogen sector but further improves the electricity sector’s adequacy and reduces consumer costs.

Capacity markets are an effective instrument to hedge risk, but their application in an integrated system has to be consistent and include both electricity and hydrogen generation capacity. Furthermore, the strong dependence of hydrogen generation on electricity prices advocates for a direct instrument to hedge risk for renewable generators such as contracts for difference, as an adequate vRES capacity improves the whole system’s performance by reducing hydrogen prices and increasing its availability.

This work seeks to resolve an outstanding problem in the use of reinforcement-learning methods for the simulation of economically-rational agents. We discuss the problem of non-stationarity, and how this subsequently limits market simulation capabilities. After explicating and isolating the source of the problem for a day-ahead electricity market, we demonstrate the application of methods which resolve this problem in simple test-cases, and prove conditions under which similar methods will work in general. Subsequently, we illustrate how these techniques can be used to solve a restricted market-design problem, in the process introducing a framework for discussing adversarial market-design for electricity markets in general. It is hoped that, insofar as they provide a new feedback-loop for market-design, these results will facilitate the design of more complex electricity-markets suitable for the energy transition.

The transition to a carbon-neutral power system has become a global imperative, ever more driven due to recent events such as the energy crisis resulting from the Ukraine invasion. The Netherlands, devoted to the large-scale development of offshore wind and solar PV, see great potential in the use of hydrogen as a gas replacing energy carrier. A knowledge gap has been identified on what this role of electrolysis will be in the future energy system and more specifically, in what quantities electricity can and should be converted to hydrogen. This research aims to address these uncertainties and provide insights into the prudent capacity for green electrolytic hydrogen production in the Netherlands by 2030 and 2040.

To this end, a model of the combined electricity and hydrogen system of the Netherlands is constructed in Linny-R, an executable graphical representation language for Mixed Integer Linear Programming (MILP). A vast array of output data from forecasting scenarios is gathered and consolidated to establish the spectrum of input variables. By systematically assessing the uncertainty, based on these ranges of input variables, and the impact, based on a sensitivity analysis, an experiment design is composed to encompass the most complete reflection of all uncertainties within computation limits. Numerous experiments are then conducted to generate outcomes, determine prudent capacity, identify influential uncertainties, and evaluate the impact of large-scale hydrogen storage on electrolysis potential.

The research results demonstrate that the potential for electrolysis increases from 8-12 GW in 2030 to 8-44 GW in 2040, indicating the cruciality of flexibility for the Dutch energy system in the coming years. The most prudent capacities for 2030 and 2040 are identified as 12 GW and 38 GW, respectively, with 9 GW being identified as the no-regret capacity to be installed by 2030. Therefore, the research recommends stimulating additional investments beyond the initial targets of 3-4 GW for 2030 to foster the Dutch hydrogen economy. Moreover, there is substantial risk of overinvestment associated with the prudent capacities, especially as a result of the growing uncertainty in 2040. Deploying additional flexible system capacity to prevent electrolysis from acting as a loss of load mitigation measure can lower the prudent capacity and reduce the risk of overinvestment. Furthermore, policy measures should focus on stimulating influential uncertainties such as hydrogen demand, peak residual load and hydrogen storage. Every additional 100GWh of underground hydrogen storage capacity enables 500MW of electrolysis potential. Moreover, the last few gigawatts of the prudent capacities will primarily serve as peak production plants with limited operational hours and margins starting at 11.00 EURO/kg required to recoup the investments.

Further research opportunities include incorporating geographical and grid aspects into the model, exploring profitable conditions for investments in the last few GW of electrolysis capacity, investigating the role of large-scale hydrogen storage, and studying the potential of electrolysis beyond national borders in the North Sea interconnected electricity generation hub.

Climate change is a very urgent issue for our society. The European Union’s commitment is increasing, as evidenced by the Paris Agreement, and individual nations are also developing strategies to become emission-free within the next few years. This has renewed interest in hydrogen, particularly that produced through electrolysis, as a complement to electrification and as a means to achieve broad economic decarbonisation. Nevertheless, the deployment of green hydrogen faces a significant challenge known as the "chicken-and-egg" threefold coordination problem, which creates a vicious cycle wherein hydrogen supply, demand, and infrastructure developments hinder one another, impeding the formation of a complete value chain. This challenge raises the possibility that only blue hydrogen will be deployed in the medium term. To this end, the main research question we want to address is, “How do hydrogen uncertainties impact the penetration of green hydrogen infrastructure in the 2030 Dutch integrated energy system?”. To answer this main question, we used an exploratory modelling approach applied to the integrated energy system of the Netherlands utilising the Calliope multi-scale energy systems modelling framework. The outcomes of our research encompass various scenarios that pertain to the future integrated energy system of the Netherlands. We devised to examine the impacts of hydrogen uncertainties and specific policies on the integration of green hydrogen infrastructure within the energy system under investigation. Specifically, we formulated these scenarios to account for fluctuations in hydrogen demand, variations in electrolyser capital costs, and the enforcement of a robust policy aimed at prohibiting blue hydrogen production by 2030. The findings derived from the scenario analysis indicate that the realisation of a transition at a sufficient pace to meet the energy objectives set for 2030 can be accomplished through the implementation of robust policies, such as the medium-term ban on blue hydrogen production. Moreover, the study reveals that policies directed towards reducing electrolyser capital costs, as opposed to policies solely focused on stimulating higher hydrogen demand, prove to be more effective in facilitating a swift transition.

Achieving the goals of the Paris Agreement requires a significant transformation of current energy systems. The energy sector has hundreds of technologies and millions of actors working together to balance the system. Researchers are using computer-based models to understand the techno-economic impacts on the energy system due to changes in one or more energy system components. Modelling to generate alternative (MGA) is an energy system optimization method which generates hundreds of equally possible near-optimal energy system configurations. From the point of view of an analyst, it becomes difficult to provide in-depth analysis for future action due to the sheer amount of data generated by the MGA optimization method. In other words, while analyzing the results of the MGA-based model, one of its strengths becomes its weakness.

This thesis aims to design a clustering-based analysis method and Python package (MGA analysis package) based on the designed method to analyze the results of the MGA based optimization model using machine learning algorithms. The package is developed for energy analysts and users of the MGA optimization method to simplify the analysis of results. MGA analysis package clusters the solution space and identifies representative solutions for each cluster. Along with that, it provides a regional equality index to assess the distribution of energy infrastructure. An interactive dashboard is designed to use the MGA analysis package with three to four user inputs and provides information graphically and in text format. The MGA analysis package is hosted on the git-hub (https://edu.nl/gmarp) for applications and further development.

The energy transition is gaining momentum in the Netherlands, but not without consequences. Due to energy grid congestion, developments are on hold as they cannot be connected to the energy grid due to limited capacity. The ACM has revised electricity grid regulations to optimize grid efficiency, which increased the risk of congestion during peak load. This can be mitigated by using congestion management and battery energy storage (BES) can be the solution by storing surplus energy during peak periods and releasing it during periods of sufficient grid capacity, presenting an opportunity for Statkraft.

This study analyses the effect of using battery energy storage systems for congestion management on the overall business case. The interactions between the battery and the energy markets is simulated using model predictive control. The business model of the battery consist of energy arbitrage and value stacking i.e. trading on multiple markets in order to generate revenue. This model uses real market data of four identified markets, aFRR, FCR, intraday and day ahead. The results of the BES simulation are used to build a business case for BES. The business case is comprised of a economic assessment, risk assessment and a sensitivity analysis. The business case for battery energy storage is positive and through research and modelling it can be concluded that the congestion management market is beneficial to the business case of the BES.

Keywords: battery energy storage (BES), congestion management, simulation, model predictive control (MPC), business case.

Following the climate goals of the Dutch government, a tremendous amount of vRES (variable Renewable Energy Sources) will be built. These energy sources will produce large amounts of power, which must travel great distances on the transmission grid. The Dutch TSO, TenneT, is responsible for the transmission grid's design, construction, and maintenance by European and Dutch law. The planning for transmission grids has become more complex since the liberalization of power markets. Because of this complexity, the planned expansions are published every two years. In energy systems, the ever-changing context of the system makes the possible future states of the system uncertain. In this research, we 1. identify the relevant uncertainties for the transmission system, 2. Identify the most vulnerable lines under these uncertainties, and 3. Look at the effect of the delays on the performance of the overall power system. This gives insights into how the transmission system must evolve to maintain the grid's performance.

Not only more power from vRES will be generated, but coal plants will also be phased out, and the demand for other energy sources (fuel, gas, etc.) will be electrified. More power flow will lead to congestion on the current grid. The Dutch Transmission System Operator (TSO) uses congestion management to alleviate acute congestion and expansion planning to prevent future congestion. To decide whether grid expansion should be used, Production Cost Models (PCMs) are used. To account for uncertainties in a system, the concept of deep uncertainty can be employed to map the uncertainty of a system. Exploratory modeling uses this concept to understand the consequences of deep uncertainty. Experimental modeling and analysis could thus be used to understand the effects of vRES on the power grid.

Direct Current Optimal Power Flow (DCOPF) is used for the formulation of the Production Cost Model (PCM) for the power network modeling. The chosen exploratory modeling framework is a simplified form of the XLRM framework, in which the policies in the model (L) is not altered. The data used as input for the model is selected from a wide range of sources, following the format of the DCOPF formulation. The tool used to build and simulate the DCOPF model is Scenario Analysis Interface for Energy Systems (SAInt) software. The EMA workbench is used to perform exploratory modeling and analysis. Two times 1,000 experiments for a week period have been run with these tools, one set in January and one set in June. To assess which lines in the transmission network might be susceptible to congestion, we analyzed the experiments' outcomes with a congestion-seeking algorithm. The adopted methodology has been verified using a 5-node model.


The results of these experiments are divided into two types of outcomes: the general system outcomes and the line outcomes; the latter has been split up based on the urgency of capacity expansion. The analysis tools by the EMA workbench were used to analyze the results of the experiments, especially the Patient Rule Induction Method (PRIM) algorithm. The experiments showed significant differences in congestion between June and January...

In the electricity system, one barrier to the energy transition is the degradation of frequency stability due to the decrease of system inertia and frequency control ancillary services (FCAS), which is caused by the replacement of inertia-abundant and governor-based conventional power plants with zero-inertia and inverter-based renewable energy sources (RES).

There are already inverter technologies for RES and battery energy storage systems (BESS) to provide virtual inertia (VI) and fast frequency response (FFR) services, which are equivalent to physical inertia and conventional FCAS. Potential providers include wind, solar, battery energy storage systems (BESS), and other types of devices with the feature of energy storage. However, there are non-technical barriers to the actual implementation. For example, in most parts of the world, these services cannot participate in the electricity market and thus there is a lack of incentive for both the provision of and investment in the VI and FFR.

In the literature, there are already proposals of possible market designs for VI and FFR that procure the services, guarantee the frequency-stability requirements, and provide payments to the service provider. The main focal point is on the formulation of security-constrained unit commitment (SCUC) and security-constrained economic dispatch (SCED) problems. The formulation needs to be accurate in modeling, be solvable, and be with reasonable computational burden. Most of them only considered the allocation of ancillary services but did not price them, or price them but only by directly assigning a shadow price. Only a few works considered explicit prices in the bid.

In this project, we consider a market design with explicit bid prices. In the SCUC and SCED problems, we adopt the state-of-the-art formulation of frequency nadir constraint and method of modeling the frequency dynamics based on the linear ramp assumption of the dynamics of frequency response (FR). With these methodologies, we will investigate the features of such a market by identifying inter-dependencies of parameters in the market setups, including the interaction between the bid price and bid amount of FR and VI, and analyzing the underlying mechanisms. The results show that the amount of FR sold depends on the bid price of FR monotonously and the amount of VI sold depends on the bid price of VI monotonously, though with different patterns. The underlying reason for such dependencies is that FR, VI, and the size of the largest unit both help mitigate frequency drop and recover it and they are influencing each other. The price of FR or VI decides the relative worthiness of each option. The size of the largest unit is also related to the amount of FR sold, which is confirmed by the formulation of the analytical nadir and the QSS constraint. However, the way that the amount of available FR influences the amount of VI sold does not show a clear pattern.

These findings provide insights into the interactions between the FR and VI products and thus provide a reference for the design of the FCAS market.

Offshore bidding zones will eventually result in lower revenues for offshore wind farm owners due to a lower electricity volume and price risk, especially when flow-based market coupling and advanced hybrid coupling are implemented in the electricity system. This results in a demand to improve the investment climate for offshore wind farm owners. One measure that can be taken to improve this climate can be by introducing CfD schemes.

Because of the number of different design elements, designing a CfD for a specific situation can be seen as a complex process that considers many factors tailored to offshore bidding zones. This research will give an overview of some CfD design choices that need to be taken with their consequences.

This thesis looks into the following designs:

- The conventional CfD design
- The advanced CfD design
- The capability-based CfD design
- The capability-based CfD design with a cap and floor strike price
- The financial CfD design with a reference generator
- The financial CfD with weather data as reference