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.@en

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

Electrification by means of renewable energy sources in electricity production (RES-E) is a key strategy to meet global climate goals established in the Paris Agreement. The increasing share of RES-E as a result of this has significant implications on the functioning of the electricity system and the adequacy of the electricity market design, as these technologies exhibit different characteristics compared to conventional generators. Their intermittent output combined with near negligible marginal costs has an effect on electricity price formation and will lead to an increased demand for flexibility services to level out these fluctuations. These trends will induce an increased investment risk for flexible generation capacity. This increased investment risk feeds into the risk-averse behaviour of investors that is already present due to market failures that exist in electricity markets. It remains unclear whether the liberalised neoclassical electricity market design provides sufficient incentives to invest in generation capacity in order to maintain system adequacy during the energy transition.
One possible market design option that is proposed to address this increased investment risk are Capacity Mechanisms. Capacity Mechanisms allow for an adequate remuneration of (flexible) generation capacity, by providing a steady income stream to firm capacity on top of revenues obtained from selling electricity. The purpose of this thesis is to evaluate the effectiveness of a capacity market - a specification of a capacity mechanism - in maintaining system adequacy in a system with an increasing share of RES-E. Additionally, it aims to fulfil the need for electricity system models that allow for performing robustness analysis as this it is seen as increasingly important to adequately account for uncertainty that is inherently related to the unfolding of the energy transition. This research was carried out by extending an existing quantitative model of the Dutch electricity system, referred to as Myopic Optimisation Detailed Operational (or MODO), with a capacity market. The methodology applied relies on myopic optimisation.
Conceptualisation of the capacity market model is based largely on the design of the NYISO Installed Capacity Market (NYISO-ICAP), as this design is relatively simple whilst being considered a successful capacity market. A thorough literature review on the NYISO-ICAP was performed to establish a profound understanding of real-world capacity market dynamics and how these can be translated to the model. The conceptualisation of the capacity market forms a basis for formalisation and is used to establish the formal rules of the capacity market model complying with the basic structure of linear programming problems. This serves as input for the implementation of the extension of the capacity market in MODO, and thus in Linny-R.
With the resulting model, the effectiveness of a capacity market in maintaining system adequacy was assessed on the performance of several Key Performance Indicators. These encompass the Supply Ratio, the average annual volume of Energy-not-Served [MWh], the average Electricity Price [€/MWh] and the total Consumer Spending [€]. A comparative analysis was performed between the performance of the energy-only market and the capacity market on the identified Key Performance Indicators in four pre-specified scenarios. The four scenarios are varied on key uncertainties underlying the unfolding of the energy transition to explore the robustness of results, such as weather conditions and the level of risk aversion of investors.
From this thesis, it can be concluded that a capacity market can be an effective and robust policy instrument to maintain system adequacy during the energy transition at a lower cost to consumers. This can specifically be seen in the increased number of investments in peaking generators compared to the energy-only market when forecasted revenues of the capacity market are sufficient, which has a positive effect on the electricity system's robustness to different weather conditions. Furthermore, when the level of a risk aversion of investors is high, a capacity market is more robust in ensuring system adequacy compared to an energy-only market.
Nonetheless, a capacity market can be prone to investment cycles which can have a negative influence on the effectiveness of a capacity market to maintain system adequacy at all times. These investment cycles are a result of the bounded rationality and myopia experienced by investors, ultimately leading to imperfect forecasting of the revenues of the capacity market. Results of this thesis have shown that in busts of investment cycles, investments in generation capacity can be insufficient to maintain system adequacy.
To conclude, a capacity market can be a viable option in order to maintain system adequacy in the energy transition. However, whether it is the most suitable option to maintain system adequacy is not clear. Novel flexibility options such as storage could potentially serve a significant role in maintaining system adequacy. If additional flexibility options have a lower perceived investment risk compared to flexible generation units, the electricity market can potentially continue to rely on its initial liberalised neoclassical market design whilst maintaining system adequacy. This requires further exploration in future research.
Furthermore, MODO and its capacity market extension fill the need for electricity system models that allow for performing robustness analysis, as it has a relatively low computation burden. Hence, an interesting avenue for future research is to evaluate the effectiveness of a capacity market in many possible futures.

What would you think if your neighbour, who owns a Tesla and therefore causes extra load on the already congested electricity grid and you would have to pay these costs? Feels unfair, doesn’t it? This is currently happening in the Netherlands and it concerns millions of households that consume electricity. Electricity consumers are currently paying for the extra loads that are caused by electric vehicles. Why do these consumers have to pay for other consumers that own Teslas? Why do these consumers have to pay without reaping the benefits? Why do these consumers have to pay for these costs while the fixed costs are rising through inflation and increasing energy prices (RTLNieuws, 2021)? Unfairness does not seem something to be desirable by society. So how does it suddenly arise? Unfairness of the network tariff system is in its core a cost allocation issue related to energy use. This issue is a recurring theme in the energy transition and it revolves around the central question of how we are going to divide the costs of the shift from fossil fuels to renewables. This question is currently a very relevant topic in the political and public debate and unfairness of the network tariffs further undermines the very necessary energy transition. The climate goals associated with this transition contributed to the development of sustainable energy technologies such as the electric vehicles, heat pumps, solar panels and wind turbines. The increasing penetration of these technologies cause extra costs for distribution system operators, the managers of the electricity grid. Electric vehicles, for example, congest the grid and therefore system operators will have to invest in the grid. The system operators will have to recover these costs from consumers through a network tariff system. The current tariff system uniformly distributes the costs for all households without making a distinction between heavy and light consumers. This means that consumers that cause extra costs for the system operators pay the same tariff as consumers that cause less costs. This was not a problem in the past because the differences in loads were small and inconsequential. This leads to an outdated network tariff system and causes unfairness for consumers. The system operators (Netbeheer Nederland) in the Netherlands luckily also recognize the seriousness of unfairness and therefore they initiated a multi-actor decision-making process with the goal of introducing a new network tariff system for small consumers in the upcoming years. This will be done in collaboration with a lot of stakeholders in the energy sector. The government (Ministry of Economic Affairs and Climate, ACM), the energy suppliers (E-NL, Vattenfall, Eneco, Essent, Engie, Greenchoice), consumer organizations (Vereniging eigen huis, Consumentenbond, Aedes) and other stakeholders are involved.

Although there is a common understanding that the use of variable renewable energy sources (VRES) is needed in our collective attempt to decarbonise society, the type of technology that we should deploy, and where is not so clear. Stakeholders from real-world projects use the outcomes from optimisation models to aid their decision-making process. One of such outcomes that decision-makers use is the price information of energy carriers in the required temporal and spatial resolution. In the current state, price information is embedded in the form of shadow prices within linear optimisation problems. As a result, price information is relatively easy to extract for conventional, power-sector focussed energy system models. However, when these energy system models are multi-carrier and sector-coupled in which energy carriers undergo several conversion stages, the extraction of price information becomes less trivial. Furthermore, even when price information is extracted in the form of shadow prices, they might not represent real-world price information. This master thesis research aims to develop a generally applicable price information generation method to extend the use of shadow-price based methods in conventional models to more complex multi-carrier fully sector-coupled models. The price information generation method is developed in Python which is tested within the Calliope modelling framework for a multi-carrier fully sector-coupled energy system. It does this by extracting the shadow prices from a linear optimisation problem of the North Sea Euro Calliope model which is adapted from the Euro-Calliope model. The shadow prices are then compared against current real-world prices for the energy carrier electricity. Results show that the shadow prices do not represent real-world prices accurately, however the use of shadow prices can be extended to understand trade-offs between different configurations of fully sector-coupled energy system models. A use case for the shadow prices has been conducted to analyse the price stability of Dutch electricity prices for different hydrogen shares within the energy system for different weather scenarios. Initial results show that the price stability of electricity in the Netherlands could be improved by increasing the share of hydrogen in the energy system. The increase of the hydrogen share within an energy system does not significantly affect the payback time of the energy system and the levelized cost of energy (LCOE) for electricity technologies. This research project shows that shadow prices could be used to understand trade-offs in different configurations of fully sector-coupled energy systems and aid the decision-making for the type and location of technologies to fulfil energy demands in the future. Recommended future research include an improvement of the North Sea Calliope model using a bottom-up approach and the analysis of other sectors within the fully sector-coupled energy system such as hydrogen and heat.

Power system planning is a strenuous exercise for both people and computers. As carbon goals and geopolit- ical complexities intensify, the number of scenarios for electricity system development is limitless. In search of robust policy and profitable investments, policymakers and investment managers rely on long-term elec- tricity system simulation models. Those seeking to simulate myopic investment behaviour in systems reliant on intermittent generation, batteries and seasonal storage then encounter a computational barrier. The sole model detailed enough to conduct such simulations – MIDO (Myopic Investment Detailed Operational) – takes over 12 hours to run a 20-year scenario. Exploring avenues to cut back computational time is key to un- locking the potential to meaningful scenario analyses. Also, due to its novelty, many of its assumptions and modeling choices were not validated with stakeholders. Research into methodologies to reduce the computational burden at an acceptable compromise to model behaviour thus holds merit.

To this end, this methodological thesis explores myopic optimisation, developing the MODO methodology: Myopic Optimisation, with Detailed Operational analysis of system performance. We have realised an 85% improvement in computational efficiency with a sufficient degree of accuracy for it to be considered as an alternative to MIDO. The essence of our approach is the replacement of an iterative investment loop with a single optimisation. In doing so, we transfer insights from the field of agent-based modeling into the field of myopic optimisation, potentially enabling the revitalisation of a research area which has seen little activity in recent years.

MODO has been implemented as a modular toolkit in Linny-R, in such a way that users need not have any technical modeling knowledge to be able to construct their own system and/ or analyse their own scenarios. The implementation in Linny-R greatly enhances model transparency and usability.

We create a projection for the state of the Dutch electricity system by 2030 and use MODO to simulate the transition path from 2030-2050. Preliminary results suggest the significant potential of both seasonal (hy- drogen) and battery (lithium-ion) storage to lower electricity prices below 2030 levels, bringing down annual consumer spending to EUR 15 bn annually in a scenario with mid-range commodity price forecasts. Addi- tionally, results affirm the functionality of MODO in more complex electricity systems.

We identify three exciting avenues for future research. The risk of contemporary investment decisions is largely mitigated through long-term contracting, such as bilateral power purchase agreements or forward ENDEX markets. Ideally, academic work would converge to the point where a first model simulates a range of potential portfolio choices based on long-term contracting, after which - based on risk appetite - the upside potential of offering excess capacity on spot markets is assessed with a methodology such as MODO. However, as the data on such long-term contracting is often kept secret, research into ways to approximate the data or work with it using ’black-box’ models could prove fruitful. Secondly, researchers could choose to build upon this work by leveraging it to conduct large-scale exploratory analyses. The case studies in this work illustrate the potential of MODO, but only provide a preliminary insight into system development. Whilst model devel- opment has been informed by a stakeholder consultation, the scenarios analysed have not. This is simplified by the highly accessible nature of MODO’s implementation in Linny-R. Lastly, we suspect that qualitative re- search into the empirical applicability of power system simulation models might prove invaluable. Research that treats decision-makers and policy analysts as clients to discover their modeling needs could result in a roadmap for the participatory development of power simulation models in order to better foster their adop- tion, and that of their findings.