Exactly predicting what the future energy system in the Netherlands will look like in 25 years is impossible, but there are ways to explore possible scenarios. A widely used tool to help give an idea of how such a system will look is energy system optimisation modelling (ESOM). TenneT, the Dutch TSO, also uses optimisation modelling to explore possible cost-effective plans for this future network. A challenge they face is not the ability to run intensive models but the need for a model that can rapidly generate a wide range of alternatives of interest. While highly detailed results with high resolution are necessary, in many cases a model is more useful when it can produce results quickly and efficiently without the need for long computation times.
This research aims to develop a flexible and exploratory energy system model for the Netherlands in 2050. This enables analysing a large set of alternatives without the high resolution of a detailed model that causes long running times. Additionally, with the modelling approach Modelling to Generate Alternatives (MGA), specifically the SPORES method, we can uncover a range of near-optimal solutions to provide insight into the technical composition of the model. With a large number of alternatives, different technology choices, technology trade-offs and spatial capacities can be examined. To create this model, the energy system modelling framework Calliope was used. The literature provides research gaps and opportunities in the current methods of using MGA. The focus for this research is aimed at developing a method to be able to create large sets of solutions without the need for long modelling times. Desk research and secondary data analysis gave insights on the current state of the Dutch energy system, and conducted interviews ensured the model was a good fit for the company and their vision of the model. The interviews resulted in a set of six questions that could be answered using MGA.
The model consists of a twelve-node Dutch energy system with both electricity and hydrogen carriers included. It also has connections to the neighbouring countries to represent the import and export of energy. The model results project a high reliance on renewable energy technologies like offshore wind and solar PV. The model also uses several types of energy storage, namely salt caverns for hydrogen storage and Li-ion batteries for electricity storage. Especially hydrogen storage is used as the main flexibility technology of the system. In comparison with the II3050 modelled energy system, the model outputs show lower use of offshore wind and more use of hydrogen. The results also show a promising possibility for Small Modular Reactors (SMR). The transmission network will need to be expanded to handle large amounts of energy from Groningen to Noord-Holland and from Zuid-Holland to Limburg. The SPORES results demonstrate that not all technologies are mandatory for the energy system. Some technologies are interchangeable within a small cost margin. BECCS-rebuilt and SMRs frequently replace hydrogen technologies or renewables in alternative configurations. This change in technique compositions also has an impact on the transmission system. Although certain reinforcements appear consistently. Directed SPORES runs show that assumptions about affordability, nuclear expansion, decentralisation, or BECCS-rebuilt availability significantly affect system architecture. Limitations of the model include fixed electricity import/export prices, costless and lossless hydrogen transport, and GDP-based spatial demand allocation. Despite these simplifications, the model allows rapid exploration of structural differences across scenarios.
This thesis advances the field of energy system optimisation modelling by integrating qualitative knowledge from expert interviews with quantitative scenario generation. The study shows how system planning questions can be addressed more quickly by using MGA to generate rapid results. It demonstrates how the MGA method SPORES can be used to generate insight into system design flexibility, infrastructure needs, and guided searching. Methodologically, the research highlights the accessibility and adaptability of open-source tools like Calliope for exploratory analysis. Future research could build on this model by incorporating varying electricity and hydrogen prices, simplified foreign demand profiles, or expanding the nearly optimal solution alternatives with a new post-processing tool, Modelling to Generate Continuous Alternatives.

Planning and decision-making have become increasingly complex, especially in the energy sector. Modeling to Generate Alternatives (MGA) enables the creation of multiple alternative solutions, enhancing the decision-making process. However, when applied to large system optimization problems, the MGA method can become computationally cumbersome. Bio-inspired metaheuristics, a branch of artificial intelligence, have the potential to overcome these computational limitations by applying metaheuristic methods that can solve multiple model solutions simultaneously. Currently, a sophisticated integration of such heuristics with MGA, specifically focused on energy system optimization, has not yet been realized.

This thesis aims to answer the question: How does a combination of MGA and bio-inspired heuristics, aimed at optimizing energy system design, compare with existing deterministic MGA methods? To address this question, a metaheuristic-MGA algorithm was developed and compared with existing spatial energy system MGA results. First, a literature review was conducted to determine the most suitable metaheuristic for this study. The review confirmed the scarcity of literature on the combination of metaheuristics and MGA in energy systems. However, relevant studies applying a metaheuristic-MGA approach to general optimization problems were identified. Based on these findings, a genetic algorithm (GA) was selected as the most appropriate method for this thesis. The mathematical formulation presented in the literature was adapted to fit the spatial optimization problem of energy systems.

The complete mathematical process of the GA-MGA algorithm was developed in Python. Next, a small energy system test model was built in Calliope to evaluate the performance of the developed GA-MGA algorithm. The algorithm’s parameters were further fine-tuned using existing parameter-tuning methods, performance measurements, and assessments of the computational time required to complete the algorithmic process.

Before applying the developed GA-MGA algorithm to a large-scale model, it needed to be scaled to prevent errors or computational inefficiencies when generating results. It was determined that the desired resolution was not feasible due to the excessive computational time required for its completion. Instead, a time-masking method was applied to the resolution, preserving high-resolution characteristics while improving computational efficiency.

The GA-MGA algorithm was then applied to a large European energy system model, and the results were compared to existing MGA results. However, due to differences in resolution, the GA-MGA-generated results did not meet the standards of the existing MGA results, making direct comparisons less robust and reliable than desired. The comparison revealed a significant difference in battery capacity deployment, with the GA-MGA solutions deploying higher quantities of battery capacity. The lack of spatial distribution data for the large model was solved by comparing the GA-MGA results to a plot of the existing MGA results. While this was not an ideal comparison, it provided an opportunity to analyze spatial deployment differences between the GA-MGA and the existing MGA results. The comparison showed that the GA-MGA algorithm favored high-capacity deployment at specific locations, whereas the existing MGA results exhibited a more diverse capacity distribution.

The limitations of the results primarily stemmed from shortcomings in spatial comparison and differences in resolution between the two modeling techniques. Another key limitation was the algorithm’s structure, which presents several opportunities for improvement in optimizing the GA-MGA approach. Despite these challenges, the theoretical combination of GA-MGA demonstrated promising potential. Future research should focus on enhancing the algorithm’s performance and conducting more in-depth comparisons with MGA results to fully evaluate its effectiveness. Further research in this area could expand access to the MGA method for tackling large, complex problems, ultimately contributing to more effective planning and decision-making processes. This, in turn, would support efforts to address major societal challenges.

The energy transition in the Netherlands, characterised by the increase of renewable energy sources (RES) and the electrification of key sectors, has placed strain on the existing electricity grid. Challenges such as volatile electricity prices, curtailment of excess renewable energy, and grid congestion highlight the limitations of the current infrastructure, particularly in urban low-voltage (LV) networks. Here, transformers get congested due to limited capacity during peak hours. Expanding the grid is often infeasible due to high costs, long timelines, and spatial constraints. However, Renewable Energy Communities (RECs) have emerged as a promising solution to these challenges by optimising the use of the existing infrastructure within local contexts.

The challenge in improving affordability, sustainability and security is that the goals are contradicting, also referred to as the Energy Trilemma. Therefore, this thesis addresses the research question: “How can Dutch urban Renewable Energy Communities be designed and operated to enhance energy affordability, sustainability, and security?”. To answer this, a multi-objective linear programming (MO LP) model was developed using the Calliope software framework in Python. The model optimizes the design and operation of RECs across three energy trilemma dimensions: affordability, sustainability, and grid security. It incorporates solar photovoltaic (PV), battery energy storage systems (BESS), and grid interactions.

The research underscores the inherent trade-offs in balancing the energy trilemma. Affordability-driven scenarios minimize costs through extensive grid reliance, increasing emissions and transformer congestion, while sustainability- and grid-security-focused scenarios emphasize self-consumption, reducing both but incurring higher costs and curtailment. Maximizing solar PV capacity cuts CO₂ emissions but leads to substantial curtailment without sufficient storage or trading. BESS mitigate imbalances by shifting energy flows in time, yet grid dependence remains unavoidable, especially in winter when PV output is low.

There is thus no universally optimal REC design, effectiveness depends on stakeholder priorities. However, key insights hold across all scenarios: Dutch urban RECs can enhance affordability, sustainability, and security with approximately 750 kW of solar PV per 200 prosumers and 200 kW MV and LV batteries for hourly balancing. Designing RECs this way, could offer a more efficient solution for mitigating urban grid congestion than defaulting to grid expansion.

Despite its contributions, the study has limitations. The model simplifies grid interactions by focusing solely on the LV grid and transformer congestion, excluding medium-voltage (MV) and high-voltage (HV) dynamics. Behavioural feedback on market dynamics, such as the impact of widespread REC adoption on electricity prices, or the diminishing business case of batteries, is also not captured. These limitations underscore the need for future research to expand the model’s scope, address emerging technologies, and integrate multi-layered grid interactions that include feedback systems.

All findings of this thesis are open source. The model that was developed to answer the research questions can be accessed at:

https://github.com/Tomdebruin/MO-LP-Energy-Community-optimisation

The European Union faces a significant challenge: energy use and production are responsible for over 75% of greenhouse gas emissions. To achieve carbon neutrality by 2050, there is a need for a shift towards decarbonising the energy grid, including innovative solutions. This research project focuses on the business park Uitgeest-Noord, located in the Netherlands. Uitgeest-Noord serves as a testing ground for a decentralised energy system that optimises energy components (solar and storage) to mitigate local-scale grid congestion. Therefore, the main research question is: Can the ideal configuration of a decentralised energy system (DES) be effectively designed to avoid local grid congestion and support capacity planning during the electrification of the Business Park Uitgeest-Noord?
To address the main research question, the thesis employs a structured methodology that includes four steps: I) theoretical context analysis, II) data collection, III) linear programming optimisation modelling, and IV) result analysis. The third step includes building a linear programming optimisation modelling setup specific to Uitgeest-Noord, with a primary objective to optimise for monetary and emissions cost classes. By running different scenarios to assess potential configurations, and analysing the results using a cview dashboard.
Key findings indicate, among other things, that integrating PV panels, EV charging infrastructure, and heat pumps without a Battery Energy Storage System (BESS) leads to significant PV curtailment and occasional grid congestion. In addition, adding BESS reduces PV curtailment and reliance on external grid supply, optimising renewable energy use. The ability to export stored electricity for revenue shows financial benefits but also highlights potential grid stress. Furthermore, battery size and PV capacity are closely linked, with cost constraints significantly impacting the system's configuration.
BESS plays a critical role in managing grid congestion and optimising renewable energy use. Flexible and scalable energy systems are essential for adapting to varying demand patterns and seasonal changes in energy generation. Therefore, accurate data collection and realistic assumptions are crucial for reliable modelling outcomes. The economic viability of different configurations is a key factor, with the model displaying that strategic use of storage and dynamic pricing can optimise financial performance.
In conclusion, this thesis project has effectively designed the ideal DES configuration for Uitgeest- Noord via a linear programming optimisation model, contributing to preventing grid congestion and supporting capacity planning. This thesis provides valuable insights into the benefits of DES and the importance of storage solutions, flexible system design, accurate data, and economic and emissions considerations. The academic value of this thesis lies in its energy modelling setup, which enables different modelling scenarios and improves performance through iterative analysis, all in a simulated environment. The reproducibility of this modelling approach makes it applicable to other case studies, advancing the understanding and practical implementation of DES in various contexts.

The ever-rising greenhouse gas emissions move the European Union to transition towards a future decarbonized energy system. To achieve this, the future energy system will mainly comprise variable renewable energy generation sources. Energy production from these sources is susceptible to weather fluctuations. Uncertainty of future weather scenarios translates into energy system models that help decision-makers to design the future renewable energy system. Energy system models often use a single weather year to simulate weather behaviour. Thus, many energy system models fail to take weather fluctuations between various years into account. Identifying energy system designs that are robust to weather fluctuations, i.e. systems that are able to suffice demand regardless of the weather circumstances, is therefore key.

The research described in this thesis has aimed to develop and test a method to give insight to decision-makers into the composition of robust and efficient energy systems, given weather uncertainty. To achieve this, the SPORES methodology has been used and extended to identify energy system configurations that are both robust and efficient. For this, a decision option space for decision-makers has been created that has been diversified based on renewable energy generation and storage technologies. To test the developed method, the North Sea region has been used as a case, as this is the region thought by policy to have great potential to house renewable generation sources in Europe.

The method developed in this research systematically covers the decision option space over three weather scenarios (worst, typical and best). From these decision spaces, configurations that meet demand with installed capacities that exist across the whole weather options space have been selected as robust. Clustering was used to identify types of energy system configurations, having commonalities in the installed generation capacities. Energy efficiency has been identified as key for measuring energy system performance. This research, therefore, takes curtailment and energy system yield into account to quantify efficiency. Using a Pareto analysis, both robust and efficiency-wise high-performing energy system configurations were identified as most promising for decision-makers.

The no-regret decisions, visualized by the SPORE-core, are minimum capacities required across the whole decision space. Results showed that robust energy systems are typically comprised of balanced configurations, meaning that solar PV and wind power both have the largest capacity of energy generation sources. The balanced configurations also contain high transmission capacities and typically no storage capacities indicating energy is distributed rather than stored. The robust and efficient configurations need additional capacity investments on top of the no-regret decisions. Especially solar PV needs a large increase in capacity when robustness and efficiency are required. Combined heat and power from biofuels and electrolysis capacity are also key to robust and efficient configurations. Additional results showed that the majority of robust and efficient configurations utilised more offshore than onshore wind capacity.

The findings of this research are based on a case of the North Sea energy system with a high level of aggregation and are thus of limited use for precise designs of the North Sea energy system. The method created in this study can be adapted to contain more detail and offers space for researchers to include their own performance indicators. However, this research already used significant computational efforts, so adding more resolution and detail will mean the computational process can be restricting. Future research should focus on using the developed method to select promising and robust energy system configurations with higher levels of detail and conduct further weather scenario analyses on the selected configuration.

Climate change, unfavorable geopolitical developments and resource scarcity pushes the fossil fuel based energy sector in the direction of renewable, carbon-neutral resources. The volatile nature of these, however, created new, technical needs for increased storage capacity and interconnectedness of demand and supporting grid, providing flexibility options. Hydrogen re-emerged as a possibility in the scientific discussion because as a potentially green energy carrier it is easy to store, transport and convert to and from electricity. The economic advantages of transporting hydrogen in its original, molecular form via pipelines rather than relying simply on the electric transmission grid are connected to the amount of energy that needs to be transported – the pipeline transportation usually requiring larger upfront investment but operating with lower unit costs. The uncertainty, however, regarding the competitiveness of hydrogen in possible end-use applications translates into unpredictability in its penetration of the future economy, bringing the return on investment of pipeline transportation options into question.
This thesis, using a quantitative analysis, compares the advantages and disadvantages of the identified, technologically possible transmission options – electric transmission, blended natural gas-hydrogen transmission, or the virtual, retrofitted and dedicated pipeline options – through the resulting average hydrogen price, volatility and grid stability of the Dutch energy grid and market under different penetration scenarios. By formulating the research as a constrained optimization problem, and using the Calliope framework to find an optimal (minimum cost) solution, it is possible to extract the shadow prices of the relevant constraints and use them as an indicator for hydrogen price and grid stability.