Increasing electricity price volatility in European energy markets presents a significant financial challenge for large industrial consumers. While academic literature has explored demand-side bidding, a knowledge gap persists in applying these strategies to complex, process-driven industries with limited operational flexibility and highly interdependent systems. This thesis aims to bridge that gap by designing, implementing, and evaluating an adaptive stochastic bidding framework to enable industrial consumers to optimise their participation in the day-ahead electricity market, minimising cost while managing risk.

A two-stage stochastic Mixed-Integer Linear Program (MILP) was developed to formulate and compare two distinct, EUPHEMIA-compatible bidding strategies: a granular stochastic hourly bidding strategy and a holistic stochastic exclusive group bids strategy. The framework incorporates Conditional Value-at-Risk (CVaR) for downside risk management and models price uncertainty using a combination of Meta's Prophet forecasting model and a Levy stable distribution to generate realistic, heavy-tailed price scenarios. The model's logic was first verified on a simplified green hydrogen system before being applied to a detailed case study of Tata Steel's IJmuiden plant, analysing both its current rigid blast furnace-basic oxygen furnace (BF-BOF) and future flexible direct reduction plant-electric arc furnace (DRP-EAF) configurations.

The core finding is that the optimal bidding strategy is fundamentally contingent on the industrial asset's specific operational flexibility and economic structure. For the current, inflexible BF-BOF system, a price-insensitive strategy that prioritises material efficiency is superior, as the financial penalties from disrupting the production chain far outweigh potential electricity cost savings. Conversely, for the future, flexible DRP-EAF system, a granular, price-sensitive hourly bids strategy becomes the most profitable approach, creating significant value by leveraging the Electric Arc Furnace for price arbitrage. Furthermore, for flexible assets whose profit margins are primarily defined by electricity costs, such as the green hydrogen system, a conservative exclusive group bids strategy is optimal due to its superior risk hedging, which prioritises capital preservation in volatile markets.

This research concludes that a universally optimal bidding method does not exist. Effective market participation requires that industrial consumers first diagnose their system’s unique techno-economic architecture and then deploy a strategy that aligns with its inherent nature: either insulating rigid processes from market volatility or actively engaging flexible assets with it.

The increasing complexity of renewable energy systems characterized by multiple energy carriers and local intermittent resources, calls for accurate tools for effective design, operation, and planning. This thesis investigates simulation-based optimization as a tool to support such decision-making processes.

To build a foundation for the proposed method, a background study was conducted on optimization theory in general and on simulation-based optimization with a primary focus on energy systems. Additionally, the functionality of the simulation software used in this thesis, The Illuminator, was explored.

Based on this foundation, a new optimization framework was developed by extending The Illuminator software and through the integration of three algorithms: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and a gradient-based algorithm (L-BFGS-B). Parallelization was implemented to increase the efficiency of the algorithms. To expand the modeling capability of The Illuminator, several new hydrogen-related component models were developed. The framework was tested across multiple domains by using three distinct scenarios: (1) a hydrogen production facility (hydrogen domain, continuous variables, system design domain), (2) a residential energy hub (electric domain, continuous variables, system operation domain), and (3) an electric vehicle charging station (electric domain, discrete variables, system planning domain).

Among the explored algorithms, Particle Swarm Optimization (PSO) proved to be the most suitable across the three presented scenarios, achieving the lowest average gaps to the best-found solutions in each case (0.107%, 0.363%, and 20.145%, respectively). Parallelization of the population-based algorithms improved the total run time by a factor of almost 5.

The results show that simulation-based optimization is a promising approach for supporting the design, operation, and planning of complex renewable energy systems.

Public communication regarding power grid policy is difficult due to the unintuitive behavior of
electricity. This thesis presents the development of an easily understandable yet accurate model of the Dutch electricity network. The topology, production and load of the contemporary grid is studied using publicly available data. An accurate model of the grid is designed using stochastic modeling and data analysis techniques. An attempt is made to simulate the model using DC power flow techniques. The software is developed to be compatible with the Illuminator system, which can visualize the simulation in a physical model. The ultimate implementation of the model was unsuccessful. The design process and attempted simulation of the model is documented in this thesis.

This thesis discusses the integration of the Illuminator software with hardware components, for the purpose of creating a table-top, Plug-and-Play energy system demonstrator. The thesis starts by introducing the motivation for an energy system demonstrator in Chapter 1. An interactive, visual demonstrator can help educate people on energy systems in a way that
is more intuitive. The main product should show the power flow on the table, implement Plug-and-Play dynamics, and be scalable. LED strips are used to visualize power flows in the table-top network, in combination with the Digispark ATtiny85. For determining the topology, static ID pairs were used. A double simulation is used to implement Plug-and-Play dynamics.
The first simulation configures the topology used by the second simulation, based on the hardware connections. The second simulation runs the Illuminator simulation. During this simulation, checks are run to see whether a physical connection has changed, such as a cable being unplugged. The simulation and then starts the reconfiguration process again.
Testing the reliability and run-time of the implementation is documented in Chapter 6, with a focus on how well the implementation scales with the size of the simulation. It was concluded that the Digispark’s communication with the Raspberry Pi would often stall, requiring error correction to be implemented. Even then, the data transfer to the Digispark from the Raspberry Pi fails on the first try an average of 48% of the time. Determining how long a setup takes to reconfigure was estimated using a computer, since the Raspberry Pi’s aren’t powerful enough to simulate dozens of models. It was determined that a reconfiguration of 20 models takes about 100 seconds.

The decarbonisation of district heating networks (DHNs) is one of key ways in achieving the net-zero climate targets, especially in densely populated and energy consumption intensive regions like South-Holland in the Netherlands. As DHNs transition from fossil-based to renewable and electrified heat sources, the interactions with the electricity distribution network (EDN) becomes increasingly more critical. Nevertheless, current operational models and planning approaches often treat the development of heat and electricity networks separately, not considering their interactions and overlooking the operational and infrastructural challenges that arise from their growing interdependence.

This thesis addresses this gap by presenting an integrated modelling framework that combines an operational optimisation model of the South-Holland DHN, developed using PyPSA, with a time-series power flow analysis of the South-Holland EDN using pandapower. The South-Holland case study is carried out in which the implemented framework simulates hourly network operations across the future energy scenarios for the years 2030, 2040 and 2050. These scenarios are driven by real-world market data of electricity, natural gas and CO2 prices, weather patterns, as well as future heat and electricity demand profiles. This master thesis is part of the TU Delft research project "DEMOSES" and is done in collaboration with Eneco and Stedin.

The results highlight that the large-scale introduction of electrified heat sources in the South-Holland DHN, such as heat pumps, electric boilers and geothermal energy plants, substantially reshapes the operation of the DHN and the loading patterns of the EDN. The operation of the DHN shifts from a more demand-responsive to a market-driven network, with a large reliance on the electricity market signals. This flexibility and responsiveness is largely driven by strategically placed thermal energy storage, especially near electrified production units. Moreover, the electrification of heat supply vastly reduces the reliance on gas and CHP units, resulting in geothermal energy, industrial waste heat and waste incineration becoming the key heat supply technologies. However, it also significantly increases the loading levels of key distribution network components, particularly on medium voltage transformers and lines, leading to critical network stress under future demand scenarios. Conversely, in future scenarios in times of high distributed energy generation, the addition of power-to-heat sources reduce the loading levels of the critical EDN components. It was identified that in times of high distributed energy generation, which results in net negative demand, the power-to-heat sources can consume power locally, lowering the amount of electrical power that needs to be transferred to the HV network, consequently reducing line and transformer loading. The reinforcement of physical assets and coordinated planning efforts between DHN and EDN operators are identified as key factors in mitigating these risks effectively.

Overall, this study provides a detailed description and analysis of the development of the integrated South-Holland heat and electricity model. In addition, the models are applied to perform multiple experiments in the case study, which allows to gain practical insights regarding the effect of large-scale electrification of the South-Holland DHN on the heat network itself and the EDN. The need for spatial-temporal coordination between heat
and electricity network operators in the operational and network planning of integrated energy systems is highlighted. The proposed methodology serves as a practical tool for decision makers and policymakers seeking to balance the decarbonisation goals of the DHN with the EDN reliability.

The Energy System Integration Demonstrator, developed by the Illuminator team at TU Delft, is a
modular, tabletop tool designed to educate and engage citizens in the benefits and challenges of the
energy transition. It simulates important aspects of national electricity grids through a combination of
Raspberry Pi control systems, 3D models, and LED visualizations.
The B.Sc. graduation project focused on enhancing the demonstrator’s visualization and interaction
capabilities. The project subgroup developed dynamic 3D models such as houses, windmills, cars,
solar panels, carbon emissions, and batteries. On-screen elements are also created to represent key
simulation agents like the battery state-of-charge and green energy percentage. Multiple dashboard
versions were designed to tailor the experience to different stakeholder audiences. The report details
the design, implementation, and validation of the visualized components.

This research investigates the optimal integration of power and heat grids to meet energy demands in the Drechtsteden region at minimal costs. Driven by the global push for decarbonization, the study explores the cost-effectiveness of reinforcing the existing power grid versus expanding the heat grid infrastructure, including the potential of waste heat utilization. A Python-based model, leveraging the PyPSA library, was developed to simulate and optimize different scenarios. It was found that reinforcing the power grid is the most cost-effective solution for satisfying the 2030 heat demand, assuming stable geopolitical conditions and material availability. However, under specific conditions, such as decreased heat grid connection costs or abundant waste heat sources, a combined approach might become more viable. The sensitivity analysis highlights the significant impact of technology costs on decision-making. Future research should incorporate resource constraints, long-term projections, and spatial limitations for a more comprehensive assessment of energy system integration.

As global efforts accelerate the transition towards renewable energy sources and decentralized en- ergy systems, the challenge of managing surplus energy during off-peak hours becomes increasingly critical. Without effective and innovative storage solutions, excess capacity from renewable energy resources is being unexploited, reducing the efficiency and sustainability of these technologies. This thesis addresses this issue by proposing the integration of Shared Hydrogen Storage Systems (SHSS) within Energy Communities (ECs), providing a viable method for storing surplus energy as green hydrogen.

By converting and storing renewable energy into hydrogen, ECs can ensure a stable green energy supply, mitigating fluctuations and enhancing energy security. This research presents a modular energy-sharing architecture that integrates blockchain-based smart contracts, with algorithms for equitable distribution and trading of hydrogen capacity between community households. Simula- tions and case studies test the algorithms for hydrogen storage sizing and fair capacity allocation, while also exploring the potential of hydrogen-based heating systems.

The results showcase the critical role of hydrogen storage in increasing the efficiency of renewable energy systems, even during periods of low demand. Two models developed from the simula- tions demonstrate the practical dynamics of using hydrogen for long-term energy storage in urban environments. This work provides a framework for the practical implementation of shared hydro- gen storage for electrification and heating, contributing to the transition towards decentralized, carbon-neutral urban energy infrastructures.

In this thesis, both a 10 MW Alkaline Electrolyser (AE) and a 10 MW Proton-Exchange Membrane (PEM) electrolyser system are evaluated through a comprehensive techno-economic feasibility study, spanning a 25-year operational lifespan. The systems are designed to serve a dual function, producing green hydrogen at 350 bar for utilisation at a hydrogen refuelling station for heavy-duty trucks, as well as recovering waste heat via a tie-in on the cooling system of the electrolyser to directly supply a medium-temperature district heating network at 70 °C. The latter is achieved by connecting the tie-in to a heat exchanger, resulting in a cost-effective heat recovery without the implementation of an expensive heat pump. Both electrolysis systems are operated at 80 °C and powered by offshore wind power, delivered to the electrolyser system through a virtual PPA.

ERA5 data on the wind speed was employed, which was converted into power data via the wind farm power curve. The wind farm power curve was produced by coupling wind farm power production data to the ERA5 wind speed. This method proved to be effective in simulating the power production of a wind farm, as it included the wind farm wake effects and the global-blockage effect.

The performance of the AE system was simulated through a semi-empirical model for both the polarization and Faraday efficiency curve, while the performance of the PEM electrolyser system was simulated by an empirical approach for the polarization curve and a semi-empirical model for the Faraday efficiency curve. A degradation efficiency method is proposed, which employs a constant degradation factor to describe the decreasing performance over the lifetime of the stack. The degradation efficiency effectively illustrated the heat-producing degradation in electrolyser cells.
The techno-economic aspect of the research involved a detailed analysis of the Levelised Costs of Hydrogen and Heat (LCoH2 and LCoHeat). The LCoH2 of green hydrogen from the AE system was 6.08 euro/kg, while the LCoHeat of the recovered waste heat was 1.57 euro/MWh. For the PEM electrolyser system, the LCoH2 was determined to be 5.59 euro/kg, while the associated LCoHeat for the recovered waste heat was 1.55 euro/MWh. The profits of selling the recovered waste can be utilised to decrease the LCoH2. When a recovered waste heat-selling price of 50 euro/MWh was assumed, the LCoH2 of the AE and PEM electrolyser system decreased by 0.64 euro/kg and 0.44 euro/kg, respectively.

The sensitivity analysis on the LCoH2 indicated that the PPA price was the most influential factor on the LCoH2, followed by the Capital Expenditures (CAPEX) of the electrolyser system, and the start-of-life stack efficiency. When assessing the LCoHeat, the sensitivity analysis revealed that the most impacting parameters on the LCoHeat were the capacity of the installed electrolysis plant, the discount rate and the CAPEX of the heat exchanger.

In response to the urgent need for sustainable energy solutions and climate change mitigation, international agreements such as the Paris Agreement have been instrumental in advocating reduced greenhouse gas emissions. As the world shifts towards renewable energy sources and electrification, there arises a heightened challenge of increased congestion and a greater demand for flexibility within electrical networks. Batteries emerge as a crucial source of added flexibility and congestion relief. However, these commercially owned batteries are not obliged to assist with grid congestion, possibly focusing solely on energy arbitrage pursuits for example. This thesis undertakes an exploration of optimizing the efficiency of energy arbitrage batteries by repositioning them to alleviate congestion. Additionally, it delves into the divergence between preferred battery locations for grid operators and battery owners. A comparative analysis is performed among energy arbitrage batteries, congestion relief batteries, and traditional reinforcements. These aspects are evaluated in terms of their contribution to grid flexibility, congestion relief, and load curtailment requirements. The study is conducted using a medium voltage network of a region in the North Rotterdam as a case study. The investigation involves the creation of a linear programming day-ahead market model and a linear programming energy arbitrage battery model. The day-ahead market model generates a price signal that guides the energy arbitrage battery’s charging and discharging decisions for profit maximization. Load and generation forecasts are provided by Stedin for the case study. A Powerfactory model simulates the effect of a congestion relief battery capacity on congestion. Through a heuristic algorithm, the optimal location and size of the energy arbitrage battery capacity are determined. By analyzing these scenarios, the study unveils the positive impact of strategically positioned energy arbitrage batteries that align discharge timing with congestion patterns. The study also highlights the significance of positioning batteries at the deepest points of radial lines to maximize benefits, even though these locations may diverge from battery owner preferences, such as solar farm sites. Interestingly, the addition of energy arbitrage batteries to these solar farm sites can exacerbate congestion due to their relatively low congestion levels. A comparative evaluation reveals that batteries surpass traditional grid reinforcement in enhancing flexibility, with congestion relief batteries outperforming energy arbitrage batteries in alleviating congestion. With the energy arbitrage battery being able to reduce congestion by 27% and the congestion relief batteries being able to reduce it by 94% with the same amount of installed capacity. Energy arbitrage scenarios may necessitate load curtailment to address congestion challenges, they may not independently resolve all congestion. In conclusion, while energy arbitrage batteries show promise in addressing congestion, their effectiveness depends on synergistic technologies and further refinement. Future research avenues may explore enhanced market models, extended predictive analyses, and intricate hybrid strategies to tackle congestion relief, considering the intricate complexities introduced by diverse network topologies.

This thesis focuses on advancing the digitization of socio-technical energy systems by facilitating the creation of Digital Twins of Energy Communities (ECs). A multi-layered architectural model was proposed to capture the different domains and interconnections within multi-energy and multi-agent ECs. Leveraging this framework, a flexible and modular co-simulation platform was realised as a tool that can be employed for enhancing research, decision-making, and policy analysis. Three study cases showcased the platform's capability to represent different ownership topologies, energy trading mechanisms and agents and control strategies. The study cases demonstrated that the holistic design and customisability of the platform allow for representing nuances and capturing cross-layer effects, thus unlocking a deeper understanding of ECs' dynamics and their members' outcomes.

Seaport operators are becoming more environmentally conscious and are looking to electrify their terminals to reduce their greenhouse gas emissions. This leads to higher energy-related costs and more congestion on the electricity grid. This thesis investigates the potential of demand response as a viable strategy to reduce energy-related costs. By modifying operational planning, energy consumption could be deferred from peak to off-peak hours, resulting in cost savings. Different potential ways within the terminal to provide demand response are identified. I propose a two-stage stochastic mixed-integer programming model to optimize operations planning, incorporating energy-related costs. Both energy demand and supply uncertainties are accounted for, exploring various scenarios for vessel arrival times and fluctuating electricity prices. The model is decomposed using a progressive hedging algorithm. Operational aspects considered in this model include vessel arrival scheduling, temperature control of refrigerated containers, allocation of handling capacity across quay cranes, yard cranes, and automated guided vehicles, as well as a charging schedule for the automated guided vehicles. A case study of the Altenwerder container terminal in Hamburg was conducted to test the model. Preliminary results suggest potential cost savings in the range of 12.0-13.2 % with a varying electricity prices based on wholesale market rates. Furthermore, it was found that stochastic modeling improved the solutions found of up to 20.6 % compared to a deterministic model. These findings underscore the substantial potential of demand response strategies in the context of container terminal operations

Before the project was started, there was an energy system integrator demonstrator. This demonstrator was purely software, there was no physical interaction possible with it. It demonstrated the energy system by simulations. The objective of this project was to design and built peripherals that represents the components of an energy system. During the project, four peripherals were made that represents a solar panel, a wind turbine, a load and a storage indicator. A hub is placed on top of the Raspberry Pi, all the peripherals are connected to the Raspberry Pi via this hub. The peripherals consisted of two parts a representation of the energy technology and a microcontroller to do the necessary signal processing. For the communication SPI protcol was used.

As the electrical grid becomes more constrained and grid reinforcement/expansion is no longer the only viable solution, electric flexibility is slowly becoming a more practical approach. Additionally, with the increasingly greater share of flexible devices being deployed, there is immense potential to solve this problem. While flexibility provision is already an implemented market mechanism, it mostly revolves around large industries that have more predictable behavior. At the residential or distribution level, said flexibility is harder to harvest due to the unpredictability and heterogeneity of the systems and actors involved. This report’s focus is creating a protocol facilitating the flexibility exchange between asset owners and the grid operator. In order to bridge these two actors, an aggregator program is created with the task of managing in a responsible way these exchanges. The protocol has used The Green Village, an aggregate of smart residential housing located on the TU Delft campus, as a physical system to base the protocol and program. Although The Green Village has been used as a reference, the protocol and program should be versatile for any application. The main goal, when developing the protocol, was to have the aggregator program take in as many tasks related to flexibility exchanges as possible to increase compatibility (interoperability). The protocol and aggregator program were also designed to facilitate modifications and upgrades (plug-and-play) while preventing communication errors (redundancy). To fulfill these requirements, the report takes the following structure. First, the different methods and frameworks enabling flexibility as well as involved actors are discussed. Then the protocol and aggregator program are explained in depth. Finally, a validation through simulation is presented and inspected.

The aim of this report is to discuss the design of the software that creates a simulation for the national electricity grid level of the Netherlands. This is done by further developing the open-source energy system integration development kit called the Illuminator. Where the goal of this software is to create an extra case, add a Graphical User Interface (GUI), and add a way to evaluate created configurations.

This report presents an energy transition model that emphasizes the importance of prioritizing innovation. It demonstrates the potential energy savings achievable through the installation of insulation layers. Additionally, it explores the increasing adoption of electric vehicles (EVs) and its influence on energy consumption within the transportation sector. The report also discusses renewable energy sources and explores energy storage methods. Lastly, the report concludes by highlighting the need for a balance between energy supply and demand, acknowledging that traditional energy sources will continue to be necessary in 2030, aligning with expectations. This comprehensive analysis serves as a foundation for future research endeavors aimed at developing a more precise model.

The climate change is evident and a big contributor to the negative effects of the climate change is the energy production sector. Therefore, the energy production must shift towards a more environment friendly mix. Changing our energy sources to mostly renewable energy sources comes with many challenges.
The way the current Dutch electricity market is designed will lead to a drop in electricity prices on the wholesale market, when the market is penetrated with a high concentration of renewable energy sources. So the electricity market mechanism needs to be reformed to have a price determination system better suited for energy sources with low marginal costs. Furthermore, a capacity remuneration system needs to be implemented to create enough investment incentive in controllable energy capacity to maintain a high level of security of supply in the Dutch energy system during the energy transition.
There exist many different forms of capacity remuneration mechanisms, but the strategic reserve and capacity markets are examined to check which one would work best in the Dutch energy system. In a strategic reserve a central authority sets the capacity volume, which will be contracted by an operator. This operator will dispatch the contracted capacity when needed. In a capacity market a central authority determines the amount of capacity each consumer should acquire through buying capacity credits on the market.
To determine which capacity remuneration mechanism would deliver the highest level of security of supply, simulations of the Dutch energy system are run. The simulations were done using the models AMIRIS and EMLab, which are coupled to give a more accurate representation of reality. The coupling takes place in the Spine Toolbox. The capacity remuneration mechanisms were created in Python and can be activated as modules in the Spine Toolbox.
The vertices are sketched of the possible future energy scenario of the Netherlands in 2050. These vertices are the so called scenarios, regional governance, national governance, European CO2-governance and international governance. The simulations were run with a scenario in which the energy demand stays equal and with a scenario where the energy demand descends similar to the international governance scenario. The international governance scenario was chosen to base the simulations on, because the simulations make profit driven decisions, similar to the international governance scenario.
The results of the simulations show that, firstly, without any governmental interference, a (near) zero-carbon energy system will not be achieved in 2050. Secondly, when running the energy-only market in a realistic scenario, no shortage hours will occur and the system had sufficient generation capacity for supplying the demand, even in a "dunkelflaute" scenario. Thirdly, the basic version of the strategic reserve offered the least costly solution for society for providing sufficient security of supply, so no significant shortage periods will occur in a realistic scenario. Finally, the best option for maintaining security of supply in the Netherlands during the transition to a zero-carbon energy system is the implementation of the yearly capacity market.

The energy transition is one of the major challenges of the 21st century, impacting the way energy is generated, conserved and consumed. Energy generation becomes more and more decentralised, intermittency and fluctuations suddenly are becoming topics of interest within day-to-day life and energy system operators are facing many new obstacles never encountered before. In this context, the anticipation for hydrogen as a resource for energy conservation and -management is big. This research focuses on the optimisation of the hydrogen pathway for investment and operational models.

The hydrogen pathway as aforementioned is divided over three technologies: hydrogen generation with means of water electrolysis, also known as 'green hydrogen', storage in compression vessels and reconversion of hydrogen into electricity in the form of a fuel cell technology (also known as Power-to-Gas).

The research focuses on identifying technical parameters and operational policies of the water electrolysis systems that can be translated into optimisation constraints, assessing the level of detail required to create an accurate optimisation model. A generic model is developed that can be scaled for further research, making different case studies and sizing possible. The research compares the performance of the models in terms of accuracy to the computational burden. The comparison is done for the level of detail and complexity added to the model.

After a literature review of technical parameters and operational policies regarding the technologies, two models were created in a mathematical framework. The two models proposed were Linear Programming (LP) and a Mixed-Integer Programming (MIP) Model. On the LP model 6 different sensitivity analysis has been performed, to be precise on Capital Expenditures (CAPEX), efficiency, lifetime, ramping rates, interest rates and finally different time horizons. The outcome of these analyses is that the technology mix can best be used in a combined manner, whereby each component of the mix contributes towards minimising the objective value: the Total Annualised Cost.

Lastly the two models are compared with different types of configurations, each with a different set of constraints. The constraints to be modelled were: minimum uptime and downtime, start-up costs, degradation due to cycling and finally the part-load operation.

Engineering has a significant cultural component in addition to its widespread perception as an engine of innovation and economic growth. This cultural component corresponds to how users and communities react or adapt to technological innovations and their influence on the advancement of such innovations. Hence, it is critical to prioritize the social aspect as much as the technical aspect of the energy transition.

With the fast-growing world and its energy demand, it becomes necessary to shift renewable energy-producing technologies to keep this planet habitable. But during the transition, it is essential to maintain the flexibility, reliability, availability, and robustness of the current energy system. With multiple actors involved in the energy system, it becomes inevitable to have a united front to make an effective transition and offer holistic solutions to the challenges. For this, communication among them is important.

This thesis has contributed to developing a toolkit catering to the cultural component of engineering and bringing different actors in the energy system to the same platform. It takes the route of co-simulation and modelling of energy system components. The modular and user-centred design of the toolkit helps to educate the communities to increase their acceptance of renewables, educate students about recent changes in the energy system, and foster communication among domain experts by simulating the challenges of energy transition and system integration. Illuminator's table top approach makes it easy for users to operate and understand the energy system demonstrations.

The outcomes show example demonstrations simulated for a specific group of users by leveraging Illuminator's modularity, table top approach, and scalability.

As the share of renewable energy generation increases, the need for energy storage also increases. Therefore, there is a need for better storage representation in the current energy modelling tools. In the present day,
the longer-term energy storage systems are not fully represented since, for existing storage systems, the self-serving nature of these leads to participation in multiple energy markets. This is because participating in other markets, like the balancing markets, can lead to higher overall profits than a storage system only participating in the wholesale market.
This thesis investigates different energy storage technologies and multiple prominent storage applications for grids. Furthermore, an overview of the European energy markets will be examined, and different design options will be discussed. These markets include frequency containment reserve (FCR), frequency regulation reserves (aFRR/mFRR) and the wholesale markets. The review of storage technologies, applications, and available markets has led to the development and simulation of single-purpose energy storage models fulfilling grid applications.
By combining the specific purpose models, a complete energy market and energy storage model representation could be created. The model created is unique since the complete energy system model allows energy storage systems to optimally dispatch over multiple markets while at the same time also influencing these markets. Multiple cases were investigated using this model, such as the influence of increasing storage capacity on the wholesale and balancing market and the influence of storage systems just performing one service, so only regulation, arbitrage or peak-shaving. Based on the model results, recommendations are made on improving the current energy market designs and how to better represent storage systems in existing energy system models.