The European Commission aims for a full circular economy (CE), an economy that aims to reuse all resources in 2050. CE is a promising way to increase welfare and wellbeing while decreasing environmental footprints. Industrial symbiosis, in which companies exchange residuals for resource efficiency, is essential to the circular transition. However, many companies are hesitant to implement business models for industrial symbiosis because of the various roles, stakes, opinions, and resulting uncertainties for business continuity.
This dissertation supports researchers, professionals, and students in understanding and shaping circular business models for industrial symbiosis networks through collaborative modelling and simulation methods. Three theoretical perspectives, design science research, complex adaptive socio-technical systems, and circular business model innovation, shed light on designing business models for industrial symbiosis. A serious game and agent-based models were developed in multiple case studies with researchers, practitioners, and students. These were then used to design circular business models and explore their efficacy under uncertain conditions, such as various behavioural intentions of potential partners in diverse natural and societal contexts.
This thesis advances business model design and experimentation by integrated simulation of social and technical aspects of industrial symbiosis. Furthermore, the research shows how simulations facilitate learning processes in designing circular business models. Ultimately, the thesis equips researchers, practitioners, and students with knowledge, tools, and methods to shape a circular economy.

This thesis aims to support the design and implementation of energy-secure thermal energy communities (TEC) by investigating their technical, behavioural and institutional settings through a collective action perspective. The thesis shows that energy-secure TEC initiatives are collective energy systems with particular characteristics and surrounding conditions. The thesis demonstrates, by building and using a number of agent-based models, that behavioural and institutional settings are relatively more influential than technical settings for establishing and sustaining the functioning of energy-secure collective thermal energy systems. In particular, a combination of aquifer thermal energy storage with heat pumps positively impacted TEC initiatives' energy security. The most crucial technical requirement for the energy security of TEC initiatives is a connection to a natural gas grid. The thesis recommends that individual households initiate their own (thermal) energy communities, and policy-makers support such initiatives.

In this paper the suitability of compressed hydrogen gas storage in salt caverns is analysed. The presence of microbial sulfate reducing bacteria create a contamination risk of H2S inside the cavern. Key cavern parameters that influence the production of H2S are highlighted by a chemical model. The model uses empirical data provided by Vattenfall, in order to predict what would happen inside an actual cavern. By doing so, it can be understood what cavern conditions are suitable for the storage of hydrogen. An analysis is done on the above ground process of a salt cavern storage plant to determine what extra separation steps are required to reach ISO limitations for hydrogen gas. The research is done by answering the following research questions: What are key cavern conditions that influence the suitability of hydrogen storage in salt caverns and for what purpose can the storage plant be implemented? The sub-research objectives are formulated as follows: 1. What are the potential process risks when storing hydrogen in salt caverns? 2. Are there substantial risks of contamination with subsurface hydrogen storage? What are the defining variables that contribute to said contamination? 3. How do these impurities build up when the salt cavern is used? 4. Is the equipment currently used in the gas storage facility in Epe useable for hydrogen gas storage? What changes should be made to the current storage process? When analysing future utilisation demands of hydrogen, one of the primary applications is energy con- version by fuel cells. There are severe limitations set by the International Organization for Standardization on the maximum amount of H2S in hydrogen gas used by fuel cells. This paper uses a chemical model based on PHREEQC to predict the chemical reactions in the cavern. In order to get close to actual results, the model input is constructed following empirical data of an existing salt cavern. With the chemical model different cases can be constructed each highlighting an important cavern constraint. What are the positive and negative forces on the production of H2S in salt caverns and what could be theoretically done to prevent H2S contamination? Primary aspects that positively contribute to H2S production, when the cavern is modelled as a batch reactor, sorted by significance are: • Bacterial growth and reduction rate • Brine volume and sulfate concentration. • Brine pH and ionic strength. • Cavern pressure and temperature. • Fe2+ and Fe3+ concentration. The chemical model only predicts what will happen in the cavern when there is no gas coming in or out, like a batch reactor. For this reason a dynamic model is constructed which predicts H2S outflow in the gas when applying different demand-cases to the cavern. The model results showed that with applying a maximum use case, which fluctuates between the maximum pressure and minimum pressure, there is still a minimum H2S output that is higher then the allowed limits. A demand curve is simulated where the cavern is used to power a hydrogen gas-turbine to profit from seasonal energy price changes. In this demand curve the cavern produces significantly less and less often, giving time for the H2S to build up. In two years, the H2S production reaches levels above the allowable limits set for hydrogen gas turbines. A gas process facility is required to eliminate H2S contamination, in order to size such a facility it was modelled in Aspen Hysys. The model is validated by using data from literature. After analyses of resulting process equipment and gas streams, the absorber tower’s efficiency is most dependant on its temperature its pressure and the absorbent flow rate. The water concentration is above ISO levels when withdrawn from the cavern. So to follow these limitations, an additional dehydration step is required. In conclusion the process is capable of accurately separating the H2S to below ISO limits. The process works using 6% of the potential chemical energy of hydrogen. The process is unable to purify the water concentrations. As a result of this research some conclusions can be made. When using salt caverns for long term hy- drogen storage can be a significant risk of H2S contamination as a result of microbial sulfate reduction. For the reference case the H2S concentrations reach above the levels set for fuel cell use and concentrations will increase in significance when utilisation of the cavern is decreased. For fuel cell application, a separation pro- cess based on MDEA gas sweetening can be used to get the hydrogen up to the demanded H2S purity. But a more economical solution would involve extensive testing of the cavern soil for any microbial activity. If there are no sulfate reducing bacteria there will be no problem. Other pre-process steps could involve increasing the pH of the brine in the cavern to avoid H2S production. As well as increasing the iron concentration in the brine.

The goal of this research is to be used as a framework for adding energy storage to aid in the connection of decentralized renewable energy generation in areas with limited connection availability to the electricity grid. The dependence on fossil fuels is reduced with the aim to reduce greenhouse gas emissions. In the Netherlands this means that natural gas is slowly faded out of our society. One consequence of this is the electrification of some functionalities that natural gas had, like heating houses and cooking. Additionally, more and more people are choosing to drive electric vehicles. On the supply side more and more renewable energy sources are being installed to increase the renewable energy mix in our electricity market. Because the supply and demand of electricity increases faster than new cables can be installed, the electricity grid is put under strain. The electricity grid was designed, decades ago, to only transport electricity from central, large energy generation plants to the consumer. However, nowadays there are also developers that are building 'decentralized' PV parks and wind farms. These decentralized generation systems are unable to be connected to the MV-grid. Energy storage has been identified to be a valuable asset to help with the penetration of renewable energy. Energy storage can help to match the supply and demand, improve the power quality, improve the variability of the power supply and smooth out the peaks of over-generation most associated with the congestion on the MV-grid. It is also possible to (temporally) replace the necessary grid reinforcement. However, energy storage is still very expensive. So it needs to be examined how energy storage can be interesting for this problem. A knowledge gap exist on energy storage for decentralized renewable energy generation for the Dutch medium-voltage grid. Additionally, no method was available to simulate such an problem. So this was researched in this study. The study has included a suitable financial model, various energy storage systems and operating strategies. Additionally, the interest of the developer of the DG plant and the DSO were included. For the developer the study has investigated the profitability of the system. The DSO is interested in minimizing the cost and resources of the connection of the hybrid DG system. From this the following research questions are formulated: Under what conditions can energy storage for decentralized renewable energy generation be economically feasible to mitigate connection scarcity on the medium-voltage grid? This was answered in three steps. Firstly, a model was built to accurately simulate energy storage in a hybrid DG system. This model must satisfy all technical requirements and be applicable to the Dutch electricity markets. Secondly, the simulation model was used to examine the hybrid system design to optimally profit from the storage system. From this it was concluded that a small energy storage system adds relatively more value in energy output and earnings than a large storage system. Additionally, it identified the pumped heat electrical storage system as the most profitable system for decentralized generation and the best performing technology is the LFP Li-ion battery. Also, storage is more beneficial for PV solar energy than for wind energy. In this section it was also found that in alleviating connection scarcity, grid reinforcement is more profitable than energy storage. And thirdly, different operating strategies for the hybrid system were investigated to optimally use the energy storage system. For a hybrid system with cable capacity limitations, peak shaving of over-generated energy is the most profitable operating strategy. This research has shown that energy storage can improve the energy output and revenue of a decentralized system with connection capacity limitations. Nevertheless, not adding energy storage is more profitable. A small energy storage system with a PV solar park can become economically feasible and more interesting than grid reinforcement. For this, energy storage technologies need to improve, especially the capital cost need to reduce, with more than half of the current cost. Additionally, this hybrid system needs to be located where grid reinforcement is costly or not possible.

Hydrogen enables renewable energy storage, and hydrogen carriers can allow easier transportation. This research was initiated to get more insight into transporting renewable hydrogen carriers (RHCs) by ship, which is one of the transportation options. The research was conducted in three steps. First, a system description with a literature study was made to select suitable RHCs, based on the production, and the performance as a hydrogen carrier. Secondly, a basic model for three transportation cases was made. Thirdly, a more detailed model was created to evaluate different transportation options. The four most attractive RHCs to be shipped are the synthetically produced: methanol, liquefied natural gas, liquid hydrogen, and liquid ammonia. In the model, short and long term options are evaluated for the same, mild and strict regulations compared to today. The following statements can be made, based on the results of the second model: • Ammonia is best suited in a scenario with strict regulation compared to today. Ammonia has a noncarbon nature; therefore, it is the most promising option in the long term. • Methanol is best suited for small scale transportation in the short and long term, in which the same regulations are active. The storage of methanol on board of the ship is relatively simple and it has a relatively high hydrogen density. • LSNG is best suited for large scale transportation in which the same regulations are active. LSNG has the highest hydrogen density of the discussed RHCs. The advantage of SNG is that during the reconversion to hydrogen, extra hydrogen will be produced because of the added steam, during steam methane reforming (SMR). • Liquid hydrogen is from a shipping point of view not the most suited for the transport of hydrogen, due to its low volumetric density and complex storage. Shipping liquid hydrogen has the advantage that it reduces extra conversion steps in other parts of the supply chain. Shipping RHCs and using the carriers as a fuel is an option which involves higher cost compared to current fossil fuel options. This relatively high share of fuel expenses using RHCs will make efficient powertrains relatively more attractive because it enables higher fuel cost savings. The most attractive engine configurations are a two-stroke internal combustion engine with a mechanical powertrain and a SOFC with a battery in an electric powertrain. The last option is especially promising for the long term, because of the technologic developments and increasing regulations in shipping. Overall the results indicate that shipping NH3 and using the carrier as a fuel is the most promising option for the transport of RHCs by ship. This research and constructed models can be used as a tool to evaluate different transportation options. However, used values are based on various sources and their corresponding assumptions, which can change significantly in the future.

The increase of the average world temperature, as a result of greenhouse gasses, is one of the greatest challenges the world is currently facing and will be facing in the future. One of the many efforts to reduce the increase of carbon dioxide, is to decarbonize the gas network by replacing natural gas with renewable gasses like green gas. Since the current gas network is not always suited for the injection of big green gas volumes, especially during summer periods, the process of decarbonizing the natural gas network requires adjustments of the current functioning of the gas network. Several technical adjustments for obtaining an increased green gas injection capacity exists, although, up to now, the potential green gas injection capacity obtained per solution was not known. Within this study, a dynamic gas network simulation model was developed wherein different gas network function strategies can be explored to obtain the potential green gas injection capacities. With the developed dynamic gas network simulation model one is capable to implement and simulate different gas distribution network configurations, specify green gas suppliers on the location of choice, simulate different gas demand scenarios and consumer profiles, adjust the city gate station pressure used for simulations of static- and dynamic pressure management, and to model the injection of excess green gas into a storage- and from the storage into the network. Within this study, the gas distribution network of Northeast Friesland, The Netherlands was analyzed on its green gas injection capacity after applying static pressure management, dynamic pressure management, and a pressure management strategy combined with storage. The gas network of Northeast Friesland was explicitly chosen since currently, green gas injection problems are experienced within this network. Following the results obtained from the simulations, a city gate station inlet pressure - demand table was defined. Within this table, the total gas demand measured within the network was plotted against the minimum city gate station inlet pressures, while still in compliance with the lower pressure boundary condition. Using this table, the optimal period to statically decrease- and increase the city gate station inlet pressures from 8.3 to 6.5 bar and from 6.5 bar to 8.3 bar, appeared to be respectively 1 May and 1 October. By changing the pressure to 6.5 bar, a safety margin of 1.5 bar was taken into account. For both static- and dynamic pressure management, green gas injection capacities ranging from 400 to 1600 m³/h, divided over three green gas suppliers, were analysed. The results were depicted against the total injectable hours, providing insight in the maximum green gas injection capacity while remaining eligible for the Stimulation of Sustainable Energy Production (SDE+) subsidy. Since with dynamic pressure management the city gate station can inject at lower inlet pressures, dynamic pressure management results in a greater green gas injection capacity. To point out, static pressure management provide 450 m³/h green gas injection capacity, whereas dynamic pressure management provides 650 m³/h green gas injection capacity without experiencing any injection problems.

The goal of this thesis is to identify under which boundary conditions
there is a business opportunity for an innovative electric thermal energy storage, with regards to the future electricity and heat markets. To meet this target, a novel optimization model has been developed as the main tool for the analysis of the storage and its profitability under different situations.

This thesis introduces an approach to support the long-term social acceptance of energy systems by addressing value conflicts embedded in regulatory and technical designs. When designing energy systems, the realisation of some values can conflict with the realisation of other values. The decision to deploy energy systems therefore inevitably entails a prioritisation of some values over others. Societal groups that do not agree with this prioritisation may decide to oppose or not to support the deployment and use of these systems. Lack of social acceptance may occur during the planning phase, but also at a later point in time as a result of value change. This can be caused by a growing mismatch between values prioritized in energy systems and how societal groups are affected. To support the social acceptance of energy systems, value conflicts embedded in energy systems need to be addressed. Methods to do so were however lacking. This thesis provides a methodological contribution by demonstrating how the literature on data science and the complexity sciences can be used to address value conflicts. This thesis answers the following research question: How can value conflicts embedded in energy systems be addressed in support of social acceptance?

We use probabilistic topic modelling to explore how the academic literature addresses value conflicts. Identified tactics can be used to specify design requirements and policy guidelines in support of the social acceptance of energy systems. Agent-based modelling is used to identify value conflicts embedded in energy systems that result from the heterogeneous properties of the affected population. Agent-based models provide insights about the type of population affected by value conflicts and hence about the severity of the resulting lack of social acceptance. This thesis contributes to the literature on social acceptance by demonstrating how long-term acceptance can be supported by drawing on insights from ethics of technology. Additionally, we provide a systematic and practical approach to integrate human values in the regulatory and technical design of infrastructures, which is critical for supporting the ongoing energy transition.

The growing penetration of renewable energy sources in electricity systems requires adapting operation models to face the inherent variability and uncertainty of wind or solar generation. In addition, the volatility of fuel prices (such as natural gas) or the uncertainty of the hydraulic natural inflows requires to take into account all these sources of uncertainty within the operation planning of the generation system. Thus, stochastic optimization techniques have been widely used in this context. From the point of view of the system operation, the introduction of wind and solar generation in the mix has forced conventional generators to be subject to more demanding schedules from the technical point of view, increasing for example the number of start-up and shutdown decisions during the week, or having to face more pronounced ramps. From the point of view of the market, all these technical issues are transferred to the market prices that are subject to greater volatility. This thesis focuses on the problem of risk management using the Conditional Value at Risk (CVaR) as a coherent risk measure. The thesis presents a novel iterative method that can be used by a market agent to optimize its operating decisions in the short term when the uncertainty is characterized by a set of random variable scenarios. The thesis analyses how it is possible to decompose the problem of risk management by means of Lagrangian Relaxation techniques and Benders decomposition, and shows that the proposed iterative algorithm (Iterative-CVaR) converges to the same solution as under the direct optimization setting. The algorithm is applied to two typical problems faced by agents: 1) optimization of the operation of a combined cycle power plant (CCGT) that has to cope with the volatility in the spot market price to build the supply curve for the futures market, and 2) strategic unit-commitment model. In a second part of the thesis the problem of market equilibrium is studied to model the interaction between several generating companies with mixed generation portfolios (thermal, hydraulic and renewable). The thesis analyses how the Nash equilibrium solution is modified at different risk-aversion level of the risk of the agents. In particular, the thesis studies how the management of hydroelectric reservoirs is modified along the annual horizon when agents are risk-averse, and it is compared with the risk-neutral solution that coincides with a centralized planning when the objective is the minimization expected operational cost.

In the transition towards low-carbon energy systems, the growth of variable renewable energy sources (V-RES) like solar and wind in the electricity systems is calling for more flexibility measures. These are needed to cope with the increased uncertainty and variability that affects the residual demand. Flexibility can be offered by traditional players in the sector, through dispatchable generation, storage, demand response, and increased interconnection. However, there are also increasing opportunities for new actors and roles. Aggregators, for example, can exploit the flexibility of small consumers and trade it on their behalf in the electricitymarkets. This flexibility can also be provided
from other sectors, such as heating and transportation. With the adoption and diffusion of electric vehicles, the aggregated capacity is considered to have significant potential to support the grid in the future. Vehicles are only used 5% of the time for driving. Thus, when parked, they could be used for providing flexibility through storage or providing vehicle-to-grid (V2G) power.

The aim of Transmission Expansion Planning (TEP) studies is to decide which, where, and when new grid elements should be built in order to minimize the total system cost. The lumpiness of the investment decisions, together with the large size of the problem, make the problem very hard to solve. Consequently, methods should be put in place to reduce the size of the problem while providing a similar solution to the one that would be obtained considering the full size problem. Techniques to model the TEP problem in a compact way, also called reduction methods, can reduce the size of the TEP problem and make it tractable. This thesis provides new techniques to reduce the size of the TEP problem in its main three dimensions: the representation made of the grid (spatial dimension), the representation made of the relevant operation situations (temporal representation), and the number of candidate grid elements to consider. In each of the three reduction techniques proposed in this thesis work, the first step consists in solving a linear relaxation of the TEP problem. Then, they make use of information that is relevant to make the network investment decisions to formulate the TEP problem in a compact way for a certain dimension. I use the potential benefits brought by candidate lines to reduce the size of the representation made of the temporal variability in the problem. Besides, I reduce the size of the network by preserving the representation made of the congested lines and partially installed lines while computing an equivalent for other network elements. Lastly, I manage to reduce the set of candidate lines to consider based on the set of expanded corridors and the amount of new capacity built in them. I also compare each of the reduction techniques that I have developed to alternative reduction methods discussed in the literature within various case studies. In each of the three reduction methods proposed, the TEP solution computed solving the TEP problem resulting from applying the proposed reduction methods is more accurate (efficient) than the ones computed applying alternative reduction methods. Besides, this solution is almost as efficient as the solution of the original TEP problem, i.e. the TEP problem that has not been reduced by the proposed reduction method. As a next step, one may explore combining the three reduction methods proposed to maximize the reduction achieved in the size of the TEP problem.

Many organizations tasked with managing public utility infrastructure routinely collect and store large volumes of data for decision making purposes in their management and maintenance processes. This data is collected, stored and analyzed within asset management data infrastructures, however, traditional data management methods are becoming increasingly inadequate. More and more, data is being provided by new sources that can communicate over the internet, collectively known as the Internet of Things (IoT). IoT may benefit the management of public utility infrastructures by providing enough quality data to generate trusted information required to make the right decisions at the right time, helping asset management organizations improve their decision-making capability. The extensible asset management data infrastructure model presented in this dissertation aims at improving our understanding of asset management through IoT. Using a Duality of Technology lens, this research takes the view that IoT is continually being socially and physically constructed, and discriminates between human activity that affects IoT, and human activity that is affected by IoT. Explorative case studies in the asset management domain are used as the main research method. Taking the view that asset management data infrastructures are complex adaptive systems ensures that the resulting model is capable of dealing with the evolution of asset management data infrastructures in the face of new technologies and new requirements. The usability of the model is tested by means of test case studies. The tests indicate that the model can be used to improve our understanding of asset management through IoT and to provide actionable insights for the achievement of expected benefits and mitigation of risks of asset management through IoT.

Among a wide variety of topics that are covered by Dynamic Security Assessment (DSA), maintaining synchronous operation and acceptable voltage profiles stand out. These two stability categories are mostly jeopardized in the seconds after a large contingency occurs. Therefore, this thesis tackles the two aspects of large disturbance stability of power systems in the short-term time scale. The classical DSA methods deal with the short-term loss of synchronism by analyzing one operating point and one contingency at a time. However, a small change in operating point may turn a stable system unstable. The first part of the thesis overcomes this gap by proposing the idea of parametrizing the stability boundary. The newly introduced method constructs the parametrized security boundaries in polynomial forms based on a reduced amount of Time Domain Simulation (TDS) data. Such a method retains the positive traits of TDS while being able to estimate a measure of stability even for those points that do not belong to the “training" set. The polynomial coefficients are further improved via SIME parametrization that has a physical meaning. Finally, when being subject to a constraint by the means of Quadratic Programming (QP), SIME parametrization also becomes competitive with direct methods in the sense of conservativeness. Nevertheless, if TDS fails, any TDS-based DSA approach is useless. Most often, the dynamics of the non-linear power system is described by the set of Differential Algebraic Equations (DAE). TDS can face problems when the DAE model experiences singularity due to the loss of voltage causality. This thesis introduces Voltage Impasse Region (V IR) as the state-space area where the voltage causality is lost due to the non-linear modeling of static loads. The entrance of a dynamic trajectory to a V IR was shown to be accompanied by non-convergence issues in TDS and significant voltage drops. Appropriate Voltage Collapse Indicators (VCIs) are also derived for each load model of interest. The thesis concluded that V IR is a structural problem of the DAE model that should always be accounted for when the short-term stability is assessed.

In this thesis, the applicability of a novel method – the Participatory Value Evaluation method – to the transition towards zero natural gas use at the local level of the case study neighborhood Hengstdal in Nijmegen was researched. The main objective of this thesis is to contribute to the development of the PVE method by researching the applicability of this novel method to the transition towards zero natural gas use at the local level of an existing neighborhood.

As a result of the research, only 6 respondents filled in the web tool and participated in the experiment. It must be concluded that it was not possible to apply the Participatory Value Evaluation method to the case study about the transition towards zero natural gas use of the neighborhood Hengstdal in Nijmegen. The process of implementing the PVE method is analyzed in order to identify factors that might have complicated the application process in this specific case study. To avoid future applications of the PVE method to suffer from the same complicating factors, several conditions are formulated. If these conditions are met, they could avoid or mitigate the impact of the complicating factors and could therefore make it possible to successfully apply the PVE method. However, future research is necessary in order to determine whether these conditions are sufficient for a successful implementation of the method.

Market organisation on energy networks is increasingly complex due to frequent emergence of new markets within and related to energy infrastructure. This research is conducted in cooperation with the Dutch Ministry of Economic Affairs and Climate Policy, and analyses the impacts of role demarcation policies on the development of the electric vehicle charging infrastructure market in the Netherlands by applying Exploratory System Dynamics Modelling and Analysis. In-depth behaviour based sensitivity analysis and scenario discovery has highlighted the most relevant dynamics on the market development over a period of 20 years. Limiting or restricting market access for network companies has showed to effectively influence market attractiveness positively and price developments negatively, both mainly on the short-term (within the first period of 10 years). The most significant and uncontrollable effects of uncertainties occur mainly on the long-term, granting the decision-maker the opportunity to adjust previous decisions when more accurate predictions can be made concerning the development of a specific market.

With increase in the number of sensors installed on sub-assemblies of industrial components, the amount of data collected is rapidly increasing. These data hold information in the areas of operation of the system and evolution of health condition of the components. Therefore, extracting the knowledge from the data can bring about significant improvements in the aforementioned areas. This dissertation provides a path for achieving such an objective. It starts by analyzing the data at the sub-assembly level of the components and creates four frameworks for analysis of operation and maintenance (O&M) for past, present and future horizons at the component level. These frameworks allow improvement in operation, maintenance planning, cost reduction, efficiency and performance of the industrial components. Next, the dissertation evaluates whether such models can be linked with system level analysis and how providing such a link could provide additional improvements for system operators. Finally, preventive maintenance (PM) in generation maintenance scheduling (GMS) in electric power systems is reviewed and updated with recent advancements such as connection to the electricity market and detailed implementation of health condition indicators into the maintenance models. In particular, maintenance scheduling through game theory in deregulated power system, for offshore wind farm (OWF) and an islanded microgrid (MG) are investigated. The results demonstrate improvements in reducing cost and increasing profit for the market agents and system operators as well as asset owners. Moreover, the models also deliver an insight on how direct integration of the collected operation data through the developed component level models can assist in improving the operation and management of maintenance for the system.

The expansion of offshore power transmission and generation in the North Seas of Europe is accelerating rapidly. This is due to several drivers, including the decarbonization and reform of the European power system, and innovations in offshore wind and high-voltage direct current transmission. So far, this European North Seas offshore grid is composed of conventional transmission lines, which perform the interconnection of onshore power systems and the wind farm connection functions separately. An integrated offshore grid is an innovative concept where some of the transmission lines perform simultaneously both the interconnection and connection functions. Earlier research leveraging optimization approaches already demonstrated that such an integrated offshore grid can provide socio-economical, technical and environmental benefits...

Due to the increasing share of renewable energy sources in modern power systems and electricity market deregulation, heavy inter-regional and cross-border power flows are becoming a commonplace in system operation. Moreover, largescale integration of renewable energy sources is expected to pace up, therefore new solutions have to be developed to integrate these intermittent sources, which are also characterized by being distributed over large geographical areas, such as offshore wind farms. Multi-Terminal High Voltage Direct Current (MTDC) networks are expected to form a solution for the integration of renewable energy sources to the existing interconnected AC grid. The type of converters used in the MTDC networks is however a subject of debate, as both Line Commutated Converters (LCCs) and Voltage Source Converters (VSCs) can be used. Moreover, the coordinated control of the MTDC networks with the AC system poses a challenge to the system operators, as it requires the consideration of both AC and DC system dynamics.
In response to these challenges, this thesis aims to discuss the following aspects of the MTDC networks: control of a hybrid MTDC with both LCCs and VSCs, as well as the utilization of an embedded VSC-MTDC for stability enhancement. The thesis also investigates the supply of passive AC systems using a hybrid MTDC network.
In the investigation of an AC/DC power system with a hybrid MTDC network, first, the combined AC/DC system is modeled. Next, a Small Signal Stability Analysis (SSSA) of the system is conducted, based on which the Power Oscillation Damping (POD) controllers were designed to enhance stability in the connected AC systems.
In the utilization of an embedded VSC-MTDC network for stability enhancement
in the AC/DC system, the operating point adjustment strategy is investigated,
which is implemented through the adjustment of setpoints for the active and reactive power controllers in the network converters. Finally, the design and placement of a Multi-Input Single Output (MISO) controller is investigated, where the control strategy is based on Modal Linear Quadratic Gaussian (MLQG) control using Wide Area Measurement Systems (WAMS) signals.

The iron law of oligarchy claims that the complex organisation will always end up in an oligarchy, no matter it was constituted originally. In that sense, the oligarchy should also happen in the Community Energy System (CES) as a complex self-govern common-pool resource system. The oligarchy situation raises the issue of the fairness and the health in the community since that subgroup of the member may steer the future decisions to be more favourable on one side than the others or may drive the rules to limit the active participation of others, or decrease the community cohesion. This paper specially concerned with the emergence of oligarchy in the CES and the effect of the iron law of oligarchy on the health and the fairness in the CES. The result shows that the iron law of oligarchy occurs in CES, but it is needed to make CES has the level of fairness and health required to support its continuity

More than 200 million Indians are without electricity, and the majority lies in the rural areas, where the central grid fails to reach due to unreliable distribution and inconsistent terrain. The solutions that reach include majorly, decentralized energy resources via solar micro-grids, or solar home systems. New projects of energy sharing are coming up which allows a household to become a consumer, as well as a producer of energy in case of excess and sell it to make profit. But these projects fail within few years due to number of social, technical, cultural, political, and environmental dynamic factors playing role in the community. Thus, the research question is raised that “Given the dynamics of rural communities of India, how can socio-technical systems of solar electrification be sustained?" This project develops an institutional innovation framework that helps answer this question. The framework is conceptualized and operationalized to understand that how the institutions around these factors can be changed by the community and individual actions in such socio-technical innovations and infrastructure systems. The study follows empirical analysis and develops simulation model (agent based model) for the case study of rural solar electrification in India, which helps in developing the institutions. The case study and the framework together emphasizes the importance of the institutional innovation approach, where institutions need to be adapted and diffused within the community to make the sustenance of the project possible in various domains. The final results show that new type of projects, labelled as hybrid projects, would be most sustained. These would be sharing projects and would only use the existing micro grids, when there is higher demand. Also, it emphasizes on the need to look at institutions development, not just as a collective perspective which happens with interactions of actions, but also a perspective of majority or collection of actions. The policies generated at two levels of usage (prioritization of resource usage, constraints on resource usage, etc.), and for acceptance of innovations (co-operative shops, in-house manufacturing, etc.) prove to help the sustenance of these electrification systems further.