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.

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.

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.

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.

“Heating and cooling remain neglected areas of energy policy and technology, but their decarbonisation is a fundamental element of a low-carbon economy.” (IEA, 2012). In The Netherlands the total heat demand needs to be reduced and the heat provision needs to become more sustainable (Kamp, 2015). By 2021, municipalities therefore need to have formulate plans on how they will realize the phase-out of natural gas in all districts in which the phase-out will take-place before 2030. In South Holland, district heating is considered a cost-effective alternative for the current gas based system. However, the Netherlands Environmental Assessment Agency (PBL) and the Province of South Holland (PZH) think differently about how district heating should be implemented and it is currently unclear what these differences exactly are and how they influence municipal policy for the phase-out of natural gas. In this thesis, we have therefore researched what the relevant differences between the two regional transition approaches are with regard to the implementation of district heating at a municipal level for realizing cost-efficient CO2 abatement. In consultation with the PBL and the PZH, we have developed four scenarios for the development of district heating. To analyse these scenarios, we have adapted an existing model of CE Delft, which can calculate the investment costs and fixed costs of district heating infrastructure. Furthermore, we have developed a dispatch model that can calculate the performance of a district heating system in terms of CO2 abatement and marginal costs of heat production. Based on this comparison of the approaches, a trade-off appears between cost-effective CO2 abatement, and maximizing the overall reduction of CO2 emissions. In which the scenarios of the PZH in general result in higher levels of CO2 abatement and the scenario of the PBL is more cost-effective. Additionally, in the light of the full phase-out of natural gas the approach of the PZH appears to suffer from higher initial investment risks but also provides better opportunities to realize large-scale district heating. However, the research has also pointed out that there are many uncertainties regarding the development of demand and supply that can average out the relatively small differences between the scenarios. One of these uncertainties is the willingness and ability of residents to participate in district heating. The decision-mandate lies with the consumer; however, current forecasts and policy processes are not sensitive to this mandate. Therefore, we have researched if Q-methodology can support municipalities in developing communication-and-decision making processes that fit with the need of heat consumers in this transition. The case study and focus group have indicated that Q-methodology can provide relevant and unique insights. However, to truly test the method it is necessary to run a larger case study in which the resulting perspectives are used to design a policy process and where this process is re-evaluated by the participants of the study.

Increased use of the distribution grid due to the uptake of distributed energy resources and the expected penetrations of Electrical Vehicles (EVs) could lead to congestion problems in the distribution grid. Congestion refers to issues related to the overheating of components or voltage issues in the distribution network. Avoiding these issues is crucial to maintain a stable, economical and reliable electricity grid. By using the flexibility of aggregated EVs large investments in grid reinforcement can be avoided. However, a holistic approach is necessary to manage the procurement of flexibility services for all stakeholders involved. One approach is the Universal Smart Energy Framework (USEF), a framework that integrates the existing electricity market with a market for flexibility services from the aggregator to the Distribution System Operator (DSO). This master thesis presents a study on the flexibility market as described by USEF from the perspective of a commercial aggregator of EVs. USEF presents a framework in which flexibility potentially provides financial opportunities for aggregators of EVs. However, it is not clear what the financial impact on the charging costs of an aggregator of EVs is and in what way an aggregator has to adapt its charging logic when trading with DSOs. Therefore, the aim of this thesis is to answer the following question: how can an aggregator of EVs offer flexibility services to a USEF compliant market at distribution level? This report presents an in-depth analysis on USEF, determines the impact of network constraints from USEF on the charging costs of an aggregator of EVs and improves the charging strategy under USEF constraints.

Intermittent generation behaviour of wind and solar power units requires Transmission System Operators (TSOs) to activate balancing power to maintain a stable grid frequency. With the current strong growth in installed renewable energy capacity in The Netherlands, it is therefore expected that the volumes of activated balancing power will grow accordingly. In the Dutch balancing power market system, the activation of balancing power is remunerated against the highest activated balancing power bid in the respective time-block. Without a proportional increase in affordable balancing power bids, the economic costs of grid balancing in The Netherlands are expected to increase as a result. Imbalance power prices for positive balancing power, the counterbalancing of net shortages of power supply, may see a particularly strong rise, as the opportunity to quickly increase grid frequency places influential restrictions on the operation status of suitable power assets. The financial opportunities inherent to this trend have gained the attention of non-traditional suppliers of technical positive balancing power potential, amongst which the energy-intensive industry and greenhouse farmers. However, limited insight into the exact value potential of market participation, and contradictory developments in the German market are currently preventing these parties to invest in unlocking their technical balancing power potential. A limited supply of new balancing power bids will further increase the economic costs of grid balancing. This study therefore sets out to identify a method to quantitatively determine the profitability potential of providing positive balancing power to the Dutch balancing power market to an individual market participant. A case study is conducted at AkzoNobel, whose sizeable chlorine plant possesses significant balancing power potential. It is found that a Unit Commitment Model based on a Mixed Integer Linear Programming solver can be used to successfully replicate the market environment. To simulate market profitability to AkzoNobel over time, it should be supplied with input data of the future development of balancing power activation, the merit order of balancing power bids, and balancing power capabilities of the AkzoNobel plant. Whilst the balancing power capabilities of the plant can be relatively simply and accurately modelled into a Unit Commitment Model, the same does not hold for the future activation of balancing power and respective prices in the merit order of balancing power bids. On the one hand, the future development of the various factors that comprise these sub-systems is characterized by significant uncertainty, as power system dynamics are notoriously hard to predict. On the other, both are the emergent behaviour from complex systems of interacting power system agents and assets. Their actions result from a large number of factors that may vary strongly on a short time scale. This causes difficulties in obtaining accurate projections of their behaviour on the required minute-by-minute time scale. The activation of a high balancing power bid price in general also implies a strong grid frequency deviation. This implies that the profitability of providing balancing power is particularly sensitive to deviations in high imbalance power prices. As imbalance power prices are primarily determined by the specific height, volatility and frequency of high balancing power activation peaks relative to the height of prices in the merit order for those respective fifteen-minute time blocks, the choice of a method to obtain the input data of balancing power activation and the merit order must be based on a clear substantiation of their accuracy. Various approaches to obtain data projections with the required accuracy have been assessed, for which the following conclusions are drawn. To determine the development of the future activation of balancing power bids it is advised to evaluate a combined method of statistical convolution of probability distributions and hidden Markov chains. However, this method must be preceded by significant additional research on critical complexities and uncertain developments in the balancing power market, for which it may not be possible to draw definite conclusions. To obtain scenarios of the various potential developments in the volume and price elements of balancing power bids in the merit order it is suggested to use a stochastic modelling approach, such as Monte Carlo simulation, to evaluate the range of different compositions the merit order may take over time. It will, however, be difficult to strategize on the results, as the potential range of states of bid prices and volumes placed by individual agents with balancing power technologies are very wide. These methods must be synthesized into an approach to determine balancing power market profitability based on the above combination of methods. Projections of the development of the key determinants of profitability in the balancing power market will inherently be associated with strong uncertainty. Considering the sensitivity of model output to inaccuracies it is very questionable whether the effort of further research weighs up against the benefits. It is concluded that it is not deemed feasible to obtain quantitative long-term indications of balancing power market profitability to an individual market participant. As a result, it is not advised to proceed with the execution of the suggested methods. It appears that alternative approaches to ensure the future affordability of balancing the power grid are more worthwhile pursuing. It is instead advised to focus continued research on public incentive schemes to attract non-conventional suppliers of positive balancing power to provide their technical potential to the market if the market fails to find an economic optimum. Continued research could also focus on enhanced regulatory designs to reduce total balancing power requirements.

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 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. @en

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.@en