Power system operation is increasingly reliant on Information and Communication Technologies (ICTs), which are essential for enhancing the resilience, reliability, and security of the future electricity supply. The advancement of ICTs has tightly integrated power grids with communication networks, giving rise to Cyber-Physical power Systems (CPS). However, this growing digitalization also increases system complexity, heightens vulnerability to cyber attacks, and alters traditional operational patterns. Consequently, this trend underscores the critical need for continued exploration and innovation in CPS to address emerging challenges. In this context, the availability of reliable test cyber-physical systems is crucial. The test CPS models must enable realistic analyses without exposing sensitive information about critical infrastructures, allowing researchers to thoroughly investigate newly introduced vulnerabilities and ensure the reliability and cyber security of CPS. To this end, we are motivated to have the following research focus: the synthetic network generation and vulnerability assessment of cyber-physical power systems...@en

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.

Voltage source converters (VSCs), particularly modular multilevel converters (MMCs), play a pivotal role in modern power grids due to their application in interconnected systems and renewable energy integration. However, their complex internal dynamics and control systems can cause stability issues under a variety of operating conditions. Impedance-based stability analysis is critical for understanding these interactions and ensuring grid reliability. Traditional small-signal models based on physics and circuit analysis often neglect nonlinearities and practical implementation details, limiting their accuracy. In contrast, black-box impedance measurement techniques necessitate significant computational resources and human effort, particularly when analysing both AC and DC side impedances.

This thesis presents a transfer learning framework for accurate impedance characterization of MMCs. The framework uses a combination of linear time-invariant (LTI) modeling and black-box impedance measurements to estimate impedances based on system-level parameters such as AC and DC side voltages, active power, and reactive power. The methodology involves pre-training artificial neural network (ANN) models on a diverse dataset derived from LTI models, followed by fine-tuning with real-time electromagnetic transient (EMT) simulations. Unlike traditional approaches, which use a single ANN model across the entire frequency range, this study discusses the use of multiple smaller ANN models tailored to specific frequency ranges and operating points, which improves prediction accuracy.

The effectiveness of the proposed transfer learning framework is demonstrated with a practical MMC converter from the CIGRE B4 DC grid test system. The results show that impedance estimation outperforms traditional ANN methods even in scenarios involving confidential converter parameters. This approach allows for precise impedance characterization while reducing computational complexity and data requirements.

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.

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.

The Dutch government's "Green deal 173" increases the challenge of electrification of mobility. The "Green deal 173" states that city centres will be emission free as from 2025 and whole cities will be emission free as from 2050. Emission free cities are in need of electric buses in the city centres. Thus, the need for charging infrastructure for new electric bus fleets is needed in urban areas. These urban areas are densely populated with not a lot of space to work in, therefore the electrification of mobility creates a new challenge for the distribution system operator, concessionaires, concession granters and municipalities. An energy hub is a solution to this challenge. An energy hub combines the new charging infrastructure with the existing electrical infrastructure in a new system, with large energy users and new technologies such as solar energy and vehicle to grid (storage). This report will explain the societal impact of different technical lay-outs of an energy hub, to do so different case studies are modelled and simulated to test the technical feasibility. Furthermore, the costs and benefits will be assessed and valued to research the business case of an energy hub, because the difference between theory and real life implementation is bound to cost reduction and/or profitability in most cases.
The results show that an energy hub can reduce the net capacity of the used lines, reduce the occurrence of peak demand and be profitable over time for the parties in the energy hub. However, all the parties involved need to work together to achieve the realisation of an energy hub. The distribution system operator, concessionaires, concession granters and municipalities need to find similarities in their priorities instead of conflicting priorities. The similarities in their priorities will be an important factor to realise an energy hub.

Electrical faults in the distribution system can lead to interruptions in customer power supply resulting in penalties that are borne by the distribution system operator. Accurate fault classification is an important step in locating the fault to achieve faster network restoration times. This reduces the operational costs of the system operator by reducing the duration of interruptions in the power supply. Furthermore, it has been found from practical experience that distortions or instabilities in fault waveforms can result in their classification and subsequent localisation being delayed, causing the loss of valuable customer minutes. The objective of this thesis is to investigate the potential of modern supervised learning techniques for the classification of faults in the distribution network. The problem is split into two parts: one aspect is to study the ability of a supervised learning classifier to differentiate between types of stable faults, and in this pursuit, to also identify better criteria for stability.

First, after a review of pertinent literature, it is found that discrete wavelet transforms and support vector machines (SVM) are suitable for fault signal processing and classification, respectively. Next, identifying characteristics, or features, from the three-phase fault current and voltage waveforms are engineered with the help of the db4 wavelet. The features are used as inputs to an SVM classifier model, and the model is tuned and validated to ensure optimal classification performance. Results showed that the developed SVM can differentiate between real-world instances of single-phase, two-phase and three-phase stable faults with a classification accuracy of 95%. A set of business rules are also developed to characterise instability by performing a windowed Fourier analysis and studying the strength of the fundamental frequency component of fault waveforms. The rules are tested on a set of fault data whose stability is uncertain, and it is found that the developed rules are able to improve upon the older method of stability analysis by increasing the rate of stable fault identification.

To keep the generation adequacy of the power system up to standards, more and more countries in Europe are setting up capacity mechanisms. In a capacity mechanism power generation units receive payments for having their capacity available when this is needed (during moments of electrical supply shortage). These payments are received on top of payments that could be received from the energy markets or ancillary service markets [4]. This gives incentives to generation units to be available when needed and create a better investment climate in generation capacity. In this way, these capacity mechanisms can increase the security of supply. In the past, countries in Europe could implement such a capacity mechanisms locally, only local capacity providers (e.g. generation units) were allowed to participate. But the European Commission has developed new regulations which state that it is allowed for capacity providers to participate in cross­border capacity mechanisms. In this way capacity providers can receive additional payments from participating in foreign capacity mechanisms. But this raises questions, because the contribution (capacity value) of a capacity providers is thought to be dependent on its location. This thesis analyzes what the effect of the new EU regulations regarding cross-­border participation is on the payments resulting from capacity mechanisms. To answer this, the relevant EU wide regulations are analyzed and combined with the regulations of the capacity mechanism as implemented in Great Britain (at the time of writing), to find a likely implementation of a capacity mechanism in which cross­-border participation is allowed. A generation adequacy model was built and combined with the likely implementation of a capacity mechanism to analyze what the expected payments from the capacity mechanism will be to local and cross-­border capacity providers. This was done to analyze how attractive it is for capacity providers to participate in two capacity mechanisms. The generation adequacy model that is used consists of two areas, these areas represent Great Britain and France. For the model the same input data was used as in [38]. A Monte Carlo sampling method was used to analyze when payments are due. It was found that with the implementation of the new regulations regarding cross-­border participation in capacity mechanisms it is very attractive for capacity providers to be participating in two capacity mechanisms. But EU regulations set a limit on the amount of capacity that is allowed to participate in a cross­border capacity mechanism. Capacity providers have to compete with other capacity providers in a market based manner to be allocated to participate in a cross­-border capacity mechanism. This will likely lead to a break even point where participating in two capacity mechanisms generates an equal income as participating in just a local capacity mechanism. To analyze how much generation units contribute to the security of supply of a cross-­border capacity mechanism, the two area adequacy model was used. An analytical method was used to calculate the capacity value of the generation units. It was found that generation units can contribute to the security of supply of a cross­-border area, although it does support the area it is located in more than the cross-­border area. When the interconnection between the areas has a larger capacity, the effect of a generation unit to the cross-­border area becomes larger. It was concluded by implementing the new regulations in the model in this thesis, that the capacity-mechanisms do not give financial incentives to invest in generation capacity for the security of supply of a cross­-border area. This might lead to sub­optimal investments, because generation units can contribute to the security of supply of a cross­-border area.

In order to decrease carbon emissions of the transport sector, the roll-out of plug-in electric vehicles (PEV) aims a replacement of conventional combustion-based vehicles. Their intended power system integration goes along with increasing the share of renewable energies. Hereby, PEV have the prerequisites for intelligent charging to support grid stability and increasing the utilisation of fluctuating renewable energies. Beside integrating PEV as an intelligent load, the approach using the PEV’s battery capacity to serve power system needs can further increase the value of PEV in the power system. Thus, for compensating additional stress on the PEV's components, ancillary services revenues are commonly measured against PEV's wearout for Vehicle-to-Grid (V2G) applications. Depending on the selected driving pattern, location, battery, regulation and revenue of ancillary services, use cases lead to different conclusions. In the scope of this thesis, it is argued that ongoing transitions in the power system and transport sector result in uncertainties how these use case and its dependencies change in the future. However, the technical prerequisites of V2G based ancillary services, which subject to physical, chemical and electrical processes, are known, whereby a broad view on use cases is essential. Hence, in this thesis, the sensitivity of these services is measured against vehicle characteristics from a technical point of view. This work identifies more than 30 technical dependencies that affect the PEV's degradation and total discharging costs. With respect to the Dutch energy market, the results demonstrate that Vehicle-to-Grid costs can lead to a significant market potential loss. However, operation conditions exists, which lead not only to a high market potential in the balancing and day-ahead market, but also in a reduction of battery degradation. Based on the sensitivity results, implications that cover the regulation, business, user and technical layers of Vehicle-to-Grid are given based on an architecture model. The presented work allows identifying use case related dependencies and therefore helps to clarify the reasons for the different results/conclusions on V2G research.

Continuous and rapid technological advancements have transformed the modern day power system. Increased global inter-connectivity has made reliable power supply a critical requirement. A small outage can cascade into a blackout causing great inconvenience and significant monetary damage. These concerns highlight the need of an additional layer of proactive approach in conventional protection schemes. The focus of such an approach would be to shift from reacting to a failure to anticipating a failure. Anticipating a failure gives time to better prepare and mitigate the failure by efficient allocation of resources in order to limit the negative consequences. Starting from the inception of the event causing the failure to the final occurrence of the failure, the time-period in between is termed as the pre-failure period where the signatures of the incipient failure can be observed. The availability of high-resolution devices has improved monitoring of grid operations during this pre-failure period. Improved monitoring enhances situational awareness leading to easier detection of incipient failure signatures. Research conducted in this field has led to development of few failure anticipation techniques but the application potential of some are restricted to specific equipment or phenomena while that of others are restricted by resource requirements. There is a need of addressing the research gap of a comprehensive failure anticipation technique that fulfills three major criteria of low computational burden, wide applicability in different scenarios and installation compatibility with existing grid monitoring devices for economical implementation. The research conducted in this thesis aims to address this research gap by developing a comprehensive failure anticipation technology titled Failure Anticipation and Diagnosis Scheme (FADS) for AC distribution systems. FADS implementation broadly comprises of three functionalities. The first functionality is concerned with quick and accurate identification of incipient failure signatures. Almost all failure anticipation techniques rely on cross-referencing historical databases or identifying specific patterns in order to detect incipient failure signatures. However, incipient failure signatures seldom manifest in same patterns. Hence, FADS relies on the fundamental aspect that pure AC sinusoids are complex exponentials. Incipient failure signatures would invariably violate certain properties of complex exponentials and manifest as waveform distortions, which would be leveraged by FADS to detect the signatures. The second functionality involves the data processing of the distortion data. Several novel parameters are introduced in the second functionality that helps in processing the data obtained from the first functionality. The use of novel parameters helps in accurate assessment of the stress experienced by the grid operations due to the event causing distortions. Such an assessment help FADS to be robust to false positives or false negatives. Finally, the third functionality involves interpretation of the information obtained through data processing. The interpretation provides metrics to rank the severity of the damage the event can inflict on grid operations along with specific inputs on the event location. This interpreted and refined data helps to provide means to the Distribution System Operator (DSO) for informed decision-making and time-efficient resource allocation for failure mitigation purposes. The different FADS functionalities work in unison to detect incipient failure signatures and extract valuable information, which can be then used to plan mitigation strategies. The different functionalities of FADS are designed to be installed in a manner such that the incremental costs of widespread FADS implementation are minimal. The evaluation of FADS in this thesis is conducted through a series of stringent and realistic test cases. The test cases are simulated on the standard IEEE-13 and IEEE-34 node test feeders. The first set of simulation studies focusses on detecting High Impedance Faults (HIF) as conventional protection schemes mostly fail to detect it. The test cases comprise of several novel stringent scenarios to evaluate the capability of FADS to accurately distinguish and detect HIF events among multiple switching events and normal grid actions at different sections of the grid. The second set of simulation studies involves recreating transient behavior generated due to real life incipient equipment failure conditions in laboratory based simulations. Simulations are used to evaluate the ability of FADS to detect and assess the incipient failure before the equipment breakdown occurs. The next set of studies is focused on analyzing how the FADS performance in previous simulation studies could be translated to assess the improvement in major reliability indices, mainly System Average Interruption Duration Index (SAIDI). Improvement in reliability indices are a major area of concern for utilities and the results obtained from FADS implementation are further quantified to provide a range of possible improvement in SAIDI value in percentage terms. Finally, the proposed benefit of FADS is illustrated through implementation on real field data provided by the Dutch DSO, Stedin B.V. In the course of FADS implementation, few shortcomings were noticed and possibilities of further improvements were identified. The final chapters of this thesis discuss the shortcomings and recommend improvements for future research studies. The functionalities of FADS are flexible and mostly user-dependent and can be systematically improved over time to make FADS a global standard for industrial and research applications for failure anticipation in AC distribution systems.@en

With the ongoing energy transition and the developments that are occurring in the electricity network, there is a possibility for FrieslandCampina to become a player in the electricity network as aggregator. An aggregator plays a central role in the electricity network between the Transport System Operator (TSO), Distribution System Operator (DSO) and the Prosumers. The TSO manages the high voltage network and is responsible for the balance and power quality in the high voltage network. The DSO manages the distribution network, and the medium and low voltage network. The DSO makes sure that the consumers always have the availability to electricity. Prosumers are a combination of consumers and private producers of electricity. The role of prosumers is a role for the dairy farmers of FrieslandCampina. FrieslandCampina has a vision to which the role of aggregator fits. Additionally, FrieslandCampina wants to investigate the possibilities to enter the electricity market. This research aims to provide a comprehensive understanding for FrieslandCampina of what an aggregator is and what kind of services an aggregator can create for the different players in the electricity network. Also, the dairy farms are going through a transformation, more and more processes are electrified and the availability of various renewable energy sources make that dairy farms can make the shift from consumer to prosumer.

The electrical infrastructure is evolving, shifting into a decentralized grid that allows the incorporation of renewable energies and mitigates the negative effects of power outages. It is predicted that this will lead to a substantial increase in the amount of microgrids deployed worldwide. Especially in locationswith a less reliable grid, the microgrids are often in off-grid or islanded operation. If an electrical fault occurs when the microgrid is not connected to the grid and there are only inverter-based power sources, the fault current will not be high enough to be detected by traditional overcurrent protection schemes.This poses a challenge that can be addressed with different strategies. So far, the proposed solutions for protecting microgrids in islanded operation are highly theoretical and have limitations.This research introduces a protection strategy for a small, low voltage off-grid microgrid with only inverter-based power sources. The microgrid has energy storage, solar energy production and generic loads. In this study, a Tesla Powerpack and Inverter are assumed as energy storage devices, since the research is conducted in collaboration with the company Tesla. The microgrid is modelled in the software PowerFactory from DIgSILENT to verify that the current and voltage magnitudes during fault will be detected properly and the protection devices will be triggered. Protection devices available in the market are proposed, and their settings specified taking into account the results of several simulations. A brief economic analysis is also included to provide a general overview of the cost of such a protection system.

The uncertainties of the current power system with a larger share of renewables and more volatile generation profiles increases the importance of assessing resource adequacy. Within this framework, the thesis “Evaluation of missing capacity and resource adequacy in an interconnected power system” aims to answer the following question: “How to reach reliability targets in the interconnected European power system?". To answer this question the concept of Missing Capacity is introduced as a possible adequacy indicator that quantifies the required capacity to reach a pre-defined reliability target in a specific region. The evaluation of missing capacity can be classified by the area of study, i.e. single or multi-area, and by the capacity expansion source, i.e. generation and/or interconnection capacity expansion. Following such classification, a methodology for the evaluation of Missing Capacity is presented. Furthermore, by analyzing the transmission capacity expansion approach the contribution of interconnectors to system adequacy is investigated.

The recent trend for monitoring and control of the distribution networks requires methods capable to calculate the network’s operating conditions. One way to monitor the distribution network is by state estimation. Although previously this method was established only for the transmission networks, its use has begun to also make sense for the distribution networks, due to the increased availability of smart meters. Typically, state estimation is a non-linear iterative procedure. Research aims to overcome this problem, by creating a non-iterative procedure. To achieve this, linearity of measurement function is a necessity. Although linearity is always desired, the measurement inputs or the procedure renders the state estimation problem difficult to formulate it in a linear fashion. The scope of this thesis is to create a linear state estimation algorithm for the distribution network. To achieve linearity, a reformulation of the states for the state estimation was used at this thesis. This reformulation created a linear optimization problem, which is easier and faster to be solved (Weighted Least Squared method). Since the measurements are not enough to provide full observability, based on these methods, additional assumptions were made. As an alternative to the primary method used for the state estimation, another optimization method was used, which is called Least Absolute Value. This method will be based on the same inputs as the previous method, but different optimization goal. The goal was to compare the two methods and find each one’s limitations. Measurements used for this thesis were provided tracked and provided by DEPsys S.A., by their main module, called GridEye. GridEye devices are synchronized SCADA-type devices, installed on cabinets of the distribution grid, based on the DSO needs. All measurements provided were from real networks within Switzerland. The smart meter device data were provided by collaborative projects of DEPsys S.A.. Smart meter devices were installed in different low-voltage consumers. Both methods were implemented correctly and were compared. Based on the results, it was obvious that the Weighted Least Square outperformed the Least Absolute Value method, in the absence of bad data. Moreover, the effect of the number of GridEye metering devices on the State Estimation was compared. While numerically and visually the outputs are comparable, the effect of having multiple devices is still important for different scenarios. Finally, bad data cases were examined for both methods under different types of errors for the smart meter devices. The smart meter devices were manually introduced to bad data, to understand the algorithm’s capabilities and limitations. An additional algorithm, in addition to the state estimation one, was implemented to not only detect and identify the location of the bad data occurrence but to correct the bad data inserted.

This thesis investigates the potential of a large pumping station in IJmuiden, the Nether-lands, for participating in Demand Response. Due to climate change, renewable energy is onthe rise. The intermittency of energy, together with its unpredictable supply, are a big hurdlefor the energy transition. Two methods are promising solutions to this problem; large scaleenergy storage and demand response. Since large scale energy storage is not yet economi-cally feasible, demand response has an important role to play in the early days of the energytransition.Using energy when it is generated requires a data-stream from the generation facilities onproduction, which is not (yet) widely available. The market price, however, is an indicationof the scarcity of energy, since it is based on the ratio between supply and demand. Besidesthat, there is a correlation between a low energy price and sustainable energy productionsince marginal costs of sustainable energy production are lower than fossil energy produc-tion. This makes using sustainable energy cheaper that fossil energy, and gives DemandResponse a business case.In this thesis, a Model Predictive Control is created that uses energy market data to minimizeenergy costs. Multiple energy markets are analyzed with respect for their suitability for thepumping station in IJmuiden to act on them. The day ahead market is called the APX inthe Netherlands, and this is where energy is bought and sold the day before consumption.The intraday market, also called the flexibility market, is where energy can be bought andsold up to 5 minutes before consumption. A strategy combining these two markets will beevaluated. This is done by using a predicted day ahead price, generated by a SARIMA model,to create a plan. This plan will then be followed, but deviations from the plan are allowedagainst intraday market price.Due to imperfections of the market (mismatch between supply and demand), imbalances areoccurring. These imbalances result in frequency deviations of the grid, and voltage devia-tions. Tenner, the Dutch TSO (Transmission system operator), is responsible for minimizingthese imbalances. In order to minimize the imbalance, TenneT gives a real-time indication ofthe imbalance on the grid, and positive contributions are rewarded while negative contribu-tions are punished. This is done through the use of the imbalance price; a price per volumeof imbalance caused or solved. The imbalance price is based on the aFRR market, wherebids can be done on possible activation. Since the imbalance market is a fast-acting market,it is not suitable for a large pumping station like IJmuiden. However, the aFRR market willbe analyzed in this thesis.The effects of expected future development, like sea level rise and energy market changes,will be analyzed and simulated as well. A higher sea level would result in more pumping, andless discharging under gravity. Which causes the the pump schedule to become less flexible.The results show that it is possible to apply demand response to a pumping station, and theintraday market makes it possible for the MPC to adjust its energy use during the day.The aFRR market analysis shows a lot of potential for the pumping station, possibly makingup for all energy costs made through the spot markets.The conclusion of this thesis is that Rijkswaterstaat can possibly save energy costs on pump-ing, based on the fixed energy price, provided by Rijkswaterstaat, they pay now. Based ona reference scenario where the MPC only minimizes energy use, and a fixed ENDEX energyprice, the proposed MPC makes about 10% less costs in the German market scenario. TheDutch market scenario does not show cost savings. In the Netherlands there is not muchcorrelation between low energy prices and renewable energy yet, since renewable energy isnot a big part of the energy mix in the Netherlands. This correlation is expected to becomemore present when the Dutch energy mix becomes more sustainable. This is expected toresult in lower CO2emission through the energy use of the pumping station. However, moreresearch is needed to confirm this.

In 2018 the Dutch government, businesses and other stakeholders started negotiating an agreement to combat climate change (het Klimaatakkoord). Energy consumption is changing and more Renewable Energy Sources (RES) are implemented. While these changes are ongoing, the Distribution System Operators (DSOs) must ensure no congestion occurs in the grid. However, it seems congestion problems are already arising. As a lot of research has been done on Energy Storage Systems (ESSs) and Electric Vehicles (EVs) for congestion prevention, research on movable ESSs is missing. This thesis proposes a method for congestion prevention by Relocatable Energy Storage Systems (RESS). A Mixed Integer Linear Program (MILP) is proposed to minimise the amount of RESS used and penalise displacement. An algorithm which includes a Minimum Cost Maximum Matching is used to determine the planning of each individual RESS. The results show that, for the test grid designed, it is beneficial to use RESS instead of statically placed ESS in the grid.

Multiple solutions for solving optimal power flow (OPF) have been employed, most of them using a centralised approach. However, there is another approach where every node is responsible for the local problem in order to reach a solution: the decentralised optimal power flow (D-OPF). In this thesis, the aim was to improve the speed and flexibility of a D-OPF algorithm based on the Consensus and Innovation (C+I) method using physical measurements from a direct current (DC) system. While in previous implementations, the system would work towards finding the solution for one point in time, the suggested implementation works by performing online optimisation, meaning that there is less time required in propagating information around the system and faster solutions are reached. A possible interaction between the physical and optimisation layers was suggested, to make online control feasible. Using current droop control for a DC system, it was possible to react to sudden changes the system and, in the long term, optimise the electrical resources. The improvements in speed were then demonstrated by the simulations results, where the time to reach the optimal solution was reduced, when compared to previous implementations. In order to increase the flexibility of the system, adaptive behaviour for the critical optimisation variables was suggested. To reduce the oscillatory behaviour of the system, some gains were made proportional to rate of change of said variables, meaning that the system didn't have to rely on user determine values in order to converge. It was also implemented a solution to calculate the line resistance between two nodes, further reducing the need for external inputs. These implementations were tested and it was concluded that it improved convergence speeds, while increasing the flexibility pf the system. Finally, a test case, based on a real existing lighting grid, was designed in order to test the algorithm under larger networks. The results showed that for a 25% increase in the size of the network there was no significant increase in the time required to reach a solution, indicating that the system can be scaled further, and might be dependent mainly on the network structure, and not its size.

To follow the rapid advance of the Energy Transition, the technology and application of the renewable energy resources and energy storage devices have been developed in an unstoppable speed. When the application of energy storage technology starts to prosper gradually over the world, there are some problems showing up while lots of advantages are being advocated. There are plenty of batteries staying unused or being wasted for over half of the lifetime of the system, especially for the behind-the-meter energy storage system. The business models are usually built just for one or two primary services for customers and grid that makes only a limited part of time occupied, leaving the essential untapped values aside. And also, it is common that the energy storage system is coupled with PV panels or wind turbines. In this case, how to increase the on-site solar energy self-consumption becomes an attractive issue considering the sizeable financial benefit it can bring. What is more, when putting the attention on the neighborhood or community level where there is more than one participator in this picture, how to create the extra value or bonus benefit using the existed system and equipment can be an interesting topic. This report will focus on the grid-size battery installed behind the meter within a commercial community. A methodology of the energy hub with two different lays providing all kinds of services will be proposed. As the result, the value of grid-size energy storage in enabling a community-level energy hub for both battery-owner and neighbors will be explored to cope with all the above problems. There will be a case study conducted in the selected Zuid-Oost Amsterdam region. In the end, how much benefit the energy hub can create for not only the battery-owner but also for the whole community and society will be discussed.

With the inclusion of Electric Vehicles Charging Stations in the city of Amsterdam, there is not enough knowledge to understand if the electrical grid infrastructure can withstand this increase in the load. In this thesis work the distribution grid of Amsterdam has been replicated and a cyber attack against the EV Charging Stations has been launched. The behaviour of the grid components has been tested and a cyber security assessment of the attack scenario has been developed.