Fault ride-through capability studies of MMCHVDC connected wind power plants have focused primarily on the DC link and onshore AC grid faults. Offshore AC faults, mainly asymmetrical faults have not gained much attention in the literature despite being included in the future development at national levels in the ENTSO-E HVDC code. The proposed work gives an event-triggered control to stabilize the system once the offshore AC fault has occurred, identified, and isolated. Different types of control actions such as proportional-integral (PI) controller and super-twisted sliding mode control (STSMC) are used to smoothly transition the post-fault system to a new steady state operating point by suppressing the negative sequence control. Initially, the effect of a negative sequence current control scheme on the transient behavior of the power system with a PI controller is discussed in this paper. Further, a non-linear control strategy (STSMC) is proposed which gives quicker convergence of the system post-fault in comparison to PI control action. These post-fault control operations are only triggered in the presence of a fault in the system, i.e., they are event-triggered. The validity of the proposed strategy is demonstrated by simulation on a 525 kV, three-terminal meshed MMC-HVDC system model in Real Time Digital Simulator (RTDS).

Nowadays, the rise in energy consumption and the integration of renewable energy resources (RES) have introduced significant challenges to the existing power grid, making it necessary to upgrade the current power system. However, considering the high variability in load and RES and the large number of possible investment options, the power system expansion planning problem results in a large mixed integer linear problem (MILP), or in some cases, a non-linear problem, impractical to solve for real-world scenarios. Time-series aggregation (TSA), capturing representative load and RES patterns, has emerged to reduce the temporal complexity, making the power system expansion planning model much easier to solve while providing similar final results.

Current TSA methods mainly rely on passive clustering, focusing on the statistical proximity between the representative periods and the full-space time-series. However, this approach does not guarantee a satisfactory solution for the final expansion planning problem in general cases, even with predefined extreme periods implemented such as days with maximum load and minimum available RES. The operation of the power system and extreme conditions are highly sensitive to the specific power system configurations, making the standalone TSA method unreliable in practical applications.

To improve the time-series aggregation in terms of the power system operation, firstly, the methods of assessing the performance of the estimated investment decision are explored directly in terms of the objective function. By introducing the full-space operational cost model for the power system with the estimated investment decision, the operational cost error made by representative periods can be obtained, referred to as the operational estimation error. The actual objective difference between the estimated investment decision and the optimal investment decision found by the full-space expansion planning model can also be evaluated, denoted as the optimality gap. It is found that the optimality gap is bounded by the difference in the operational estimation error of representative periods for the power system with the two investment decisions. As the operations of the power system with the estimated and optimal investment decisions are better estimated, the simplified model can provide a closer investment decision.

Subsequently, looking into the operational estimation error, the performance of representative periods in estimating the full-space operational cost is highly unevenly distributed among the full-space time-series. Representative periods fail to accurately estimate a minor portion of the full-space time-series, causing extremely high operational estimation errors that contribute to the majority of the total operational estimation error. This uneven distribution remains as the number of representative periods increases, indicating that standalone TSA methods are not capable of capturing the extreme conditions of the specific power system operation.

Therefore, to improve the time-series aggregation in terms of its ability to better estimate the operation of the power system, bad-performing representative periods and original periods with high operational estimation error can be identified and prioritized, forming a feedback enhancement loop. Case studies show that the feedback enhancement with re-clustering on the bad-performing representative periods improves the optimality gap by more than 50% compared with the standard mean-based clustering method.

The management of electric vehicle (EV) batteries involves accurately estimating their state-of-charge (SOC), which indicates remaining usable energy. Precise SOC estimation extends battery life, increases usable capacity, and enhances vehicle performance by allowing a greater depth of discharge without increasing battery weight or size. Traditional SOC estimation via Coulomb counting accumulates errors and requires correction using the Open-Circuit-Voltage (OCV). Modern silicon graphite anodes exhibit voltage hysteresis, making accurate SOC determination difficult. Introducing a hysteresis factor, ranging from -1 to 1, helps interpret OCV values within this hysteresis region.

This thesis proposes using a black box approach and machine learning models to predict the hysteresis factor for SOC correction more accurately. The research aims to enhance SOC accuracy in real-world EV environments, beyond laboratory settings. Improved SOC estimation through machine learning could significantly advance battery management systems (BMS), yielding both economic and environmental benefits by increasing EV efficiency. The study addresses the existing gaps in SOC estimation for voltage hysteresis, considering the vehicle as a system by incorporating driving cycles, BMS technical limitations, and cell chemistry. By improving EV reliability and efficiency, this research aims to promote broader acceptance and contribute to a sustainable future in personal transportation.

This thesis describes the design of a low-cost class-S power quality analyzer based on a Raspberry Pi 4. Capable of detecting power frequency, magnitude, voltage dips and swells, harmonics, and total harmonic distortion, the project consists of four main components: the communication protocol, the interface, the algorithms for power quality parameters, and the database. Each with its subdivisions. The communication module, which uses the I2C protocol, is chosen for its high sampling rates and built-in acknowledgement system, ensuring robust and fast operation. The interface module features a secure login process with two-step verification, a graphical user interface for real-time monitoring, and integration with algorithms for power quality parameter calculation. The algorithm module includes Fast Fourier Transform for harmonic detection, zero crossing method for power factor and frequency, and peak detection for voltage dips and swells. The database, powered by MariaDB on Raspberry Pi 4, securely manages the received data with restricted access for increased security, allowing remote access only from specified IP addresses.

The increase in non-linear loads of modern electronics raises concerns over power quality. Additionally, existing power-quality analyzers are expensive and not intended for household use. This thesis aims to develop a single-phase, user-friendly power-quality analyzer using a Raspberry Pi 4 Model B with an emphasis on low cost and class S specifications. The design was split into modules consisting of analog to digital conversion, voltage sensing, and current sensing. Sub-modules were added for circuit protection and PCB. Various approaches are discussed before circuit design, simulation, and testing occur. A functioning prototype was assembled on a dedicated PCB while not exceeding the set budget of €250.00. However, it could not be determined whether the class S specifications were achieved due to insufficient testing. A variety of improvements have been suggested.

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

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

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

Cyber-physical systems are vulnerable to malicious attacks, which can lead to severe consequences. Active detection methods have emerged as a promising approach for identifying such attacks. However, existing active detection methods are susceptible to malicious parameter identification attacks, where attackers exploit eavesdropped data to identify and manipulate the active detection mechanisms. In this work, we propose two methods to address the issue of malicious parameter identification: the system immersion coding method and the hybrid multiplicative watermarking method. These approaches have a primal focus on disturbing the identification of attackers and defending against malicious parameter identification. Besides, as active detection methods, both of them are capable of detecting multiple attacks.

The system immersion coding method, derived from the privacy solution in federated learning, is adapted to enhance its capability to detect malicious attacks by merging the input information and defend malicious parameter identification by leveraging its privacy-preserving properties. This method involves mapping the plant output into a higher-dimensional space and introducing carefully defined noise, which can create arbitrarily large disturbances without compromising performance. The introduced disturbance disrupts the attacker's parameter estimation. Theoretical conditions are provided to discuss the detection performance of replay attacks, control-signal-injection zero-dynamics attacks, and sensor-signal-injection zero-dynamics attacks. However, we also identify that the system immersion coding method is vulnerable to known-plaintext attacks.

Watermarking is a promising active diagnosis technique for the detection of highly sophisticated attacks. Motivated by the computational hardness problems of cryptography analysis, we propose a hybrid multiplicative watermarking scheme as an active diagnosis technique. In this scheme, watermarking parameters are periodically updated based on the dynamics of unobservable states in specifically designed piecewise affine (PWA) hybrid systems. We conduct a theoretical analysis to assess the impact of this scheme on closed-loop performance, demonstrating its stability preservation. We also provide conditions to detect replay attacks and control-signal-injection zero-dynamics attacks. Furthermore, we demonstrate that the proposed approach makes it challenging for an eavesdropper to reconstruct watermarking parameters, considering both computational complexity and systems theoretic perspectives.

The proliferation of Distributed Energy Resources (DERs) is decentralizing the power system, with more and more capacity installed in the distribution grids. Concurrently, the energy sector is embracing the Internet of Things (IoT) paradigm, resulting in the emergence of the Internet of Energy. However, this transformation introduces new concerns regarding cyber security. As the number of interconnected devices increases, the possible attack surface for malicious actors expands. Recognizing this challenge, researchers are investigating the potential cyber security benefits of applying blockchain in power systems. Blockchain offers some secure-by-design features, such as the immutability of the stored data, that can be leveraged to improve the cyber security of smart grids.
In this work, a blockchain-based application for the monitoring and control of a feeder in the Low-Voltage (LV) distribution grid is designed and tested. A smart contract is created and deployed in a private Ethereum blockchain utilizing the Proof of Authority (PoA) consensus mechanism. The blockchain application enhances the cyber security of the LV distribution system in three ways. First, it detects cyber attacks targeting DERs by comparing the setpoints received by prosumers with smart meter measurements. Second, it prevents cyber attacks by enabling the exchange of measurements and setpoints on-chain and by preventing unreliable prosumers from participating in the voltage regulation market. Third, it mitigates the effects of cyber attacks on the steady-state voltage magnitudes by enforcing a novel voltage regulation mechanism, in which a new metric is proposed to quantify the power-to-voltage relationship while considering the location of the power exchange.
The efficacy of the blockchain application is tested in a co-simulation environment together with a modeled LV distribution network, simulated in DigSILENT PowerFactory. The distribution network model is first used to assess the impact of cyber attacks manipulating the setpoints of Battery Energy Storage Systems (BESSs), which have been identified as the most critical DERs. The simulation results demonstrate that the considered cyber attacks can force the disconnection of inverters by causing violations of the acceptable steady-state voltage magnitudes. One of the scenarios demonstrates that a cyber attack targeting half of the BESSs in a feeder can lead to the collapse of the voltage, causing a local outage. Finally, the results of the co-simulation of the blockchain-based monitoring and control system, achieved by the Open Platform Communications Unified Architecture (OPC UA) communication protocol and by a series of clients managing the data streams, demonstrate its efficacy in detecting cyber attacks and mitigating their impact on the voltage magnitude across the feeder, thus reducing the number of disconnected DERs.

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

Electrical faults in the distribution network can lead to interruptions in the power supply of the customers. Therefore penalties are applied to the DSOs if they overcome the benchmark set based on all the DSOs reliability performance. Hence, the fast restoration of the power supply is crucial for the grid operator in order for the operational costs to be decreased. The traditional way of fault locating is performed with the help of so-called Fault-current Passage Indicators. This can be improved by automatic estimation of the fault location using analysis of the current and voltage signals during the fault, which also depends on the accurate fault classification. However, the existence of distortions and instabilities in some of the fault waveforms leads to an unreliable fault loop-impedance/-reactance. As a result, the location of the fault has to be performed in the traditional way which leads to a delay in the restoration of the power supply.

The objective of this thesis is to explore the potential of modern signal processing and machine learning (ML) techniques for the development of an explainable classification scheme of faults in the distribution network. ML explainability is a huge part of the research held for this thesis, as it is very important for the fault analysis department to understand the model and for the grid operators to trust the results of the model. The problem is divided into two parts. The first part concerns the construction of an explainable ML-based classification model that can accurately differentiate between types of stable faults. The second part concerns the identification of suitable criteria for single-phase and multi-phase fault stability. Combined with a fault classifier based on the first part research, this will lead to a classification and location scheme that can incorporate all fault types.

First, a literature study is done. The pre-modeling explainability stage is the most suitable one and based on that, the Short-time Fourier Transform (STFT) is chosen as the signal processing technique since it can lead to more explainable features than other techniques, e.g. the Wavelet Transform (WT). Also, the Support Vector Machines (SVMs) are selected as the supervised learning technique, as it performs well in classification problems and it is not a method as complicated as for example the Artificial Neural Network (ANN). Next, three different feature sets are shaped appropriately from the three-phase fault current and voltage signals, which are the inputs for the SVM classifier model, so then this model can be tuned and evaluated. The model with the symmetrical feature set is selected serving suitably both the performance and explainability requirements. Following that, a set of stability rules are developed to better identify the stable faults that previously were classified as unstable. These rules are tuned based on a set of initially labeled unstable faults. In the end, the ML classification and the stability model combined with section selection rules constitute the final classification scheme. This is tested on hidden data and the results indicate good performance in classifying faults, locating faults and identifying stable faults that initially would be classified as unstable. In particular, the performance of the proposed scheme on that test set has f1-score of 95.1% exceeding 93.3% of the current algorithm tested on the same data, errors regarding the fault loop-impedance/reactance are located within the acceptable limits and 4 stable faults correctly identified that previously were seen as unstable.

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

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

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

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

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

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

Under the increasing electrification of end uses in the energy transition towards more renewable integration, the electricity price keeps gaining importance on every scale from individual well-being to the competitiveness of an economy. Though scarce in the scientific literature, Long-Term Electricity Price Projection (LEPP) has great potentials in decision-making and planning, as well as complementing the long-term energy scenarios. This study takes features from the Dutch, Spanish and Danish data in five years (2015-2019) to train deep neural networks in the conditional Wasserstein Generative Adversarial Nets with Gradient Penalty (cWGAN-GP) framework, in order to project Day-Ahead Market (DAM) price series under Dutch 2050 energy scenarios.

The LEPP to 2050 is made possible by normalising the selected markets. As a result, the conditions unprecedented in Dutch data are covered in the normalised and combined data set. Generally, under scenarios with high proportions of hydrogen power in the energy portfolio, the cWGAN-GP model projects that DAM price series would have slightly lower mean and daily standard deviation than the 2019 level. Whereas much lower mean and daily standard deviation are projected when natural gas is still the fuel of the most frequent final generating technology. To explore the possible application of the projector model, the German DAM prices series in 2019 have been projected and evaluated, and the projections under Dutch 2050 energy scenarios have been used in calculating the generic profit potential of energy storage.

Five findings can be summarised from the main results. Firstly, from a literature survey and importance analyses, seven features are shown relevant to the DAM price in the combined data set, namely month of the year, day of the week, total hourly load forecast, national daily mean temperature, fuel cost of the most frequent final generating technology, hourly renewable power generation forecast and total installed renewable power capacity. Secondly, it has been found that two of the four proposed market state normalisation solutions, the Renewable Scarcity Factor (RSF) and the Renewable-Load Ratio (RLR) help the cWGAN-GP model strike a balance between price value distribution and hourly inter-dependencies. Thirdly, in this LEPP study, the cWGAN-GP model performs better than the Conditional Variational Auto-Encoder (CVAE) and multivariate Gaussian distribution (mGaus) models. Compared with the two alternatives, the cWGAN-GP model produces samples in better quality while remaining sensitive to temporal conditions. Fourthly, projections by the cWGAN-GP model are more realistic than those made by the Energy Transition Model (ETM), with price values varying continuously in smooth boundaries. Finally, the fuel cost of the most frequent final generating technology is found critical to LEPP. The annual mean and daily standard deviation of the DAM price series are expected to rise significantly when natural gas is mostly replaced by hydrogen power in the national energy portfolio.

Whereas in the past, the Distribution System Operator (DSO) almost never encountered congestion in their grids, nowadays, with the increase of connected renewable energy sources, this will become more prevalent. To forecast congestion on transformer stations, with the goal of mitigating it, the Dutch DSO Stedin uses machine learning models with standard loss functions for regression. These fall short in predicting congestion peaks, as loss functions like MSE are unaware of the prediction goal, which is minimizing the cost associated with the congestion. Without knowledge of this goal, a loss function will not put any extra importance on finding congestion peaks and is more likely to miss them.

In this thesis, the use of a a custom loss function is explored, which incorporates the different costs associated with congestion mitigation. This function aims to improve upon existing congestion forecasts by having a loss function in line with the congestion forecasts goal. For this research, the case of substation Middelharnis is used. This substation encounters congestion due to connected wind parks and distributed photovoltaics. The congestion encountered will be solved by grid expansion in 2024, but until then will have to be mitigated using the redispatching market GOPACS.

A custom function was generated using two cost components: a congestion fee, which needs to be paid if wind parks are disconnected and GOPACS costs, which are the price of buying flexible power on the redispatch market. The function takes into account two-time horizons: costs associated with buying GOPACS power day-ahead acting on a prediction of the load, and the resulting cost on the day itself if any remaining congestion was resolved via the congestion fee. The contribution of this function is that it depends on the difference between the costs of the prediction and the cost of the realization, instead of the cost of the difference between prediction and realization.

Four models were trained, each with a different loss function: MSE, Pinball, and Cost. The cost model is trained twice and for each model, a different value for GOPACS cost is used. The results show that the cost models outperform the Pinball and MSE-trained models, when the performance is evaluated on the final cost metric. However, if the GOPACS price is high, the cost model becomes conservative in predicting congestion peaks. It is not expected that this will be an issue in the future, as with more renewable energy, the energy price and subsequently the GOPACS price will be lower.

This thesis work investigates and provides an analysis of the potential benefits of an electricity - gas - heat integrated energy system, putting extra focus on the waste heat potential from fuel cells and electrolysers. The main focus is given to the low-voltage distribution grid level, and a case study is presented for the Drechtsteden subnetwork operated by Stedin, which, together with the IEPG department at TU Delft, is the promoter of this study. The novelty items of this work consist in the analysis of the waste heat potential of hydrogen conversion assets connected to a district heating network along with the inclusion of electricity and hydrogen markets, all in the contest of an energy system optimization.

The integration between these three energy sectors is assumed to be chiefly driven by the operation of electrolysers and fuel cells. Two other main assumptions set the base for this work: first, the gas sector is assumed to be entirely repurposed to operate with hydrogen and, secondly, the waste heat coming from fuel cells and electrolysers is assumed to be the main thermal energy input of a district heating network. An electricity and a hydrogen market have also been modelled to simulate the interaction of this region with the external grid.
The analysis is carried out by means of a linear optimization algorithm coded using oemof, an open source python package for multi-energy system modelling and optimization. Most of the input data (i.e. energy demand and generation profiles) comes from the Integrale Ifrastructuurverkenning 2030-2050 study from TenneT, Gasunie and the Dutch DSOs, which has been regionalized for the Drechsteden region is order to optimize the investments on the main energy assets (transformers, hydrogen substations, electrolysers, fuel cells, batteries) needed to run the future energy system.

An optimal system configuration is also calculated for a "reinforcement" scenario, in which energy sectors remain independent, and a comparison with the system integration scenario is presented. The overall system costs appear to be only about 5% lower for the system integration scenario. The costs imputable to assets is higher with system integration due to the addition of expensive hydrogen conversion assets, but, in this scenario, the operation on the energy market (driven chiefly by hydrogen exports) is more advantageous.
Finally, a sensitivity analysis on the share of heat demand to be satisfied by district heating and on the price of batteries is carried out, along with an investigation on the effect of adding thermal storage to the system.

Renewable energy sources have become a cost-competitive and green option for supplying power to the grid in recent years. Nonetheless, their variable nature poses a problem to the regular operation of the electrical grid by introducing severe fluctuations of large magnitudes and/or short-duration known as ramps. There is a lack of research in the literature on characterizing ramp events induced by wind-based hybrid power plants. The main research question of this study is how to characterize the ramping behaviour of wind-based hybrid power plants and what impact they have on the system. The application of the different methods to detect and assess the implications of ramps were presented in this thesis using wind and solar power based on the reference location.

Due to dependence on threshold values that vary across the literature and the limitations associated with calculating thresholds as a percentage of installed capacity, it was demonstrated that binary ramp definitions are not ideal and result in under-reporting. On the other hand, the wavelet approach extracts ramp events from the generation using statistically determined threshold values. As a result, the problem of under-detection of ramp events is mitigated. The proposed approach of "significant ramps" allows the evaluation of which ramp events are important and which are far less disruptive and may be ignored.

It was demonstrated that anti-correlation between wind and solar resources alone is not adequate to promise a smoother output as it does not provide sufficient information about ramp events. Anti-correlations at shorter time resolutions, such as 15 minutes or an hour, could be preferable. While seasonal anti-correlation may benefit national system adequacy, it does not benefit daily ramping events.

The optimal wind-PV capacity size for decreasing the total number of ramps was such that wind turbines filled the grid capacity, as solar power would result in extra ramps. It was observed that solar over-planting leads to a significantly increased number of ramp events, whereas wind over-planting results in a minimal change in ramp events. A penalty price was proposed to internalize the severity of ramp events, which could influence the choice between wind and solar over-planting. A solution was presented to mitigate ramp incidents in a hybrid power plant using a battery which was found to be more effective and/or more economical in minimizing ramps compared to over-planting.

The proposed "significant wavelet ramp approach" is shown to be a useful metric for characterizing wind-based hybrid power plant ramp occurrences. For a future in which variable renewable energy sources account for a substantial portion of the energy mix, it is proposed that demand information be considered when defining ramp events. More attention must be paid to power ramp occurrences, either by penalizing ramps or enforcing tougher grid codes. The ramp events must be considered at the sizing and development stage, with the possibility of including a ramp-mitigating battery strategy. A thorough examination of ramp events in hybrid power plants demonstrates the importance of minimizing and managing ramp events for both the system operator and the producer.

The European Green Deal states that the European Union will have to cut 55% of carbon-dioxide (CO2) levels and implement a share of at least 40% of renewable energy sources in the electricity sector by 2030, compared to levels of 1990. While renewable energy technologies are effective in cutting CO2 levels, they also bring challenges like variability and unpredictability which affect power system stability. The TradeRES project was founded in order to create and test new innovative electricity market designs that incorporate near 100% renewable energy generation. Multiple research groups across the EU participate in TradeRES, including TU Delft and TNO. In order to be able to research future system stability, this project seeks to improve investment decision-making in the energy models EM-Lab and COMPETES, provided by TU Delft and TNO respectively. To fully utilize the sophistication of both models and to prevent development of a new model, this thesis focuses on the coupling of the models through soft-linking and its validation in a Dutch energy transition context.

This thesis presents a conceptual approach and implementation towards the aforementioned soft-linking. First, the definition and requirements of soft-linking are defined to mitigate ambiguity created by previous work. These requirements are then implemented in a conceptual model describing data exchange, data mapping and timing of the models in the context of soft-linking. The conceptual model describes how the strengths of both models are utilized in the coupling. EM-Lab is an agent-based model used for studying investment behavior and has strong policy and market modules. COMPETES is an optimization model that has a very sophisticated and detailed dispatch module. The nature of these models provide coupling potential and constraints. A series of design choices describe how these models interact in the soft-linking, and how the decision was made to couple specific modules from EM-Lab with COMPETES to improve overall investment decision-making.

The implementation entails the recreation of EM-Lab market modules with Python and the soft-linking of these modules with COMPETES using the software kit SpineToolbox. From EM-Lab, the CO2 market and the capacity market were recreated in Python. These modules could be used to complement COMPETES and improve overall investment decision-making. Pseudo-code of the implemented algorithms is provided and a thorough description of the programmatic interactions of the models is given. Also, the SpineToolbox implementation is shown and elaborated on.

To ensure correctness, the soft-linking is validated and verified. Standard verification methods like face validity and static functional testing are used. For validation, the soft-linking is used to generate results in case studies which have been used to validate COMPETES. These case studies resemble a Dutch energy transition scenario. The results from the soft-linking are then compared to the validated COMPETES results. As the differences in results could all be explained, there is no reason to assume the soft-linking is invalid.

Results of the soft-linking are provided and contain graphs regarding the CO2 market, capacity market, dispatch, and investment and decommissioning decisions. The CO2 market presents volatile and extreme results, begging the question whether the current implementation is sufficient for future power system analysis. However, the results do suggest that the soft-linking has effect on investment and decommissioning decision-making. In conclusion, additional work, like representation of mechanisms that help stabilize the CO2 prices, is required in order to use this soft-linking for future power system analysis. The potentials of soft-linking are explored and suggestions are made for future work.

Transition from fossil fuels to sustainable sources of energy like wind and solar is the need of the hour. All over the globe, plans are in motion to achieve this goal. This implies addition of new elements to the grid in the form of Distributed Energy Resources (DERs). These affect the working of distribution grids and to ensure reliable as well as safe operation, it is important to keep a track on the grid’s state regularly which is essential to a Distribution System Operator (DSO). For this very reason, Distribution System State Estimator (DSSE) has been introduced and has been a prominent topic of interest which has been discussed in literature over the past two decades. Because of lack in observability of the network owing to unavailability of measurements and the stochastic load profiles of the distribution network, DSSE poses its own challenges. For this very reason, it is necessary to validate the working of a suitable DSSE that is affected by the continuous changes in the grid. By selecting a suitable algorithm, this thesis attempts to solve the observability issue by introduction of pseudo-measurements. The work in this thesis comprises of sensitivity analysis of Weighted Least Squares(WLS) algorithm tested on two networks in the Netherlands; a synthetic model of an anonymized distribution network and a part of Stedin’s distribution network which has limited measuring devices data available. The percentage of pseudo-measurements is varied to determine the state estimator’s accuracy. Also the statistical validity of the algorithm against measures like bias and consistency is determined with the help of scenarios generated by using Latin Hypercube Sampling (LHS). The results obtained prove the effectiveness of the selected algorithm for DSSE and are important for the DSOs to make critical decisions when needed for grid operation.