Batteries are an essential tool for energy transition. They provide the ability to reduce imbalances by absorbing the forecast errors for consumers and renewable energy sources. This has the added benefit that congestions occurring due to supply and demand mismatches would be prevented. Furthermore, they allow generating a stable power consumption for a longer period by utilizing the available reserves. The electricity markets are all power markets, where the products grant an amount of power for a certain period. Therefore, the introduction of a battery would make it easier to buy power for a portfolio of consumers. Currently, it is not yet feasible to use a battery for the consumer segment as the revenues generated are simply too small to pay the battery back before it reaches the end of its lifetime. This report aims to describe a machine learning-based battery operating algorithm that can be utilized for the consumer market. The operating algorithm will be trading on the day ahead and the voluntary aFRR market while satisfying the demand of the consumer(s). The trading is done based on the forecast generated by different machine learning models. For the day ahead market, two forecasting models are tested, namely an XGBoost algorithm and a Dense Neural Network. For the imbalance market, a simple Dense Neural Network and a Long Short-Term Memory (LSTM) are compared and for the consumer forecast, an XGBoost model is compared to a Lasso. All forecasts are combined to create a profit-maximizing algorithm. Lastly, a comparison is made between having an individual battery for every homeowner and aggregating a group of consumers together in a larger battery is compared. The results show that forecasting an aggregated group of consumers has a significantly lower error compared to forecasting individual demand patterns. The large errors in the individual forecast result in a large opportunity cost, based on the Value of Lost Load (VoLL), which renders the benefits of owning a battery useless again. However, the aggregated group of consumers combined into a single large battery is profitable. Considering the current prices (22Q1) the battery generated enormous profits, such that the battery would be break-even in less than 2 years.

As the share of solar and wind in the energy mix increases, the inherent intermittency of these energy sources pose a great challenge for the Dutch power grid. The occurrence of problems with congestion and balancing power supply and demand are becoming more common. Companies in the Netherlands show potential to implement means for smart use of energy to alleviate these kind of problems. However, the gap between academic solutions and the industry is often too large. This research presents a method to find the optimal design of an integrated energy system in the Netherlands. A use case at Picnic is used to apply the proposed method. A flexible load scheduling optimisation algorithm is presented to explore the financial benefit of an integrated energy system that combines a photovoltaic system, an energy storage system, a cold storage system and a fleet of electric vehicles. The financial performance of different system designs are compared. Results show that the optimised load schedules for the EV fleet and CS system achieve a 2.6% decrease of energy costs with respect to the benchmark of Picnic. A PV system turns out to be beneficial for every size that the grid connection allows. This research finds 600 square meters to be optimal in the case of Picnic. An energy storage system would make optimised load schedules obsolete due to its flexibility. An energy storage system with a capacity of 250 kWh is found to be optimal according to the performance analysis. However, significant limitations in the assumptions of the storage system advise against installing it. Further research is required to elaborate the different elements in the proposed model. Different markets are suggested to use as a basis for load scheduling and a broader set of system designs is suggested to analyse their performance.

Electricity grids worldwide are experiencing increased peak demands and decreasing simultaneity due to higher shares of Renewable Energy Sources (RES). It is expected that many grids will soon reach their limits. One solution to mitigate these issues is exploiting flexibility in e.g. electric vehicles. In this work, a shrinking horizon model predictive controller is constructed to optimally charge and discharge EVs with respect to the day ahead electricity price or grid carbon intensity. The model takes into account that users with a dual rate electricity plan only want to charge during their off-peak hours. A feature to implement a household PV setup in the optimization is included. The possible consequences in terms of associated carbon emissions, utility costs and user costs are analysed using a simulation based on data from 4279 charging sessions that took place between June 23, 2021 and June 23, 2022. The sessions are split in 2855 weekday sessions (duration between 4 and 24 hours) and 1424 weekend sessions (duration between 4 and 60 hours). It is found that using current circumstances, minimizing the carbon emissions using bidirectional charging results in a higher price (5.5 %) for the utility than using the current state of the art, unidirectional charging minimizing the wholesale electricity cost. Bidirectional charging minimizing the wholesale electricity cost results in higher emissions compared to unidirectional charging (2.8 %), and even compared to uncontrolled charging (0.9 – 3.6 %). The reason for this seems to be a negative correlation between carbon intensity and wholesale price during the times that vehicles are typically connected although this needs further investigation to be confirmed.

Grid congestion, which results from a larger demand for electricity transport than the available transport capacity, is a challenge in the Dutch energy transition. The growing pace of decentralized renewable energy development in less densely populated areas and the electrification of demand leads to congestion, which potentially hinders the connection of new renewable projects to the grid and slows down the energy transition. Due to the intermittent behaviour of renewable resources, the grid connection capacity is not used to its full capacity at all times. Cable pooling is introduced as a possible solution to congestion, allowing an existing and a new renewable resource to share a grid connection and improving the utlilisation of the current grid infrastructure. This thesis’ main objective is to develop a calculation framework to assist developers to evaluate the economic potential of a cable pooling location by optimizing the Net Present Value (”NPV”) of a shared grid connection, considering technical, regulatory, legal, and financial aspects. It aims to provide developers with a tool for making a preliminary decision on whether to continue development for a potential cable pooling location. The Dutch context is used to identify different grid connecting possibilities, different combinations of wind and solar to form a hybrid farm and different revenue streams. These are used in a methodology to find the optimal installed capacity of the added resource and the impact of on-site storage to one of the hybrid farms. The approach considers the influence of market prices on the technology specific cable pooling business case at an hourly level and accounts for long-term market developments. Other factors accounted for in the tool are the hourly export capacity, defined as the residual space after the export of the existing farm, land size and costs of installation. A case study of a solar farm oriented to the east-west is used to test the tool and draw conclusions on the potential of cable pooling for this case study. The additions of a wind and solar resource with either south or east-west generation all yield positive NPV values with the highest values seen for the wind addition. Sensitivity tests for technical and economic inputs show that the results are sensitive to weather data and various economic inputs, but the results of this case study are robust. The case study however considers a relatively large grid connection. Generalizing the results by considering a smaller grid connection shows the value of complementary production patterns. The impact on the cost-benefit framework by adding a battery is considered. The addition of a battery adds value by peak-shifting the produced energy, but not enough to cover the costs of the battery without any subsidy or alternative revenue streams. In conclusion, cable pooling shows potential for this case study, for different revenue streams and additions. The tool used to obtain the results can be tailored to different case studies and input scenarios and shows the economical attractiveness of a cable pooling location, based on the Dutch context. The Dutch context shows a promising potential for cable pooling as a method to deal with congestion, but not many projects are present yet. Considering different parties sharing one grid connection, coming to an agreement on the terms can form a hurdle. Therefore, the introduction of more transparent cable capacity calculations by DSOs and a separate subsidy for cable pooling projects could help incentivize the development of more cable pooling projects. The impact of developments such as the ”Use-itor-lose-it” on the cable pooling potential should be monitored closely. Other solutions to dealing with congestion, such as using the fault reserves, should not be disregarded.

Offshore wind power plants are subject to adverse electrical conditions that are more severe than onshore cases, due to the long cable connection to the shore. Among these conditions are amplified voltage distortion at (inter)harmonic frequencies, as well as stability issues. An important aspect of wind turbine generator control is the phase-locked loop (PLL), a method to synchronize the output current with the grid voltage. In the case of voltage distortion, an error occurs in the estimated phase-angle of the PLL, which results in an additional distortion in the output current. This research focuses on 5 different variants of PLLs and compares their behaviour when implemented in a grid-side inverter of a wind turbine. The comparison is based on deriving the equivalent small-signal output impedance of the inverter for each PLL, which can be used for stability analysis of the wind farm connected to the onshore grid by a typical cable connection. A conclusion is drawn on the effect of each PLL variant on the output distortion as well as the stability.

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.

Thin-film silicon technology creates electricity out of micrometer thick silicon absorber layers, which makes this technology less material heavy compared to classic crystalline technology. This advantage can be further exploited with the transition towards flexible thin-film technology, where the non-modular cost can be further reduced compared to traditional crystalline solar parks ect. However, thin films have limited absorption coefficients at higher wavelengths which means the optical pathlength must be maximised to overcome this limitation. In literature, the highest efficiencies are obtained with the creation of periodic textures resulting in 10.2%, 12.7% or 14% for nanocrystalline, micromorph and triple junction silicon technology. All these cells are made on silicon substrates which means the back of the cell is textured but if the front side of the solar cell is textured, efficiencies can outperform the current holding records. A methodology is designed to create periodic micro-textures on Corning glass to improve the total absorption of lower energetic wavelengths. However, the creation of random micro-textures (Aluminum Zinc oxideand Indium Tin Oxide sacrificial texturing)(AZO-/ ITO textures) is also researched because different methodologies exist and limited knowledge exists on which methodology results in the highest amount of scattering. Second, a comparison between random and periodic textures must be made. Both types of textures undergo optical- and physical parametrisation after which both aspects are correlated to each other to gain a deeper understanding of light management. For random textures, Haze-values between 93-86% are obtained. These high values are obtained because the created craters are characterised by an increased depth for identical crater widths. Second, each methodology has its characteristic depth-width ratio which explains the optical superiority of one (ITO textures). For periodic textures, Haze-values lie between 50-3% with a maximum obtained aspect ratio of 0.18 but the optical response is not comparable to random textures because diffraction is the dominant light management technique. Therefore Angular Intensity Distribution measurements must be performed, which resulted in the conclusion that the created ITO textures stay superior (also compared to literature) while the AZO textures have a similar performance compared to the periodic texture. This translates itself in a superior external quantum efficiency (EQE) of ITO from 800nm on. Between 600-800nm, the periodic textures are superior.

To cut down immense greenhouse gases emission and energy consumption in the rapidly urbanizing world, a holistic understanding and rethinking of our dynamic urban energy system are inevitable. Performing bottom-up building energy modelings at urban scale based on Geographic Information System (GIS) and semantic 3D city models could be a promising option to provide quantitative and integrated energy solutions. Nevertheless, input uncertainties either caused by limited data accessibility in most cities or parameters with stochastic variability (e.g. house occupancy profile) become one of the biggest obstacles to produce reliable and acceptable building energy modeling results. This study aims to address the heating demand simulation performance gap caused by input uncertainties. In this case study based on Amsterdam residential building stock, parameter importance ranking of the 14 simulation inputs are first derived according to the sensitivity analysis. The selected key uncertain parameters are then modeled in a probabilistic distribution way at postcode 6 level (approximately or slightly more than 10 buildings). Model calibration is based on the Bayesian approach and given six years (2010-2015) of gas consumption data to infer parameter posterior distributions. After the training phase, the calibrated annual heating demand simulation results of the validation years show significant improvement in modeling accuracy. Comparing the baseline and the calibrated simulation results, the averaged absolute percentage errors of energy use intensity (EUI) among at least 84 valid postcodes have decreased from 24.96% to 8.31% in 2016 and from 19.93% to 7.70% in 2017 respectively. The calibrated urban building energy model would be most interested by municipalities, urban planners, and engineering consultancies. It can be used to evaluate long-term energy supply and demand strategies, identify building renovation saving potential, perform large-scale building performance mapping, and carry out retrofit measures assessment.

In earlier times the climate crisis was ignored. However, these days it is acknowledged as one of the most important challenges the world is facing. With increased awareness of anthropogenic emissions, most sectors are changing rapidly. One of those sectors is the offshore wind energy sector. As a consequence of the Paris Climate Agreement, the European Commission adoptedan offshore renewable strategy that aims to install at least 60 GW of offshore wind by 2030 and up to 300 GW by 2050. With the increasing capacity of offshore wind being constructed, new wind farms will move gradually further offshore. This, therefore, requires a need for significant investments in large-scale wind farms and in the required grid infrastructure to be able to transmit the large amounts of electricity produced offshore to the consumers onshore.Thus the need for cost-efficient integration of the North Sea transmission arises. Currently, existing offshore wind farms are being investigated as potential gateways in order to achieve this offshore transmission network integration in a cost-efficient manner.In this master thesis project, the effects of various future connection schemes and market setups on the business cases of offshore wind farms were investigated. The study focuses on a location in the North Sea close with similar geographical characteristics as a large sandbank called the Doggerbank. First, a literature study was performed to obtain background knowledge about the offshore situation. Relevant assets were identified, multiple offshore gridtypologies were found and different offshore governance models and market setups have been identified. In addition, the theoretical background of constructing a business case for offshore wind farms from a Dutch and British perspective was explored. The main cost drivers were identified and can be distinguished in capital expenditures and operational expenditures. In addition, these cost categories were quantified in order to set up a business case for an offshore wind farmat a specified location. The cost category taking up most of the investment was found to be the grid connection, taking approximately 30% of the total investments. In addition, British built wind farms require more initial investment with respect to the Dutch wind farms, due to a difference in governance models between the countries. Next, the methods by which a wind farm can collect income was explored. Various categories were identified being; revenueby selling the commodity, power purchase agreement, financial support schemes and green certificates. Historical wind data was used to determine a wind profile. In addition, the historical market data was added to quantify the cash flow of an offshore wind farm. Last, an expression of the Levelized Cost of Energy (LCOE) was found for a Dutch and British constructed offshore wind farm respectively.To obtain insights into the effects of the North Sea transmission integration on the business case of offshore wind farms, a scenario-based modelling & simulation approach was adopted. First, a conceptual model has been developed. This was done by, first, identifying the situationin which the problem occurs and second, determining the modelling scope. Next, the in- and outputs were explained and the simplifications used for the model were presented accordingly. Various scenarios were developed in order to be able to simulate distinct future situations in which a wind farm may be required to operate. These system changes are predominantly caused by different connection scenarios or by a change in market setups. First, a base model was developed, to understand the basic operations of a wind farm. Next, the reference case using a Dutch and British wind farm connected to their own national electricity market was simulated that represents the current situation of existing offshore wind farms. Then, a cross-border connection was added to the reference case under the current home market setup. Subsequently, a change in market setup, the offshore bidding zone, was introduced.The first insights were obtained through simulation of the reference case, which represents the current situation. Large wind farm projects far from shore were found to be most likely still dependent on support through subsidy schemes. This holds for both a Dutch and British perspective regarding the construction. The first scenario on which the effects were simulated, describes a situation in which a connection to a foreign market is introduced to the reference case. The results of these simulations show no substantial changes in the cash flow of theoffshore wind farms. However, large amounts of costs imposed on society were identified under this scenario. When the offshore bidding zone was introduced as a new market setups in a cross-border connection various results were found. First, for a Dutch wind farm in this connection and under this market setup, the revenues tent to increase with respect to the reference case. However, for a British wind farm a clear decline in the collected revenues by the wind farm developers was observed. Nevertheless, due to the regulatory conflicts thatwere identified in the cross-border connection scenario under the current home market setup, a change to the offshore bidding zone market setup seems desirable. This implies that under current regulation, the offshore bidding zone market setup shows no issues being compliant with EU legislation and National regulation.An additional step was performed in this master thesis project, that adopts the objective to identify instruments that could mitigate these declines in revenues under an offshore bidding zone market setup. These instruments are regarded as mitigation options or mitigation schemes. The academic method of a policy scorecard was adopted, and additional desk research was performed to identify current proposals for mitigation schemes in the literature. Eight mitigation schemes were found from which seven were applicable to an offshore biddingzone market setup. These could be distinguished into support through operations and through regulatory changes. It was found that, within the scope of this research, which is looking into the economic effects and the feasibility of the identified mitigation schemes, the Contract for Difference scheme seems to be the best choice as it provides stability for the wind farm developers, and is the most cost-efficient option with respect to society. Good alternatives were identified to be the redistribution of congestion income and granting windfarm developers a so-called "Transmission System Operator -light" certificate that allows for a share in the congestion income. However, there is a broader political impact, in terms of financial budget requirements on a member state level, general public support for support schemes and overall effectiveness of support schemes in broader policy objectives. As a consequence, it is recommended for future research to further investigate different domains than the economically and feasibility domains that have been presented in this research. In order to solve the current issue, thesedomains require additional investigated to obtain this broader perspective and to eventually be able to make political decisions.

In this thesis, an algorithm that calculates the hosting capacity of independent PV, or EV charger systems was created. The hosting capacity is based on slow voltage variations and voltage unbalances caused by worst case PV generation and EV charging scenarios. The algorithm was run for four different commercial and industrial networks, and a range of network impedances. The outcome of the algorithm resulted into two figures for each of the networks, representing the possible installed capacity of PV and EV charger systems, respectively.

One of the main challenges of today is the energy transition from fossil fuels to green renewable energy. One new concept that can aid this transition is the smart charging of electric vehicles. Smart charging is utilizing the flexible availability of an electric vehicle to charge on a time when this is most valuable, while complying to the constraint that it needs to be fully charged when the vehicle unplugs. This research focuses on the use of smart charging for cost optimization on the ETPA intraday market, in combination with the day ahead market. During this research it has first been investigated what the properties of the intraday market and the available electricity fleet were and which difficulties they pose. One of the most challenging properties were the limited volume constraints placed on the orders in the limit order and the constraint that the sessions had a binary charge speed, meaning they can either fully charge or not charge at all. This combination creates and NP-hard optimization problem. The second main difficulty is online nature of the market, meaning a solution for the hard optimization problem has to be done within seconds. Current research in the field of smart charging on the intraday markets and smart charging in general has not been able to provide this research with a feasible scheduling optimization method. This leads us to the main contribution of this research: the proposal of a new optimization method which computes an (online) approximate scheduling of a fleet, with binary charge settings, that optimizes on the costs of multiple electricity markets including the ETPA intraday and day ahead market. Through empirical testing it has been shown that re optimization in an online environment can be done within maximally a quarter of a second, with a fleet of 10000 EVs per day. Testing has also shown that it can schedule these EVs with a maximum error or 33 kW per PTU, which is an error of 0.16% on a 10000 session per day fleet. The newly proposed optimization methods has immediately been utilized to test the maximum trading possibilities of the ETPA intraday market. Only purchasing on the day ahead and intraday market has shown some possible opportunities, but the real opportunity lies within full trading on the day ahead and intraday market. Cost reductions of up to 6ct/kW could be achieved compared to not smart charging at all. Online tests, using no forecasting model, has shown almost no cost reduction compared to only smart charging on the day ahead market. This is due to the algorithm trading when the least of profit can be made, wasting the profit possibility. This research has provided the first building blocks to achieve a optimization model for smart charging on the ETPA intraday market. For the optimization model to be completely ready it must be able to optimize over PTUs, that have different lengths, and be able to make use of forecasted prices and volumes computed by an external forecasting model.

In the field of photovoltaics, tandem cells have emerged as a promising technology with high power conversion efficiencies. The academic society grew interest toward this technology due to its high generation capabilities at low production costs. For the time being, most of the research efforts are limited to the lab performance of these cells. However, real life performance studies allow for better understanding of the design effects and yield potentials of this technology. The commercially available yield prediction tools do not involve energy yield prediction models for tandem modules. In addition, they fall short at modelling all the aspects influencing the PV energy yield. The PVMD Toolbox, developed by the Photovoltaic Materials and Devices group at Delft University of Technology, is proposed to serve this need. This thesis presents the work done to develop version 4 of the toolbox. A calibrated lumped element model (CLEM) was developed to simulate the electric performance at the cell level. The CLEM combines the accuracy of physical models with the speed of lumped-element models to generate hundreds of thousands of simulations within a single minute. Additional models were implemented in the toolbox to account for the effects of cell interconnections and metallization on the energy output. Besides, a cell mapping algorithm was developed to reduce the AEY simulation time. This algorithm proved beneficial by reducing the number of required electric simulations by 86% at the small cost of 0.226% bias. Afterwards, the accuracy of the thermal and electric models was validated against two datasets. The simulated results showed a great agreement to the measurements with total energy yield deviations from the measurement of 2.65% and 4.15%, compared to 7.43% in version 3. Therefore, version 4 of the toolbox offers more accurate simulations with a reduced computation speed by a factor of 45. The CLEM and cell interconnection models were utilized to perform energy yield simulations on tandem modules. Case studies were performed on c-Si/tandem modules to investigate the implications of design choices. After optimizing the STC output of four design options, the toolbox was used to simulate the energy yield for each of them. Then, the optical and electric performance of the modules were studied. The tandem modules proved advantageous, with energy yield increase ranging between 12.91% and 27.13% compared to SHJ modules. In addition, specific yield computations confirmed the sensitivity of tandem modules to meteorological conditions. The final result of this thesis is a first-time combination of modelling spectral irradiance, thermal, and cell and module electric aspects for energy yield simulations of tandem modules.