This thesis covers the top­level design of a DC microgrid of a tiny house community on the roof ofa high­rise building in Rotterdam. This DC microgrid consists of 12 tiny houses, a common usagearea, Renewable Energy Supply (RES) methods, using solar and wind energy, and an Energy Storagesystem (ESS). This design is part of a complete DC smart grid for such a community with two othersubgroups focusing on the control and software, theCNSgroup, and power line communication, thePLCgroup.In this thesis, three design phases are discussed; demand estimation, storage & supply design, andtopology design. Subsequently, the resulting grid design is validated. The first phase resulted in anestimation of hourly, daily, and monthly energy usage. Using a model of the generation, 61.2푚2Photo­Voltaic (PV) panels and 6 Vertical Axis Wind Turbines (VAWT)s were selected. In order to handle thevarying energy generation of theRESs, differentESSoptions are considered, and 4 Li­Ion batteriesare chosen. This combination of storage and supply resulted in a grid availability of 93.73%. In thelast design phase, the topology of the community is designed, which resulted in a 400 VDC unipolar ring-­based series­ connected multi­bus configuration, which effectively operates in radial form to re­duce complexity and enables easier fault location detection. The topology design also considers theconverter requirements, wiring, and stability and safety considerations. A cost analysis is made ofthe entire grid resulting in an estimated total cost of around €100,000. Lastly, design verification isperformed on the proposed design, which resulted in functional results during 100% demand, with amaximum voltage drop of 1.82% and during 150% demand, with a maximum voltage drop of 2.80%.

Future energy systems are expected to rely increasingly more on distributed energy resources (DER). Prosumers that own energy resources such as photovoltaic (PV) generation and energy storage systems, will play a crucial role in the realization of DER’s in future power systems. Limited grid connections due to local electricity congestion make it more difficult for prospective industrially-sized prosumers to find suitable locations for their business operation. Energy resources such as batteries and PV, can be used to fulfil the desired electricity demands while adhering to the congestion related limits. By combining multiple energy resources and controlling them together the operational cost is drastically reduced. This thesis develops a stochastic optimization model that is able to find the optimal dispatch strategy for energy resources while adhering to constraints set by the DSO in a congested area. Also, a deterministic model is developed to be used as a comparison to the stochastic model. A deterministic model is simple and fast but it does not accurately represent the stochastic nature of PV generation, electricity consumption, and market prices. The stochastic model uses a set of different input scenarios for each time step and finds the optimal strategy considering all scenarios. In this thesis two models that consider stochasticity are developed: a stochastic and a robust model. For the robust model, the imposed grid constraints from the Distribution System Operator (DSO) have to be respected at each time step for each scenario and are classified as hard constraints. The model is able to find a optimal strategy for a week in each season when considering 11 scenarios. All predetermined constraints are respected for each simulation and while this makes the strategy reliable it also makes it conservative. For the second stochastic model, the DSO grid supply constraint is designed to be flexible and allow for occasional overshoot. In this case, flexibility is obtained by constraining the statistical distribution of grid power rather than the value at each time step and scenario. The calculated control strategy results in a simulated revenue increase between 3.67% and 4.78% depending on the season. For each season the objective cost reduces relatively more than the occurrence of grid overshoot. The term grid overshoot is used to indicate a situation where the DSO imposed grid limit is exceeded for a time step. The overshoot remains within the predetermined value threshold of 4% for all simulations of the stochastic optimization model.

This report presents an analysis of the emergence of residential batteries from a consumer’s perspective and their effect on the load peak of the distribution grid. First, the future of residential batteries was evaluated in social and technical context by mapping the present situation, its trends, and its possible drivers. Three different battery control strategies were analysed: maximising self-consumption, market price arbitrage, and load peak reduction. The corresponding battery profile was determined with a rule-based approach. Second, load summation was used to simulate battery development scenarios within the distribution network, after which the effect of residential batteries was determined on a selected substation with a load peak analysis. From consumer perspective, residential batteries are driven by the phase-out of the net-metering scheme, increasing wholesale daily price spread, a decline of investment costs, or renewed tariff structures. The results showed that residential batteries become economically attractive when net-metering is abolished and the daily electricity price spread increases. The load profile results of the selected substation show a reduction of both size and number of overload hours for all control methods in case of medium and low emergence of residential batteries. In case the batteries are used to reduce individual load peaks, this reduction is largest and is smallest when batteries are used for market price arbitrage. Battery usage for self-consumption and peak reduction mainly operate during summer months when PV generation is higher. Therefore, demand load peaks in winter are not relieved as much. Although it was found that grid reinforcement is still not alleviated for the selected substation, the maximum and minimum load peak are unified leading to a reduction of required reinforcement capacity. It is found that retail price regulations strongly influence which control method will be dominant. Incentives to increase self-consumption or reduce load peaks will relieve pressure on the grid. However, critical grid congestion moments are not explicitly relieved. Therefore, further research is required to identify methods to utilise the batteries for DSO control during critical peak moments or winter seasons with low self-consumption usage.

This thesis will discuss the design, simulations and measurements of a powerline communication system that utilizes FSK modulation in order to transfer information from a transmitter to a receiver at a baud rate of 1kbps, in order to reach the requirement of having an effective data rate of at least 6bps. This system is designed specifically to be implemented in the smart DC grid of a sustainable "tiny house" community. This thesis investigates the different subsystems needed to create this communication system and compares different methods and implementations. This research led to the conclusion that the communication system was successfully build and could reliably reach a baud rate of 700bps, which appeared to be more than enough to reach a data rate of 6bps. The system also showed to be resistant to 'high levels' of noise on the communication channel as defined in the CELENEC standard. Furthermore, in order to decrease the bit error rate further, additional bit error detection and correction has been implemented using microcontrollers. This led to a robust and nearly error free communication system over a tested powerline of 50 meters long.

Increasing distributed generation of and demand for electrical energy results ever more in problems like congestion. Forecasting the demand, photovoltaic power generation and the number of electric vehicles connected to charging station for a residential neighbourhood can be an important part of a smarter distribution network for such a neighbourhood and can thereby increase the usage of renewable energy. In this thesis an overview of the design steps for making such a forecasting system is given and the steps are applied to a fictional dutch residential neighbourhood of the future. Some key findings are that for accurately forecasting the load, the temperature and time are the most important features. For accurately forecasting the PV power generation, especially the irradiation is most important, but time, horizontal view and humidity are also important features. Furthermore, it is shown that random forest regression models can accurately forecast both the demand and PV power generation with an accuracy above 90%. Artificial neural networks are also adequate models for the forecasting problems, but because they are harder to understand and not necessarily better, it is recommended to start with random forest regressors before making neural networks. Support vector machines seem less suitable for these particular forecasting problems.

In this thesis the build plans for a new residential neighbourhood are used as a basis for a distribution network of the neighbourhood of the future. Data is used to simulate power flows and an analysis is given on the current method used by DSO's. Furthermore the discussion is opened to suggest different methods for constructing sustainable distribution networks.

This thesis shows the detailed design of the control and software of a DC microgrid of a tiny house community on the roof of a high-rise building in the city of Rotterdam in the Netherlands, consisting of twelve tiny houses powered by solar and wind energy. This thesis is part of a project with two other subgroups, focusing on the microgrid design and the powerline communication. First, an introduction to the problem is given together with a description of the tiny house community. After that, the general program of requirements is presented, as well as the requirements of this subgroup. Next, an artificial neural network design is presented, which is used to forecast solar and wind generation and energy demand. The designed dense neural network resulted in predictions with mean errors of 10.11%, 12.56%, and 6.95% as a fraction of the maximum value for solar generation, wind generation, and energy demand, respectively. The predictions functioned as an input for the model predictive controller, which used them to place restrictions on appliances in the community when necessary, to reduce dependency on the main power grid of Rotterdam. Using a mathematical optimization algorithm, a simulation of one year showed that the controller could reduce the grid dependency up to 25%, compared to simulating without the controller. The conclusion summarises the achieved results, discusses whether the requirements are met, and considers possible future works.

The electrification of all sectors is essential to achieve carbon neutrality by 2050. In terms of mobility, many nations around the globe envisage a zero-emission fleet resulting in a massive deployment of Electric Vehicles (EV). Nevertheless, this goes hand-in-hand with some challenges related to congestion and voltage problems in power grids caused by inadequate grid capacity. When grid reinforcement cannot keep up with the increasing demand for electricity, both in terms of planning and excessive financial means, smarter, more efficient solutions should solve the problem. This thesis aims at providing an optimal strategy for consumers’ flexibility distribution by smartly charging EVs in Low Voltage (LV) grids. In that way, grid reinforcement should be reduced to a minimum. In order to do so, the main contribution of this work is covered by the development of a smart, optimal charging model that tries to comply with grid constraints including voltage and congestion boundaries. Mathematical (near) real-time optimisation is used together with the receding horizon optimisation principle. The work of this thesis relates to a typical urban LV grid in the Netherlands. First, the required level of grid reinforcement was investigated by means of quantifying the voltage and congestion problems in the LV grid. This was done by applying uncontrolled charging scenarios up to 2050 for both winter and summer. After investigating the uncontrolled scenarios, the need for an optimal smart charging strategy became apparent. This was developed afterwards. The results showed that in 2050, 94.5% of line congestion and 100% of transformer congestion and voltage problems could be bypassed with smart charging only. This was achieved by implementing grid constraints at the most vulnerable locations in the power grid. Consequently, the need for grid reinforcement could be almost completely avoided till at least 2050. Lastly, this work distinguishes itself from others by extensively reflecting on the integration of three concepts: a novel recently developed tariff structure, the effect of bidirectional charging, as well as the necessity for grid constraints. This allows summarising the benefits for all stakeholders including the distribution system operator, the charge point operator and EV owners. The final conclusion can be made that including these three concepts results in one of the most favourable investigated strategies for all three parties together.

With the advancements in technology and the enforcement of governmental regulations and incentives, the share of electric vehicles (EVs) in the mobility sector is on the rise. Although this aids the transition into a greener future with net-zero carbon emissions, the increase in EV penetration rates can have immense impacts on the grid and its operations. Coordinated charging strategies can help relieve grid stress by adhering to grid codes and requirements while carrying-out EV charging routines. Coordinated charging strategies can be of three types: centralized, decentralized, and price-oriented. The aim of this thesis is to investigate and mitigate the impacts of EV charging on Dutch residential grids, namely the impacts of voltage magnitude regulation and distribution transformer loading. This thesis proposes a decentralized coordinated charging strategy with local voltage control at its essence. The proposed charging strategy effectively allocates the charging power by prioritizing users based on their preferences, which are communicated to the charge controller through an IoT platform. Furthermore, in order to investigate whether users would be inclined to enroll into a coordinated charging routine, the impact on the user is also taken into account in this thesis by comparing to what extent can a user charge their EV battery with a coordinated charging strategy when compared with an uncoordinated one. Finally, all relevant and practical results from this thesis will be integrated into DNV's Next Generation Grid Operation Knowledge Framework to aid DNV employees in the future.

Technological advances, cost reduction, depletion of fossil fuels, environmental concerns, and growing energy demand are expanding photovoltaic solar energy (PV) in more latitudes and locations. A simple and effective procedure to assess the PV potential of a particular region is to analyse its climatic conditions. In general, climatic studies use the K¨oppen-Geiger (KG) climate classification as a reference. However, KG is solely based on temperature and precipitation, resulting in an unsatisfactory scheme for analyses in the PV field, since the most important variable, solar irradiation, is not considered. Thus, in 2019 Ascencio-V´asquez et al. developed a new worldwide classification based on temperature, precipitation, and solar irradiation: the K¨oppen-Geiger-Photovoltaic (KGPV) climate classification. Even though KGPV is a good improvement, it just consists of a simplified version of the KG groups subdivided into four levels of irradiation: low, medium, high, and very high. Hence, the climate parameters are not considered in a combined manner in the sorting process. In this project, a new worldwide climate classification directly applicable to PV has been developed. Machine Learning proved to be a convenient tool to achieve this objective. First, supervised learning served to identify and assess the climate variables more correlated to the specific energy yield. More specifically, a Linear Regression model was implemented. Subsequently, these variables were used to create the classification by applying k-means, a clustering algorithm. The classification was optimised following a comprehensive qualitative analysis, resulting in a scheme based on seven climate variables and 20 clusters. By contrast, KGPV considers five variables. Even though it contemplates 24 groups at first, half of them are neglected based on a land-surface ratio and population density criterion, resulting in a classification based on 12 clusters. Hence, the methodology proposed in this work enables identifying new relevant regions. Moreover, “Machine Learning driven PV-climate classification” presents a satisfactory correlation with the specific energy yield, except for very low values, where the correlation is minor. Lastly, the relationship between climate and degradation rate was explored. The complexity and non-linear behaviour of degradation demand an alternative approach. Random Forests was proposed, but it showed poor performance. It is necessary to be able to predict non-linearities and, at the same time, keep a logical mathematical relation between the supervised and clustering algorithms. In this regard, Multivariate Adaptive Regression Spline (MARS) might be a promising option.

Overhead power lines play a key role in the transmission and distribution of electrical power. Traditionally, the operating limits of these overhead lines are fixed and determined through static line rating. These limits are designed under conservative conditions such as high loads and extreme weather scenarios. However, the global energy demand is expected to increase substantially in a few decades. Furthermore, there will be higher levels of penetration of intermittent renewable generation in the power system due to energy transition in the future. In this thesis, a Dynamic Line Rating (DLR) method is developed to enable the determination of the varying operating limits of the overhead lines. It is realized by calculating the maximum allowable current for the line considering the real-time monitored electrical and weather quantities while meeting the requirements for the design and security criteria of the line. This is a more economical and feasible solution to improve the existing power system compared to constructing additional infrastructure. This thesis aims to develop a real-time dynamic line monitoring system by utilizing Phasor Measurement Unit (PMU) data for a 50kV distribution grid. The Real-Time Digital Simulator(RTDS) is used with an interface to MATLAB to realize the DLR model at computational speeds equivalent to real-time operations. A parametric sensitivity analysis is conducted to understand the influence of each parameter in the DLR model and its working. Case studies are conducted by using the DLR model on the 50kVdistribution grid, which showed an increase of 20% on the line ampacity. Lastly, a comparative analysis of the DLR model and the P341 MiCOM DLR relay results are performed by applying RTDS Hardware-in-the-loop (HIL) testing. The results of the DLR model are found to be in accordance with those obtained by the relay which also validates the model. Finally, recommendations and future work are proposed such as DLR implementation for cables using PMU data.

Climate change is one of the biggest challenges of this time. One of the necessary changes to combat this challenge is a shift from conventional power resources, such as coal and natural gas, to large scale deployment of renewable resources, such as wind and solar power. However, renewable energy resources are volatile because they depend on the weather, which poses a problem because supply and demand should always be exactly matched. Along with a shift in energy production, also appliances that use non renewable energy are becoming electric, e.g., electric heating and cooling in buildings and electric vehicles. This causes an increase in electricity demand and is a burden on the main grid. To reduce the demand on the grid, it is important that a part of the energy is produced locally by distributed renewable resources, such as residential solar panels. To maintain a stable operation of the grid, the smart grid paradigm has to be adopted. Advancements in information and communication technologies provide the means for an efficient management of the grid using various services, such as distributed generation and storage. Because of the conflicting nature of the agents in an electricity network, game theory is a widely used framework to tackle this problem. In a realistic model of a power grid coupling constraints must be taken into account, because the agents have shared resources. Therefore, the economic dispatch problem is modeled as a noncooperative game with coupling constraints, called a generalized game. Three distributed algorithms are derived to reach a generalized Nash equilibrium. It is shown through simulations that these algorithms have the potential to ensure a safe and efficient operation of the grid, while minimizing the cost of power usage. To reduce the cost even further, a method is presented to make more optimal use of the storage units.

Microgrids are a promising tool that can help transition the electricity grid towards a smart grid. They can provide significant benefits to the power grid in the form of increased reliability and flexibility. The microgrid controller sets the electricity consumption and generation of all controllable devices within the microgrid and controls how much electricity is exchanged with the main electricity grid. Model Predictive Control (MPC) has been proposed as a method for developing such microgrid controllers. MPC can deal well with the many different constraints that are imposed on the control of the microgrid. Because controlling a microgrid involves switching devices on and off, microgrids are often modelled as a hybrid system. A downside of MPC for hybrid systems is that, a mixed integer optimization problem must be solved at every control step. Solving mixed integer programs is computationally complex, especially for large scale problems. The difficulty of the optimization problem limits the response time of the microgrid controller. Parametric MPC has been suggested as a way to reduce the computational complexity of the controller and to create an efficient single level MPC controller. In parametric MPC the control input is parametrized according to a set of parameters and a control law. Instead of determining the inputs directly by solving an optimization problem, the optimization problem determines the optimal parameters of the control law. This method can reduce the number of optimization variables significantly and increases the scalability of the controller. To ensure the parametric controller performs well a good parametric control law should be chosen. In this research a new way to determine the parametric control law is proposed. The control law is represented as a combination of expressions. These expressions are represented using a set of expression trees. Using expression trees a wide array of different non-linear functions can be represented. During an offline optimization step, optimal control inputs are determined using a regular MPC controller on a set of scenarios. Expression trees that are able to parametrize the control inputs well are then learned from these control inputs using a genetic algorithm. By using learning methods to determine the control law, the design of the parametric controller can be automated. Using this method there is no need for the control system engineer to determine a parametric control law through trial and error testing. This can speed up the design process and could allow parametric MPC to be used for systems for which input parametrizations are difficult to find. The effectiveness of the proposed approach is illustrated through a case study in which a microgrid is simulated with 2 controllable generators, renewable generation, an uncontrollable load and local energy storage. The performance of the parametric MPC controller determined in the offline optimization is compared with a handcrafted parametric MPC controller and a regular MPC controller. The estimated economic cost of operating the simulated microgrid using the different controllers is compared. Furthermore the computational complexity of the different controllers is analysed. The results show that the offline trained parametric controller achieves similar performance in both operating cost and computational complexity as the handcrafted parametric controller. This shows that the proposed offline optimization algorithm can be used to determine an effective control law for a parametric MPC controller. This will make it easier to design parametric MPC controllers for different systems in the future.

The impedance-based approach is promising to analyse the harmonic emission and stability of a converter-based system, e.g., an EV fast charging station. When extracting the accurate impedance model of an EV charger, the knowledge of the charger’s circuit and controller parameters is indispensable. However, EV chargers’ manufacturers do not disclose the design parameters of chargers since they are confidential designs. Therefore, developing an approach to estimate the charger’s design parameters is practically beneficial for establishing the charger’s impedance model and thereby analysing the harmonic and stability of the charger-grid system. Recent works on physics-informed machine learning provide a promising data-efficient approach to estimating unknown parameters of a system with a known physical model. However, to the author’s knowledge, such a technique has not been explored extensively in the power electronics domain. Thus, exploring whether PINN is also suitable for the parameter estimation of power electronic converters and how to implement this technique is worthwhile. In this thesis, to begin with, a Physics Informed Neural Net model is developed as a proof of concept to estimate the parameters of the boost converter. And subsequently applied to the AFE converter of the Fast-Charging Station to explore the scalability and generalisation of the model developed. Implementing the physics model within the neural net is still an open problem; hence the physics parameters are estimated similarly to the weights and biases during the training process using the existing optimiser. This approach requires diving deeper into the development of the neural net, and hence instead of eager execution, TensorFlow’s computational graph method is used to build the neural net and the physics model. This allows complete flexibility to build the network from scratch with added complexity. In the PINN model developed for the boost converter, the mean estimation error was 0.89%, and the parameters were estimated in 25.76 seconds. Because of the complexity of the physics model, optimizing the PINN model was challenging for the AFE converter. With a mean estimation error of 30.25%, the PINN model for the AFE converter took 361 seconds to execute. For both models, less than 150 data points collected over 25 switching cycles peak-to-peak values are used. The proposed PINN model developed in this thesis thus proves to be highly data-efficient with quick convergence. Despite the success of the developed PINN model, several drawbacks of PINN-based parameter estimation are also noticed and summarised.

The electrification of modern-day society keeps increasing and the demand for electricity grows along with it. Technologies like EVs and heat pumps are both part of the new types of demand, while PV systems and wind farms are meant to be our main new sources of generation. Contrary to traditional resources connected to the electricity network, most of these resources tend to be placed and operated in a distributed manner, increasing the demand for transport capacity within the LV and MV grid. This demand cannot always be met however, which leads to situations of congestion. This thesis seeks to reduce this congestion by means of optimising the grid topology in line with the seasonal variations in the supply and demand of electricity. This is done by means of implementing a two-stage reconfiguration algorithm. The benefit of network reconfiguration is that it is a short-term and low-cost solution which can be implemented by the DSO without relying on other external parties. In the first stage of the reconfiguration algorithm, the positions of the normally open switches within the network are optimised in order to adjust the power flow therein. These optimised positions are subsequently used in the second stage to calculate the network variables. The first stage is implemented as a MILP optimisation in Python, while the second stage consists of a Newton-Raphson calculation in the commercial software PowerFactory. This distinction is made to enhance the accuracy of the final network parameters, while still being able to optimise the switch positions in a deterministic manner. Rather than day-ahead or real-time reconfiguration, as is often considered in most literature, this thesis focuses on seasonal reconfiguration to accommodate for the manual operation of the switches present within the MV grid in the Netherlands. These manually operated switches severely reduce the frequency by which reconfiguration actions can be performed, but that does not mean that the topology of the grid cannot be enhanced. The presented reconfiguration algorithm is able to consistently reduce congestion within the analysed network, completely removing it or reducing its severity. It also outperforms two optimisation options in PowerFactory with regards to the objective function value. Those being an iterative exploration of meshes and a genetic algorithm.