This thesis investigates the optimal sizing and siting of energy storage systems (ESS) within a distribution grid, focusing on the provision of ancillary services and the intelligent power control of flexible loads, including electric vehicles (EVs), heat pumps (HPs), and renewable energy sources (RES). The study aims to investigate the use of ESS in combination with EVs to participate in the automatic frequency restoration reserves (aFRR) market. The effect of different seasons and the sensitivity of electricity market prices to the optimal sizing are analysed.

The first level involves day-ahead scheduling of flexible loads (EVs, HPs) and ESS allocation. This stage utilises mixed-integer programming (MIP) to achieve optimal ESS sizing and siting based on day-ahead electricity prices, predicted data on EV arrivals and departures, and building occupancy. Additionally, the aFRR prices are known a day ahead, and the most lucrative time intervals for revenue maximisation are selected. The objective function minimises the total cost, which consists of penalties related to customer satisfaction, grid import/export cost, revenues from aFRR provision, and BESS CAPEX and O\&M cost. Two different MIP formulations were used to solve the optimisation in Gurobi using Python programming language.

The second level involves real-time control, where ESS operation is re-optimized using real-time data. This stage adjusts for forecast errors through rolling horizon optimisation. Within this level, the scheduled aFRR reserves are deployed.

The findings reveal that the optimal integration of ESS, considering additional revenue from aFRR provision, is achieved by using a centralised ESS closer to the substation. In contrast, optimal ESS integration for energy arbitrage alone involves sharing capacity between nodes but requires a lower CAPEX cost of BESS to be economical. The results indicate that optimal BESS allocation can differ based on specific conditions, such as imbalance price distribution, grid limits, peak load and EV flexibility. To maximise revenue and efficiency, the study suggests placing the BESS at nodes with the lowest resistance. Winter and summer results are similar in the optimal placement and sizing of BESS, although the operation of BESS and EVs is different. It was observed that during the winter, V2G utilisation was higher. The main difference for the seasons is in the case of only energy arbitrage. The cost savings from ancillary service provision of either BESS, EVs or both show greater potential for summer. Both of the proposed optimisations can find a solution, although the non-convex MIQCP formulation did not guarantee a global optimum for all cases. However, the iterative method proved to be more robust but had a longer simulation time.

This master thesis focuses on developing a Physics-Informed Neural Network (PINN) to predict the degradation of lithium-ion batteries, aiming for a balance between computational efficiency, data requirements, and prediction accuracy. A thorough comparison between physical models and data-driven models was conducted, assessing their theoretical foundations, computational demands, and performance in predicting battery behavior. This evaluation led to the selection of the most suitable model for integration into the PINN framework, demonstrating the significant potential of PINNs as performance models for lithium-ion batteries.
The implementation revealed that while integrating physical laws into the neural network reduces data requirements and enhances interpretability, achieving high accuracy in degradation predictions remains challenging. The study highlights the strengths and limitations of PINNs for battery degradation prediction, showing they can effectively bridge the gap between purely data-driven approaches and traditional physical models. However, further advancements are necessary to refine these models and enhance their predictive capabilities. This thesis emphasizes the need for ongoing research and development to fully utilize PINNs in battery lifecycle management, pointing to their potential for more efficient and accurate degradation predictions.

This study explores the integration of diverse flexible assets, including electric vehicles (EVs), stationary battery energy storage (BESS), heat-pumps and photovoltaics, into a localised smart grid to enhance aFRR ancillary service provision and lower overall grid costs. The performance of the grid was evaluated by simulating and analysing various loads, including residential and commercial building heating, EV charging profiles, as well as photovoltaic generation and battery energy storage. This analysis covered six cases across different seasons and years, with each case assessed over a oneday period. Within the grid, three different nodes are analysed; a Residential, Commercial and Mixed node, all with different power consumption and behaviour profiles differentiating between the seasons. The findings demonstrate the potential of EVs to deliver a significant amount of aFRR ancillary power, while still satisfying minimum state of charge requirements set by vehicle owners. Utilising both BESS and EVs in aFRR results in substantial grid cost savings and, in some years, potential profits. However, participation in
aFRR services results in a higher average cost for imported energy (without taking into account aFRR revenue). This rise is attributed to maintaining sufficient capacity for aFRR provision, where the revenue per unit of energy is generally higher. The combination of vehicle-to-grid and BESS-to-grid without aFRR provision showed the lowest cost of imported energy, since here only day-ahead prices are used to optimise load scheduling. In addition, operating a localised grid with regards to imbalance prices can result in 80-160% portfolio savings for balancing responsible parties (BRPs), due to the concept of passive balancing, where the combination of EV and BESS showed to be most promising. The research contributes to the field by providing an insight and potential framework to leverage flexible assets in both commercial and residential smart grid environments, showcasing a significant step toward sustainable energy management, taking into account the perspective of multiple market parties.

The EU strives to lower greenhouse gas emissions. To reach this goal, many energy intensive processes in the residential sector such as heating and transportation will be electrified using heat pumps and electric vehicles (EVs) respectively. Simultaneously, a transition of electricity generation to sustainable sources will take place, necessitating an increased adoption of rooftop photovoltaic (PV) systems.

The adoption of PV systems, heat pumps and EVs, also known as low carbon technologies (LCTs), can increase three-phase unbalance in low voltage (LV) distribution networks as many of these components will be connected to a single phase of the three-phase network. Threephase unbalance is undesirable in a three-phase system, as it causes among others, energy losses and a suboptimal use of network capacity.

The aim of this thesis is to evaluate the impact of different combinations and penetration levels of LCTs on three-phase unbalance in different real LV distribution networks through simulations and how unbalance is affected by LCT location, season and LCT control schemes.

Simulations were performed on six different grids, varying in level of urbanization and loading, with increasing levels of LCT penetration (0%, 50%, 80%, 100%). In these simulations, LCTs were integrated in varying combinations (PV & EV, PV & HP and PV & EV & HP). For every simulation, the maximum and mean voltage unbalance factor (VUF) was determined. Seasonal effects and the effect of an LCT control scheme were also evaluated.

Simulations showed that the voltage unbalance factor exceeded the legal limit of 3% for two of the six grids for high levels of LCT penetration when all LCTs are integrated. Combining all three LCTs resulted in the highest unbalance levels. Varying the locations of the LCTs resulted in significant differences in unbalance levels. Comparing a winter week with a summer week, the overall unbalance is similar, however, the contribution of the PV systems to the unbalance is increased, while the contribution of EV chargers and heat pumps is decreased.
The effect of the LCT control scheme was limited.

As the integration of LCTs will increase considerably in the near future, three-phase unbalance levels exceeding the limit of 3% will occur more often. To prevent unbalance levels from structurally exceeding the legal limit of 3%, more effective control schemes should be designed and implemented.