Integration of Electric Vehicles into Power Distribution Systems – The Norwegian Case Study; Using High-Performance Multi-Period AC Optimal Power Flow Solver

With the massive increase in the integration of Electric Vehicles (EVs), their share of energy consumption has shifted from fossil fuels to electricity. This shift in turn heavily relies on the safe, reliable and cost-efficient operation of electricity distribution networks. Traditionally, grids were built to transfer electricity from the generation side to the consumer at the endpoint, and neither designed to be smartly controlled, nor to integrate battery storage, photovoltaic solar panels (PV), wind energy and shiftable loads on the consumer side. The shift toward an active controllable distribution grid with active end-users requires new methods and tools for planning and operation. With this perspective, the aim of this PhD thesis is to develop a tool to integrate and optimise a large number of Stationary Energy Storage Systems (SESS) and EVs in the distribution grid. The thesis has provided several key contributions: C 1. Develop a high-performance and memory-efficient multi-period AC optimal power flow solver called “BATTPOWER”, integrating a large number of intertemporal constraints. BATTPOWER takes advantage of several methods and sparse structures to speed-up the solution proposal significantly, as it: 1) incorporates the analytical first and second partial derivatives of constraints and objective functions with respect to all optimisation variables, 2) explores the sparsity structures of partial derivatives, 3) reorders the Jacobian matrix of the KKT structure, and 4) uses a sparse Schur-Complement algorithm for the multi-period structure of the KKT matrix. C 2. Convert a large-scale local distribution grid into a standard research format, and simulate the integration of a large fleet of EVs. The simulation results reveal that the maximum share of EVs it could accommodate is around 20% (220 EVs out of 1113 estimated passenger cars) through an uncoordinated (dumb) charging strategy with no grid reinforcement. C 3. Propose a centralised charging scheduling strategy using multi-period AC optimal power flow and integrating operational grid constraints which it solves the large-scale local distribution network (974 buses, 1023 lines, 2 generators and 1113 EVs) within 790 seconds. C 4. Simulation results using the proposed centralised charging scheduling strategy reveal that the local grid could accommodate 36% (400 EVs out of 1113) without considering grid operational constraints. However, it manages to schedule all EVs (100%) though consideration of operational grid constraints in the optimisation problem. C 5. Simulation results of the combined EV and PV cases signify the increasing growth of EV and PV penetration simultaneously with similar growing rates can lead to: 1) a more stable voltage profile, 2) lower line/transformer overloading problems, and 3) higher social-welfare and, lower and cost-efficient operation. Moreover, from the game theoretical perspective, an Agent-Based Modelling (ABM) simulation is conducted to investigate the increasing impact of a largescale EV fleet on the power system. In this mode the agents’ incentives are: 1) to maximise state of charge (SOC), 2) to maximise SOC and minimise electricity price, and 3) no incentive (when they arrive, they charge). The EV agents are modelled through different charging strategies. Their different behaviour indicates how these incentives might affect their charging at different times and locations. This could be a useful tool for policymakers and researchers alike who like to estimate the variability of future demand. In addition, a low-voltage area hosting 54 end-users is analysed by using real power measurements obtained from smart meters in load flow analysis. The possibility of a fast charger in the low voltage grid has been assessed, and an optimal location for fast charging is proposed. The optimisation model aims to minimize the grid loss along with voltage fluctuations. The results show that the EV-hosting capacity of the grid is sufficient for a majority of the end-users, but the weakest power cable in the system will be overloaded at around 20% EV penetration level. The network tolerated an EV penetration of 50% with regard to the voltage levels at all end-users. Finally, the scenarios of EV and home batteries are compared with the objective of investigating the economic potential of utilising PV and storage at an end-user level. This is performed with a dynamic programming algorithm to minimise the electricity costs under four different grid tariff structures. When utilising an EV battery together with rooftop PV, the cost is reduced by a maximum of 19.2%, whereas a home battery installation together with PV reduces the cost by a maximum of 14.4%. Overall, this PhD thesis primarily provides a mathematical backbone for an efficient solution of Multi-Period AC Optimal Power Flow (MPOPF), which could be taken as a stage for further development and future computationally efficient control systems. In addition, it looks into a path toward the sustainable operation and planning of power systems, more specifically focused on the electrification of the transport sector in combination with PV