Optimal placement of large-scale electric vehicle and distributed generation in power system to enhance power quality

Abstract

The widespread adoption of electric vehicles (EVs) and renewable distributed generators (REDGs), including photovoltaic (PV) systems and wind turbine generators, has garnered significant attention in global power systems. These energy sources are recognized as environmentally friendly. However, substantial integration of EVs and REDGs can lead to voltage fluctuations that exceed acceptable limits and result in reverse power flows at interconnection points within the power grid. Such excessive voltage variations can adversely affect consumer electric loads, while reverse power flows can disrupt the overall power transmission system. Consequently, the extensive integration of EVs and REDGs poses challenges for both consumers and power utilities. To address these issues, previous studies have suggested reactive power control strategies, such as employing power electronic converters linked to distributed generations (DGs) to mitigate voltage deviations. This research proposes a method for the optimal integration of EVs through bidirectional charging and REDGs within power systems, aiming to effectively manage voltage, active power, and reactive power flows at interconnection points. Additionally, it involves identifying suitable locations and sizes for electric vehicle charging stations and calculating associated system costs. The control objectives are framed as an optimization problem, which is addressed using a hybrid genetic algorithm combined with an improved particle swarm optimization algorithm (HGAIPSO). The research was structured into three distinct sections, with the efficacy of the proposed methodology illustrated through numerical simulations conducted in MATLAB. The initial section focused on identifying the optimal location for the charging station and the appropriate number of electric vehicles (EVs) per charging station within the power system, utilizing the GA, PSO, IPSO, and HGAIPSO algorithms. This analysis was performed on an IEEE-30 bus system. The simulation outcomes from this initial case revealed that the strategic placement and coordination of EV charging stations, as facilitated by the HGAIPSO algorithm, led to a reduction in power losses, an enhancement of voltage profiles, and an overall improvement in power quality. Specifically, the results indicated a decrease in real power loss of 40.70%, 36.24%, and 42.94% for types 1, 2, and 3 of EV allocation, respectively, while the voltage profile at the buses improved to approximately 1.01 pu. The second section involved the allocation of EVs to function as loads in a grid-to-vehicle (G2V) system and as generators in a vehicle-to-grid (V2G) system, in conjunction with renewable energy distributed generators (REDGs). This was tested on a more advanced distribution network, specifically the IEEE-118 bus test system, employing the HGAIPSO algorithm. The simulation results indicated that the proposed HGAIPSO method significantly improves power quality and reduces the overall installation costs when compared to the baseline scenario. The final section provided a comparative analysis regarding computation time and iterations between the proposed HGAIPSO and various other optimization techniques, including GA, PSO, and IPSO. This analysis was conducted on the IEEE-118 bus system with the allocation of V2G, G2V, and REDGs. The simulation results demonstrated that the proposed HGAIPSO method is faster and more effective in terms of computation time for complex networks, achieving optimal solutions more efficiently.