Please use this identifier to cite or link to this item: http://dx.doi.org/10.18419/opus-11382
|Title:||The integration of electric vehicles in the smart grid|
|Abstract:||In recent years, the rising trend of Electric Vehicles (EVs) as a clean mode of transportation is regarded crucial for a sustainable future. Impact of heavy traffic flow in the transportation system will have significant implications on distribution network load due to widely varying EV penetration rate and market price incentives. Naturally, it becomes imperative to study the joint stochastic operational planning of Transportation Network (TN) and Power Distribution Network (PDN) with targeted service radius. EV routing protocols have stochastic nature owing to several environmental factors and traffic elements which may alter the path chosen by EV users. This ultimately may increase or reduce the energy consumption of EVs limited-resource battery, which in turn has a cascading effect on the power system network since EVs might need to recharge either frequently or sparsely. In this research project, we aim to find the inter-relation between TN and PDN in a complex bi-level optimization problem where operational costs and social welfare is modeled. We take an interdisciplinary approach by establishing a stochastic multi-agent simulation-based platform with the objective of minimizing the social welfare cost of the interdependent TN and PDN systems. The conjunction between overlaid networks has been extensively described. The distributed vehicle load on the TN based on random EV mobility behavior poses a challenge to estimate the charging load on the PDN. The spatial and temporal traffic distribution of TN influences the loads connected to PDN through charging stations. We assess the impact of large-scale EV integration into the PDN through mutual coupling of both the networks. Our methodology aims to solve the coupled optimization problems, i.e., optimal EV routing using traffic assignment problem and optimal power flow (OPF) using branch flow model. The route choice of EV users is determined by Dijkstra’s shortest path algorithm which minimizes the travel cost. Utilizing Multi-Agent Systems (MAS), we generate semi-realistic samples of EV mobility trip data to eventually develop an Optimal TransportationPower Network Flow (OTPNF) model. We employ a Dynamic User Equilibrium model to get the optimal traffic distribution in TN. Through the joint optimization of both networks taking into consideration network constraints, we try to achieve cost minimal system optimal solution. The IEEE 30 test system is adapted to Low Voltage (LV) network to examine the EV charging impact on grid. Simulation results show mutual economic benefits by maximizing social welfare of both the networks. We optimized total power generation by 10.86% and found an optimal solution for both networks which reduced overall system cost by 35%. We also reduced transmission power losses by 23.5% using the same loads and generator costs with our Genetic Algorithm.|
|Appears in Collections:||05 Fakultät Informatik, Elektrotechnik und Informationstechnik|
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