Browsing by Author "Sadhu, Kaushik"

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    The integration of electric vehicles in the smart grid
    (2021) Sadhu, Kaushik
    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.
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    Optimal joint operation of coupled transportation and power distribution urban networks
    (2022) Sadhu, Kaushik; Haghshenas, Kawsar; Rouhani, Mohammadhadi; Aiello, Marco
    The number of Electric Vehicles (EVs) and consequently their penetration level into urban society is increasing which has imperatively reinforced the need for a joint stochastic operational planning of Transportation Network (TN) and Power Distribution Network (PDN). This paper solves a stochastic multi-agent simulation-based model with the objective of minimizing the total cost of interdependent TN and PDN systems. Capturing the temporally dynamic inter-dependencies between the coupled networks, an equilibrium solution results in optimized system cost. In addition, the impact of large-scale EV integration into the PDN is assessed through the mutual coupling of both networks by solving the optimization problems, i.e., optimal EV routing using traffic assignment problem and optimal power flow using branch flow model. Previous works in the area of joint operation of TN and PDN networks fall short in considering the time-varying and dynamic nature of all effective parameters in the coupled TN and PDN system. In this paper, a Dynamic User Equilibrium (DUE) network model is proposed to capture the optimal traffic distribution in TN as well as optimal power flow in PDN. A modified IEEE 30 bus system is adapted to a low voltage power network to examine the EV charging impact on the power grid. Our case study demonstrates the enhanced operation of the joint networks incorporating heterogeneous EV characteristics such as battery State of Charge (SoC), charging requests as well as PDN network’s marginal prices. The results of our simulations show how solving our defined coupled optimization problem reduces the total cost of the defined case study by 36% compared to the baseline scenario. The results also show a 45% improvement on the maximum EV penetration level with only minimal voltage deviation (less than 0.3%).