Optimizing Residential Energy Costs with PEV-based Energy Storage and Particle Swarm Optimization

Abstract

This research addresses the critical challenge of optimizing electrical energy usage amidst the increasing integration of variable renewable energy sources and the widespread adoption of electric vehicles (EVs). The core objective is to alleviate the energy cost burden for residential consumers by leveraging the capacity of Plug-in Electric Vehicles (PEVs) as an auxiliary energy source. Through innovative utilization of PEVs for both charging and discharging actions based on dynamic electricity pricing and the Energy Price Tag (EPT)—the cost associated with stored battery energy—this study introduces a novel approach aimed at cost reduction and efficiency enhancement. The methodology employs a Particle Swarm Optimization (PSO) algorithm to determine the optimal real-time pricing for PEV charging and discharging activities, comparing these costs with the prevailing half-hourly grid prices to judiciously manage energy supply and demand. This strategy not only proposes a way to decrease residential energy expenses but also aims to lessen the overall strain on the electrical grid, thereby contributing to the reduction of reliance on traditional power generation methods. The implementation of the PSO-based charging strategy in MATLAB demonstrates its potential to shift energy consumption from peak to off-peak hours, further alleviating grid load and promoting a more sustainable energy consumption model. Simulation results affirm that the proposed PSO charging strategy effectively reduces monthly energy costs for residential EV owners, showcasing its viability as a scalable solution for energy cost management and grid stability enhancement in the era of renewable energy and electric mobility