Game-Theoretic Demand-Side Management for Fair Cost Distribution in Community Energy Storage and Electric Vehicle Charging

Abstract

Advancements in rechargeable batteries and environmental awareness campaigns have highlighted the importance of electric vehicles (EVs) in recent times. The influx of EVs has posed challenges in almost all areas of technology, including demand-side management (DSM). Extra generating units are switched ON to meet the resultant higher electricity demand, thus reducing the sustainability of the system. To overcome this challenge, effective DSM techniques integrating renewable energy sources are proposed to efficiently utilize the existing generating capacity. The primary goal is to fairly distribute available resources among smart homes and EV owners using the Shapley value and tau value. In this work, two scenarios are examined. First, a community energy storage (CES) approach is adopted to maximize CES revenue, reduce the grid peak-to-average ratio (PAR), and minimize electricity costs. Second, a coordinated group of EVs is utilized to minimize the impact of charging loads during peak hours while concurrently reducing EV charging costs. Simulation results show a reduction in the grid PAR from 2.468 to 1.799, or 27.1%, together with an average reduction of approximately 3% in the electricity cost of participating smart homes. In the EV scenario, optimal scheduling reduces total charging expenditure by 24.8% and lowers the system peak by 2.65% relative to uncoordinated charging of the same fleet, with the total cost distributed among the vehicles by the Shapley value. Benchmarking against a proportional-to-demand rule shows that the tau-value allocation coincides with proportional sharing, whereas the Shapley allocation shifts 4.2% of the allocation away from the household contributing most to the system peak. The framework provides a fair and individually rational cost allocation layer for community-scale peer-to-peer energy markets.