Game theoretic approach for energy scheduling in demand side management for smart grid applications

Abstract

The emergence of Game theory (GT) enabled with demand side management (DSM) has the applications in the field of smart grid applications. A mathematical method called game theory uses desirable rules to identify the circumstances under which all actors can win. Various agents can be used to optimize their gains. In terms of customer utility, demand response algorithms are categorized as agents in terms of customer utility. A centralized demand response (DR) scheduling algorithm that meets the varied energy consumption needs of a community can be difficult owing to the differences among residents. A non-cooperative DR-GT model is proposed to improve individual benefits in the energy consumption scheduling algorithm. The appliance information comprises different power levels to categorize the residents, which reduces the scheduling traffic between the residents and aggregator. There is a 23% reduction in the peak-to average ratio and increase in renewable energy usage by 13–25%, as better scheduling based on the flexibility of consumer loads and pricing schemes. Smart grid efficiency is improved by 23–30%, owing to reduced energy losses, fewer system imbalances, and lower wears on grid infrastructure.