Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid

TLDR

Demand‑side management programs mainly address utility‑customer interactions, assuming time‑varying tariffs can differentiate energy usage. The paper proposes an autonomous, distributed demand‑side energy management system leveraging two‑way digital communication in future smart grids. The system models users as players in a game‑theoretic energy‑consumption scheduling game, where each user applies a best‑response strategy to the current total load and tariffs. The Nash equilibrium of the game yields global cost minimization, preserves user privacy, incentivizes participation, and simulation shows reductions in peak‑to‑average ratio, total energy costs, and individual daily charges.

Abstract

Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges.