GTES: An Optimized Game-Theoretic Demand-Side Management Scheme for Smart Grid

TLDR

Demand‑side management in smart grids focuses on optimizing energy consumption, yet prior work typically optimizes from either the user’s or the power company’s perspective. This study aims to reduce the peak‑to‑average power ratio by jointly optimizing users’ energy schedules through consideration of the interaction between consumers and the power company. The authors introduce a game‑theoretic energy schedule (GTES) that uses a consumption‑dependent price model, a value‑minus‑cost objective, and a round‑robin, two‑step centralized game, and evaluate its performance with computer simulations.

Abstract

Demand-side management in smart grids has emerged as a hot topic for optimizing energy consumption. In conventional research works, energy consumption is optimized from the perspective of either the users or the power company. In this paper, we investigate how energy consumption may be optimized by taking into consideration the interaction between both parties. We propose a new energy price model as a function of total energy consumption. Also, we propose a new objective function, which optimizes the difference between the value and cost of energy. The power supplier pulls consumers in a round-robin fashion and provides them with energy price parameter and current consumption summary vector. Each user then optimizes his own schedule and reports it to the supplier, which, in turn, updates its energy price parameter before pulling the next consumers. This interaction between the power company and its consumers is modeled through a two-step centralized game, based on which we propose our game-theoretic energy schedule (GTES) method. The objective of our GTES method is to reduce the peak-to-average power ratio by optimizing the users' energy schedules. The performance of the GTES approach is evaluated through computer-based simulations.