Reward fairness-based optimal distributed real-time pricing to enable supply-demand matching

Real-time pricing (RTP) is the pivotal component of demand response (DR) that promotes the power utilization of users in a cost-efficient way. This paper presents a reward fairness-based distributed RTP (RFbDRTP) framework for electric users to reduce the electricity consumption cost as well as experience the fairness from participating in DR. In RFbDRTP, a behavior welfare model (BWM) is developed to exploit the reward fairness strategy that returns the cost savings as a reward to the users in proportion to their load increase or decrease in DR. The model can motivate users to react to the price signal through adjusting their electricity consumption patterns, to in turn enable optimal distributed RTP for matching supply-demand. In this paper, users are assumed to interact with each other because of pricing based on the imbalance between load demand and electricity supply. We formulate the interactions among the users into a noncooperative game and give a sufficient condition to ensure the unique equilibrium in the game. After that, we develop a distributed algorithm and give a sufficient convergence condition of the algorithm. The simulation results show that the proposed RFbDRTP is effective in motivating users to participate in DR and matching demand with supply, and the distributed algorithm can converge to the equilibrium with a significant convergence rate. (c) 2020 Elsevier B.V. All rights reserved.

Automated summary

The paper proposes a distributed real-time pricing framework based on reward fairness to motivate users to participate in demand response and achieve supply-demand matching. Through a behavior welfare model and noncooperative game, a distributed algorithm is developed to ensure convergence to equilibrium.