A real-time pricing scheme considering load uncertainty and price competition in smart grid market

Yeming Dai; Yan Gao; Hongwei Gao; Hongbo Zhu; Lu Li

doi:10.3934/jimo.2018178

Article Contents

2020, Volume 16, Issue 2: 777-793. Doi: 10.3934/jimo.2018178

This issue Previous Article Multi-objective robust cross-market mixed portfolio optimization under hierarchical risk integration Next Article An executive model for network-level pavement maintenance and rehabilitation planning based on linear integer programming

A real-time pricing scheme considering load uncertainty and price competition in smart grid market

1.
School of Business, Qingdao University, Qingdao 266071, China
2.
School of Mathematics and Statistics, Qingdao University, Qingdao 266071, China
3.
School of Management, University of Shanghai for Science and Technology, Shanghai 200093, China

Received: October 2017

Revised: July 2018

Early access: December 2018

Published: February 2020

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Abstract

As a powerful tool of Demand Response (DR) techniques in smart grid market, Real-time Pricing (RTP) may optimize the electricity consumption pattern of users and improve the efficiency of electricity market. In this paper, a multi-leader-follower Stackelberg Game (SG) based on RTP is established to model the strategic interaction behavior between multiple electricity retailers and multiple users while simultaneously considering the power load uncertainty of users and the price competition among electricity retailers. In the game model, electricity retailers aim to seek their revenue maximization while the optimal power consumption competition among the users is taken into account. Lagrange multiplier method is utilized to solve the Nash Equilibriums (NE) of two non-cooperative games, and the closed-form optimal solution is obtained, then the Stackelberg Equilibrium (SE) consisting of the optimal real-time prices of electricity retailers and the power consumption of users is given. Finally, the numerical analysis results verify that the proposed scheme can reduce the real-time electricity price and increase the users' satisfaction under feasible constraint, which shows the effectiveness and better performance of proposed RTP scheme.

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Mathematics Subject Classification: Primary: 91A10; Secondary: 91A15.

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Figure 1. RTP data from ComEd

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Figure 2. Optimal real-time pricing of retailers

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Figure 3. Optimal real-time pricing of retailers when $ \sigma = 0.1,0.2,0.3 $

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Figure 4. Expected revenue of electricity retailers

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Figure 5. Aggregate power load of user 1 and user 2

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Figure 6. Expected power payoff of user 1 and user 2

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