Joint Optimization Control of Energy Storage System Management and Demand Response

Authors

Xueying Gao, School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China;
Tang Hao, School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China;
Gangzhong Miao, School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China;
Zhaowu Ping, School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China;

Abstract

Abstract: The joint optimization problem of energy management and demand response were studied in order to reduce the long-run cost of electricity users equipped with energy storage unit and smart applications, and to increase their benefits meanwhile. The goals were achieved by controlling both the energy storage unit (charging, discharging, or idle) and the load service (access or delay). Based on the random nature of solar photovoltaic, load demand electricity and electricity price, the joint optimization problem was modeled as infinite-horizon Markov decision process model, and Q-learning algorithm was proposed to find the optimal solution. Simulation results show that the joint control increases the user’s long-term income.

Recommended Citation

Gao, Xueying; Hao, Tang; Miao, Gangzhong; and Ping, Zhaowu (2020) "Joint Optimization Control of Energy Storage System Management and Demand Response," Journal of System Simulation: Vol. 28: Iss. 5, Article 22.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss5/22

First Page

1165

Revised Date

2015-03-02

DOI Link

https://doi.org/

Last Page

1172

CLC

TP202

Recommended Citation

Gao Xueying, Tang Hao, Miao Gangzhong, Ping Zhaowu. Joint Optimization Control of Energy Storage System Management and Demand Response[J]. Journal of System Simulation, 2016, 28(5): 1165-1172.