储能系统能量调度与需求响应联合优化控制

系统仿真学报 ›› 2016, Vol. 28 ›› Issue (5): 1165-1172.

储能系统能量调度与需求响应联合优化控制

高雪莹, 唐昊, 苗刚中, 平兆武

合肥工业大学电气与自动化工程学院,安徽合肥 230009

收稿日期:2014-12-22 修回日期:2015-03-02 发布日期:2020-07-03
作者简介:高雪莹(1990-),女,安徽颍上,硕士生,研究方向为强化学习在智能电网中的应用。
基金资助:
国家自然科学基金(61174186,61374158,71231004),高等学校博士学科点专项科研基金(20130111110007),教育部新世纪优秀人才计划项目(NCET-11-0626)

Joint Optimization Control of Energy Storage System Management and Demand Response

Gao Xueying, Tang Hao, Miao Gangzhong, Ping Zhaowu

School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China

Received:2014-12-22 Revised:2015-03-02 Published:2020-07-03

摘要/Abstract

摘要： 为了增加含有储能单元及智能用电设备的用户的长期收益,研究了储能系统能量调度及需求响应联合优化问题,对储能单元动作(充电,放电,闲置)及可延时负荷动作(接入,不接入)进行优化控制。建立包括用电经济性及满意度的优化目标函数,根据太阳能光伏发电、负载用电需求以及电网电价的随机特性,将该联合优化问题建模为无穷时段马尔可夫决策过程模型,进而引入Q学习算法对优化问题进行求解。仿真结果表明联合优化控制比单独对电池动作控制或者需求响应控制使用户获得了更高的长期收益。

关键词: 智能电网, 储能管理, 需求响应, 马尔可夫决策过程, 强化学习

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.

Key words: smart grid, management of energy storage, demand response, MDP, reinforcement learning

中图分类号:

TP202

高雪莹, 唐昊, 苗刚中, 平兆武. 储能系统能量调度与需求响应联合优化控制[J]. 系统仿真学报, 2016, 28(5): 1165-1172.

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.

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