摘要
高比例间歇性分布式电源与电动汽车接入配电网时,容易导致功率与电压频繁、快速、剧烈波动。文中结合数据驱动与物理建模方法,提出了一种配电网双时间尺度有功无功协调优化策略。针对短时间尺度(分钟级或秒级)的功率波动,以静止无功补偿器、分布式电源无功功率为决策变量,以网损最小为目标函数,计及物理约束,针对平衡与不平衡配电网分别构建了二阶锥与二次规划模型。针对长时间尺度(小时级)的优化,以有载调压变压器分接头变比、可投切电容电抗器挡位、储能系统充放电功率为动作,以网损为代价,计及节点电压越限惩罚,构建了马尔可夫决策过程。为克服连续-离散动作空间维数灾,采用一种基于松弛-预报-校正的深度确定性策略梯度强化学习求解算法。通过IEEE 33节点与IEEE 123节点配电系统验证了所提方法的有效性。
When a high proportion of intermittent distributed generators(DGs)and electric vehicles are connected to the distribution network,it is easy to cause frequent,fast and dramatic fluctuations in power and voltage.This paper combines both the data-driven and physical modeling approaches to propose a coordinated optimal strategy for dual-timescale active and reactive power in distribution networks.For the short-timescale(minute or second)power fluctuations,a second-order cone programming(SOCP)model and a quadratic programming model are constructed for balanced and unbalanced distribution networks,respectively,with static var compensator(SVC)and reactive power of DG as decision variables and network loss minimization as the objective function,taking into account physical constraints.For the long-timescale(hour scale)optimization,the Markov decision process(MDP)is constructed with the tap ratio of on-load transformer changers(OLTCs),the tap position of switchable capacitors reactors(SCRs),and the charging/discharging power of energy storage systems(ESSs)as the actions,network loss as the cost,taking into account the crossing penalty of bus voltage.To overcome dimension curses in continuous-discrete action space,this paper uses a relaxation-prediction-correction based deep deterministic policy gradient(DDPG)reinforcement learning algorithm.The effectiveness of the proposed method is verified by IEEE 33-bus and IEEE 123-bus distribution systems.
作者
张剑
崔明建
姚潇毅
何怡刚
ZHANG Jian;CUI Mingjian;YAO Xiaoyi;HE Yigang(School of Electrical and Automation Engineering,Hefei University of Technology,Hefei 230009,China;School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China;Bengbu Electric Power Supply Company of State Grid Anhui Electric Power Company,Bengbu 233000,China;School of Electrical and Automation,Wuhan University,Wuhan 430072,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2023年第20期64-71,共8页
Automation of Electric Power Systems
基金
国家自然科学基金资助项目(52207130)。
关键词
主动配电网
分布式电源
深度强化学习
二阶锥规划
二次规划
active distribution network
distributed generator(DG)
deep reinforcement learning
second-order cone programming(SOCP)
quadratic programming(QP)
