随着大量分布式光伏接入配电网,配电网在应对网络重构和源荷储不确定性等方面面临较大挑战.因此提出一种主动配电网两阶段电压控制策略,第一阶段对主动配电网联络开关进行集中控制,以小时为调度周期并以网损最小为目标进行网络重构,建立混合整数二阶锥规划模型进行求解.第二阶段对光伏和储能系统进行实时电压控制,将实时电压控制问题转换为马尔科夫博弈过程(Markov game process,MGP)并实行多智能体建模,采用离线训练-在线运行的方法.相比于传统的两阶段均采用数学规划的方法,所提控制策略不依赖于精确的配网潮流模型,对通信要求低、求解速度更快.最后在改进的IEEE 33节点系统算例验证了所提控制策略的有效性.
Two-Stage Voltage Control Strategy Based on Multi-Agent Reinforcement Learning
With a large number of distributed photovoltaic(PV)access to the distribution network,the distribution network faces greater challenges in dealing with network reconstruction and source-load-storage uncertainty,etc.Therefore,a two-stage voltage control strategy for active distribution networks is proposed.In the first stage,the contact switch of the active distribution network is centrally controlled.The network reconstruction is carried out with the goal of minimizing the network loss in an hourly scheduling period,and a mixed integer second-order cone planning model is established to solve the problem.In the second stage,the real-time voltage control of photovoltaic and energy storage systems is carried out,and the real-time voltage control problem is converted into the Markov game process(MGP).The multi-agent model is implemented and the offline training-online operation method is adopted.Compared with the traditional two-stage mathematical planning approach,the control strategy proposed does not rely on an accurate distribution network power flow model,has low communication requirements and a faster solution speed.Finally,the ef-fectiveness of the proposed control strategy is verified by the improved IEEE 33-node calculation examples.
distributed photovoltaicdistributed energy storagedistribution network reconstructionmulti-agent reinforcement learningvoltage control
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维普
