分布式能源在电力系统中的渗透率不断提升,使得配电网系统呈现出更大的复杂性和不确定性,这将对电力网络的可靠性产生影响.为确定配电网系统中可再生能源发电机组的最优安装位置和容量,文章结合随机模糊期望值算子和马尔科夫蒙特卡洛法(Markov Chain Monte Carlo,MCMC),提出了一种可靠性评估框架.该模型建立了风电和光伏出力的多状态概率密度函数,采用随机模糊期望值算子模拟配电网功率损耗和电压稳定性的不确定性.在考虑配电系统拓扑结构的情况下,利用MCMC模拟配电网系统中所有非源元件的随机性,由指数分布生成配电网组件故障事件及恢复时间.最后,在IEEE-33节点标准配电网上,对系统平均停电次数、系统平均停电持续时间、电量不足期望值 3 种可靠性指数进行评价,实验结果证明了所提出方法的有效性.
Reliability assessment of distribution network considering uncertainty of distributed renewable energy
With the increasing penetration of distributed energy in the power system and its output uncertainty,the distribution system presents greater complexity and uncertainty,which will have an impact on the reliability of the power network.In order to determine the optimal installation location and capacity size of renewable units in the distribution network system,this paper proposes a reliability assessment framework by combining the stochastic fuzzy expected value operator and Markov Monte Carlo method.The model first establishes the multi-state probability density functions of wind and PV outputs,and subsequently employs the stochastic fuzzy expected value operator to simulate the uncertainties of power loss and voltage stability in the distribution network.The stochastic nature of all non-source components in the distribution system is modeled using the Markov Monte Carlo method to generate distribution network component failure events and recovery times from an exponential distribution,considering the topology of the distribution system.Three reliability indices,namely,average system outage number,average system outage duration,and power shortage expectation,are evaluated on the IEEE-33 node standard distribution network,and the experimental results demonstrate the effectiveness of the proposed method.
万方数据
维普
