基于条件风险价值的含风电电力系统旋转备用效益研究  

Study on the Benefit from Spinning Reserve in Wind Power Integrated Power System Based on Conditional Value at Risk

作　　者：刘兴宇[1] 温步瀛[1] 江岳文[1] Liu Xingyu Wen Buying Jiang Yuewen(College of Electrical Engineering and Automation Fuzhou University Fuzhou 350108 China)

机构地区：[1]福州大学电气工程与自动化学院,福州350108

出　　处：《电工技术学报》2017年第9期169-178,共10页Transactions of China Electrotechnical Society

基　　金：福建省自然科学基金项目资助(2013J01176)

摘　　要：由于风电出力的波动性和间歇性,大规模风电并网使得旋转备用效益和风险的矛盾更加突出。考虑系统上、下旋转备用的容量成本和能量成本,以及因购买上旋转备用而减少的失负荷损失和因购买下旋转备用而减少的弃风损失,以期望旋转备用效益最大和系统损失的条件风险价值(CVaR)最小为两个目标,建立基于条件风险价值的含风电电力系统旋转备用效益-风险模型。采用蒙特卡罗法模拟实际负荷功率和风电出力的预测偏差,并改进多目标粒子群优化算法,用于求解得到期望旋转备用效益-风险有效前沿和日前旋转备用计划,以及不同可靠性水平、置信水平对期望旋转备用效益和风险的影响。最后,通过算例验证了该模型和算法的可行性。Due to the fluctuation and intermittent of wind power,large-scale wind power grid connection makes the contradiction between the spinning reserve benefit and risk more prominent. The capacity cost and the energy cost of the system up and down spinning reserve, the reduced power loss due to the purchase of up spinning reserve and the reduced wind loss due to the purchase of the down spinning reserve are considered. To establish the spinning reserve benefit-risk model of wind power integrated system based on conditional value at risk（ CVaR） with the goal of maximum expected spinning reserve benefit and minimum CVaR value of system loss. Monte Carlo method is used to simulate the actual load power and wind power prediction error, and the multi-objective particle swarm optimization algorithm is improved to find the efficient frontier of expected spinning reserve benefit-risk and the day-ahead spinning reserve plan,and the influence of different reliability level and confidence level on expected spinning reserve benefit and risk. Finally,the example shows the feasibility of the model and the algorithm.

关 键 词：旋转备用效益 备用容量 风电并网 条件风险价值 多目标粒子群优化算法 

分 类 号：TM614[电气工程—电力系统及自动化]