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基于加权半方差的含风电电力系统旋转备用效益研究
引用本文:刘兴宇,温步瀛,江岳文.基于加权半方差的含风电电力系统旋转备用效益研究[J].电力自动化设备,2017,37(9).
作者姓名:刘兴宇  温步瀛  江岳文
作者单位:福州大学 电气工程与自动化学院,福建 福州 350108,福州大学 电气工程与自动化学院,福建 福州 350108,福州大学 电气工程与自动化学院,福建 福州 350108
基金项目:福建省自然科学基金资助项目(2013J01176)
摘    要:在考虑系统旋转备用的容量成本和能量成本、因购买旋转备用而减少的停电损失,以及旋转备用效益的离散程度的基础上,引入风险偏好系数,并采用证券投资组合中的加权半方差度量风险。以期望旋转备用效益最大和风险最小为优化目标,建立基于加权半方差的含风电电力系统旋转备用效益-风险模型。采用蒙特卡洛模拟法模拟实际负荷功率偏差和风电出力预测偏差,并通过多目标纵横交叉算法求得期望旋转备用效益-风险有效前沿和日前旋转备用计划,以及风险偏好系数、可靠性水平、预测偏差、失负荷损失价格、旋转备用价格对期望旋转备用效益和风险的影响。算例验证了所提模型的合理性。

关 键 词:电力系统  旋转备用效益  备用容量  风险偏好系数  加权半方差  风电
收稿时间:2016/9/1 0:00:00
修稿时间:2017/7/20 0:00:00

Research of spinning reserve benefit based on weighted semi-variance for power system with wind power
LIU Xingyu,WEN Buying and JIANG Yuewen.Research of spinning reserve benefit based on weighted semi-variance for power system with wind power[J].Electric Power Automation Equipment,2017,37(9).
Authors:LIU Xingyu  WEN Buying and JIANG Yuewen
Affiliation:College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China,College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China and College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
Abstract:With the consideration of the capacity cost and energy cost of system spinning reserve, the reduced outage cost due to the purchase of spinning reserve and the dispersion degree of spinning reserve benefit, the risk bias coefficient is introduced and the weighted semi-variance of security portfolio is adopted to measure the risk. A spinning reserve benefit-risk model with the maximum expected spinning reserve benefit and minimum risk as its optimization objectives is built based on the weighted semi-variance for the power system with wind power. The Monte Carlo method is used to simulate the actual load-power deviation and wind power prediction error, and the multi-objective crisscross optimization algorithm is used to obtain the effective frontier of expected spinning reserve benefit-risk and the day-ahead spinning reserve plan, as well as the influences of risk bias coefficient, reliability level, prediction error, load-loss price and spinning reserve price on the expected spinning reserve benefit and risk. The rationality of the proposed model is verified by the example.
Keywords:electric power systems  spinning reserve benefit  reserve capacity  risk bias coefficient  weighted semi-variance  wind power
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