考虑需求响应的光伏发电系统两阶段随机调度模型  

A two-stage stochastic scheduling model for photovoltaic power generation system considering demand response

作　　者：关钦月[1] 王波[1] 朱成亮 马恒瑞 张黎明[1] 杨艳[3] GUAN Qinyue;WANG Bo;ZHU Chengliang;MA Hengrui;ZHANG Liming;YANG Yan(School of Electrical Engineering and Automation丒 Wuhan University,Wuhan 430072 .China;State Grid Jiaxing Power Supply Company,Jiaxing 314000,China;Tus-institute for Renewable Energy,Qinghai University, Xining 810016 , China)

机构地区：[1]武汉大学电气与自动化学院,湖北武汉430072 [2]国网嘉兴供电公司,浙江嘉兴314000 [3]青海大学启迪新能源学院,青海西宁810016

出　　处：《武汉大学学报（工学版）》2019年第4期331-336,343,共7页Engineering Journal of Wuhan University

基　　金：国家自然科学基金资助项目(编号:51777142;51477121);青海省自然科学基金资助项目(编号:2019-ZJ-950Q)

摘　　要：西北地区光伏发电比例的不断上升,为电力系统调度带来了巨大的困难和挑战.为此,将需求侧响应作为虚拟备用来克服传统调度备用形式单一的劣势;采用基于不确定性参数模糊化的两阶段随机模型,在充分考虑光伏发电出力的随机性与波动性的基础上充分体现了机侧备用与虚拟备用的经济特性,建立了基于预测误差场景概率的高比例光伏发电系统需求侧联合调度模型;采用细菌群体趋药性算法进行求解,以经典的6机系统和实际西北光伏发电系统作为研究对象进行了仿真分析,从而证明了所提调度模型的有效性以及可行性.The rising proportion of photovoltaic(PV) power generation in northwest China has brought great difficulties and challenges to power system scheduling. In this paper, the demand side response is used as virtual standby to overcome the disadvantage of traditional scheduling standby. A two-stage stochastic model based on the fuzziness of uncertain parameters is adopted to fully reflect the economic characteristics of both machine side reserve and virtual reserve based on the randomness and volatility of photovoltaic power generation output. The bacterial population chemotaxis algorithm is used to solve the problem;and the simulation analysis is carried out on the classic 6 machine system and the actual northwest PV system, so as to prove the effectiveness and feasibility of the proposed scheduling model.

关 键 词：预测误差场景概率 两阶段随机模型 需求侧响应 虚拟备用 细菌群体趋药性算法 

分 类 号：TM73[电气工程—电力系统及自动化]