考虑最优风电投标量的高载能用户电价决策模型  

Price Decision Model of High-Load Users Considering Optimal Wind Bidding Strategy

作　　者：胡殿刚[1] 齐晓琳 李韶瑜[1] 王春艳 艾欣 

机构地区：[1]国网甘肃省电力公司,甘肃省兰州市730050 [2]新能源电力系统国家重点实验室(华北电力大学),北京市昌平区102206 [3]河南平芝高压开关有限公司,河南省平顶山市467013

出　　处：《电网技术》2016年第8期2265-2272,共8页Power System Technology

基　　金："国网甘肃省电力公司科技项目(52272214003Y)"项目的资助

摘　　要：由于风电的随机性和波动性等特点导致常规电源的调峰压力增大,并受到输电通道的制约,各地的弃风率逐年攀升。将日前市场风电最优投标与用户侧需求响应相结合,在日前市场以风电场利益最大为目标,通过报童模型求解出最优风电投标量;在需求侧采用适用于高载能用户的尖峰电价模型,令高载能用户分组分时段地参与需求侧响应。综合考虑风电消纳能力、电网利益、高载能用户投切成本与火电机组运行成本,在尖峰日和非尖峰日分别采取不同的多目标优化模型,考虑负荷、电网以及电价等约束,采用粒子群算法进行模型求解。结果表明,将最优风电投标量引入需求响应策略中可以降低系统成本,且用尖峰电价激励高载能用户可以实现削峰填谷、消纳风电的目的。Due to randomness and fluctuation of wind power, pressure of regulating peak load is increasing. Because of restriction of transmission channel, wind power curtailment rate is increasing year by year. This paper, combining optimal wind bidding strategy in day-ahead market（DAM） and demand response（DR）, uses newsboy model to solve optimal wind bidding with maximized profit of wind power plant as objective. In demand side, high-load users use critical peak pricing（CPP） model to participate in demand response in different groups and time slots. Objective functions contain wind power accommodation level, power grid profit, operation cost of high-load users and heat-engine plant. Considering constrains of users, grid and price, particle swarm optimization（PSO） is used to solve different multi-objection models in CPP and NCPP days. Simulation results show that by combination of optimal wind bidding and DR, system＇s cost is reduced. And applying CPP to high-load users can avoid peak load and enhance wind power accommodation level.

关 键 词：风电投标 高载能用户 尖峰电价 粒子群优化 

分 类 号：TM715[电气工程—电力系统及自动化]