文章摘要
高鹏,陈红坤,张光亚,等.主动配电网中消纳高渗透率风电的风电源规划[J].电力系统自动化. DOI: 10.7500/AEPS20180131006.
GAO Peng,CHEN Hong-kun,ZHANG Guang-ya, et al.Planning of Distributed Wind Turbine for High-Permeability Wind Power Accommodation in Active Distribution Networ[J].Automation of Electric Power Systems. DOI: 10.7500/AEPS20180131006.
主动配电网中消纳高渗透率风电的风电源规划
Planning of Distributed Wind Turbine for High-Permeability Wind Power Accommodation in Active Distribution Networ
DOI:10.7500/AEPS20180131006
关键词: 风速模糊性  风电源规划  高渗透风电  需求侧管理  模糊随机机会约束
KeyWords: fuzziness of wind speed  wind power generation planning  high permeability of wind power  distributed generation planning  active management  demand side management
上网日期:2018-07-05
基金项目:
作者单位E-mail
高鹏 武汉大学 18696120360@163.com 
陈红坤 武汉大学 chkinsz@163.com 
张光亚 国网山西省朔州供电公司 595601451@qq.com 
赵莉莉 国网山西省朔州供电公司 jxry22@163.com 
边小军 国网山西省朔州供电公司 234415@163.com 
摘要:
      本文考虑主动管理(AM)和需求侧管理(DSM)措施,针对分布式风电源(WTG)出力模糊随机的运行特性,建立了主动配电网中分布式电源双层模糊随机机会约束规划模型。首先将风速处理为一个随机模糊变量,以处理实际风速因历史统计数据不足产生的误差;在此基础上,考虑主动管理和需求侧管理措施,构建双层模糊随机机会约束规划模型,上层以模糊随机年利润最大为目标,下层以WTG有功削减费用和负荷中断费用之和最小为目标,各项静态安全指标均以模糊随机机会约束考虑,采用遗传算法、蒙特卡洛模拟(MCS)和原对偶内点法对上下层模型进行求解。最后通过IEEE14节点配电系统进行了仿真计算,验证了所提模糊随机机会约束规划模型的有效性和合理性。
Abstract:
      Taking active management(AM) measures and demand side management(DSM) measures, as well as operating characteristics of WTGs in the planning stage, a bi-layer fuzzy random chance constrained model on distributed generation planning for active distribution network is built. The wind speed is treated as a random fuzzy variable in order to deal with error of actual wind speed due to lack of historical data. On this base, a bi-layer fuzzy random chance constrained planning model is constructed. The maximum annual fuzzy random profit is regarded as the objective function for the upper-layer of the built planning model and the minimum total cost of active power reduction of DG and load interruption is taken as the objective function of lower-layer of the built planning model. All static safety indicators are considered in the form of fuzzy random chance constraints. The upper- and lower-layer of the planning model are solved by genetic algorithm, Monte Carlo simulation(MCS) and prime-dual interior point method. The validity and rationality of the bi-layer planning model are verified by simulation example of IEEE 14-bus system.
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