Suzhou Electric Appliance Research Institute
期刊号: CN32-1800/TM| ISSN1007-3175

Article retrieval

文章检索

首页 >> 文章检索 >> 往年索引

考虑风电接入的电力系统动态随机环境/经济调度

来源:电工电气发布时间:2017-04-21 14:21 浏览次数:8
考虑风电接入的电力系统动态随机环境/经济调度
 
王锐1,朱超2,张帅3
(1 国网江苏省电力公司泰州供电公司,江苏 泰州 225300;2 国网江苏省电力公司检修分公司,江苏 南京 211102;
3 国网江苏省电力公司无锡供电公司,江苏 无锡 214000)
 
    摘 要:考虑风电固有的随机性、波动性和间歇性,兼顾环境污染和经济性的多优化目标,针对不同时间断面,构建风电接入后的电力系统动态随机环境/经济调度模型。引入机会约束规划理论,设计基于非劣排序的随机多目标粒子群优化算法,应用模糊集合理论和熵权法建立综合最优解的提取方法。算例分析验证了该模型和算法的可行性和有效性。
    关键词:大规模风电;环境/经济调度;多目标优化;机会约束规划
    中图分类号:TM614;TM734      文献标识码:A      文章编号:1007-3175(2017)04-0010-07
 
Dynamic and Stochastic Environmental Economic Dispatch for Power Systems Integrated with Large-Scale Wind Power
 
WANG Rui1, ZHU Chao2, ZHANG Shuai3
(1 Taizhou Power Supply Company of Jiangsu Electric Power Co., Ltd , Taizhou 225300, China;
2 Jiangsu Electric Power Maintenance Branch Company, Nanjing 2111 02, China;
3 Wuxi Power Supply Company of Jiangsu Electric Power Co., Ltd, Wuxi 214000, China)
 
    Abstract: Giving consideration to the randomness, volatility and intermittent, multi-optimization objective of environmental pollution and economy, and aiming at fracture surface at different time, this paper established a dynamic and stochastic environmental economic dispatch model for power system integrated with large-scale wind power. The chance constraint planning theory was introduced. This paper designed a superior multi-objective particle swarm optimization algorithm based on Pareto non-dominated sorting mechanism and used the fuzzy set theory and entropy weights to extract the comprehensive optimal solution. The calculated example analysis verifies the feasibility and validity of this model and its algorithm.
    Key words: large-scale wind power; environmental economic dispatch; multi-objective optimization; chance-constrained programming
 
参考文献
[1] 雷亚洲. 与风电并网相关的研究课题[J]. 电力系统自动化,2003,27(8):84-89.
[2] 张粒子,周娜,王楠. 大规模风电接入电力系统调度模式的经济性比较[J]. 电力系统自动化,2011,35(22):105-109.
[3] 吴栋梁,王扬,郭创新,等. 电力市场环境下考虑风电预测误差的经济调度模型[J]. 电力系统自动化,2012,36(6):23-28.
[4] 任博强,彭鸣鸿,蒋传文,等. 计及风电成本的电力系统短期经济调度建模[J]. 电力系统保护与控制,2010,38(14):67-72.
[5] HETZER J, YU D C, BHATTARAI K.An economic dispatch model incorporating wind power[J]. IEEE Transactions on Energy Conversion,2008,
23(2):603-611.
[6] 张昭遂,孙元章,李国杰,等. 计及风电功率不确定性的经济调度问题求解方法[J]. 电力系统自动化,2011,35(22):125-130.
[7] MIRANDA V, HANG P S.Economic dispatch model with fuzzy wind constraints and attitudes of dispatchers[J].IEEE Transactions on Power Systems,2005,20(4):2143-2145.
[8] WANG L, SINGH C.PSO-based multi-criteria economic dispatch considering wind power penetration subject to dispatcher’s attitude[C]//Proceeding of IEEE North American Power Symposium,2006:269-276.
[9] 陈海焱,陈金富,段献忠. 含风电场电力系统经济调度的模糊建模及优化算法[J]. 电力系统自动化,2006,30(2):22-26.
[10] 江岳文,陈冲,温步瀛. 含风电场的电力系统机组组合问题随机模拟粒子群算法[J]. 电工技术学报,2009,24(6):129-137.
[11] 孙元章,吴俊,李国杰. 基于风速预测和随机规划的含风电场电力系统动态经济调度[J]. 中国电机工程学报,2009,29(4):41-47.
[12] 赵俊华,文福拴,薛禹胜,等. 计及电动汽车和风电出力不确定性的随机经济调度[J]. 电力系统自动化,2010,34(20):22-29.
[13] 田廓,曾鸣,鄢帆,等. 考虑环保成本和风电接入影响的动态经济调度模型[J]. 电网技术,2011,35(6):55-59.
[14] DUTTA P, SINHA A K.Environmental economic dispatch constrained by voltage stability using PSO[C]//IEEE Internayional Conference on Industrial Technology,2007:1879-1884.
[15] KING R T F A, RUGHOOPUTH H C S, DEB K.Stochastic evolutionary multiobjective environmental/economic dispatch[C]//IEEE Congress on Evolutionary Computation,2006:946-953.
[16] 刘盛松,邰能灵,侯志俭,等. 基于最优潮流与模糊贴近度的电力系统环境保护研究[J]. 中国电机工程学报,2003,23(4):21-26.
[17] 彭春华,孙惠娟. 基于非劣排序微分进化的多目标优化发电调度[J]. 中国电机工程学报,2009,29(34):71-76.
[18] 文瑛,元昌安. 改进的多目标粒子群优化方法[J].计算机工程与设计,2010,31(12):2846-2848.
[19] ALVAREZ-BENITEZ J, EVERSON R, FIELDSEND J.A MOPSO algorithm based exclusively on pareto dominance concepts[C]//Third International Conference, Evolutionary Malti-Criterion Optimization,2005:459-473.
[20] 吴亚丽,徐丽青. 基于差分演化的改进多目标粒子群优化算法[J]. 系统仿真学报,2011,23(10):2211-2215.
[21] 徐丽青,吴亚丽. 求解环境经济调度问题的多目标差分粒子群优化算法[J]. 西安理工大学学报,2011,27(1):62-68.
[22] DEB K, PRATAP A, AGARWAL S, et al.A fast and elitist multiobjective genetic algorithm: NSGA-II[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.