计及分类需求响应的孤岛微网并行多目标优化调度
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摘要
孤岛微网优化调度中的源荷协调性非常重要,却常被忽视。针对孤岛微网的运行特点,计及分类负荷需求响应,构建了以系统运行成本及污染排放最小化为目标的微网源荷协调多目标优化调度模型。并引入多核并行运算环境和多种群并行交叉变异机制,构建新型的并行多目标微分进化(PMODE)算法对模型进行高效求解,以调和常规智能算法寻优深度和速度间的矛盾。结合应用结果分析了分类负荷发生转移及削减前后对微网内各分布式电源的出力影响和负荷的峰谷调整状况。表明所提出的孤岛微网源荷协调优化调度模型可有效实现节能减排和提升风光消纳率,并验证了PMODE算法的优越性能。
As the source-load coordination in the dispatch optimization of island micro-grid is very important but often neglected, based on the operation characteristics of island micro-grid and load classified demand response, a multi-objective optimal dispatch model considering source-load coordination of micro-grid with goals of minimizing operation cost and pollution emission is established. By introducing multi-core parallel computing environment and multi-population parallel crossover mutation mechanism, a new Parallel Multi-objective Differential Evolution(PMODE)algorithm is designed to solve the model effectively, in order to harmonize the contradiction between optimization depth and speed of conventional intelligent algorithm. Based on the application results, the influence of classified load transfer and pre-and after cutting on the output of each distributed power in microgrid and the load adjustment of peak-valley are analyzed. The results show that the proposed optimal dispatch model can effectively realize energy saving and emission reduction and improve the utilization rate of wind/photovoltaic power, verifying the superior performance of PMODE.
As the source-load coordination in the dispatch optimization of island micro-grid is very important but often neglected, based on the operation characteristics of island micro-grid and load classified demand response, a multi-objective optimal dispatch model considering source-load coordination of micro-grid with goals of minimizing operation cost and pollution emission is established. By introducing multi-core parallel computing environment and multi-population parallel crossover mutation mechanism, a new Parallel Multi-objective Differential Evolution(PMODE)algorithm is designed to solve the model effectively, in order to harmonize the contradiction between optimization depth and speed of conventional intelligent algorithm. Based on the application results, the influence of classified load transfer and pre-and after cutting on the output of each distributed power in microgrid and the load adjustment of peak-valley are analyzed. The results show that the proposed optimal dispatch model can effectively realize energy saving and emission reduction and improve the utilization rate of wind/photovoltaic power, verifying the superior performance of PMODE.
引文
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[21]别朝红,胡国伟,谢海鹏,等.考虑需求响应的含风电电力系统的优化调度[J].电力系统自动化,2014,38(13):115-120.BIE Chaohong,HU Guowei,XIE Haipeng,et al.Optimal scheduling of wind power system considering demand response[J].Automation of Electric Power Systems,2014,38(13):115-120.
[2]ZHANG Fan,ZHAO Huiying,HONG Mingguo.Operation of networked microgrids in a distribution system[J].CSEE Journal of Power&Energy System,2016,1(4):12-21.
[3]唐巍,高峰.考虑用户满意度的户用型微电网日前优化调度[J].高电压技术,2017,43(1):140-148.TANG Wei,GAO Feng.Optimal operation of household microgrid day-ahead energy considering user satisfaction[J].High Voltage Engineering,2017,43(1):140-148.
[4]孙川,汪隆君,许海林.用户互动负荷模型及其微电网日前经济调度的应用[J].电网技术,2016,40(7):2009-2015.SUN Chuan,WANG Longjun,XU Hailin.An interaction load model and its application in microgrid day-ahead economic scheduling[J].Power System Technology,2016,40(7):2009-2015.
[5]张忠,王建学,曹晓宇.基于负荷分类调度的孤岛型微网能量管理方法[J].电力系统自动化,2015,39(15):17-23,109.ZHANG Zhong,WANG Jianxue,CAO Xiaoyu.Island microgrid energy management based on load classification scheduling[J].Automation of Electric Power Systems,2015,39(15):17-39,109.
[6]许汉平,李姚旺,苗世洪,等.考虑可再生能源消纳效益的电力系统“源-荷-储”协调互动优化调度策略[J].电力系统保护与控制,2017,45(17):18-25.XU Hanping,LI Yaowang,MIAO Shihong,et al.Optimization dispatch strategy considering renewable energy consumptive benefits based on“source-load-energy”coordination in power system[J].Power System Protection and Control,2017,45(17):18-25.
[7]梁捷.基于可中断负荷分级削减的发电调度模型研究[J].智慧电力,2018,46(7):61-66.LIAN Jie.Study on power generation scheduling model based on hierarchical reduction of interruptible load[J].Smart Power,2018,46(7):61-66.
[8]曾鸣,杨雍琦,向红伟,等.兼容需求侧资源的“源-网-荷-储”协调优化调度模型[J].电力自动化设备,2016,36(2):102-111.ZENG Ming,YANG Yongqi,XIANG Hongwei,et al.Coordination and optimization scheduling model of"source-network-load-reserve"compatible with demand side resources[J].Electric Power Automation Equipment,2016,36(2):102-111.
[9]何舜,郑毅,蔡旭,等.基于荷-储型微网的需求侧管理系统运行优化[J].电力系统自动化,2015,39(19):15-20.HE Shun,ZHENG Yi,CAI Xu,et al.Demand side management system operation optimization based on charge-storage type microgrid[J].Automation of Electric Power Systems,2015,39(19):15-20.
[10]ADIKA C O,WANG L F.Autonomous appliance scheduling for household energy management[J].IEEETransactions on Smart Grid,2014,5(2):673-682.
[11]LOGEN T,SRININ A D,KHAMABADKONE A,et al.Multiagent system for real-time operation of a microgrid in real-time digital simulator[J].IEEE Transactions on Smart Grid,2012,3(2):925-933.
[12]郭帅,刘鲁嘉,杨萌,等.计及可控负荷的微网自治调度模型[J].电网技术,2017,41(1):48-56.GUO Shuai,LIU Lujia,YANG Meng,et al.Autonomous scheduling model for microgrid considering controllable loads[J].Power System Technology,2017,41(1):48-56.
[13]齐先军,程桥,吴红斌,等.激励型需求响应对配电网运行可靠性的影响[J].电工技术学报,2018,33(22):5319-5326.QI Xianjun,CHENG Qiao,WU Hongbin,et al.Impact of incentive-based demand response on operational reliability of distribution network[J].Transactions of China Electrotechnical Society,2018,33(22):5319-5326.
[14]PENG C,SUN H,GUO J,et al.Multi-objective optimal strategy for generating and bidding in the power market[J].Energy Conversion and Management,2012,57(1):13-22.
[15]王蓓蓓,刘小聪,李扬.面向大容量风电接入考虑用户侧互动的系统日前调度和运行模拟研究[J].中国电机工程学报,2013,36(22):35-44.WANG Beibei,LIU Xiaocong,LI Yang.Day-ahead generation scheduling and operation simulation considering demand response in large-capacity wind power integrated systems[J].Proceedings of the CSEE,2013,36(22):35-44.
[16]刘故帅,肖异瑶,贺禹强,等.考虑新能源类型的电力系统多目标并网优化方法[J].电力系统保护与控制,2017,45(10):31-37.LIU Gushuai,XIAO Yiyao,HE Yuqiang,et al.Multi-objective optimal method considering types of grid connected new energy of electric power system[J].Power System Protection and Control,2017,45(10):31-37.
[17]DEB K,PRATAP A,AGARWAL S,et al.A fast and elitist multi-objective genetic algorithm:NSGA-II[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[18]PATTANAIK J K,BASU M,DASH D P.Opposition-based differential evolution for hydrothermal power system[J].Protection and Control of Modern Power Systems,2017,2(2):40-56.DOI:10.1186/s41601-017-0033-5.
[19]彭春华,于蓉,孙惠娟.基于K-均值聚类多场景时序特性分析的分布式电源多目标规划[J].电力自动化设备,2015,35(10):58-65.PENG Chunhua,YU Rong,SUN Huijuan.Multi-objective programming of distributed power supply based on time series characteristic analysis of K-means clustering[J].Electric Power Automation Equipment,2015,35(10):58-65.
[20]舒航,李强,郑琴.基于并行粒子群优化算法的变分资料同化[J].解放军理工大学学报(自然科学版),2014,15(6):583-590.SHU Hang,LI Qiang,ZHENG Qin.Variational data assimilation based on parallel particle swarm optimization algorithm[J].Journal of PLA University of Science and Technology(Natural Science Edition),2014,15(6):583-590.
[21]别朝红,胡国伟,谢海鹏,等.考虑需求响应的含风电电力系统的优化调度[J].电力系统自动化,2014,38(13):115-120.BIE Chaohong,HU Guowei,XIE Haipeng,et al.Optimal scheduling of wind power system considering demand response[J].Automation of Electric Power Systems,2014,38(13):115-120.