基于粒子校正优化的智能小区需求响应调度策略A Scheduling Strategy Based on Particle Correction for Intelligent Power Demand Response
陆俊;彭文昊;朱炎平;祁兵;崔高颖;
摘要(Abstract):
针对复杂智能用电环境配电网侧波动最小优化目标下的智能用电小区多用户日负荷需求响应调度问题,提出一种基于粒子校正优化的智能用电小区用电需求响应调度策略。首先综合考虑智能家居的基本负荷、可调度负荷、电动汽车负荷和储能装置负荷等约束条件,建立了计及罚系数的多用户智能用电调度模型;然后为求解该模型,提出基于粒子校正的混合粒子群算法,通过解决用户负荷分配最优求解中偏离约束问题,实现优化调度的效果;最后进行算例仿真实验,经过对比分析,结果表明文中所提策略对包含多种新型用能负荷的用户需求响应调度有着良好的负荷平抑配电网波动效果,能较好地实现配电网削峰填谷的目的。
关键词(KeyWords): 需求响应;智能用电模型;粒子校正;调度策略
基金项目(Foundation): 国家电网公司总部科技项目(智能电网用户行为辨识方法与互动化模式研究);; 国家重点研发计划项目课题(2016YFB0901104)~~
作者(Author): 陆俊;彭文昊;朱炎平;祁兵;崔高颖;
Email:
DOI: 10.13335/j.1000-3673.pst.2017.2825
参考文献(References):
- [1]田世明,王蓓蓓,张晶.智能电网条件下的需求响应关键技术[J].中国电机工程学报,2014,34(22):3576-3589.Tian Shiming,Wang Beibei,Zhang Jing.Key technologies for demand response in smart grid[J].Proceedings of the CSEE,2014,34(22):3576-3589(in Chinese).
- [2]中国网.电力需求侧管理城市综合试点工作中央财政奖励资金管理暂行办法[J].中华人民共和国国务院公报,2012(31):39-40.Chinanet.The central financial reward fund management interim measures about power demand side management integrated and pilot city work[J].The Bulletin of the State Council of the People's Republic of China,2012(31):39-40(in Chinese).
- [3]Faruqui A,Palmer J.The discovery of price responsiveness:a survey of experiments involving dynamic pricing of electricity[J].Social Science Electronic Publishing,2012,4(1):1-13.
- [4]王卿然,谢国辉,张粒子.含风电系统的发用电一体化调度模型[J].电力系统自动化,2011,35(5):15-18.Wang Qingran,Xie Guohui,Zhang Lizi.An integrated generation-consumption dispatch model with wind power[J].Automation of Electric Power Systems,2011,35(5):15-18(in Chinese).
- [5]王雅,曾成碧,苗虹,等.基于K-means聚类的有序充放电多目标调度模型[J].电力建设,2016,37(7):99-104.Wang Ya,Zeng Chengbi,Miao Hong,et al.Multi-objective scheduling model for coordinated charging and discharging based on K-means clustering[J].Electric Power Construction,2016,37(7):99-104(in Chinese).
- [6]吴红斌,侯小凡,赵波,等.计及可入网电动汽车的微网系统经济调度[J].电力系统自动化,2014,38(9):77-84.Wu Hongbin,Hou Xiaofan,Zhao Bo,et al.Economical dispatch of microgrid considering plug-in electric vehicles[J].Automation of Electric Power Systems,2014,38(9):77-84(in Chinese).
- [7]Li N,Chen L,Dahleh M A.Demand response using linear supply function bidding[J].IEEE Transactions on Smart Grid,2015,6(4):1827-1838.
- [8]阳小丹,李扬.家庭用电响应模式研究[J].电力系统保护与控制,2014,42(12):51-56.Yang Xiaodan,Li Yang.Research on household electricity response mode[J].Power System Protection and Control,2014,42(12):51-56(in Chinese).
- [9]侯建朝,胡群丰,谭忠富.计及需求响应的风电-电动汽车协同调度多目标优化模型[J].电力自动化设备,2016,36(7):22-27.Hou Jianchao,Hu Qunfeng,Tan Zhongfu.Multi-objective optimization model of collaborative WP-EV dispatch considering demand response[J].Electric Power Automation Equipment,2016,36(7):22-27(in Chinese).
- [10]刘振亚.智能电网技术[M].北京:中国电力出版社,2010:279-325.
- [11]陈思.智能电网中考虑电动汽车储能特性的家庭用电策略研究[D].成都:电子科技大学,2014.
- [12]Jian L,Xue H,Xu G,et al.Regulated charging of plug-in hybrid electric vehicles for minimizing load variance in household smart microgrid[J].IEEE Transactions on Industrial Electronics,2013,60(8):3218-3226.
- [13]Tan Z,Yang P,Nehorai A.An optimal and distributed demand response strategy with electric vehicles in the smart grid[J].IEEE Transactions on Smart Grid,2014,5(2):861-869.
- [14]赵远东,方正华.带有权重函数学习因子的粒子群算法[J].计算机应用,2013,33(8):2265-2268.Zhao Yuandong,Fang Zhenghua.Particle swarm optimization algorithm with weight function's learning factor[J].Journal of Computer Applications,2013,33(8):2265-2268(in Chinese).
- [15]苗振华.基于交叉库与并行变异的自适应遗传算法[D].大连:大连理工大学,2015.
- [16]Office of Energy Efficiency&Renewable Energy,EERE,Commercial and residential hourly load profiles for all TMY3locations in the united states[DB/OL].[2013-07-02].http://energy.gov/eere/buildings/commercial-buildings-integration.
- [17]OPA,Simulated wind generation data,Ontario Power Authority,2007[DB/OL].[2010-05-15].http://www.powerauthority.on.ca/integrated-power-system-plan/simulated-wind-generation-data.
- [18]田立亭,史双龙,贾卓.电动汽车充电功率需求的统计学建模方法[J].电网技术,2010,34(11):126-130.Tian Liting,Shi Shuanglong,Jia Zhuo.A statistical model for charging power demand of electric vehicles[J].Power System Technology,2010,34(11):126-130(in Chinese).
- [19]黄宇,杨健维,何正友.基于双层离散粒子群优化的智能小区车辆与家庭互动调度策略[J].电网技术,2015,39(10):2690-2696.Huang Yu,Yang Jianwei,He Zhengyou.A dispatching strategy for V2H of intelligent community based on bilayer discrete particle swarm optimization[J].Power System Technology,2015,39(10):2690-2696(in Chinese).
- [20]安甦.主动负荷系统需求响应行为特征分析[D].北京:华北电力大学,2013.