发电系统中微电网功率负荷优化调度研究
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摘要
为使微电网能够对负荷持续供电以及让用户选择合适的调度策略,对发电系统中微电网功率负荷优化调度进行研究。搭建包含太阳能光伏电池、风力发电机、燃料电池、微型燃气轮机、柴油发电机、储能装置六个分布式电源的微电网数学模型,建立考虑运行成本、环境成本的多目标函数。由于此微电网比已有微电网包含的分布式电源多,导致结构复杂,变量增多,各微电源的输出功率占比优化较难,因此在满足系统的约束条件下,采用GA-PSO算法进行求解。通过算例验证,结果显示:搭建的模型能够保证微电网对负荷持续供电,并提供不同的调度策略供用户选择。然后与多目标粒子群算法(MOPSO)进行对比分析,得出了GA-PSO算法的可行性和优越性,为微电网的设计提供理论基础。
To enable microgrid to continuously load the power supply and to allow the user to select the appropriate scheduling strategy,the optimal dispatch of microgrid power load in power generation system was studied. Mathematical models of six distributed powers were built up,including micro grid contains solar cells,wind turbines,fuel cell,micro gas turbine,diesel generator,and energy storage device. A multi-objective function considering operating cost and environmental cost was established. Because of the micro grid includes more distributed power than the existing micro grid,so that the structure is complicated,which results in the increase of variables,and difficult optimization of each micro power output. Therefore,GA-PSO algorithm was used to solve the problem to meet the constraint conditions of the system. Through the example verification,the results show that the model can guarantee the micro grid to load continuous power supply,and provide different scheduling strategies for the user to choose. Then,compared with the multi-objective particle swarm optimization( MOPSO) algorithm,the feasibility and advantages of GA-PSO algorithm were obtained,which provides a theoretical basis for the design of microgrid.
To enable microgrid to continuously load the power supply and to allow the user to select the appropriate scheduling strategy,the optimal dispatch of microgrid power load in power generation system was studied. Mathematical models of six distributed powers were built up,including micro grid contains solar cells,wind turbines,fuel cell,micro gas turbine,diesel generator,and energy storage device. A multi-objective function considering operating cost and environmental cost was established. Because of the micro grid includes more distributed power than the existing micro grid,so that the structure is complicated,which results in the increase of variables,and difficult optimization of each micro power output. Therefore,GA-PSO algorithm was used to solve the problem to meet the constraint conditions of the system. Through the example verification,the results show that the model can guarantee the micro grid to load continuous power supply,and provide different scheduling strategies for the user to choose. Then,compared with the multi-objective particle swarm optimization( MOPSO) algorithm,the feasibility and advantages of GA-PSO algorithm were obtained,which provides a theoretical basis for the design of microgrid.
引文
[1]刘勇智,李蒙湘,范冰洁,王珍.含微型燃气轮机的微电网优化经济性研究[J].计算机仿真,2015,(10):144-148.
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[9]常俊晓,游文霞,肖隆恩.含风电的发电资源优化调度与仿真研究[J].计算机仿真,2015,(4):120-123,128.
[10]庞振国,邢方方,别少勇,杜乾.含风电系统的环境经济优化调度与仿真研究[J].计算机仿真,2016,(10):100-103,133.
[11]吕立召,刘觉民,刘忠仁,楚文斌.计划孤岛方式下微电源控制系统的仿真研究[J].计算机仿真,2011,(6):304-307.
[12]王魁,张步涵,周杨,李俊芳.基于混沌量子粒子群算法的含风电场电力系统实时调度[J].电力系统自动化,2011,(22):141-146.
[13]翟云峰,蒋云峰,易国伟,潘浩,代希雷.基于改进蝙蝠算法的微电网优化调度[J].电力建设,2015,(6):103-108.
[14]丁明,张颖媛,茆美琴,刘小平,徐宁舟.包含钠硫电池储能的微网系统经济运行优化[J].中国电机工程学报,2011,(4):7-14.
[15]杨健.基于改进粒子群算法的微网实时能量优化调度[D].南京邮电大学,2014.
[16]陈洁,杨秀,朱兰,张美霞.基于遗传算法的热电联产型微网经济运行优化[J].电力系统保护与控制,2013,(8):7-15.
[2]张栋华,李征,蔡旭.基于量子行为粒子群算法的微电网优化配置[J].计算机仿真,2014,(08):120-124,208.
[3]王江波,费标青,王越,杨仁刚.户用微电网能量调度日前计划研究[J].电测与仪表,2013,(8):81-86,107.
[4]王佳,刘娇娇,邹毅军,王志鹏,谭伟.基于混合PSO算法的孤岛运行微电网优化调度研究[J].自动化与仪表,2015,(12):15-19.
[5]郑漳华,艾芊,徐伟华,施婕,解大,韩利.智能电网经济运行的多目标调度优化策略[J].电网技术,2010,(2):7-13.
[6]王金全,黄丽,杨毅.基于多目标粒子群算法的微电网优化调度[J].电网与清洁能源,2014,(1):49-54.
[7] G L Zheng,Y C Gao,L J Wan. Realization of automatic meter reading system based on Zig Bee with improved routing protocol[C]. 2010 Asia-Pacific Power and Energy Engineering Conference,March 28-31,Chengdu,China,2010.
[8] Z Y Wang,G L Zheng. The application of NILM in energy e-valuation of smart home:Contrast with IPv6[C]. 2011 Interna-tional Conference on Electric Information and Control Engi-neering(ICEICE),April 15-17,Wuhan,China,2011.
[9]常俊晓,游文霞,肖隆恩.含风电的发电资源优化调度与仿真研究[J].计算机仿真,2015,(4):120-123,128.
[10]庞振国,邢方方,别少勇,杜乾.含风电系统的环境经济优化调度与仿真研究[J].计算机仿真,2016,(10):100-103,133.
[11]吕立召,刘觉民,刘忠仁,楚文斌.计划孤岛方式下微电源控制系统的仿真研究[J].计算机仿真,2011,(6):304-307.
[12]王魁,张步涵,周杨,李俊芳.基于混沌量子粒子群算法的含风电场电力系统实时调度[J].电力系统自动化,2011,(22):141-146.
[13]翟云峰,蒋云峰,易国伟,潘浩,代希雷.基于改进蝙蝠算法的微电网优化调度[J].电力建设,2015,(6):103-108.
[14]丁明,张颖媛,茆美琴,刘小平,徐宁舟.包含钠硫电池储能的微网系统经济运行优化[J].中国电机工程学报,2011,(4):7-14.
[15]杨健.基于改进粒子群算法的微网实时能量优化调度[D].南京邮电大学,2014.
[16]陈洁,杨秀,朱兰,张美霞.基于遗传算法的热电联产型微网经济运行优化[J].电力系统保护与控制,2013,(8):7-15.