基于功率预测滚动时域全局优化微电网能效控制
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摘要
本文针对微网能量协调的最优经济要求,在传统之上,使用了模型预测控制框架下的滚动时域优化(receding horizon contrcl,RHC)方法,并采用支持向量机(SVM)来预测风机功率和负荷状态。结合微电网模型特点,提出一种具有柔性框架结构的改进型复杂过程全局优化进化算法(IEACOP)。该算法在通用框架内嵌入具有搜索机制的各类子方法,在充分考虑风力发电机和蓄电池的外特性数学模型以及约束条件的前提下,采用无限折射映射混沌模型改进多样性初始种群的生成策略。并应用局部搜索法对"超越策略"产生的超矩形区域进行深度搜索,提高局部最优解的求解效率。仿真结果表明,该方法充分发挥了IEACOP优势,满足微电网能量协调控制的要求,改进的复杂过程全局优化进化方法能够使用较少的调节参数完成微电网能量协调优化的可行解搜索,实例证实了所提出方法的有效性。
As for the optimal economic requirements of energy coordination of micro-grid, receding horizon control(RHC) method under model predictive control framework is used traditionally and the support vector machines(SVMS) is sued to predict power of fan and load status. An improved complex process global optimization algorithm(IEACOP) with flexible frame structure is proposed in this paper with combination of feature of the micro-grid model. In this algorithm, each type of sub-method with search mechanism is embedded into the general framework. On the premise of giving full consideration to outer characteristic mathematical model and operational constraints of the wind generator and battery, the strategy of using infinite refraction mapping chaotic model to improve the diversity of the initial population generation is adopted. The local search method to deeply search the hyper-rectangle area caused by "transcendence method" is used to improve the solution efficiency of local optimal solution. It is shown by the simulation result that the method makes full use of advantage of the RCH and IE ACOP and meets the requirements of micro-grid energy coordination control. The IE ACOP method can use less adjustment parameters to finish the feasible solution of micro-grid coordinate optimization. The effectiveness of the proposed method is verified by the real example.
As for the optimal economic requirements of energy coordination of micro-grid, receding horizon control(RHC) method under model predictive control framework is used traditionally and the support vector machines(SVMS) is sued to predict power of fan and load status. An improved complex process global optimization algorithm(IEACOP) with flexible frame structure is proposed in this paper with combination of feature of the micro-grid model. In this algorithm, each type of sub-method with search mechanism is embedded into the general framework. On the premise of giving full consideration to outer characteristic mathematical model and operational constraints of the wind generator and battery, the strategy of using infinite refraction mapping chaotic model to improve the diversity of the initial population generation is adopted. The local search method to deeply search the hyper-rectangle area caused by "transcendence method" is used to improve the solution efficiency of local optimal solution. It is shown by the simulation result that the method makes full use of advantage of the RCH and IE ACOP and meets the requirements of micro-grid energy coordination control. The IE ACOP method can use less adjustment parameters to finish the feasible solution of micro-grid coordinate optimization. The effectiveness of the proposed method is verified by the real example.
引文
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[3]杨新法,苏剑,吕志鹏,等.微电网技术综述[J].中国电机工程学报,2014,34(1):57-70.YANG Xinfa,SU Jian,LYU Zhipeng,et al. Overview on micro-grid technology[J]. Proceedings of the CSEE, 2014, 34(1):57-70.
[4] ZAKARIAZADEH A,JADID S,SIANO P. Smart microgrid energy and reserve scheduling with demand response using stochastic optimization[C]//Electr. Power Energy Syst. 2014:523-533.
[5]MAJUMDER R G,LEDWICH A G. Power management and power flow control with back-to-back converters in a utility connected microgrid[C]//IEEE Trans. Power Syst. 2010:821-834.
[6]鲍薇.多电压源型微源组网的微电网运行控制与能量管理策略研究[D].中国电力科学研究院,2014.
[7]郝雨辰,窦晓波,吴在军,等.微电网分层分布式能量优化管理[J].电力自动化设备,2014,34(1):154-162.HAO Yuchen,DOU Xiaobo,WU Zaijun,et al. Hierarchical and distributed optimization of energy management for microgrid[J]. Electric Power Automation Equipment, 2014,34(1):154-162.
[8] HAO Yucheng,DOU Xiaobo, WU Zaijun,et al. Hierarchical and distributed optimization of energy management for microgrid[J]. Electric Power Automation Equipment,2014(1):154-161.
[9] PRODAN I,ZIO E. A model predictive control framework for reliable microgrid energy management[J]. Electrical Power and Energy Systems, 2014(61):399-409.
[10] KATIRAEI F, IRAVANI R, HATZIARGYRIOU N, et al.Microgrids management[J]. Power and Energy Magazine,IEEE,2008,6(3):54-65.
[11]郑金华.多目标进化算法及其应用[M].北京:科学出版社,2007.
[12]ZHENG Jinhua. Multi-objective evolutionary algorithm and application[M]. Beijing:science press, 2002.
[13]王晓晴,唐加福,韩毅.分散搜索算法研究进展[J].系统仿真学报,2009,21(11):3155-3160.WANG Xiaoqing,TANG Jiafu,HAN Yi. Advances in scatter search[J]. Journal of System Simulation, 2009, 21(11):3155-3160.
[14] WANG Xiaoqing,TANG Jiafu,HAN Yi. Advances in scatter search[J]. Journal of System Simulation, 2009(21):3155-3160.
[15] NELDER J A, MEAD R. A simple method for function minimization[J]. Computer Journal, 1965,7(4):308-313.
[16] JARVENTAUSTA P, REPO S, RAUTIAINEN A, et al.Smart grid power system control in distributed generation environment[J]. Annual Reviews in Control, 2010, 34(2):277-286.
[17]Bendtsen,J.,Trangbaek,K.,Stoustrup,J. et al. Hierarchical model predictive control for resource distribution[C]//201049th IEEE Conference on Decision and Control, 2010:2468-2473.
[18]白鹏,张喜斌,张斌,等.支持向量机理论及工程应用实例[M].西安:西安电子科技大学出版社,2008.
[19] BAI Peng, Zhang Xibin, Zhang Bin, et al., Support vector machine and its application in mixed gas infrared spectrumanalysis[M]. Xi'an:Xi'an Electronic Sience&Technology University Press:42-44(in Chinese).