计及电动汽车不确定性的家庭微电网实时能量调度策略
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摘要
EV和可再生能源的发展促使V2H系统成为研究的热点。EV在V2H系统中扮演着可控负载和移动储能的双重角色,具有削峰填谷、后备电源的作用,可以有效提高电网的经济性和可靠性。然而,EV行为的不确定性对微电网经济和稳定运行产生了重大影响。针对上述问题,提出了基于马尔可夫链蒙特卡罗(Markov chain Monte Carlo,MCMC)的方法对EV的随机性进行建模,并在此基础上建立了基于模型预测控制技术的全局在线优化算法。该算法可以在执行微电网能量优化管理的过程中充分考虑EV的储能特性,进而降低系统运行成本。不同情况下的算例分析验证了所提能量优化管理策略的有效性。
Research on V2H systems attracts increasing attention with development of EV and renewable energy. As a controllable load and mobile energy storage, EV plays a role of peak shaving and valley filling, and can be used as backup power, thus improving economy and reliability of microgrid. However, due to uncertainty of EV, reliability and economic operation of microgrid face new challenges. To address this issue, an EV travel model based on MCMC(Markov chain Monte Carlo) is established and an energy management solution based on model predictive control is designed in this paper. The proposed solution can reduce system operational cost by fully considering the energy storage property of the EV. Experiments under different conditions are conducted to verify the proposed energy dispatch strategy.
Research on V2H systems attracts increasing attention with development of EV and renewable energy. As a controllable load and mobile energy storage, EV plays a role of peak shaving and valley filling, and can be used as backup power, thus improving economy and reliability of microgrid. However, due to uncertainty of EV, reliability and economic operation of microgrid face new challenges. To address this issue, an EV travel model based on MCMC(Markov chain Monte Carlo) is established and an energy management solution based on model predictive control is designed in this paper. The proposed solution can reduce system operational cost by fully considering the energy storage property of the EV. Experiments under different conditions are conducted to verify the proposed energy dispatch strategy.
引文
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[2]段小宇,胡泽春,崔岩,等.长时间尺度下的电动汽车有序充放电调度[J].电网技术,2018,42(12):4037-4044.Duan Xiaoyu,Hu Zechun,Cui Yan,et al.Optimal charging and discharging strategy for electric vehicles under large timescales[J].Power System Technology,2018,42(12):4037-4044(in Chinese).
[3]梁子鹏,陈皓勇,王勇超,等.含电动汽车的微网鲁棒经济调度[J].电网技术,2017,41(8):2647-2658.Liang Zipeng,Chen Haoyong,Wang Yongchao,et al.Robust economic dispatch of microgrids containing electric vehicles[J].Power System Technology,2017,41(8):2647-2658(in Chinese).
[4]Muratori M.Impact of uncoordinated plug-in electric vehicle charging on residential power demand[J].Nature Energy,2018,3(3):193.
[5]Lund P D,Lindgren J,Mikkola J,et al.Review of energy system flexibility measures to enable high levels of variable renewable electricity[J].Renewable and Sustainable Energy Reviews,2015(45):785-807.
[6]李博彤,唐锴.计及EV储能特性的微电网规划模型与算法[J].电力建设,2017,38(2):60-65.Li Botong,Tang Kai.Planning model and algorithm of microgrid considering energy storage characteristics of electric vehicles[J].Electric Power Construction,2017,38(2):60-65(in Chinese).
[7]Nguyen D T,Le L B.Joint optimization of electric vehicle and home energy scheduling considering user comfort preference[J].IEEETransactions on Smart Grid,2014,5(1):188-199.
[8]Corchero C,Cruz-Zambrano M,Heredia F J.Optimal energy management for a residential microgrid including a vehicle-to-grid system[J].IEEE Transactions on Smart Grid,2014,5(4):2163-2172.
[9]Lin H,Liu Y,Sun Q,et al.The impact of electric vehicle penetration and charging patterns on the management of energy hub-A multiagent system simulation[J].Applied Energy,2018(230):189-206.
[10]Wang Y,Wang B,Chu C C,et al.Energy management for a commercial building microgrid with stationary and mobile battery storage[J].Energy and Buildings,2016(116):141-150.
[11]Wu X,Hu X,Moura S,et al.Stochastic control of smart home energy management with plug-in electric vehicle battery energy storage and photovoltaic array[J].Journal of Power Sources,2016(333):203-212.
[12]仉梦林,胡志坚,王小飞,等.基于动态场景集和需求响应的二阶段随机规划调度模型[J].电力系统自动化,2017,41(11):68-76.Zhang Menglin,Hu Zhijian,Wang Xiaofei,et al.Two-stage stochastic programming scheduling model based on dynamic scenario sets and demand respons[J].Automation of Electric Power Systems,2017,41(11):68-76(in Chinese).
[13]季振亚,黄学良,张梓麒,等.基于随机优化的综合能源系统能量管理[J].东南大学学报(自然科学版),2018,48(1):45-53.Ji Zhenya,Huang Xueliang,Zhang Ziqi,et al.Energy management for integrated energy systems based on stochastic optimization[J].Journal of Southeast University(Natural Science Edition),2018,48(1):45-53(in Chinese).
[14]Chib S,Greenberg E.Understanding the metropolis-hastings algorithm[J].The American Statistician,1995,49(4):327-335.
[15]Muratori M.Impact of uncoordinated plug-in electric vehicle charging on residential power demand-supplementary data[R].National Renewable Energy Laboratory-Data(NREL-DATA).Golden,CO(United States):National Renewable Energy Laboratory,2017.
[16]Wu J,Xing X,Liu X,et al.Energy management strategy for grid-tied microgrids considering the energy storage efficiency[J].IEEETransactions on Industrial Electronics,2018(65):9539-9549.
[17]徐冬冬.微电网多时间尺度优化算法研究[D].广州:华南理工大学,2015.
[18]Mohsenian-Rad A H,Leon-Garcia A.Optimal residential load control with price prediction in real-time electricity pricing environments[J].IEEE Transactions on Smart Grid,2010,1(2):120-133.