有可再生能源和电力存储设施并网的智能电网优化用电策略
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摘要
大量可再生能源和存储设施集中或分布接入电网,缓解了电网的供给压力,但同时也对电力系统安全造成新的威胁。合理使用新能源和可存储设施使之更好为电网服务,是现代电网亟待解决的一个问题。本文对有可存储设备和可再生能源并网的电力系统进行研究,根据可再生能源在实际生活中的情形,将其划分为两类:私人新能源发电和公共新能源发电,其中私人新能源发电可供自身直接使用,多余部分并入电网,而公共新能源发电直接并入电网,然后针对上述复杂情形,结合用户实际需求,以所有用户效用最大化、成本最小化为目标函数,建立优化模型,给出了一种既有可存储设备又有可再生能源复杂并网情况下用户优化用电策略——包括家用电器、新能源、以及存储设备充放电策略。对模型的性质进行研究,考虑到模型是凸规划,强对偶成立,用拉格朗日对偶算法给出了模型的解。求解过程中,由于目标函数是非光滑的,采用光滑化的技术将目标函数光滑化,将非光滑问题转化为光滑问题,进一步利用拟牛顿下降法求解。该策略能确保新能源得到优先、充分利用,体现用户效用最大化、成本最小化,同时可以避免由于新能源并网可能会造成电网不稳定情况的出现;光滑化的方法不但适用于本文,经过适当改进后也可适用于其他目标函数为非光滑的情况。仿真结果验证了模型的合理性和算法的可行性。
With a large number of renewable energy and storage facilities centralized or distributed accessible to the grid,the supply pressure of the power grid is eased,but at the same time,a new threat to the safety of power systems arises.Rational use of new energy and storage facilities to better serve the grid is an urgent problem for the modern grid.In this paper,a study on smart grid with the integration of renewable energy and storage equipments is made.According to the complex situation of renewable energy,they are first diviede two categories:one is private renewable energy and the other is public renewable energy.Private renewable energy can be used for users directly,and the excess part will be put into the grid.However,public renewable energy will be put into the griddirectly.Then,aiming at the above complex situation,combining with the actual demand of the user,an optimized strategy of rational use of renewable energy and storage equipment is given based on the maximization of users' utility and the minimization of users' cost.The properties of the model are also studied.Considering that the model is a convex programming and strong duality is founded,the solution of the model is given by Lagrangian dual algorithm.In the process of solving,because the objective function is non-smooth,the smoothing method is used to smooth the objective function,which transforms the optimal value of the non-smooth function into that of the smoothing function,and then the problem is further solved by the quasi-Newton method.This strategy not only gives priority to the use of renewable energy but also makes the most of it while maximizing users utility and minimizing cost.This strategy also avoids instability of grid caused by renewable energy,and the method of smoothing is applicable not only to this article,but also to the case where the objective functions are not differentiable.Simulation results verify the rationality of the model and the feasibility of the algorithm.
With a large number of renewable energy and storage facilities centralized or distributed accessible to the grid,the supply pressure of the power grid is eased,but at the same time,a new threat to the safety of power systems arises.Rational use of new energy and storage facilities to better serve the grid is an urgent problem for the modern grid.In this paper,a study on smart grid with the integration of renewable energy and storage equipments is made.According to the complex situation of renewable energy,they are first diviede two categories:one is private renewable energy and the other is public renewable energy.Private renewable energy can be used for users directly,and the excess part will be put into the grid.However,public renewable energy will be put into the griddirectly.Then,aiming at the above complex situation,combining with the actual demand of the user,an optimized strategy of rational use of renewable energy and storage equipment is given based on the maximization of users' utility and the minimization of users' cost.The properties of the model are also studied.Considering that the model is a convex programming and strong duality is founded,the solution of the model is given by Lagrangian dual algorithm.In the process of solving,because the objective function is non-smooth,the smoothing method is used to smooth the objective function,which transforms the optimal value of the non-smooth function into that of the smoothing function,and then the problem is further solved by the quasi-Newton method.This strategy not only gives priority to the use of renewable energy but also makes the most of it while maximizing users utility and minimizing cost.This strategy also avoids instability of grid caused by renewable energy,and the method of smoothing is applicable not only to this article,but also to the case where the objective functions are not differentiable.Simulation results verify the rationality of the model and the feasibility of the algorithm.
引文
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[29]王宏杰,高岩.基于非光滑方程组的智能电网实时定价[J].系统工程学报,2018,33(3):320-327.
[2]Nunna H K,Doolla S.Demand response in smart distribution system with multiple microgrids[J].IEEE Transactions on Smart Grid,2012,3(4):1641-1649.
[3]Guerrero J M,Blaabjerg F,Zhelev T,et al.Distributed generation:Toward a new energy paradigm[J].Industrial Electronics Magazine IEEE,2010,4(1):52-64.
[4]Bahramirad S,Reder W,Khodaei A.Reliability-constrained optimal sizing of energy storage system in a micro-grid[J].IEEE Transactions on Smart Grid,2012,3(4):2056-2062.
[5]Xu Zhanbo,Guan Xiaohong,Jia Qingshan,et al.Performance analysis and comparison on energy storage devices for smart building energy management[J].IEEE Transactions on Smart Grid,2012,3(4):2136-2147.
[6]Silva A M L D,Nascimento L C,Rosa M A D,et al.Distributed energy resources impact on distribution system reliability under loadtransfer restrictions[J].IEEE Transactions on Smart Grid,2012,3(4):2048-2055.
[7]Wang K,Ciucu F,Lin C,et al.A Stochastic power network calculus for integrating renewable energy sources into the power grid[J].IEEE Journal on Selected Areas in Communications,2012,30(6):1037-1048.
[8]Momoh J.Smart grid:Fundamentals of design and analysis[M].New Jersey:Jokn Wiley&Sons,Inc.,2012.
[9]吴振信,石佳.基于STIRPAT和GM(1,1)模型的北京能源碳排放影响因素分析及趋势预测[J].中国管理科学,2012,20(S2):803-809.
[10]Sioshansi R.Evaluating the impacts of real-time pricing on the cost and value of wind generation[J].IEEE Transactions on Power Systems,2010,25(2):741-748.
[11]Vittal V.The impact of renewable resources on the performance and reliability of the electricity grid[J].The bridge,2010,40(1):5-12.
[12]Amin S M,Wollenberg B F.Toward a smart grid:Power delivery for the 21st century[J].IEEE Power&Energy Magazine,2005,3(5):34-41.
[13]Groot R J W,Morren J,Slootweg J G.Smart integration of distribution automation applications[C]//IEEE Pes International Conference and Exhibition on Innovative Smart Grid Technologies,IEEE,2012:1-7.
[14]Wang Wenye,Lu Zhuo.Survey cyber security in the smart grid:Survey and challenges[J].Computer Networks,2013,57(5):1344-1371.
[15]Deng Ruilong,Yang Zaiyue,Chow M Y,et al.A survey on demand response in smart grids:mathematical models and approaches[J].IEEE Transactions on Industrial Informatics,2015,11(3):570-582.
[16]Cheung K W,Rios-Zalapa R.Smart dispatch for large grid operations with integrated renewable resources[C]//Innovative Smart Grid Technologies,IEEE,2011:1-7.
[17]Makarov Y V,Etingov P V,Ma J,et al.Incorporating uncertainty of wind power generation forecast into power system operation,dispatch,and unit commitment procedures[J].IEEE Transactions on Sustainable Energy,2011,2(4):433-442.
[18]徐智威,胡泽春,宋永华,等.充电站内电动汽车有序充电策略[J].电力系统自动化,2012,36(11):38-43.
[19]杨珺,冯鹏祥,孙昊,等.电动汽车物流配送系统的换电站选址与路径优化问题研究[J].中国管理科学,2015,23(9):87-96.
[20]常方宇,黄梅,张维戈.分时充电价格下电动汽车有序充电引导策略[J].电网技术,2016,40(9):2609-2615.
[21]Yang Zaiyue,Sun Lihao,Chen Jiming,et al.Profit maximization for plug-in electric taxi with uncertain future electricity prices[J].IEEE Transactions on Power Systems,2014,29(6):3058-3068.
[22]Raziei A,Hallinan K P,Brecha R J.Cost optimization with solar and conventional energy production,energy storage,and real time pricing[C]//Innovative Smart Grid Technologies Conference,IEEE,2014:1-5.
[23]Cecati C,Citro C,Siano P.Combined operations of renewable energy systems and responsive demand in a smart grid[J].IEEE Transactions on Sustainable Energy,2011,2(4):468-476.
[24]Samadi P,Mohsenian-Rad H,Wong V W S,et al.Utilizing renewable energy resources by adopting DSM techniques and storage facilities[C]//IEEE International Conference on Communications,IEEE,2014:4221-4226.
[25]Qian Liping,Zhang Yingjun,Huang Jianwei,et al.Demand response management via real-time electricity price control in smart grids[J].IEEE Journal on Selected Areas in Communications,2013,31(7):1268-1280.
[26]代业明,高岩,高红伟,等.基于需求响应的智能电网实时电价谈判模型[J].中国管理科学,2017(3):102-110.
[27]王宜举,修乃华.非线性最优化理论与方法.第2版[M].北京:科学出版社,2016.
[28]高岩.非光滑优化[M].北京:科学出版社,2008.
[29]王宏杰,高岩.基于非光滑方程组的智能电网实时定价[J].系统工程学报,2018,33(3):320-327.