基于WCVaR评估的虚拟发电厂能量市场收益——风险模型
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摘要
由于虚拟发电厂中存在风电机组等出力波动的可再生分布式电源,为了量化风电机组出力不确定性对虚拟发电厂进行能量交易时的收益风险,在考虑了风电机组出力不确定性因素后,利用最差情景下的条件风险价值(WCVaR)方法构建了包含运行成本、售电收益和交互收益的虚拟发电厂能量市场的收益—风险模型,然后采用遗传算法对其进行求解,并通过算例分析了不同风险水平下的机组出力情况、交互收益和碳排放量,以及虚拟发电厂在不同风险水平下进行能量交易时的WCVaR,验证了所提出模型的正确性,为分析风电机组出力对虚拟发电厂的收益、风险和减碳的影响提供了理论支持。
Because of the existence of numerous renewable distributed generators with output fluctuation in the virtual power plant,such as wind turbines,in order to quantify the profit risk in virtual power plant energy trading due to uncertainty of wind turbine output,after the uncertainty factors of wind turbine output are taken into consideration,a benefit-risk model of virtual power plant in energy market including operation cost,electricity-sale revenue and trading revenue is developed by the method of worst-case conditional value-at-risk(WCVaR),which is solved by the genetic algorithm.The output of different generating units,benefits and carbon emission reductions are analyzed at different risk levels through case study,and the different values of WCVaR in virtual power plant energy transactions at different risk levels are analyzed as well,which proves the validity of the proposed model while providing theoretical support for analysis of the effect of wind turbine output on the revenue,risk,and carbon reduction of the virtual power plant.
Because of the existence of numerous renewable distributed generators with output fluctuation in the virtual power plant,such as wind turbines,in order to quantify the profit risk in virtual power plant energy trading due to uncertainty of wind turbine output,after the uncertainty factors of wind turbine output are taken into consideration,a benefit-risk model of virtual power plant in energy market including operation cost,electricity-sale revenue and trading revenue is developed by the method of worst-case conditional value-at-risk(WCVaR),which is solved by the genetic algorithm.The output of different generating units,benefits and carbon emission reductions are analyzed at different risk levels through case study,and the different values of WCVaR in virtual power plant energy transactions at different risk levels are analyzed as well,which proves the validity of the proposed model while providing theoretical support for analysis of the effect of wind turbine output on the revenue,risk,and carbon reduction of the virtual power plant.
引文
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[19]周任军,胡军,罗潇,等.发电资产最优组合分配的WCVaR风险度量方法[J].长沙理工大学学报(自然科学版),2009,6(1):36-42.ZHOU Renjun,HU Jun,LUO Xiao,et al.Generation asset optimal portfolio allocation based on worst-case CVaR[J].Journal of Changsha University of Science and Technology(Natural Science),2009,6(1):36-42.
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[2]ANDREWS G.Market based instruments:Australia’s experience with trading renewable energy certificates[C]//Workshop on Good Practices in Policies and Measures,October8-10,2001,Copenhagen,Denmark:7p.
[3]XIA Yuhang,LIU Junyong.Optimal scheduling of virtual power plant with risk management[J].Journal of Power Technologies,2016,96(1):49-56.
[4]闫涛,渠展展,惠东,等.含规模化电池储能系统的商业型虚拟电厂经济性分析[J].电力系统自动化,2014,38(17):98-104.DOI:10.7500/AEPS20140131001.YAN Tao,QU Zhanzhan,HUI Dong,et al.Economic analysis of the virtual power plants with large-scale battery energy storage systems[J].Automation of Electric Power Systems,2014,38(17):98-104.DOI:10.7500/AEPS20140131001.
[5]夏榆杭,刘俊勇.基于分布式发电的虚拟发电厂研究综述[J].电力自动化设备,2016,36(4):100-106.XIA Yuhang,LIU Junyong.Review of virtual power plant based on distributed generation[J].Electric Power Automation Equipment,2016,36(4):100-106.
[6]XIA Y H,LIU J Y,HUANG Z W,et al.Carbon emission impact on the operation of virtual power plant with combined heat and power system[J].Frontiers of Information Technology&Electronic Engineering,2016,17(5):479-488.
[7]PANDZIC H,KUZLE I,CAPUDER T.Virtual power plant mid-term dispatch optimization[J].Applied Energy,2013,101:134-141.
[8]MASHHOUR E,MOGHADDAS-TAFRESHI S M.Bidding strategy of virtual power plant for participating in energy and spinning reserve markets:Part one problem formulation[J].IEEE Trans on Power Systems,2011,26(2):949-956.
[9]PEIK-HERFEH M,SEIFI H,SHEIKH-EL-ESLAMI M K.Decision making of a virtual power plant under uncertainties for bidding in a day-ahead market using point estimate method[J].International Journal of Electrical Power&Energy Systems,2013,44(1):88-98.
[10]TASCIKARAOGLU A,ERDINC O,UZUNOGLU M,et al.An adaptive load dispatching and forecasting strategy for a virtual power plant including renewable energy conversion units[J].Applied Energy,2014,119:445-453.
[11]夏榆杭,刘俊勇,冯超,等.计及需求响应的虚拟发电厂优化调度模型[J].电网技术,2016,40(6):1666-1674.XIA Yuhang,LIU Junyong,FENG Chao,et al.Optimal scheduling model of virtual power plant considering demand response[J].Power System Technology,2016,40(6):1666-1674.
[12]PETERSEN M K,HANSEN L H,BENDTSEN J,et al.Heuristic optimization for the discrete virtual power plant dispatch problem[J].IEEE Trans on Smart Grid,2014,5(6):2910-2918.
[13]周明,李庚银,严正,等.考虑备用需求和风险的供电企业最优购电计划[J].电网技术,2005,29(3):33-38.ZHOU Ming,LI Gengyin,YAN Zheng,et al.Optimal electricity procurement schedule for load service entities incorporating with reserve and risks[J].Power System Technology,2005,29(3):33-38.
[14]王壬,尚金成,冯旸,等.基于CVaR风险计量指标的发电商投标组合策略及模型[J].电力系统自动化,2005,29(14):5-9.WANG Ren,SHANG Jincheng,FENG Yang,et al.Combined bidding strategy and model for power suppliers based on CVaR risk measurement techniques[J].Automation of Electric Power Systems,2005,29(14):5-9.
[15]杨甲甲,何洋,邹波,等.电力市场环境下燃煤电厂电煤库存优化的CVaR模型[J].电力系统自动化,2014,38(4):51-59.DOI:10.7500/AEPS20130508014.YANG Jiajia,HE Yang,ZOU Bo,et al.A CVaR-based coal inventory optimization model for coal-fired power plants in electricity market environment[J].Automation of Electric Power Systems,2014,38(4):51-59.DOI:10.7500/AEPS20130508014.
[16]CHASSIN D P,FULLER J C.On the equilibrium dynamics of demand response in thermostatic loads[C]//Proceedings of the 44th Hawaii International Conference on System Sciences,January 4-7,2011,Hawaii,USA:6p.
[17]卫志农,余爽,孙国强,等.虚拟电厂的概念与发展[J].电力系统自动化,2013,37(13):1-9.WEI Zhinong,YU Shuang,SUN Guoqiang,et al.Concept and development of virtual power plant[J].Automation of Electric Power Systems,2013,37(13):1-9.
[18]ZHU S S,FUKUSHIMA M.Worst-case conditional value-atrisk with application to robust portfolio management[J].Operations Research,2009,57(5):1155-1168.
[19]周任军,胡军,罗潇,等.发电资产最优组合分配的WCVaR风险度量方法[J].长沙理工大学学报(自然科学版),2009,6(1):36-42.ZHOU Renjun,HU Jun,LUO Xiao,et al.Generation asset optimal portfolio allocation based on worst-case CVaR[J].Journal of Changsha University of Science and Technology(Natural Science),2009,6(1):36-42.
[20]SEGURO J V,LAMBERT T W.Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis[J].Journal of Wind Engineering and Industrial Aerodynamics,2000,85(1):75-84.
[21]葛炬,王飞,张粒子.含风电场电力系统旋转备用获取模型[J].电力系统自动化,2010,34(6):32-36.GE Ju,WANG Fei,ZHANG Lizi.Spinning reserve model in the wind power integrated power system[J].Automation of Electric Power Systems,2010,34(6):32-36.