Optimal Bidding Strategy Based on Two-Stage Stochastic Programming for Virtual Power Plant

doi:10.3969/j.issn.1000－7229.2018.09.009

Abstract

Abstract: Considering the uncertainty of the market price and the power of renewable energy, this paper adopts virtual power plant （VPP） model to aggregate distributed energy （electric vehicles, demand response, etc.） to participate in electricity market transactions, to improve the stability and market competitiveness of VPP by optimizing and coordinating distributed energy sources. Using multi-scenario method to simulate uncertainty of day-ahead market clearing price and the output of wind power plant, with the goal of maximizing the operating efficiency, the optimal trading strategy model of VPP based on two-stage stochastic programming is formulated. In the first-stage, day-ahead market consider the uncertainty of the price；in the second-stage, balance market consider the uncertainty of the wind power plant production. In addition, the risk associated with the VPP profit is explicitly taken into account through the incorporation of the conditional value-at-risk metric, so as to realize the preference of economy and risk. Finally, the influence of risk appetite and uncertainty on the profit and risk loss of VPP are analyzed, which can provide valuable reference for different risk appetite of VPP.

Key words: virtual power plant（VPP）, stochastic programming, electric vehicle, demand response, conditional value at risk（CVaR）

CLC Number:

TM 61
F 426

ZHOU Bo，LU Lin，GAO Hongjun，TAN Xinyi，WU Honghao. Optimal Bidding Strategy Based on Two-Stage Stochastic Programming for Virtual Power Plant[J]. Electric Power Construction, 2018, 39(9): 70-77.

References

［1］NIKONOWICZ  B, MILEWSKI J. Virtual power plants-general review: Structure, application and optimization［J］. Journal of Power Technologies, 2012, 92（3）: 135-149.

［2］ZIADI Z, TAIRA S, OSHIRO M, et al. Optimal power scheduling for smart grids considering controllable loads and high penetration of photovoltaic generation［J］. IEEE Transactions on Smart Grid, 2014, 5（5）: 2350-2359.

［3］刘吉臻．大规模新能源电力安全高效利用基础问题［J］．中国电机工程学报，2013，33（16）：1-8.

LIU Jizhen．Basic issues of the utilization of large-scale renewable power with high security and efficiency［J］． Proceedings of the CSEE，2013，33（16）：1-8．

［4］陈春武，李娜，钟朋园，等．虚拟电厂发展的国际经验及启示［J］．电网技术，2013，37（8）：2258-2263.

CHEN Chunwu，LI Na，ZHONG Pengyuan，et al．Review of virtual power plant technology abroad and enlightenment to China［J］．Power System Technology，2013，37（8）：2258-2263．

［5］MASHHOUR E, MOGHADDAS-TAFRESHI S M. Bidding strategy of virtual power plant for participating in energy and spinning reserve markets—Part I: Problem formulation［J］. IEEE Transactions on Power Systems, 2011, 26（2）: 949-956.

［6］MASHHOUR E, MOGHADDAS-TAFRESHI S M. Bidding strategy of virtual power plant for participating in energy and spinning reserve markets—Part II: Numerical analysis［J］. IEEE Transactions on Power Systems, 2011, 26（2）: 957-964.

［7］KARDAKOS E G, SIMOGLOU C K, BAKIRTZIS A G. Optimal offering strategy of a virtual power plant: A stochastic bi-level approach［J］. IEEE Transactions on Smart Grid, 2016, 7（2）: 794-806.

［8］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．

［9］牛文娟，李扬，王蓓蓓．考虑不确定性的需求响应虚拟电厂建模［J］．中国电机工程学报，2014，34（22）：3630-3637.

NIU Wenjuan，LI Yang，WANG Beibei．Demand response based virtual power plant modeling considering uncertainty［J］．Proceedings of the CSEE，2014，34（22）：3630-3637．

［10］卢志刚，王荟敬，赵号，等．含V2G的虚拟电厂双层逆鲁棒优化调度策略［J］．电网技术，2017，41（4）：1245-1252.

LU Zhigang，WANG Huijing，ZHAO Hao，et al．Strategy of bilevel inverse robust optimization dispatch of virtual power plant containing V2G［J］．Power System Technology，2017，41（4）：1245-1252．

［11］PANDZIC H, MORALES J M, CONEJO A J, et al. Offering model for a virtual power plant based on stochastic programming［J］. Applied Energy, 2013, 105: 282-292.

［12］PANDZIC H, KUZLE I, CAPUDER T. Virtual power plant mid-term dispatch optimization［J］. Applied Energy, 2013, 101: 134-141.

［13］SHAYEGAN-RAD A, BADRI A, ZANGENEH A. Day-ahead scheduling of virtual power plant in joint energy and regulation reserve markets under uncertainties［J］. Energy, 2017, 121: 114-125.

［14］马春艳，董春发，吕志鹏，等．计及随机因素的商业型虚拟发电厂短期交易与优化运行策略［J］．电网技术，2016，40（5）：1543-1549.

MA Chunyan，DONG Chunfa，L Zhipeng，et al．Short-term trading and optimal operation strategy for commercial virtual power plant considering uncertainties［J］．Power System Technology，2016，40（5）：1543-1549．

［15］RIVEROS J Z, BRUNINX K, PONCELET K, et al. Bidding strategies for virtual power plants considering CHPs and intermittent renewables［J］. Energy Conversion and Management, 2015, 103: 408-418.

［16］TAJEDDINI M A, RAHIMI-KIAN A, SOROUDI A. Risk averse optimal operation of a virtual power plant using two stage stochastic programming［J］. Energy, 2014, 73: 958-967.

［17］MOGHADDAM I G, NICK M, FALLAHI F, et al. Risk-averse profit-based optimal operation strategy of a combined wind farm-cascade hydro system in an electricity market［J］. Renewable energy, 2013, 55: 252-259.

［18］XU Z, HU Z, SONG Y, et al. Risk-averse optimal bidding strategy for demand-side resource aggregators in day-ahead electricity markets under uncertainty［J］. IEEE Transactions on Smart Grid, 2017, 8（1）: 96-105.

［19］TAN Z, WANG G, JU L, et al. Application of CVaR risk aversion approach in the dynamical scheduling optimization model for virtual power plant connected with wind-photovoltaic-energy storage system with uncertainties and demand response［J］. Energy, 2017, 124: 198-213.

［20］ ARGIENTO R, FARANDA R, PIEVATOLO A, et al. Distributed interruptible load shedding and micro-generator dispatching to benefit system operations［J］. IEEE Transactions on Power Systems, 2012, 27（2）: 840-848.

［21］周亦洲，孙国强，黄文进，等．计及电动汽车和需求响应的多类电力市场下虚拟电厂竞标模型［J］．电网技术，2017，41（6）：1759-1766.

ZHOU Yizhou，SUN Guoqiang，HUANG Wenjin，et al．Strategic bidding model for virtual power plant in different electricity markets considering electric vehicles and demand response［J］．Power System Technology，2017，41（6）：1759-1766．

［22］许少伦, 严正, 冯冬涵,等. 基于多智能体的电动汽车充电协同控制策略［J］. 电力自动化设备, 2014, 34（11）: 7-13.

XU Shaolun, YAN Zheng, FENG Donghan, et al. Cooperative charging control strategy of electric vehicles based on multi-agent［J］. Electric Power Automation Equipment, 2014, 34 （11）: 7-13.

［23］ HUANG X, YE Y, ZHANG H. Extensions of kmeans-type algorithms: a new clustering framework by integrating intracluster compactness and intercluster separation［J］. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25（8）: 1433-1446.

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