电力市场下风电电力系统旋转备用风险-成本模型
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摘要
为了量化风电出力的随机性和波动性对电力系统备用容量的影响,利用条件风险价值方法,在电力市场环境下构建了包含了常规机组的运行成本、排污成本、期望停电成本、旋转备用成本在内的风电电力系统旋转备用的风险-成本模型,在Matlab环境下利用量子差分进化算法对模型进行求解,通过仿真分析了量子差分进化算法的优势、不同风险水平对系统上下旋转备用容量的影响,以及不同置信度下系统总的运行成本和条件风险值,得出了风险水平越高(对风电的态度愈保守),系统的上下旋转备用越小,而系统的上下旋转备用容量的置信度增加,系统总的运行成本和CVa R值则降低的结论。
In order to quantify the impact caused by the fluctuation and randomness of wind power,the method of conditional value at risk( CVa R) is proposed to build the spinning reserve model of the electric power system incorporated wind power which includs the conventional unit operation cost,the pollution cost,expected energy not supplied cost and spinning reserve capacity cost in the environment of electricity market,and the model is solved by quantum-inspired and differential evolution algorithm in Matlab environment,and then the advantages of quantum differential evolution algorithm,the impact of different profit risk levels on spinning reserve capacity and the influence of different degrees of confidence on the system total operation cost and the value of CVa R are analyzed by simulation examples. It is concluded that the higher the risk level( the more conservative attitude towards wind power),the smaller the up-down spinning reserve of the system,and the higher the confidence of the up-down spinning reserve capacity of the system,the lower the total operating cost and CVa R value of the system.
In order to quantify the impact caused by the fluctuation and randomness of wind power,the method of conditional value at risk( CVa R) is proposed to build the spinning reserve model of the electric power system incorporated wind power which includs the conventional unit operation cost,the pollution cost,expected energy not supplied cost and spinning reserve capacity cost in the environment of electricity market,and the model is solved by quantum-inspired and differential evolution algorithm in Matlab environment,and then the advantages of quantum differential evolution algorithm,the impact of different profit risk levels on spinning reserve capacity and the influence of different degrees of confidence on the system total operation cost and the value of CVa R are analyzed by simulation examples. It is concluded that the higher the risk level( the more conservative attitude towards wind power),the smaller the up-down spinning reserve of the system,and the higher the confidence of the up-down spinning reserve capacity of the system,the lower the total operating cost and CVa R value of the system.
引文
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[2]崔杨,冯鑫源,王铮,等.出力受限风电场群有功分配多目标优化策略[J].可再生能源,2016,34(11):1610-1616.CUI Yang,FENG Xinyuan,WANG Zheng,et al. Multi objective optimization strategy on active power allocation of clustered wind farms with limited output[J]. Renewable Energy,2016,34(11):1610-1616.
[3]WANG J,SHAHIDEHPOUR M,LI Z. Security-constrained unit commitment with volatile wind power generation[J]. IEEE Transactions on Power Systems,2008,23(3):1319-1327.
[4] BHATTACHARYA M,PARAMATI S R,OZTURK I,et al.The effect of renewable energy consumption on economic growth:evidence from top 38 countries[J]. Applied Energy,2016,162:733-741.
[5]殷桂梁,张雪,操丹丹,等.考虑风电和光伏发电影响的电力系统最优旋转备用容量确定[J].电网技术,2015,39(12):3497-3504.YIN Guiliang,ZHANG Xue,CAO Dandan,et al. Determination of optimal spinning reserve capacity of power system considering wind and photovoltaic power affects[J]. Power System Technology,2015,39(12):3497-3504.
[6]曲翀,王秀丽,姚力,等.基于条件成本收益分析的旋转备用优化配置[J].电力系统自动化,2014,38(14):62-69.QU Chong,WANG Xiuli,YAO Li,et al. Optimal configuration of spinning reserve based on conditional cost/benefit analysis[J]. Automation of Electric Power Systems,2014,38(14):62-69.
[7]王雁凌,许传龙,岳巍澎.时变可靠性约束下含风电系统旋转备用的随机规划模型[J].电网技术,2013,37(5):1311-1316.WANG Yanling,XU Chuanlong,YUE Weipeng. A stochastic programming model for spinning reserve of power grid containing wind farms under constraint of time-varying reliability[J].Power System Technology,2013,37(5):1311-1316.
[8]ORTEGA-VAZQUEZ M A,KIRSCHEN D S. Estimating the spinning reserve requirements in systems with significant wind power generation penetration[J]. IEEE Transactions on Power Systems,2009,24(1):114-124.
[9]许云辉,李仲飞.基于收益序列相关的动态投资组合选择——动态均值-方差模型[J].系统工程理论与实践,2008,28(8):123-131.XU Yunhui,LI Zhongfei. Dynamic portfolio selection based on serially correlated return-dynamic mean-variance formulation[J]. System Engineering Theory and Practice,2008,28(8):123-131.
[10]HENDRICKS D. Evaluation of value-at-risk models using historical data(digest summary)[J]. Economic Policy Review Federal Reserve Bank of New York,1996,2(1):39-67.
[11]XIA Y,LIU J. Optimal scheduling of virtual power plant with risk management[J]. Journal of Power Technologies,2016,96(1):49-56.
[12]ROCKAFELLAR R T,URYASEV S. Conditional value-at-risk for general loss distributions[J]. Journal of banking&finance,2002,26(7):1443-1471.
[13]XIA Y,LIU J,HUANG Z,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.
[14]TEWARI S,GEYER C J,MOHAN N. A statistical model for wind power forecast error and its application to the estimation of penalties in liberalized markets[J]. IEEE Transactions on Power Systems,2011,26(4):2031-2039.
[15]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.
[16]LI P,LI S. Quantum-inspired evolutionary algorithm for continuous space optimization based on Bloch coordinates of qubits[J]. Neurocomputing,2008,72(1):581-591.
[17]MOHANTY B,PANDA S,HOTA P K. Controller parameters tuning of differential evolution algorithm and its application to load frequency control of multi-source power system[J]. International journal of electrical power&energy systems,2014,54:77-85.
[18]张里,刘俊勇,刘友波,等.风速相关性下的最优旋转备用容量[J].电网技术,2014,38(12):3412-3417.ZHANG Li,LIU Junyong,LIU Youbo,et al. Optimalspinning reserve capacity of power grid considering wind speed correlation[J]. Power System Technology, 2014, 38(12):3412-3417.