考虑需求响应的多目标概率最优潮流问题研究
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摘要
文章研究了用户基于节点综合电价信息参与需求响应的多目标概率最优潮流问题。首先,建立同时考虑系统运行费用和碳税费用的多目标概率最优潮流模型,并采用乔列斯基因子分解方法考虑负荷的相关性,采用Pair-Copula方法考虑风电场风速的高维相关性,采用拟蒙特卡罗模拟方法进行求解;然后,提出以潮流方程对应的拉格朗日乘子作为节点综合电价,建立需求响应模型并计算得到响应后的负荷;最后,统计所有需求响应后的负荷、运行费用、潮流分布等变量的概率特征,分析不同需求响应指标对概率最优潮流结果的影响。结果表明,用户参与需求响应在降低节点综合电价、改变负荷大小的同时,能够降低线路发生输电阻塞的可能性,对电网安全、稳定及经济运行起到一定的促进作用。
This paper focuses on the problem of multi-objective probabilistic optimal power flow(MPOPF) considering demand response based on locational comprehensive price. First of all, a MPOPF model that jointly considering system operation cost and carbon tax was established. The dependences of load were considered by Cholesky factorization method, and high dimensional dependences of wind speed were considered by Pair-Copula method, respectively. Quasi-Monte Carlo simulation method was applied to solve MPOPF.Then, the locational comprehensive price(LCP) could be obtained by interior point method since it was equivalent to the Lagrange multipliers of the corresponding power flow equations. Finally, a demand response model based on LCP could be established, and the statistical characteristics of load, operating cost, as well as the distribution of power flow after demand response were calculated. Simulation results show that the demand response based on LCP has positive effects on reducing the LCP as well as changing the size of load. At the same time, it can also reduce the possibility of transmission congestion, and plays an important role in promoting the security, stability and economic operation of power grid.
This paper focuses on the problem of multi-objective probabilistic optimal power flow(MPOPF) considering demand response based on locational comprehensive price. First of all, a MPOPF model that jointly considering system operation cost and carbon tax was established. The dependences of load were considered by Cholesky factorization method, and high dimensional dependences of wind speed were considered by Pair-Copula method, respectively. Quasi-Monte Carlo simulation method was applied to solve MPOPF.Then, the locational comprehensive price(LCP) could be obtained by interior point method since it was equivalent to the Lagrange multipliers of the corresponding power flow equations. Finally, a demand response model based on LCP could be established, and the statistical characteristics of load, operating cost, as well as the distribution of power flow after demand response were calculated. Simulation results show that the demand response based on LCP has positive effects on reducing the LCP as well as changing the size of load. At the same time, it can also reduce the possibility of transmission congestion, and plays an important role in promoting the security, stability and economic operation of power grid.
引文
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[12]曹佳,严正,李建华,等.含风电场交直流混联系统的概率潮流计算[J].电力自动化设备,2016,36(11):94-100.
[13]LI X,CAO J,DU D J.Probabilistic optimal power flow for power systems considering wind uncertainty and load correlation[J].Neurocomputing,2015,148:240-247.
[14]LI X,CAO J,DU D J.Two-point estimate method for probabilistic optimal power flow computation including wind farms with correlated parameters[J].Communications in Computer and Information Science,2013,355:417-423.
[15]LI X,CAO J,LU P.Probabilistic load flow computation in power system including wind farms with correlated parameters[C]//2nd IETRenewable Power Generation Conference(RPG 2013),Beijing:IET,2013:1-4.
[16]CAO J,YAN Z.Probabilistic optimal power flow considering dependences of wind speed among wind farms by pair-copula method[J].International Journal of Electrical Power&Energy Systems,2017,84:296-307.
[17]温晓虹.高维相关建模的Pair-Copula方法及其应用[D].厦门:厦门大学,2009,33-35.
[18]ZOU B,XIAO Q.Solving Probabilistic Optimal Power Flow Problem Using Quasi Monte Carlo Method And Ninth-Order Polynomial Normal Transformation[J].IEEE Transactions on Power Systems,2014,29(1):300-306.
[19]LI X,CAO J,DU D J.Comparison of Levenberg-Marquardt Method and Path Following Interior Point Method for the Solution of Optimal Power Flow Problem[J].International Journal of Emerging Electric Power Systems,2012,13(3):1-21.
[2]TAYLOR J A.Financial storage rights[J].IEEE Transactions on Power Systems,2015,30(2):997-1005.
[3]TAYLOR J A,NAYYAR A,CALLAWAY D S,et al.Consolidated dynamic pricing of power system regulation[J].IEEE Transactions on Power Systems,2013,28(4):4692-4700.
[4]曾丹,姚建国,杨胜春,等.应对风电消纳中基于安全约束的价格型需求响应优化调度建模[J].中国电机工程学报,2014,34(31):5571-5578.
[5]别朝红,胡国伟,谢海鹏,等.考虑需求响应的含风电电力系统的优化调度[J].电力系统自动化,2014,38(13):115-120.
[6]宋嗣博,郭红霞,杨萍,等.基于节点边际电价的电力市场分区策略研究[J].电力建设,2017,38(9):132-138.
[7]刘小聪,王蓓蓓,李扬,等.基于实时电价的大规模风电消纳机组组合和经济调度模型[J].电网技术,2014,38(11):2955-2963.
[8]赵洪山,赵航宇,侯杰群,等.需求响应对配电网供电可靠性影响分析[J].电力自动化设备,2017,37(1):8-14.
[9]徐辉,焦扬,蒲雷,等.计及不确定性和需求响应的风光燃储集成虚拟电厂随机调度优化模型[J].电网技术,2017,41(11):3590-3597.
[10]WANG Y,WANG C,MILLER C J,et al.Locational marginal emissions:analysis of pollutant emission reduction through spatial management of load distribution[J].Applied Energy,2014,119:141-150.
[11]曹佳,马洪艳,刘扬,等.基于节点电价的需求响应策略研究[J].电网技术,2016,40(5):1536-1542.
[12]曹佳,严正,李建华,等.含风电场交直流混联系统的概率潮流计算[J].电力自动化设备,2016,36(11):94-100.
[13]LI X,CAO J,DU D J.Probabilistic optimal power flow for power systems considering wind uncertainty and load correlation[J].Neurocomputing,2015,148:240-247.
[14]LI X,CAO J,DU D J.Two-point estimate method for probabilistic optimal power flow computation including wind farms with correlated parameters[J].Communications in Computer and Information Science,2013,355:417-423.
[15]LI X,CAO J,LU P.Probabilistic load flow computation in power system including wind farms with correlated parameters[C]//2nd IETRenewable Power Generation Conference(RPG 2013),Beijing:IET,2013:1-4.
[16]CAO J,YAN Z.Probabilistic optimal power flow considering dependences of wind speed among wind farms by pair-copula method[J].International Journal of Electrical Power&Energy Systems,2017,84:296-307.
[17]温晓虹.高维相关建模的Pair-Copula方法及其应用[D].厦门:厦门大学,2009,33-35.
[18]ZOU B,XIAO Q.Solving Probabilistic Optimal Power Flow Problem Using Quasi Monte Carlo Method And Ninth-Order Polynomial Normal Transformation[J].IEEE Transactions on Power Systems,2014,29(1):300-306.
[19]LI X,CAO J,DU D J.Comparison of Levenberg-Marquardt Method and Path Following Interior Point Method for the Solution of Optimal Power Flow Problem[J].International Journal of Emerging Electric Power Systems,2012,13(3):1-21.