考虑变压器分接头动作次数限制的综合无功优化
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摘要
无功功率的分布不仅与电压幅值有着密切的关系，而且与有功功率的损耗也有着密切的关系。因而电力系统无功优化问题是电力系统研究领域的一个重要课题，但是近年来由于电力市场的实行，传统的无功优化模型不能够适应电力市场的要求，研究适应电力市场要求的无功优化模型具有较重要的理论意义和较大的实用价值。
在传统的电力系统无功优化的数学模型中，一般以系统有功网损最小为目标函数，为了适应电力市场的实际情况，本文提出的无功优化数学模型不仅考虑了系统有功损耗还考虑了无功功率的费用。在算法上采用了具有较强的全局寻优能力和较好的适应性的新算法――遗传算法和Alopex方法相结合的算法作为无功优化的求解方法。
为了减少变压器分接头动作次数，延长设备使用寿命，本文提出了一种分段计算的方法来满足一天的无功优化。该方法根据一天的负荷水平和变化趋势以及一天内变压器分接头动作次数的限制，将一天划分为几个分段然后对每一个分段分别进行无功优化计算。这种方法能够比较容易地满足一天内变压器分接头动作次数的限制，而且对于大部分时间段只进行发电机端电压和补偿容量的优化计算，这样便减小了计算时间。通过对算例的计算结果表明，本文提出的方法是可行的。
The distribution of reactive power has close relations with not only voltage amplitude but also the power loss. So the reactive power optimization problem is an important subject in the research domain of power system, but because of the realization of power market in recent years, the traditional reactive power optimization model can’t adapt to the requirements of power market, the study on reactive power optimization model which can adapt to the requirements of power market has theoretical meaning and practical value.
In the traditional mathematical model of reactive power optimization, usually taking the minimization of the power loss as the objective function, in order to adapt to the actual conditions in the power market, the mathematical model of reactive power optimization described in this paper considers not only system power loss but also the price of the reactive power. We use a new algorithm which has the ability of global search and better adaptability―Genetic Algorithm integrated with Alopex in this paper as the solution method.
In order to reduce the switching operations of transformer, increase the service time of equipments, we proposed a sectionalized based reactive power optimization method in the paper to satisfy the requirements of one-day reactive power optimization, this method divides one day into several sections and performs reactive power optimization separately according to the load level, its changing tread and the maximum allowable number of switching operations. This method can meet constraints of the maximum allowable number of switching operations easily, moreover optimization calculation only includes voltage of generator and compensation capacity for most time segments, so it improves calculation speed. The results of the calculation examples demonstrate that the method described in the paper is feasible.
在传统的电力系统无功优化的数学模型中，一般以系统有功网损最小为目标函数，为了适应电力市场的实际情况，本文提出的无功优化数学模型不仅考虑了系统有功损耗还考虑了无功功率的费用。在算法上采用了具有较强的全局寻优能力和较好的适应性的新算法――遗传算法和Alopex方法相结合的算法作为无功优化的求解方法。
为了减少变压器分接头动作次数，延长设备使用寿命，本文提出了一种分段计算的方法来满足一天的无功优化。该方法根据一天的负荷水平和变化趋势以及一天内变压器分接头动作次数的限制，将一天划分为几个分段然后对每一个分段分别进行无功优化计算。这种方法能够比较容易地满足一天内变压器分接头动作次数的限制，而且对于大部分时间段只进行发电机端电压和补偿容量的优化计算，这样便减小了计算时间。通过对算例的计算结果表明，本文提出的方法是可行的。
The distribution of reactive power has close relations with not only voltage amplitude but also the power loss. So the reactive power optimization problem is an important subject in the research domain of power system, but because of the realization of power market in recent years, the traditional reactive power optimization model can’t adapt to the requirements of power market, the study on reactive power optimization model which can adapt to the requirements of power market has theoretical meaning and practical value.
In the traditional mathematical model of reactive power optimization, usually taking the minimization of the power loss as the objective function, in order to adapt to the actual conditions in the power market, the mathematical model of reactive power optimization described in this paper considers not only system power loss but also the price of the reactive power. We use a new algorithm which has the ability of global search and better adaptability―Genetic Algorithm integrated with Alopex in this paper as the solution method.
In order to reduce the switching operations of transformer, increase the service time of equipments, we proposed a sectionalized based reactive power optimization method in the paper to satisfy the requirements of one-day reactive power optimization, this method divides one day into several sections and performs reactive power optimization separately according to the load level, its changing tread and the maximum allowable number of switching operations. This method can meet constraints of the maximum allowable number of switching operations easily, moreover optimization calculation only includes voltage of generator and compensation capacity for most time segments, so it improves calculation speed. The results of the calculation examples demonstrate that the method described in the paper is feasible.
引文
[1] 何仰赞，温增银等，电力系统分析,华中理工大学出版社，1996，7～10。[2] 陈衍，电力系统稳态分析，水利电力出版社，1985。
[3] 韩祯祥，吴国炎等，电力系统分析，浙江大学出版社，1993
[4] 华志明，岳湖山，电力系统稳态计算，重庆大学出版社，1991
[5] 西安交通大学等，电力系统计算，水利电力出版社，1978
[6] 诸俊伟等，电力系统分析，中国电力出版社，1995
[7] J Carpentier，Contribution a l’etude du Dispatching Economique，Bull. Soc. Fr. Elec，1962
[8] 许文超，郭伟，电力系统无功优化的模型及算法综述，电力系统及其自动化学报，2003，2
[9] 刘万福，电网无功优化规划方法综述，内蒙古电力，1994，2
[10] 南志远，王小兵，电力系统无功优化与电压控制，华北电力学院学报，1992
[11] 朱太秀，电力系统优化潮流与无功优化，电网技术，1996，11
[12] H.W.Dommel，W.F.Tinney，Optimal Power Flow Solution，IEEE Trans On PAS，1968
[13] Sun D I，et al，Optimal Power Flow by Newton Approach，IEEE Trans On PAS，1984
[14] 徐建亭，王秀英等，电力系统电压无功的序列二次规划算法，电力系统自动化，2001，12
[15] Burchett R C，Kapp H H，Quadratically Convergent Optimal Power Flow，IEEE Trans On PAS，1984
[16] Shibimi M A EI，Reactive Power Optimization Using Modified Linear Programming Approach，IEEE Trans On PAS，1975
[17] Back T，Hammel C，Evolutionary Computation：Comment on the History and Current State，IEEE Trans On Evolutionary Computation，1997
[18] Glover F，Laguna M，A Tabu Search Approach，Management Science，1993
[19] Gan D Q，Qu Z H，Large Scale VAR Optimization and Planning by Tabu Search，Electric Power Systems Research，1996
[20] Huang Y C，Yang H T，Solving the Capacitor Placement Problem Using Tabu Search Approach，IEEE Trans on Power Systems，1996
[21] 刘玉田，马莉，基于Tabu搜索方法的电力系统无功优化，电力系统自动化，2000
[22] 朱心飚，丁洪，人工神经网络简介，机床，1993
[23] 陈卫田 施颂椒，基于神经网络的非线性自适应控制，控制理论与应用，1996
[24] 彭建春，王耀南等，基于神经网络的静止无功补偿器自校正内模控制，电网技术，1997
[25] 杨争林，孙雅明，基于ANN无功预测和优化策略相结合的变电站电压和无功综合自动控制，天津电力技术，1999
[26] Narendra K. S.，Parthasarthy K.，Identification Control of Dynamic System Using Neural Networks，IEEE Trans Neural Networks，1990
[27] 易继锴，侯媛彬，智能控制技术，北京工业大学出版社，1999
[28] 吴疑，遗传算法在电力系统无功优化中的应用综述，广东电力，2002
[29] 段玉倩，贺家里，遗传算法及其改进，电力系统及其自动化学报，1998
[30] LEE K Y，BAI X M，Optimization method for reactive power planning by using a modified simple genetic algorithm，IEEE Trans on PS，1995
[31] 杨以涵，L.L.Lai等，遗传算法在电力系统无功优化中的应用，中国电机工程学报，1995
[32] 周双喜，杨彬，实现无功优化的新算法——遗传算法，电力系统自动化，1995
[33] WU Q H，CAO Y J，Optimal reactive power dispatch using an adaptive genetic algorithm，Electrical power & systems，1998
[34] 于尔铿，韩放等，电力市场，中国电力出版社，1998
[35] Lamont J W，Jian Fu，Cost Analysis of Reactive Power Support，IEEE Transaction on Power System，1999
[36] Baughman M L，Siddiqi S N，Real time Pricing of Reactive Power，IEEE Transaction on Power System，1991
[37] Shangyou Hao，Alex Papalexopoulos，Reactive Power Pricing and Management，IEEE Transaction on Power System，1997
[38] 戴彦，倪以信等，基于潮流组成分析及成本分摊的无功功率电价，电力系统自动化，2000
[39] 吴国丙，任震等，电力市场环境下的无功服务及其成本分析，继电器，2002
[40] Unnikrishnan K P，Venugopal K P，Learning in Connectionist Networks Using the Alopex Algorithm，Proceedings of UNCC，1992
[41] Harth E，Tzanakou E，Vision Res，1974
[42] 徐立新，王常虹等，神经网络训练的一种随机方法，哈尔滨工业大学学报，1997
[3] 韩祯祥，吴国炎等，电力系统分析，浙江大学出版社，1993
[4] 华志明，岳湖山，电力系统稳态计算，重庆大学出版社，1991
[5] 西安交通大学等，电力系统计算，水利电力出版社，1978
[6] 诸俊伟等，电力系统分析，中国电力出版社，1995
[7] J Carpentier，Contribution a l’etude du Dispatching Economique，Bull. Soc. Fr. Elec，1962
[8] 许文超，郭伟，电力系统无功优化的模型及算法综述，电力系统及其自动化学报，2003，2
[9] 刘万福，电网无功优化规划方法综述，内蒙古电力，1994，2
[10] 南志远，王小兵，电力系统无功优化与电压控制，华北电力学院学报，1992
[11] 朱太秀，电力系统优化潮流与无功优化，电网技术，1996，11
[12] H.W.Dommel，W.F.Tinney，Optimal Power Flow Solution，IEEE Trans On PAS，1968
[13] Sun D I，et al，Optimal Power Flow by Newton Approach，IEEE Trans On PAS，1984
[14] 徐建亭，王秀英等，电力系统电压无功的序列二次规划算法，电力系统自动化，2001，12
[15] Burchett R C，Kapp H H，Quadratically Convergent Optimal Power Flow，IEEE Trans On PAS，1984
[16] Shibimi M A EI，Reactive Power Optimization Using Modified Linear Programming Approach，IEEE Trans On PAS，1975
[17] Back T，Hammel C，Evolutionary Computation：Comment on the History and Current State，IEEE Trans On Evolutionary Computation，1997
[18] Glover F，Laguna M，A Tabu Search Approach，Management Science，1993
[19] Gan D Q，Qu Z H，Large Scale VAR Optimization and Planning by Tabu Search，Electric Power Systems Research，1996
[20] Huang Y C，Yang H T，Solving the Capacitor Placement Problem Using Tabu Search Approach，IEEE Trans on Power Systems，1996
[21] 刘玉田，马莉，基于Tabu搜索方法的电力系统无功优化，电力系统自动化，2000
[22] 朱心飚，丁洪，人工神经网络简介，机床，1993
[23] 陈卫田 施颂椒，基于神经网络的非线性自适应控制，控制理论与应用，1996
[24] 彭建春，王耀南等，基于神经网络的静止无功补偿器自校正内模控制，电网技术，1997
[25] 杨争林，孙雅明，基于ANN无功预测和优化策略相结合的变电站电压和无功综合自动控制，天津电力技术，1999
[26] Narendra K. S.，Parthasarthy K.，Identification Control of Dynamic System Using Neural Networks，IEEE Trans Neural Networks，1990
[27] 易继锴，侯媛彬，智能控制技术，北京工业大学出版社，1999
[28] 吴疑，遗传算法在电力系统无功优化中的应用综述，广东电力，2002
[29] 段玉倩，贺家里，遗传算法及其改进，电力系统及其自动化学报，1998
[30] LEE K Y，BAI X M，Optimization method for reactive power planning by using a modified simple genetic algorithm，IEEE Trans on PS，1995
[31] 杨以涵，L.L.Lai等，遗传算法在电力系统无功优化中的应用，中国电机工程学报，1995
[32] 周双喜，杨彬，实现无功优化的新算法——遗传算法，电力系统自动化，1995
[33] WU Q H，CAO Y J，Optimal reactive power dispatch using an adaptive genetic algorithm，Electrical power & systems，1998
[34] 于尔铿，韩放等，电力市场，中国电力出版社，1998
[35] Lamont J W，Jian Fu，Cost Analysis of Reactive Power Support，IEEE Transaction on Power System，1999
[36] Baughman M L，Siddiqi S N，Real time Pricing of Reactive Power，IEEE Transaction on Power System，1991
[37] Shangyou Hao，Alex Papalexopoulos，Reactive Power Pricing and Management，IEEE Transaction on Power System，1997
[38] 戴彦，倪以信等，基于潮流组成分析及成本分摊的无功功率电价，电力系统自动化，2000
[39] 吴国丙，任震等，电力市场环境下的无功服务及其成本分析，继电器，2002
[40] Unnikrishnan K P，Venugopal K P，Learning in Connectionist Networks Using the Alopex Algorithm，Proceedings of UNCC，1992
[41] Harth E，Tzanakou E，Vision Res，1974
[42] 徐立新，王常虹等，神经网络训练的一种随机方法，哈尔滨工业大学学报，1997