地区电网无功电压优化控制系统的研究
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摘要
无功／电压优化控制是保证电力系统安全、经济运行的一项有效手段，是提高电压质量的重要措施，因此研究地区电网的电压／无功控制策略，既具有理论意义，又具有实际应用价值。
在无功电压优化中，在追求网损目标最小及较高电压质量的同时，通常并未考虑限制每个调度周期内设备的动作次数，因此，可能会造成某些设备频繁动作，同时每个时间段的优化中，动作设备数可能会过多，这样对于电网运行的质量，如网损的减少、设备的寿命、系统的扰动等，综合来看并不是最优。本文提出了一种考虑设备动作成本和限制设备次数的新的无功电压优化方法，从而能对电网运行的网损、设备寿命和动作次数进行综合优化。
借助于负荷预测的结果，可以对全天(例如24个时间点，即24次优化)按照传统的无功电压优化方法进行预优化。在预优化中，可以同时算出每个时间点的动作设备数以及全天的动作总数。因而可以在实时控制中，对那些全天动作过频繁的设备引入惰性因子。利用该惰性因子，不仅可以避免设备动作次数越限，同时可以将设备动作合理地分布到全天的各个时段。
采用的混合优化算法并进行了如下改进：引入灵敏度指导优化进程，减少搜索的盲目性；根据不同设备的调节范围，采用不同二进制长度进行编码，以减少无效搜索；采用可变交叉、变异率，以适应搜索进程的需要；种群大小根据搜索进程动态改变，在保证搜索质量的同时提高速度；在目标函数中引入模糊逻辑中的隶属度来计及负荷的轻重，以实现逆调压的目标等。
将本文所提出的算法应用于某地区电网实时电压无功控制，体现出了较好的实用性，较高的适应性及实时性。
In voltage-reactive power optimization, with the pursuit of the minimization of system losses and higher electric power quality, the restrict of the equipment operations number during a specific time period is often not considered, so some equipment may be operated frequently and the number of operated equipment in each optimization can be quite high. The equipment duration and system operation quality are not comprehensively optimized in this manner.
In this paper, a new voltage-reactive power optimization method, which considers the equipment operational cost and confines the number of operated equipment in each optimization, is introduced. The system losses, endurance of equipment and operated times could be calculated optimally and comprehensively.
By the result of load forecasting, the pre-optimization is made for a whole day (e.g. 24 time-points, i.e. 24 optimizations) in conventional voltage-reactive power optimization method . In the pre-optimization, not only the number of equipment operation at one point of time but also the number of operations during a whole day can be calculated. So in real-time control, inertia factors are introduced when equipment are operated too frequenctly within a whole day. With the utilization of such factors, the number of operations can be reasonably distributed among the whole day, and the violation of operating limits can be avoided.
Furthermore, the hybrid optimal strategy is introduced and improved: introducing sensitivity to guide the searching course, in order to reduce the blindness of search; coding based on range of value corresponding equipment operation, to avoid invalid search; adopting variable cross over and mutation rate, to adapt it to the requirement of the searching progress; making dynamic variation of searching population, to ensure the optimization quality while accelerate the searching course; adopting the membership degree of fuzzy logic to objective function, to take into account the load status and to realize the counter voltage adjustment.
The proposed algorithm has been used in real-time control of voltage-reactive power in a regional power network system and are of high practicability, good adaptability and fast speed.
在无功电压优化中，在追求网损目标最小及较高电压质量的同时，通常并未考虑限制每个调度周期内设备的动作次数，因此，可能会造成某些设备频繁动作，同时每个时间段的优化中，动作设备数可能会过多，这样对于电网运行的质量，如网损的减少、设备的寿命、系统的扰动等，综合来看并不是最优。本文提出了一种考虑设备动作成本和限制设备次数的新的无功电压优化方法，从而能对电网运行的网损、设备寿命和动作次数进行综合优化。
借助于负荷预测的结果，可以对全天(例如24个时间点，即24次优化)按照传统的无功电压优化方法进行预优化。在预优化中，可以同时算出每个时间点的动作设备数以及全天的动作总数。因而可以在实时控制中，对那些全天动作过频繁的设备引入惰性因子。利用该惰性因子，不仅可以避免设备动作次数越限，同时可以将设备动作合理地分布到全天的各个时段。
采用的混合优化算法并进行了如下改进：引入灵敏度指导优化进程，减少搜索的盲目性；根据不同设备的调节范围，采用不同二进制长度进行编码，以减少无效搜索；采用可变交叉、变异率，以适应搜索进程的需要；种群大小根据搜索进程动态改变，在保证搜索质量的同时提高速度；在目标函数中引入模糊逻辑中的隶属度来计及负荷的轻重，以实现逆调压的目标等。
将本文所提出的算法应用于某地区电网实时电压无功控制，体现出了较好的实用性，较高的适应性及实时性。
In voltage-reactive power optimization, with the pursuit of the minimization of system losses and higher electric power quality, the restrict of the equipment operations number during a specific time period is often not considered, so some equipment may be operated frequently and the number of operated equipment in each optimization can be quite high. The equipment duration and system operation quality are not comprehensively optimized in this manner.
In this paper, a new voltage-reactive power optimization method, which considers the equipment operational cost and confines the number of operated equipment in each optimization, is introduced. The system losses, endurance of equipment and operated times could be calculated optimally and comprehensively.
By the result of load forecasting, the pre-optimization is made for a whole day (e.g. 24 time-points, i.e. 24 optimizations) in conventional voltage-reactive power optimization method . In the pre-optimization, not only the number of equipment operation at one point of time but also the number of operations during a whole day can be calculated. So in real-time control, inertia factors are introduced when equipment are operated too frequenctly within a whole day. With the utilization of such factors, the number of operations can be reasonably distributed among the whole day, and the violation of operating limits can be avoided.
Furthermore, the hybrid optimal strategy is introduced and improved: introducing sensitivity to guide the searching course, in order to reduce the blindness of search; coding based on range of value corresponding equipment operation, to avoid invalid search; adopting variable cross over and mutation rate, to adapt it to the requirement of the searching progress; making dynamic variation of searching population, to ensure the optimization quality while accelerate the searching course; adopting the membership degree of fuzzy logic to objective function, to take into account the load status and to realize the counter voltage adjustment.
The proposed algorithm has been used in real-time control of voltage-reactive power in a regional power network system and are of high practicability, good adaptability and fast speed.
引文
[1] Grudinin N. Reactive Power Optimization Using Successive Quadratic Programming Method. IEEE Trans on Power Systems, 1998, 13(4).
[2]. Ding Qia, Li Naihu, Wang Xiaodong. Implementation of Interior Point Method Based Voltage/Reactive Power Optimization. In: IEEE Power Engineering Society Winter Meeting. Singapore:2000.
[3] W. N. W Abdullah, H. Saibon A. A. M Zain. Genetic Algorithm For Optimal Reactive Power Dispatch. Proceedings of EMPD 1998, Vol 1.
[4] 程新功，厉吉文，曹立霞等．基于电网分区的多目标分布式并行无功优化研究(J)．中国电机工程学报，2003，23(10)．
[5] 许诺，黄民翔．原对偶内点法与定界法在无功优化中的应用(J)．电力系统自动化学报，2000，12(3)．
[6] 李亚男，张粒子，杨以涵．考虑电压约束裕度的无功优化及其内点解法(J)．中国电机工程学报，2001，21(9)．
[7] 倪炜，单渊达．具有优化路径的遗传算法应用于电力系统无功优化(J)．电力系统自动化，2000，24(21)．
[8] J. R. Gomes, O. R. Saavedra. Optimal Reactive Power Dispatch Using Evolutionary Computation: Extended Algorithms. IEEE Transactions on Power Systems, 1995, Vol 10.
[9] Hidenori Aoki, Yoshibumi Mizutani. Reactive Power Control By Genetic Algorithm. IEEE Power Engineering Society Winter Meeting, 2000.
[10] 何仰赞，温增银等．电力系统分析．华中理工大学出版社，1996．
[11] Yutian Liu, Li Ma, Jianjun Zhang. GA/SA/TS Hybird Algorithms for Reactive Power Optimization, IEEE Tran. On Power Systems, January 2000.
[12] G. A. Taylor, Y. H. Song, M. R. lrving, M. E. Bradkey, T. G. Williams. A review of algorithmic and heuristic based methods for voltage/VAr control. 5th internationall Power Engineering Conference(IPEC2001), Singapore, 17th-19th. May 2001.
[13] I. Hano etal. Real Time Control of System Voltage and Reactive Power. IEEE Transaction on Power Apparatus and System, Vol. PAS-88, No. 10, October 1969.
[14] Zhu J Z, Chang C S. Reactive power optimisation using an analytic hierarchical process and a nonlinear optimisation neural network approach(C). I0EE Proceedings on Generation, Transmission and Distribution. 1998, 145(1).
[15] 文劲宇，江振华等．基于遗传算法的无功优化在鄂州电网中的实现，电力系统自动化，2000年1月，Vol．24．No．2．
[16] 周双喜，蔡虎．应用于电力系统无功优化的改进遗传算法，电网技术，1997年12月，Vol．21 No．12．
[17] 靳龙章，丁毓山．电网无功补偿实用技术，中国水利水电出版社，1997．
[18] 黄华，熊信，吴耀武等．基于BOX算法的无功优化配置，电力系统自动化，2000，10月，Vol．24 No．20．
[19] 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，Vol．19 No．11．
[20] 程浩忠．基于遗传算法的电力系统无功优化，上海交通大学学报，1998，Vol．32 No．1．
[21] 顾丹珍，徐瑞德．一种地区电网多目标无功优化的新方法——改进模拟退火算法．电网技术．1998，22(1)．
[22] 王凌．智能优化算法及其应用．北京：清华大学出版社，施普林格出版社，2001．
[23] 赵登福，周文华，张伏生等．遗传算法在无功优化应用中的改进．电网技术．1998，22(10)．
[24] 杨若黎，顾基发．一种高效的模拟退火全局优化算法．系统工程理论与实践．1997，vol 5．
[25] Mamandur K R C, Chenoweth R D. Optimal control of reactivepower flow for improvements in voltage profiles and for realpower loss minimization[J]. IEEE Transaction on PAS, 1981, 100(7).
[26] Laguna M, Glover F. Bandwidth packing: a tabu search approach[J]. Management science, 1993, Vol. 39 No. 4.
[27] Abdul-Rahman K H, Shahidehpour S M, Daneshdoost M. AI Approach to Optimal VAR Control with Fuzzy Reactive Loads. IEEE Trans on Power Systems, 1995, Vol. 10 No. 1.
[28] Jwo WS, Liu CW, Liu C C, etal. Hybrid Expert System and Simulated Annealing Approach to Optimal Reactive Power Planning. IEE Proceedings——Generation, Transmission & Distribution, 1995, Vol. 142 No. 4.
[29] Wu Q H, Cao Y J, Wen JY. Optimal Reactive Power Dispatch Using an Adaptive Genetic Algorithm. Int Jof Electrical Power & Energy Systems, 1998, Vol. 20 No. 8.
[30] Goldberg D E. Genetic algorithms in search, optimization, and machine learnning. New York:Addison-Wesley, 1989.
[31] Davis L(ed). Handbook of genetic algorithms. New York:NY Van Nostrand Reinhold, 1991.
[32] Nara K. Implementatin of genetic algorithm for distribution systems loss minimum reconfiguration. IEEE Trans PWRS, 1992, Vol 7 No3.
[33] Youshimi M. Optimal economic power dispatch using genetic algorithms. 3 rd ANNPS Japan, 1993.
[34] Cen Wenhui, Zhao Qing, Dai Wenxiang. Selection of optimal drop point for external source based on genetic algorithm. Electric Power System Automation(in Chinese), 1995, Vol. 19 No. 2.
[35] Iba K. Reactive power optimization by genetic algorithm. IEEE Trans on Power Systems, 1994, Vol. 9 No. 2.
[36] Iba. K. Optimal VAR allocation by genetic algorithm. Proceeding of 1993 IEEE ANNPS-93, Yokoyama, Japan, 1993.
[37] Cheng Haozhong, Liao Peihong. Optimal placement planning of compensating capacitors in distribution power systems. Electric Power System Automation(in Chinese), 1987, Vol. 11 No. 6.
[38] 王洪章，熊信艮等．基于改进Tabu搜索算法的电力系统无功优化，电网技术，2002年1月，
Vol. 26 No. 1.
[39]. Kennedy J, Eberhart R. Particle Swarm Optimization. IEEE Int'l Conf on Neural Network, Perth, Australia, 1995, Vol 4.
[40]. Clerc, M. The swarm and the queen: toward a deterministic and adaptive particle swarm optimization. Proc of the Congress of Evolutionary Computation, Washington, DC, 1999.
[41] Eberhart, R. Shi Y. Comparing inertia weights and constriction factors in particle swarm optimization. Proc. of Conf. on Evolutionary Computation, Piscataway, July 2000. Vol 1.
[42] Kennedy J, Eberhart R. A Discrete Binary Version of the Particle Swarm Algorithm. Proc. of IEEE International Conference on System, Man, and Cybernetics, Piscataway, 1997, Vol 5.
[43] 余健明，杜刚，姚李孝．结合灵敏度分析的遗传算法应用于配电网无功补偿优化规划．电网技术，2002，26(7)．
[44] Yoshida H, Kawata K, Takayama, S, et al. A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Transaction on Power System. Nov 2000. Vol 15.
[45] Shi Y, Eberhart R. A modified particle swarm optimizer. IEEE International Conf. on Evolutionary Computation, Anchorage, Alaska, USA, May 1998.
[46] 黄晓烁．基于电压稳定的ATC分析和改进遗传算法在无功优化中的应用，浙江大学硕士学位论文，2003，3．
[47] Malachi Y, Singer S. A genetic algorithm for reactive power/voltage control problem. The 21st IEEE Convention of the 11-12 April 2000.
[48] 王凌，郑大钟．一种GASA混合优化策略．控制理论与应用，2001，Vol 4．
[2]. Ding Qia, Li Naihu, Wang Xiaodong. Implementation of Interior Point Method Based Voltage/Reactive Power Optimization. In: IEEE Power Engineering Society Winter Meeting. Singapore:2000.
[3] W. N. W Abdullah, H. Saibon A. A. M Zain. Genetic Algorithm For Optimal Reactive Power Dispatch. Proceedings of EMPD 1998, Vol 1.
[4] 程新功，厉吉文，曹立霞等．基于电网分区的多目标分布式并行无功优化研究(J)．中国电机工程学报，2003，23(10)．
[5] 许诺，黄民翔．原对偶内点法与定界法在无功优化中的应用(J)．电力系统自动化学报，2000，12(3)．
[6] 李亚男，张粒子，杨以涵．考虑电压约束裕度的无功优化及其内点解法(J)．中国电机工程学报，2001，21(9)．
[7] 倪炜，单渊达．具有优化路径的遗传算法应用于电力系统无功优化(J)．电力系统自动化，2000，24(21)．
[8] J. R. Gomes, O. R. Saavedra. Optimal Reactive Power Dispatch Using Evolutionary Computation: Extended Algorithms. IEEE Transactions on Power Systems, 1995, Vol 10.
[9] Hidenori Aoki, Yoshibumi Mizutani. Reactive Power Control By Genetic Algorithm. IEEE Power Engineering Society Winter Meeting, 2000.
[10] 何仰赞，温增银等．电力系统分析．华中理工大学出版社，1996．
[11] Yutian Liu, Li Ma, Jianjun Zhang. GA/SA/TS Hybird Algorithms for Reactive Power Optimization, IEEE Tran. On Power Systems, January 2000.
[12] G. A. Taylor, Y. H. Song, M. R. lrving, M. E. Bradkey, T. G. Williams. A review of algorithmic and heuristic based methods for voltage/VAr control. 5th internationall Power Engineering Conference(IPEC2001), Singapore, 17th-19th. May 2001.
[13] I. Hano etal. Real Time Control of System Voltage and Reactive Power. IEEE Transaction on Power Apparatus and System, Vol. PAS-88, No. 10, October 1969.
[14] Zhu J Z, Chang C S. Reactive power optimisation using an analytic hierarchical process and a nonlinear optimisation neural network approach(C). I0EE Proceedings on Generation, Transmission and Distribution. 1998, 145(1).
[15] 文劲宇，江振华等．基于遗传算法的无功优化在鄂州电网中的实现，电力系统自动化，2000年1月，Vol．24．No．2．
[16] 周双喜，蔡虎．应用于电力系统无功优化的改进遗传算法，电网技术，1997年12月，Vol．21 No．12．
[17] 靳龙章，丁毓山．电网无功补偿实用技术，中国水利水电出版社，1997．
[18] 黄华，熊信，吴耀武等．基于BOX算法的无功优化配置，电力系统自动化，2000，10月，Vol．24 No．20．
[19] 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，Vol．19 No．11．
[20] 程浩忠．基于遗传算法的电力系统无功优化，上海交通大学学报，1998，Vol．32 No．1．
[21] 顾丹珍，徐瑞德．一种地区电网多目标无功优化的新方法——改进模拟退火算法．电网技术．1998，22(1)．
[22] 王凌．智能优化算法及其应用．北京：清华大学出版社，施普林格出版社，2001．
[23] 赵登福，周文华，张伏生等．遗传算法在无功优化应用中的改进．电网技术．1998，22(10)．
[24] 杨若黎，顾基发．一种高效的模拟退火全局优化算法．系统工程理论与实践．1997，vol 5．
[25] Mamandur K R C, Chenoweth R D. Optimal control of reactivepower flow for improvements in voltage profiles and for realpower loss minimization[J]. IEEE Transaction on PAS, 1981, 100(7).
[26] Laguna M, Glover F. Bandwidth packing: a tabu search approach[J]. Management science, 1993, Vol. 39 No. 4.
[27] Abdul-Rahman K H, Shahidehpour S M, Daneshdoost M. AI Approach to Optimal VAR Control with Fuzzy Reactive Loads. IEEE Trans on Power Systems, 1995, Vol. 10 No. 1.
[28] Jwo WS, Liu CW, Liu C C, etal. Hybrid Expert System and Simulated Annealing Approach to Optimal Reactive Power Planning. IEE Proceedings——Generation, Transmission & Distribution, 1995, Vol. 142 No. 4.
[29] Wu Q H, Cao Y J, Wen JY. Optimal Reactive Power Dispatch Using an Adaptive Genetic Algorithm. Int Jof Electrical Power & Energy Systems, 1998, Vol. 20 No. 8.
[30] Goldberg D E. Genetic algorithms in search, optimization, and machine learnning. New York:Addison-Wesley, 1989.
[31] Davis L(ed). Handbook of genetic algorithms. New York:NY Van Nostrand Reinhold, 1991.
[32] Nara K. Implementatin of genetic algorithm for distribution systems loss minimum reconfiguration. IEEE Trans PWRS, 1992, Vol 7 No3.
[33] Youshimi M. Optimal economic power dispatch using genetic algorithms. 3 rd ANNPS Japan, 1993.
[34] Cen Wenhui, Zhao Qing, Dai Wenxiang. Selection of optimal drop point for external source based on genetic algorithm. Electric Power System Automation(in Chinese), 1995, Vol. 19 No. 2.
[35] Iba K. Reactive power optimization by genetic algorithm. IEEE Trans on Power Systems, 1994, Vol. 9 No. 2.
[36] Iba. K. Optimal VAR allocation by genetic algorithm. Proceeding of 1993 IEEE ANNPS-93, Yokoyama, Japan, 1993.
[37] Cheng Haozhong, Liao Peihong. Optimal placement planning of compensating capacitors in distribution power systems. Electric Power System Automation(in Chinese), 1987, Vol. 11 No. 6.
[38] 王洪章，熊信艮等．基于改进Tabu搜索算法的电力系统无功优化，电网技术，2002年1月，
Vol. 26 No. 1.
[39]. Kennedy J, Eberhart R. Particle Swarm Optimization. IEEE Int'l Conf on Neural Network, Perth, Australia, 1995, Vol 4.
[40]. Clerc, M. The swarm and the queen: toward a deterministic and adaptive particle swarm optimization. Proc of the Congress of Evolutionary Computation, Washington, DC, 1999.
[41] Eberhart, R. Shi Y. Comparing inertia weights and constriction factors in particle swarm optimization. Proc. of Conf. on Evolutionary Computation, Piscataway, July 2000. Vol 1.
[42] Kennedy J, Eberhart R. A Discrete Binary Version of the Particle Swarm Algorithm. Proc. of IEEE International Conference on System, Man, and Cybernetics, Piscataway, 1997, Vol 5.
[43] 余健明，杜刚，姚李孝．结合灵敏度分析的遗传算法应用于配电网无功补偿优化规划．电网技术，2002，26(7)．
[44] Yoshida H, Kawata K, Takayama, S, et al. A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Transaction on Power System. Nov 2000. Vol 15.
[45] Shi Y, Eberhart R. A modified particle swarm optimizer. IEEE International Conf. on Evolutionary Computation, Anchorage, Alaska, USA, May 1998.
[46] 黄晓烁．基于电压稳定的ATC分析和改进遗传算法在无功优化中的应用，浙江大学硕士学位论文，2003，3．
[47] Malachi Y, Singer S. A genetic algorithm for reactive power/voltage control problem. The 21st IEEE Convention of the 11-12 April 2000.
[48] 王凌，郑大钟．一种GASA混合优化策略．控制理论与应用，2001，Vol 4．