考虑多个分布式电源接入配电网的多目标无功优化调度
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摘要
坚强智能电网的建设促进了分布式电源(Distributed Generation,DG)并网技术的发展,DG并网对配网进行无功优化不仅能够提高电压质量、降低有功网损,还增加了配网运行的灵活性、经济性与安全性。以系统有功功率损耗最低与电压偏压量最小为双目标函数,建立无功优化模型。针对目前无功优化问题尚缺乏一种能兼顾求解的高效性与全局搜索最优性的方法,文中将一种新的启发式算法-鲸鱼优化算法(WOA)运用到电网无功优化调度中,对多个DG接入的IEEE 33节点系统进行无功优化仿真分析。研究表明DG并网增加了配网的稳定性,并且证明了WOA算法在解决此问题上的鲁棒性和有效性。
The construction of a strong smart grid has promoted the development of DG (distributed generation) grid-connected technologies. DG grid-connecting reactive power optimization has not only improved voltage quality,reduced active power network losses,but also increased the flexibility,economy,and security of distribution network operations. In this paper,the reactive power optimization model is established with the minimum active power loss and the minimum voltage bias as the objective function. Aiming at the current reactive power optimization problem,there is still a lack of a method that can take into account both the efficiency of the solution and the optimality of the global search. A new heuristic algorithm,the whale optimization algorithm (WOA),is applied to reactive power optimization scheduling and performed on IEEE33-node system with larges DGs access. The research shows that the DG grid-connection increases the stability of the distribution network,and the WOA algorithm is robust and effective in solving this problem.
The construction of a strong smart grid has promoted the development of DG (distributed generation) grid-connected technologies. DG grid-connecting reactive power optimization has not only improved voltage quality,reduced active power network losses,but also increased the flexibility,economy,and security of distribution network operations. In this paper,the reactive power optimization model is established with the minimum active power loss and the minimum voltage bias as the objective function. Aiming at the current reactive power optimization problem,there is still a lack of a method that can take into account both the efficiency of the solution and the optimality of the global search. A new heuristic algorithm,the whale optimization algorithm (WOA),is applied to reactive power optimization scheduling and performed on IEEE33-node system with larges DGs access. The research shows that the DG grid-connection increases the stability of the distribution network,and the WOA algorithm is robust and effective in solving this problem.
引文
[1]雷敏,杨万里,彭晓波,等.基于改进简化粒子群算法的含DG的配电网无功优化[J].华北电力大学学报,2015,42(1):39-44.Lei Min,Yang Wanli,Peng Xiaobo,et al.Reactive Power Optimization of the Distribution Network with Distributed Generation Based on Improved Simple PSO Algorithm[J].Journal of North China University of Electric Power,2015,42(1):39-44.
[2]赵晶晶,符杨,李东东.考虑双馈电机风电场无功调节能力的配电网无功优化[J].电力系统自动化,2011,35(11):33-38.Zhao Jingjing,Fu Yang,Li Dongdong.Reactive power optimization network considering regulation capability of DFIG wind farm[J].Automation of Electric Power Systems,2011,35(11):33-38.
[3]王成山,罗凤章.配电系统综合评价理论与方法[M].北京:科学出版社,2012.
[4]林济铿,石伟钊,吴乃虎,等.计及离散变量基于互补约束全光滑牛顿法的无功优化[J].中国电机工程学报,2012,32(1):93-100.Lin Jikeng,Shi Weizhao,Wu Naihu,et al.Reactive Power Optimization With Discrete Variables Based on Complementarity Constraints Smooth Newton Method[J].Proceedings of the CSEE,2012,32(1):93-100.
[5]谷永刚,肖凯,夏经德,等.利用增量二次规划和启发式方法的电力系统动态无功优化[J].西安交通大学学报,2010,44(8):106-111.Gu Yonggang,Xiao Kai,Xia Jingde,et al.Incremental Quadratic Programming and Heuristic Combination Algorithm for Dynamic Optimal Reactive Power in Power System[J].Journal of Xi’an Jiaotong University,2010,44(8):106-111.
[6]姚煜,蔡燕春.离散粒子群与内点法结合的电力系统无功优化[J].电力系统保护与控制,2010,38(3):48-52.Yao Yu,Cai Yanchun.A hybrid strategy based on DPSO and IPM for optimal reactive power flow[J].Power System Protection and Control,2010,38(3):48-52.
[7]熊虎岗,程浩忠,李宏仲.基于免疫算法的多目标无功优化[J].中国电机工程学报,2006,(11):102-108.Xiong Hugang,Cheng Haozhong,Li Hongzhong.Reactive power optimization for medium voltage distribution network based on improved particle swarm optimization[J].Proceedings of the CSEE,2006,(11):102-108.
[8]曾学强,刘志刚,符伟杰,等.改进差分进化算法在电力系统无功优化中的应用[J].电网技术,2012,36(2):121-125.Zeng Xueqiang,Liu Zhigang,Fu Weijie,et al.Application of improved differential evolution algorithm in reactive power optimization[J].Power System Technology,2012,36(2):121-125.
[9]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,2013(10):109-113.Cheng Xingong,Li Jiwen,Cao Lixia,et al.Multi-objective distributed parallel reactive power optimization based on subarea division of the power systems[J].Proceedings of the CSEE,2013(10):109-113.
[10]张丽,徐玉琴,王增平,等.包含分布式电源的配电网无功优化[J].电工技术学报,2011,26(3):168-174.Zhang Li,Xu Yuqin,Wang Zengping,et al.Reactive power optimization for distribution system with distributed generators[J].Transactions of China Electrotechnical Society,2011,26(3):168-174.
[11]李晶,王素华,谷彩连.基于遗传算法的含分布式发电的配电网无功优化控制研究[J].电力系统保护与控制,2010,38(6):60-63.Li Jing,Wang Suhua,Gu Cailian.Research of reactive power optimization control in distribution networks based on genetic algorithm[J].Power System Protection and Control,2010,38(6):60-63.
[12]赵俊光,王主丁,张宗益,等.基于节点补偿容量动态上限的配电网无功规划优化混合算法[J].电力系统自动化,2009,33(23):69-74.Zhao Junguang,Wang Zhujiang,Zhang Zongyi,et al.A dynamic upper limit to node compensation based hybrid optimization algorithm for reactive power planning in distribution networks[J].Automation of Electric Power Systems,2009,33(23):69-74.
[13]赵昆,耿光飞.基于改进遗传算法的配电网无功优化[J].电力系统保护与控制,2011,39(5):57,62-68.Zhao Kun,Geng Guangfei.Reactive power optimization of distribution network based on improved genetic algorithm[J].Power System Protection and Control,2011,39(5):57,62-68.
[14]付英杰,汪沨,谭阳红.基于Pareto最优解的含分布式电源配电网无功优化[J].电力系统及其自动化学报,2017,29(1):18-23.Fu Yingjie,Wang Feng,Tan Yanghong.Reactive Power Optimization of Distribution Network Containing Distributed Generation Based on Pareto optimal[J].Journal of Electric Power Systems and Automation,2017,29(1):18-23.
[15]S.Mirjalili,A.Lewis,The whale optimization algorithm[J].Adv.Eng.Softw.2016,95:51-67.
[16]牛培峰,吴志良,马云鹏,等.基于鲸鱼优化算法的汽轮机热耗率模型预测[J].化工学报,2017,68(3):1049-1057.Niu Peifeng,Wu Zhiliang,Ma Yunpeng,et al.Prediction of steam turbine heat consumption rate based on whale optimization algorithm[J].CIESC Journal,68(3):1049-1057.
[17]崔东文.鲸鱼优化算法在水库优化调度中的应用[J].水利水电科技进展,2017,37(3):72-76.
[18]徐继亚,王艳,纪志成.基于鲸鱼优化算法WKELM的滚动轴承故障诊断[J].系统仿真学报,2017,29(9):2189-2197.Xu Jiya,Wang Yan,Ji Zhicheng.Fault Diagnosis Method of Rolling Bearing Based on WKELM Optimized by Whale Optimization Algorithm[J],Journal of System Simulation,2017,29(9):2189-2197.
[2]赵晶晶,符杨,李东东.考虑双馈电机风电场无功调节能力的配电网无功优化[J].电力系统自动化,2011,35(11):33-38.Zhao Jingjing,Fu Yang,Li Dongdong.Reactive power optimization network considering regulation capability of DFIG wind farm[J].Automation of Electric Power Systems,2011,35(11):33-38.
[3]王成山,罗凤章.配电系统综合评价理论与方法[M].北京:科学出版社,2012.
[4]林济铿,石伟钊,吴乃虎,等.计及离散变量基于互补约束全光滑牛顿法的无功优化[J].中国电机工程学报,2012,32(1):93-100.Lin Jikeng,Shi Weizhao,Wu Naihu,et al.Reactive Power Optimization With Discrete Variables Based on Complementarity Constraints Smooth Newton Method[J].Proceedings of the CSEE,2012,32(1):93-100.
[5]谷永刚,肖凯,夏经德,等.利用增量二次规划和启发式方法的电力系统动态无功优化[J].西安交通大学学报,2010,44(8):106-111.Gu Yonggang,Xiao Kai,Xia Jingde,et al.Incremental Quadratic Programming and Heuristic Combination Algorithm for Dynamic Optimal Reactive Power in Power System[J].Journal of Xi’an Jiaotong University,2010,44(8):106-111.
[6]姚煜,蔡燕春.离散粒子群与内点法结合的电力系统无功优化[J].电力系统保护与控制,2010,38(3):48-52.Yao Yu,Cai Yanchun.A hybrid strategy based on DPSO and IPM for optimal reactive power flow[J].Power System Protection and Control,2010,38(3):48-52.
[7]熊虎岗,程浩忠,李宏仲.基于免疫算法的多目标无功优化[J].中国电机工程学报,2006,(11):102-108.Xiong Hugang,Cheng Haozhong,Li Hongzhong.Reactive power optimization for medium voltage distribution network based on improved particle swarm optimization[J].Proceedings of the CSEE,2006,(11):102-108.
[8]曾学强,刘志刚,符伟杰,等.改进差分进化算法在电力系统无功优化中的应用[J].电网技术,2012,36(2):121-125.Zeng Xueqiang,Liu Zhigang,Fu Weijie,et al.Application of improved differential evolution algorithm in reactive power optimization[J].Power System Technology,2012,36(2):121-125.
[9]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,2013(10):109-113.Cheng Xingong,Li Jiwen,Cao Lixia,et al.Multi-objective distributed parallel reactive power optimization based on subarea division of the power systems[J].Proceedings of the CSEE,2013(10):109-113.
[10]张丽,徐玉琴,王增平,等.包含分布式电源的配电网无功优化[J].电工技术学报,2011,26(3):168-174.Zhang Li,Xu Yuqin,Wang Zengping,et al.Reactive power optimization for distribution system with distributed generators[J].Transactions of China Electrotechnical Society,2011,26(3):168-174.
[11]李晶,王素华,谷彩连.基于遗传算法的含分布式发电的配电网无功优化控制研究[J].电力系统保护与控制,2010,38(6):60-63.Li Jing,Wang Suhua,Gu Cailian.Research of reactive power optimization control in distribution networks based on genetic algorithm[J].Power System Protection and Control,2010,38(6):60-63.
[12]赵俊光,王主丁,张宗益,等.基于节点补偿容量动态上限的配电网无功规划优化混合算法[J].电力系统自动化,2009,33(23):69-74.Zhao Junguang,Wang Zhujiang,Zhang Zongyi,et al.A dynamic upper limit to node compensation based hybrid optimization algorithm for reactive power planning in distribution networks[J].Automation of Electric Power Systems,2009,33(23):69-74.
[13]赵昆,耿光飞.基于改进遗传算法的配电网无功优化[J].电力系统保护与控制,2011,39(5):57,62-68.Zhao Kun,Geng Guangfei.Reactive power optimization of distribution network based on improved genetic algorithm[J].Power System Protection and Control,2011,39(5):57,62-68.
[14]付英杰,汪沨,谭阳红.基于Pareto最优解的含分布式电源配电网无功优化[J].电力系统及其自动化学报,2017,29(1):18-23.Fu Yingjie,Wang Feng,Tan Yanghong.Reactive Power Optimization of Distribution Network Containing Distributed Generation Based on Pareto optimal[J].Journal of Electric Power Systems and Automation,2017,29(1):18-23.
[15]S.Mirjalili,A.Lewis,The whale optimization algorithm[J].Adv.Eng.Softw.2016,95:51-67.
[16]牛培峰,吴志良,马云鹏,等.基于鲸鱼优化算法的汽轮机热耗率模型预测[J].化工学报,2017,68(3):1049-1057.Niu Peifeng,Wu Zhiliang,Ma Yunpeng,et al.Prediction of steam turbine heat consumption rate based on whale optimization algorithm[J].CIESC Journal,68(3):1049-1057.
[17]崔东文.鲸鱼优化算法在水库优化调度中的应用[J].水利水电科技进展,2017,37(3):72-76.
[18]徐继亚,王艳,纪志成.基于鲸鱼优化算法WKELM的滚动轴承故障诊断[J].系统仿真学报,2017,29(9):2189-2197.Xu Jiya,Wang Yan,Ji Zhicheng.Fault Diagnosis Method of Rolling Bearing Based on WKELM Optimized by Whale Optimization Algorithm[J],Journal of System Simulation,2017,29(9):2189-2197.