基于SEOA算法的抽水蓄能梯级水库调度研究
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摘要
针对含混合式抽水蓄能电站的梯级水库发电调度的维数灾问题,在考虑历史径流序列随机性和非线性特点的基础上,建立含混合式抽水蓄能电站的梯级水库长期优化调度模型。文中以调度期内混合式抽水蓄能电站梯级水库发电量最大为目标函数,引入改进社会情态优化(SEOA)算法编程求解。仿真结果表明,新方法在混合式抽水蓄能电站梯级水库发电优化调度问题中既能减少传统算法的冗余计算,又可有效地结合决策者的主观偏好和利益倾向。对比动态规划算法,新方法在较短时间内得到年发电量为66.96×10~8kWh的高质量解。
Aiming at the curse of dimensionality of power generation dispatching in cascade reservoirs with hybrid pumped storage power stations,based on the randomness and nonlinear characteristics of historical runoff series,a longterm optimal dispatching model of cascade reservoirs with hybrid pumped storage power stations was established.In the scheduling period,the maximum generating capacity of the cascade pumped storage power station was selected as the objective function,and the improved social emotional optimization algorithm was introduced to solve the problem.Taking a mixed flow cascade power station as an example,the simulation results showed that the new method could reduce the redundant calculation of traditional algorithms,and effectively combined the decision-maker's subjective preference as well as interest propensity in the optimal operation of cascade type reservoirs in hybrid pumped storage power stations. Compared with the dynamic programming algorithm,the new method used a high-quality solution with an annual power generation of 66.96×10~8 kWh in a short period of time.
Aiming at the curse of dimensionality of power generation dispatching in cascade reservoirs with hybrid pumped storage power stations,based on the randomness and nonlinear characteristics of historical runoff series,a longterm optimal dispatching model of cascade reservoirs with hybrid pumped storage power stations was established.In the scheduling period,the maximum generating capacity of the cascade pumped storage power station was selected as the objective function,and the improved social emotional optimization algorithm was introduced to solve the problem.Taking a mixed flow cascade power station as an example,the simulation results showed that the new method could reduce the redundant calculation of traditional algorithms,and effectively combined the decision-maker's subjective preference as well as interest propensity in the optimal operation of cascade type reservoirs in hybrid pumped storage power stations. Compared with the dynamic programming algorithm,the new method used a high-quality solution with an annual power generation of 66.96×10~8 kWh in a short period of time.
引文
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[15] 邱骁奇,胡志坚.基于改进教与学优化算法的配电网重构[J].电力系统保护与控制,2016(12):42-49.Qiu Xiaoqi,Hu Zhijian.Reconfiguration of distribution network based on improved teaching-learning-based optimization algorithm[J].Power System Protection and Control,2016(12):42-49.
[16] 祝勇俊,范浩泽,刘文波,等.基于教与学改进算法的自适应前照灯控制设计[J].机电一体化,2016(5):43-49.Zhu Yongjun,Fan Haoze,Liu Wenbo,et al.The design of adaptive headlamps control system based on optimized TLBO[J].Mechatronics,2016(5):43-49.
[2] 张培,纪昌明,吴月秋,等.多维向量空间决策模型及其应用研究[J].中国农村水利水电,2017(6):70-73.Zhang Pei,Ji Changming,Wu Yueqiu,et al.Multidimensional vector space decision model and its application[J].China Rural Water and Hydropower,2017(6):70-73.
[3] Pe X,Rez-Di X,Az J I,et al.Optimal short-term operation and sizing of pumped storage power plants in systems with high penetration of wind energy[C].Madrid:Proceedings of the 7th International Conference on the European Energy Marke,2010.
[4] 徐飞,陈磊,金和平,等.抽水蓄能电站与风电的联合优化运行建模及应用分析[J].电力系统自动化,2013,37(1):149-154.Xu Fei,Chen Lei,Jin Heping,et al.Modeling and application analysis of optimal joint operation of pumped storage power station and wind power[J].Automation of Electric Power System,2013,37(1):149-154.
[5] 陈博,施梦奇,张智,等.水火电力系统联合经济调度[J].通信电源技术,2017,34(3):63-64.Chen Bo,Shi Mengqi,Zhang Zhi,et al.Joint economic dispatch of water and fire power system[J].Telecommunication Power Technology,2017,34(3):63-64.
[6] 李文武,熊小翠,吴稀西.含混合式抽水蓄能电站的梯级水库长期随机优化调度研究[J].水电能源科学,2014(9):55-58.Li Wenwu,Xiong Xiaocui,Wu Xixi.Long-term stochastic optimization research of cascaded reservoirs operation with hybrid pumped storage power stations[J].Hydroelectric Energy Science,2014(9):55-58.
[7] 王乐,周章,尉志勇,等.风电-抽水蓄能联合系统的优化运行研究[J].电网与清洁能源,2014,30(2):70-75.Wang Le,Zhou Zhang,Wei Zhiyong,et al.Research on optimal operation of hybrid wind power and pumped hydro storage system[J].Power System and Clean Enery,2014,30(2):70-75.
[8] 肖白,丛晶,高晓峰,等.风电-抽水蓄能联合系统综合效益评价方法[J].电网技术,2014,38(2):400-404.Xiao Bai,Cong Jing,Gao Xiaofeng,et al.A method to evaluate comprehensive benefits of hybrid wind power-pumped storage system[J].Power System Technology,2014,38(2):400-404.
[9] 徐飞飞,简献忠.基于细菌觅食算法的含风电场电网无功优化[J].电子科技,2015,28(6):5-8.Xu Feifei,Jian Xianzhong.Wind farm reactive power optimization by bacteria foraging algorithm[J].Electronic Science and Technology,2015,28(6):5-8.
[10] 盛四清,孙晓霞.风电-抽水蓄能联合运行优化模型[J].电力系统及其自动化学报,2016,28(11):100-103.Sheng Siqing,Sun Xiaoxia.Operational optimization model for combined operation of wind power andpumped-storage plant[J].Proceedings of the CSU-EPSA,2016,28(11):100-103.
[11] 黄海新,邓丽,文峰,等.基于实时电价的用户用电响应行为研究[J].电力建设,2016,37(2):63-68.Huang Haixin,Deng Li,Wen Feng,et al.Customer response behavior based on real-time pricing[J].Electric Power Construction,2016,37(2):63-68.
[12] 沈风,唐建勋,吴琼.智能电网环境下负荷响应对系统消纳风电能力影响的研究[J].电测与仪表,2016,53(11):88-94.Shen Feng,Tang Jianxun,Wu Qiong.Research on the effect of load response on system consumption capability of wind power in smart grid[J].Electrical Measurement & Instrumentation,2016,53(11):88-94.
[13] 韩小琪,宋璇坤,李冰寒,等.风电出力变化对系统调频的影响[J].中国电力,2010,43(6):26-29.Han Xiaoqi,Song Xuankun,Li Binghan,et al.Study of impact of wind power variable output on frequency regulation[J].Electric Power,2010,43(6):26-29.
[14] 张强,孙建平,武昊.基于改进的教与学算法的线性自抗扰在负荷频率控制中的应用[J].仪器仪表用户,2017(6):39-43.Zhang Qiang,Sun Jianping,Wu Hao.Application of linear active disturbance rejection based onimprovedteaching-learning-based optimization algorithmin load frequency control[J].Instrumentation Customer,2017(6):39-43.
[15] 邱骁奇,胡志坚.基于改进教与学优化算法的配电网重构[J].电力系统保护与控制,2016(12):42-49.Qiu Xiaoqi,Hu Zhijian.Reconfiguration of distribution network based on improved teaching-learning-based optimization algorithm[J].Power System Protection and Control,2016(12):42-49.
[16] 祝勇俊,范浩泽,刘文波,等.基于教与学改进算法的自适应前照灯控制设计[J].机电一体化,2016(5):43-49.Zhu Yongjun,Fan Haoze,Liu Wenbo,et al.The design of adaptive headlamps control system based on optimized TLBO[J].Mechatronics,2016(5):43-49.
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