考虑需求响应的水火电优化调度改进型花朵授粉算法
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摘要
针对花朵授粉算法(FPA)寻优能力的不足,提出3种策略对其进行改进.双向学习策略能够加强FPA的局部搜索能力;仿嗅觉搜索策略不仅能增加种群的多样性,还能提升算法的全局寻优能力;动态转换概率策略能够有效地平衡全局搜索与局部搜索之间的切换.基于上述策略,提出一种具有更强搜索能力的改进型花朵授粉算法(IFPA),并在此基础上提出一种新的水火电优化调度模型.该模型在考虑火电站煤耗成本最小和供电公司利润最大的同时,还考虑采用一定的补偿策略使得消费者降低电能的需求.最后,利用IFPA解决考虑需求响应的水火电优化调度.仿真结果表明,改进的算法具有收敛速度快、精度高等优点,考虑了需求响应的水火电优化调度模型可降低消费者对电能的需求,进而降低火电站的煤耗成本.
In order to enhance the searching ability of the flower pollination algorithm(FPA), this paper presents an improved flower pollination algorithm(IFPA) with three strategies, i.e., a double-direction learning strategy to advance the local searching ability, an imitative osphresis search strategy to strengthen the diversity of population and global searching ability, and a dynamic switching probability strategy to balance the switch between global and local searching. On this basis, a hydrothermal scheduling model with demand respond is proposed, which minimizes the fuel cost, maximizes the benefit of power supply company and reduces the electricity demand by providing an appropriate compensation to customers. Finally, the presented model is solved by using the IFPA. The simulation results show that the IFPA has outstanding performance, such as fast convergence speed and high accuracy, and the proposed model incorporating demand response can reduce the demand for electrical supply of customers and fuel cost of thermal power plants.
In order to enhance the searching ability of the flower pollination algorithm(FPA), this paper presents an improved flower pollination algorithm(IFPA) with three strategies, i.e., a double-direction learning strategy to advance the local searching ability, an imitative osphresis search strategy to strengthen the diversity of population and global searching ability, and a dynamic switching probability strategy to balance the switch between global and local searching. On this basis, a hydrothermal scheduling model with demand respond is proposed, which minimizes the fuel cost, maximizes the benefit of power supply company and reduces the electricity demand by providing an appropriate compensation to customers. Finally, the presented model is solved by using the IFPA. The simulation results show that the IFPA has outstanding performance, such as fast convergence speed and high accuracy, and the proposed model incorporating demand response can reduce the demand for electrical supply of customers and fuel cost of thermal power plants.
引文
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[18]郭壮志,吴杰康,孔繁镍.基于仿电磁学算法和数据包络分析的水火电力系统多目标优化调度[J].中国电机工程学报,2013,33(4):53-61.(Guo Z Z,Wu J K,Kong F N.Multi-objective optimization scheduling for hydrothermal power systems based on electromagnetism-like mechanism and data envelopment analysis[J].Proc of the CSEE,2013,33(4):53-61.)
[19]Feng Z K,Niu W J,Zhou J Z,et al.Scheduling of short-term hydrothermal energy system by parallel multi-objective differential evolution[J].Applied Soft Computing,2017,61(12):58-71.
[20]覃晖,周建中.基于多目标文化差分进化算法的水火电力系统优化调度[J].电力系统保护与控制,2011,39(22):90-97.(Qin H,Zhou J Z.Optimal hydrothermal scheduling based on multi-objective cultured differential evolution[J].Power System Protection and Control,2011,39(22):90-97.)
[21]Yang X S,Karamanoglu M,He X.Multi-objective flower algorithm for optimization[J].Procedia Computer Science,2013,18(1):861-868.
[22]Salgotra R,Singh U.Application of mutation operators to flower pollination algorithm[J].Expert Systems with Applications,2017,79(13):112-129.
[23]Nwulu N I,Xia X.Optimal dispatch for a microgrid incorporating renewables and demand response[J].Renewable Energy,2017,101(2):16-28.
[24]吴杰康,唐力,基于模糊机会约束规划的水火电力系统多目标随机调度模型[J].中国电机工程学报,2011,31(25):26-34.(Wu J K,Tang L.Multi-objective stochastic scheduling models for hydrothermal plants based on fuzzy chance constrained programming[J].Proc of the CSEE,2011,31(25):26-34.)
[25]邓创,鞠立伟,刘俊勇,等.基于模糊CVaR理论的水火电系统随机调度多目标优化模型[J].电网技术,2016,40(5):1447-1454.(Deng C,Ju L W,Liu J Y,et al.Stochastic scheduling multi-objective optimization model for hydro-thermal power systems based on fuzzy CVaR theory[J].Power System Technology,2016,40(5):1447-1454.)
[26]仉梦林,胡志坚,王小飞,等,水火电力系统短期实用化调度模型及求解方法[J].电力自动化设备,2016,36(12):115-122.(Zhang M L,Hu Z J,Wang X F,et al.Practical short-term dispatch model of hydro-thermal power system and solution[J].Electric Power Automation Equipment,2016,36(12):115-122.)
[2]Holland J H.Adaptation in natural and artificial system[M].Ann Arbor:University of Michigan Press,1975:89-120.
[3]Dorigo M,Maniezzo V,Colorni A.Ant system:Optimization by a colony of cooperating agents[J].IEEETrans on Systems,Man,and Cybernetics-Part B,1996,26(1):29-41.
[4]Karaboga D,Basturk B.On the performance of artificial bee colony(abc)algorithm[J].Applied Soft Computing,2008,8(1):687-697.
[5]Pan W.A new fruit fly optimization algorithm:Taking the financial distress model as an example[J].Knowledge-Based Systems,2012,26(2):69-74.
[6]Yang X S.Flower pollination algorithm for global optimization[M].Berlin:Springer Heidelberg,2012:242-243.
[7]Salgotra R,Singh U.A novel bat flower pollination algorithm for synthesis of linear antenna arrays[J].Neural Computing&Applications,2018:30(7):2269-2282.
[8]Nabil E.A modified flower pollination algorithm for global optimization[J].Expert Systems with Applications,2016,57(15):192-203.
[9]Zhou Y,Wang R,Luo Q.Elite opposition-based flower pollination algorithm[J].Neurocomputing,2016,188(18):294-310.
[10]Draa A.On the performances of the flower pollination algorithm-qualitative and quantitative analyses[J].Applied Soft Computing,2015,34(9):349-371.
[11]Ram J P,Babu T S,Dragicevic T,et al.A new hybrid bee pollinator flower pollination algorithm for solar pv parameter estimation[J].Energy Conversion&Management,2017,135(5):463-476.
[12]Abdelaziz A Y,Ali E S,Elazim S M A.Flower pollination algorithm to solve combined economic and emission dispatch problems[J].Engineering Science&Technology An International J,2016,19(2):980-990.
[13]Sayed A E F,Nabil E,Badr A.A binary clonal flower pollination algorithm for feature selection[J].Pattern Recognition Letters,2016,77(9):21-27.
[14]Yuan X,Cao B,Yang B,et al.Hydrothermal scheduling using chaotic hybrid differential evolution[J].Energy Conversion&Management,2008,49(12):3627-3633.
[15]Zhang J,Wang J,Yue C.Small population-based particle swarm optimization for short-term hydrothermal scheduling[J].IEEE Trans on Power Systems,2012,27(1):142-152.
[16]张景瑞,龙健,岳超源,等.水火电力系统短期优化调度的一种改进粒子群算法[J].控制与决策,2011,26(3):407-412.(Zhang J R,Long J,Yue C Y,et al.A modified particle swarm optimizer for short-term hydrothermal scheduling with cascaded reservoirs[J].Control and Decision,2011,26(3):407-412.)
[17]侯贸军,罗春辉,隗霖捷,等.基于微分进化算法的水火电短期优化调度的研究[J].电力系统保护与控制,2015,43(9):54-61.(Hou M J,Luo C H,Wei L J,et al.Study on short-term hydrothermal scheduling based on differential evolution[J].Power System Protection and Control,2015,43(9):54-61.)
[18]郭壮志,吴杰康,孔繁镍.基于仿电磁学算法和数据包络分析的水火电力系统多目标优化调度[J].中国电机工程学报,2013,33(4):53-61.(Guo Z Z,Wu J K,Kong F N.Multi-objective optimization scheduling for hydrothermal power systems based on electromagnetism-like mechanism and data envelopment analysis[J].Proc of the CSEE,2013,33(4):53-61.)
[19]Feng Z K,Niu W J,Zhou J Z,et al.Scheduling of short-term hydrothermal energy system by parallel multi-objective differential evolution[J].Applied Soft Computing,2017,61(12):58-71.
[20]覃晖,周建中.基于多目标文化差分进化算法的水火电力系统优化调度[J].电力系统保护与控制,2011,39(22):90-97.(Qin H,Zhou J Z.Optimal hydrothermal scheduling based on multi-objective cultured differential evolution[J].Power System Protection and Control,2011,39(22):90-97.)
[21]Yang X S,Karamanoglu M,He X.Multi-objective flower algorithm for optimization[J].Procedia Computer Science,2013,18(1):861-868.
[22]Salgotra R,Singh U.Application of mutation operators to flower pollination algorithm[J].Expert Systems with Applications,2017,79(13):112-129.
[23]Nwulu N I,Xia X.Optimal dispatch for a microgrid incorporating renewables and demand response[J].Renewable Energy,2017,101(2):16-28.
[24]吴杰康,唐力,基于模糊机会约束规划的水火电力系统多目标随机调度模型[J].中国电机工程学报,2011,31(25):26-34.(Wu J K,Tang L.Multi-objective stochastic scheduling models for hydrothermal plants based on fuzzy chance constrained programming[J].Proc of the CSEE,2011,31(25):26-34.)
[25]邓创,鞠立伟,刘俊勇,等.基于模糊CVaR理论的水火电系统随机调度多目标优化模型[J].电网技术,2016,40(5):1447-1454.(Deng C,Ju L W,Liu J Y,et al.Stochastic scheduling multi-objective optimization model for hydro-thermal power systems based on fuzzy CVaR theory[J].Power System Technology,2016,40(5):1447-1454.)
[26]仉梦林,胡志坚,王小飞,等,水火电力系统短期实用化调度模型及求解方法[J].电力自动化设备,2016,36(12):115-122.(Zhang M L,Hu Z J,Wang X F,et al.Practical short-term dispatch model of hydro-thermal power system and solution[J].Electric Power Automation Equipment,2016,36(12):115-122.)