水火电力系统短期节能发电优化调度的研究
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摘要
本文以提高水火电力系统联合运行的经济和环保效益为总目标,从减少化石燃料的使用量和降低燃煤机组发电成本两方面考虑,将含梯级水电站电力系统短期发电调度问题化为4个具有时序的优化子问题:即梯级水电站发电量最大、水电耗水量最小、火力发电污染物排放最小以及火力发电总成本最小。以此建立的优化调度模型不仅可以确定火电的最佳出力和水电的最佳蓄放水策略,还可描述水电和火电的互补作用,充分体现节能和效益的理念。在模型求解上,针对粒子群算法易陷入局部最优的缺点,在标准粒子群优化算法中引入自适应惯性权重和最差粒子,避免了算法的局部最优和过早收敛;针对各目标量纲不同,权重系数难以合理确定的多目标优化问题,运用满意度函数和欧式距离函数对其进行归一化处理,并采用改进粒子群算法对处理后的目标进行优化求解。算例仿真验证了所建模型的正确性及算法的有效性。
This article aiming at improving the economy of hydrothermal combined power system operation and environmental benefits,we can solve the problems from the two aspects of reducing the use of fossil fuels and the cost of coal-fired generating units. We can convert the optimal scheduling of short-term study of water and thermal into a sequence of four optimization sub-problems: maximal cascaded energy output, the minimum of hydroelectric water consumption,the minimum of thermal power pollutant discharge and the minimum of thermal power cost. The optimization scheduling model can not only determine the optimal output of thermal power and the best water storage strategy of hydropower, but also describe the complementary roles of hydropower and thermal power,fully embodies the concept of energy conservation and efficiency.In the view of the shortcoming that particle swarm optimization(PSO) algorithm is easy to fall into local optimum, worst particle and adaptive inertia weight are is introduced in the standard particle swarm optimization algorithm,avoiding the premature convergence and falling into local optimum. For the multi-objective optimization problem which each target are different dimension and weight coefficient is hard to reasonably determine, desirability function and Euclidean distance function are used to the normalize processing, and the improved particle swarm optimization algorithm is adopted to optimize target after processing.The examples simulation verify the correctness of the model and the effectiveness of the algorithm in this article.
This article aiming at improving the economy of hydrothermal combined power system operation and environmental benefits,we can solve the problems from the two aspects of reducing the use of fossil fuels and the cost of coal-fired generating units. We can convert the optimal scheduling of short-term study of water and thermal into a sequence of four optimization sub-problems: maximal cascaded energy output, the minimum of hydroelectric water consumption,the minimum of thermal power pollutant discharge and the minimum of thermal power cost. The optimization scheduling model can not only determine the optimal output of thermal power and the best water storage strategy of hydropower, but also describe the complementary roles of hydropower and thermal power,fully embodies the concept of energy conservation and efficiency.In the view of the shortcoming that particle swarm optimization(PSO) algorithm is easy to fall into local optimum, worst particle and adaptive inertia weight are is introduced in the standard particle swarm optimization algorithm,avoiding the premature convergence and falling into local optimum. For the multi-objective optimization problem which each target are different dimension and weight coefficient is hard to reasonably determine, desirability function and Euclidean distance function are used to the normalize processing, and the improved particle swarm optimization algorithm is adopted to optimize target after processing.The examples simulation verify the correctness of the model and the effectiveness of the algorithm in this article.
引文
[1]Bisanovic S,Hajro M,Dlakic M.Hydrothermal self-scheduling problem in a day-ahead electricity market[J].Electric Power Systems Research,2008,78(9):1579-1596.
[2]邵宝珠,王优胤,宋丹.电力期货在水火电资源优化配置中的应用[J].电网技术,2010,34(11):170-175.
[3]吴宏宇,管晓宏,翟桥柱,等.水火电联合短期调度的混合整数规划方法[J].中国电机工程学报,2009,29(28):82-88.
[4]Yu B H,Yuan X H,Wang J W.Short-term hydrothermal scheduling using particle swarm optimization method[J].Energy Conversion and Management,2007,48(7):1902-1908.
[5]陈建建,赵彩虹,高星辰,等.配电网中分布式电源的优化配置研究[J].电气技术,2016,17(5):36-40,44.
[6]韩冬.水火电短期优化调度模型和算法的研究[D].哈尔滨:哈尔滨工业大学,2009.
[7]赵彪,孙智华,宗薇薇.含风/火/水发电的电力系统中长期优化调度[J].电气技术,2016,17(9):34-38.
[8]艾琳,华栋.基于模糊优化和内点法的水火电系统短期经济调度[J].电力自动化设备,2008,28(8):46-50.
[9]张晓花,朱昌平,陈星莺,等.计及节能减排的梯级水火电优化调度研究[J].华东电力,2012(7):1104-1107.
[10]卢有麟,周建中,王永强,等.水火电力系统多目标环境经济调度模型及其求解算法研究[J].电力系统保护与控制,2011,39(23):93-100.
[11]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.
[12]陈道君,龚庆武,张茂林,等.考虑能源环境效益的含风电场多目标优化调度[J].中国电机工程学报,2011,31(13):10-17.
[13]周德建,杨莉,郭义明,等.基于免疫算法的水火电联合调度研究[J].电力系统保护与控制,2012(22):32-37.
[14]胡国强.梯级水电站优化调度模型与算法研究[D].北京:华北电力大学,2007.
[15]吴杰康,唐力.含不确定性负荷的水火电力系统随机优化调度[J].中国电机工程学报,2012,32(28):36-43.
[2]邵宝珠,王优胤,宋丹.电力期货在水火电资源优化配置中的应用[J].电网技术,2010,34(11):170-175.
[3]吴宏宇,管晓宏,翟桥柱,等.水火电联合短期调度的混合整数规划方法[J].中国电机工程学报,2009,29(28):82-88.
[4]Yu B H,Yuan X H,Wang J W.Short-term hydrothermal scheduling using particle swarm optimization method[J].Energy Conversion and Management,2007,48(7):1902-1908.
[5]陈建建,赵彩虹,高星辰,等.配电网中分布式电源的优化配置研究[J].电气技术,2016,17(5):36-40,44.
[6]韩冬.水火电短期优化调度模型和算法的研究[D].哈尔滨:哈尔滨工业大学,2009.
[7]赵彪,孙智华,宗薇薇.含风/火/水发电的电力系统中长期优化调度[J].电气技术,2016,17(9):34-38.
[8]艾琳,华栋.基于模糊优化和内点法的水火电系统短期经济调度[J].电力自动化设备,2008,28(8):46-50.
[9]张晓花,朱昌平,陈星莺,等.计及节能减排的梯级水火电优化调度研究[J].华东电力,2012(7):1104-1107.
[10]卢有麟,周建中,王永强,等.水火电力系统多目标环境经济调度模型及其求解算法研究[J].电力系统保护与控制,2011,39(23):93-100.
[11]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.
[12]陈道君,龚庆武,张茂林,等.考虑能源环境效益的含风电场多目标优化调度[J].中国电机工程学报,2011,31(13):10-17.
[13]周德建,杨莉,郭义明,等.基于免疫算法的水火电联合调度研究[J].电力系统保护与控制,2012(22):32-37.
[14]胡国强.梯级水电站优化调度模型与算法研究[D].北京:华北电力大学,2007.
[15]吴杰康,唐力.含不确定性负荷的水火电力系统随机优化调度[J].中国电机工程学报,2012,32(28):36-43.