基于CE-PSO算法的风、火、梯级水电系统联合优化调度
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摘要
为促进风电更好地消纳、减少传统化石能源造成的污染,充分发挥梯级水电站的调节能力,制定了一种包含风、火、梯级水电系统的联合优化调度策略。该策略确定了优先风电上网、火电平稳出力、梯级水电补偿运行的调度模式,并依据该策略建立了多目标联合优化模型。针对传统粒子群算法存在容易陷入局部最优解的缺点,采用基于鲶鱼效应改进的粒子群算法对模型进行求解,并在MATLAB软件中进行仿真。算例的仿真结果表明,基于鲶鱼效应改进的粒子群算法能够有效地应用于风、火、梯级水电联合优化调度问题中,能够发挥出梯级水电站调节特性的同时有效地增加风电的上网电量、提高系统的综合效益。
In order to promote wind power to better absorb and reduce the pollution caused by traditional fossil energy and give full play to the adjustment capability of cascaded hydropower stations, a combined optimal scheduling strategy of wind, fire and cascade hydropower stations is proposed in this paper. This strategy determines the priority of the wind power grid, stable output of thermal power, and the scheduling mode of cascade hydropower compensation operations, and establishes a multi-objective joint optimization model based on this strategy. In view of the fact that the traditional particle swarm optimization algorithm is easy to fall into the local optimal solution, this paper solves the model based on catfish effect improved particle swarm algorithm, and conducts simulation in MATLAB. The example of the simulation results show that the algorithm based on catfish effect improved particle swarm algorithm can be efficiently applied to wind, fire, optimal scheduling of cascade hydropower joint, and it can give full play of the control characteristics of the cascade hydropower stations effectively. It can increase the quantity of wind power connection and improve the comprehensive benefit of the system as well.
In order to promote wind power to better absorb and reduce the pollution caused by traditional fossil energy and give full play to the adjustment capability of cascaded hydropower stations, a combined optimal scheduling strategy of wind, fire and cascade hydropower stations is proposed in this paper. This strategy determines the priority of the wind power grid, stable output of thermal power, and the scheduling mode of cascade hydropower compensation operations, and establishes a multi-objective joint optimization model based on this strategy. In view of the fact that the traditional particle swarm optimization algorithm is easy to fall into the local optimal solution, this paper solves the model based on catfish effect improved particle swarm algorithm, and conducts simulation in MATLAB. The example of the simulation results show that the algorithm based on catfish effect improved particle swarm algorithm can be efficiently applied to wind, fire, optimal scheduling of cascade hydropower joint, and it can give full play of the control characteristics of the cascade hydropower stations effectively. It can increase the quantity of wind power connection and improve the comprehensive benefit of the system as well.
引文
[1]王开艳,罗先觉,吴玲,等.清洁能源优先的风–水–火电力系统联合优化调度[J].中国电机工程学报, 2013, 33(13):27-35.
[2]杨苹,叶超.风电优先上网的风水火电力系统联合优化调度[J].广东电力, 2017, 30(4):31-36.
[3]卢有麟. 流域梯级大规模水电站群多目标优化调度与多属性决策研究[D].武汉:华中科技大学,2012.
[4]肖欣,周渝慧,何时有,等.含流域梯级水电的水火风互补发电系统联合运行优化[J].电力自动化设备, 2018, 38(2):100-108.
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[6]童晓霞,孙宁宁,李亚龙.动态蚁群算法在梯级水库优化调度中的应用[J].中国农村水利水电, 2014(6):86-89.
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[9]薛美娟,杨晓萍,马啸远.基于最优潮流理论的风电、梯级水电短期联合优化调度[J].水利学报,2014,45(3):368-375.
[10]FUENTES-LOYOLA R, QUINTANA V H. Medium-term hydrothermal coordination by semidefinite programming[J].IEEE Transactions on Power Systems,2003,18(4): 1515-1522.
[11]安源,黄强,丁航,等.水电—风电联合运行优化调度研究[J].西安理工大学学报, 2016, 32(3):333-337.
[12]易文周,田立伟.一种基于混沌搜索和鲶鱼效应策略的粒子群算法[J].计算机应用与软件, 2013, 30(5):311-315.
[13]刘方,纪昌明,向腾飞,等.基于鲶鱼效应多目标粒子群算法的水库水沙联合优化调度[J].中国农村水利水电,2012(11):4-8.
[14]纪昌明,刘方,喻杉,等.基于鲶鱼效应粒子群算法的梯级水库群优化调度[J].电力系统保护与控制, 2011, 39(19):63-68.
[15]杨晓萍,王文坚,薛斌,等.风、火、水电短期联合优化调度研究[J].水力发电学报,2013,32(4):199-203.
[2]杨苹,叶超.风电优先上网的风水火电力系统联合优化调度[J].广东电力, 2017, 30(4):31-36.
[3]卢有麟. 流域梯级大规模水电站群多目标优化调度与多属性决策研究[D].武汉:华中科技大学,2012.
[4]肖欣,周渝慧,何时有,等.含流域梯级水电的水火风互补发电系统联合运行优化[J].电力自动化设备, 2018, 38(2):100-108.
[5]吴成国,王义民,黄强,等.基于加速遗传算法的梯级水电站联合优化调度研究[J].水力发电学报, 2011, 30(6):171-177.
[6]童晓霞,孙宁宁,李亚龙.动态蚁群算法在梯级水库优化调度中的应用[J].中国农村水利水电, 2014(6):86-89.
[7]FLETEN S E,KRISTOFFERSEN T K. Short-term hydropower production planning by stochastic programming [J]. Computers & Operations Research,2008,35(8) : 2656-2671.
[8]葛晓琳. 水火风发电系统多周期联合优化调度模型及方法[D].保定:华北电力大学, 2013.
[9]薛美娟,杨晓萍,马啸远.基于最优潮流理论的风电、梯级水电短期联合优化调度[J].水利学报,2014,45(3):368-375.
[10]FUENTES-LOYOLA R, QUINTANA V H. Medium-term hydrothermal coordination by semidefinite programming[J].IEEE Transactions on Power Systems,2003,18(4): 1515-1522.
[11]安源,黄强,丁航,等.水电—风电联合运行优化调度研究[J].西安理工大学学报, 2016, 32(3):333-337.
[12]易文周,田立伟.一种基于混沌搜索和鲶鱼效应策略的粒子群算法[J].计算机应用与软件, 2013, 30(5):311-315.
[13]刘方,纪昌明,向腾飞,等.基于鲶鱼效应多目标粒子群算法的水库水沙联合优化调度[J].中国农村水利水电,2012(11):4-8.
[14]纪昌明,刘方,喻杉,等.基于鲶鱼效应粒子群算法的梯级水库群优化调度[J].电力系统保护与控制, 2011, 39(19):63-68.
[15]杨晓萍,王文坚,薛斌,等.风、火、水电短期联合优化调度研究[J].水力发电学报,2013,32(4):199-203.