梯级水电站和水火电站群优化调度的PSO算法
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摘要
梯级水电站和含梯级水电站的水火电力系统的优化调度一直是学术和工程界研究的前沿和热点。近年来,我国加快了水电能源开发的进程,随着大规模梯级电站的不断开发和陆续投产,对梯级水电站进行联合优化调度是未来流域梯级水电站运行的既定方式。梯级水电站的联合优化调度需综合考虑水电站间串联约束、水库各用水部门间的错综复杂关系和水文径流与系统负荷的不确定性；含梯级水电站的水火电力系统的联合优化调度还需考虑水火电间电量分配关系。因此,它们的优化调度隶属于高维、强约束的非线性优化问题范畴,问题的求解成为制约联合调度的一个障碍。梯级水电的迅猛发展和联合调度所面临的障碍急迫需要寻找新的优化算法。本文结合系统工程、最优化理论、计算机科学和智能计算技术等理论和方法,对流域梯级水电站和水火电力系统联合优化调度的求解展开研究,取得了一些具有一定理论意义和实际应用效果的研究成果。主要创新性成果如下：
(1)从流域公司的视角,构建了一个梯级总发电效益最大的优化调度模型。在动态自适应惯性权重PSO算法基础上引入最优向导集和全面学习思想,为调度问题的求解构建多向导粒子群算法。以我国西南某流域梯级电站为例,对算法的应用效果进行验证。与现有方法的比较表明该算法在进化后期探索能力强,能够找到更优的结果。进一步,将研究成果应用于澜沧江流域梯级水电站不同上网电价政策的比较。
(2)针对水火电力系统优化调度问题,建立考虑电量平衡、水量平衡、梯级水电站串联约束和水火电力系统自身特性的复杂调度模型。在惯性权重线性递减PSO算法中引入变异和迁徙操作构建改进粒子群算法用于水火联调问题的求解。仿真结果表明,改进粒子群算法能获得较好的结果。
(3)针对当前众多PSO算法在求解复杂约束优化问题时需采用较大规模的种群,从而面临耗时长、收敛慢的弊端,在系统总结、吸收多向导粒子群算法和改进粒子群算法特点和优势的基础上提出一个基于小种群的粒子群算法。该算法引入变异、迁徙和DE加速操作以增强小种群的多样性并提高收敛速度。为尽量避免罚函数法的使用,算法还对候选个体进行修复。采用经典水火电力系统案例对算法的求解效果进行验证,结果表明,小种群粒子群算法只需3-5个粒子即可求解复杂水火联调问题,并且无论在收敛速度、优化结果还是计算耗时上,它都比现有算法更具竞争力。
(4)针对含梯级水电站的水火电力系统机组组合问题,建立了考虑煤耗成本与启动费用之和最小的水火电力系统机组组合优化模型。结合二进制粒子群算法,对小种群粒子群算法进行完善和扩展,构建基于小种群的混合粒子群算法求解机组组合问题。在电力系统机组组合问题上获得验证后,将算法应用于水火电力系统的机组组合优化的求解。
(5)针对水火电力系统多目标调度,构建考虑煤耗成本、污染物排放、电量损耗、梯级总蓄能、调峰效益和生态影响的多目标优化调度模型。以最常见的水火电力系统环境经济调度问题为例,从两方面对优化问题求解展开研究：其一,将环境经济两目标调度问题转为单目标的环境经济组合调度问题,并采用小种群粒子群算法求解；其二,结合Pareto占优理论,构建基于小种群的多目标粒子群算法,对环境、经济两目标进行同时优化。在小种群多目标粒子群算法的效果获得验证后,将它应用于澜沧江流域梯级水电站多目标调度问题的求解。
The optimizations of cascaded hydropower stations and hydrothermal power systems with cascaded reservoirs have always been the forefront and hotspot of academic and engineering research. Especially in recent years, China is gradually accelerating the development process of hydropower. The large-scale cascaded hydropower stations are developed continuously and put into operation gradually. The joint optimal scheduling for cascaded hydropower stations will be the vested operation mode of the cascaded stations in future. The joint scheduling for cascaded stations must consider the intricate relationships among hydropower stations and departments of water utilization, and the uncertainty hydrological runoff and system load. The joint scheduling for hydrothermal power systems must consider the load dispatch among hydro and thermal plants additionally. All these make the scheduling issues be high-dimensional, strong constrained and nonlinear optimization problems. The solution of the optimization problems will be an obstacle of the joint optimal scheduling of cascaded hydropower stations. Finding a novel algorithm has been one of the most important issues due to the rapid development of cascaded hydropower stations and the obstacle faced. In this thesis, the optimal scheduling of cascaded hydropower stations and hydrothermal power systems with cascaded reservoirs are deeply studied by adopting systems engineering theory, optimization methods, computer science, and techniques of swam intelligence. Some conclusions with theoretical and practical value are obtained. The main innovation achievements are as follows:
(1) A mathematical model which maximizes the total benefits of cascaded hydropower stations is established. Multi-guide particle swarm optimizer (MGPSO), which introduces a multi-guide set into the selection of flying leaders, is proposed to solve the optimization problem. This approach is verified through an application to the scheduling of the cascaded hydropower stations on some river in southwest China. Simulations show the algorithm's strong exploration ability in the later evolution stage. After being validated, MGPSO is applied to the comparisons among the different on-grid prices of cascaded hydropower stations in Lancang River Basin.
(2) A complex scheduling model for hydrothermal power systems is established with consideration of water balance of hydro reservoirs, system load balance, and physical limitations of generators and reservoirs. A modified PSO is developed to solve this optimization problem. In the proposed algorithm, a mutation operation acting on the selection of particles' personal best, and a migration operation which regenerates newly diverse population are introduced. The simulations show the modified PSO has fast convergence and the optimal solution obtained is more competitive.
(3) A small population-based PSO (SPPSO) approach is proposed to solve the complex constrained hydrothermal scheduling problem. In this approach, small population (3-5 particles) instead of large population including hundred of particles is adopted in order to avoid more CPU time consumed and lower convergence speed. The operations of mutation, migration and DE-acceleration are incorporated into the SPPSO to enhance its competitive performance. In addition, a special repair procedure instead of penalty function approaches is also employed to handle the complex constraints of hydrothermal scheduling problem. Three hydrothermal test systems from literature are considered to test the proposed algorithm. The results have been reported through the comparisons with those obtained by other population-based evolutionary techniques. It is found that the proposed SPPSO can obtain more competitive solutions within faster convergence and shorter time consumed.
(4) A mathematical model minimizing the total power production cost including both fuel and start-up cost is present for the short-term unit commitment of hydrothermal power systems. Aiming at the solution of this optimization problem, a small population-based hybrid particle swarm optimization (SPHPSO) approach is proposed basing on the improvement and expansion of the SPPSO. The hybrid algorithm employs the binary particle swarm optimization and the SPPSO to handle the variables of discrete unit status and continuous power, respectively. The SPHPSO is used to solve the hydrothermal unit commitment problem after it is validated in thermal unit commitment problem.
(5) Aiming at multi-objective hydrothermal scheduling problems, a mathematical model with consideration of fuel cost, emissions of atmospheric pollutants, transmission losses, energy storage, peaking benefits and ecological impact of cascaded hydropower plants is built. In order to solve the short-term environmental economic hydrothermal scheduling problem, one of the famous multi-objective hydrothermal scheduling problems, two solution approaches are proposed. Multi-objective is coverted into a single objective through weight factors and penalty value factors firstly, and then the SPPSO is adopted to solve the converted optimization problem in one of the solution approaches. Small population-based multi-objective PSO basing on Pareto-domination theory (SPMPSO), which is extended from the above SPPSO, is presented aiming at optimizing the different objective simultaneously in the other solution approach. After it is verified in the famous hydrothermal test systems, the SPMPSO is applied to the multi-objective hydrogenation scheduling of the cascaded hydropower stations among Lancang River Basin.
(1)从流域公司的视角,构建了一个梯级总发电效益最大的优化调度模型。在动态自适应惯性权重PSO算法基础上引入最优向导集和全面学习思想,为调度问题的求解构建多向导粒子群算法。以我国西南某流域梯级电站为例,对算法的应用效果进行验证。与现有方法的比较表明该算法在进化后期探索能力强,能够找到更优的结果。进一步,将研究成果应用于澜沧江流域梯级水电站不同上网电价政策的比较。
(2)针对水火电力系统优化调度问题,建立考虑电量平衡、水量平衡、梯级水电站串联约束和水火电力系统自身特性的复杂调度模型。在惯性权重线性递减PSO算法中引入变异和迁徙操作构建改进粒子群算法用于水火联调问题的求解。仿真结果表明,改进粒子群算法能获得较好的结果。
(3)针对当前众多PSO算法在求解复杂约束优化问题时需采用较大规模的种群,从而面临耗时长、收敛慢的弊端,在系统总结、吸收多向导粒子群算法和改进粒子群算法特点和优势的基础上提出一个基于小种群的粒子群算法。该算法引入变异、迁徙和DE加速操作以增强小种群的多样性并提高收敛速度。为尽量避免罚函数法的使用,算法还对候选个体进行修复。采用经典水火电力系统案例对算法的求解效果进行验证,结果表明,小种群粒子群算法只需3-5个粒子即可求解复杂水火联调问题,并且无论在收敛速度、优化结果还是计算耗时上,它都比现有算法更具竞争力。
(4)针对含梯级水电站的水火电力系统机组组合问题,建立了考虑煤耗成本与启动费用之和最小的水火电力系统机组组合优化模型。结合二进制粒子群算法,对小种群粒子群算法进行完善和扩展,构建基于小种群的混合粒子群算法求解机组组合问题。在电力系统机组组合问题上获得验证后,将算法应用于水火电力系统的机组组合优化的求解。
(5)针对水火电力系统多目标调度,构建考虑煤耗成本、污染物排放、电量损耗、梯级总蓄能、调峰效益和生态影响的多目标优化调度模型。以最常见的水火电力系统环境经济调度问题为例,从两方面对优化问题求解展开研究：其一,将环境经济两目标调度问题转为单目标的环境经济组合调度问题,并采用小种群粒子群算法求解；其二,结合Pareto占优理论,构建基于小种群的多目标粒子群算法,对环境、经济两目标进行同时优化。在小种群多目标粒子群算法的效果获得验证后,将它应用于澜沧江流域梯级水电站多目标调度问题的求解。
The optimizations of cascaded hydropower stations and hydrothermal power systems with cascaded reservoirs have always been the forefront and hotspot of academic and engineering research. Especially in recent years, China is gradually accelerating the development process of hydropower. The large-scale cascaded hydropower stations are developed continuously and put into operation gradually. The joint optimal scheduling for cascaded hydropower stations will be the vested operation mode of the cascaded stations in future. The joint scheduling for cascaded stations must consider the intricate relationships among hydropower stations and departments of water utilization, and the uncertainty hydrological runoff and system load. The joint scheduling for hydrothermal power systems must consider the load dispatch among hydro and thermal plants additionally. All these make the scheduling issues be high-dimensional, strong constrained and nonlinear optimization problems. The solution of the optimization problems will be an obstacle of the joint optimal scheduling of cascaded hydropower stations. Finding a novel algorithm has been one of the most important issues due to the rapid development of cascaded hydropower stations and the obstacle faced. In this thesis, the optimal scheduling of cascaded hydropower stations and hydrothermal power systems with cascaded reservoirs are deeply studied by adopting systems engineering theory, optimization methods, computer science, and techniques of swam intelligence. Some conclusions with theoretical and practical value are obtained. The main innovation achievements are as follows:
(1) A mathematical model which maximizes the total benefits of cascaded hydropower stations is established. Multi-guide particle swarm optimizer (MGPSO), which introduces a multi-guide set into the selection of flying leaders, is proposed to solve the optimization problem. This approach is verified through an application to the scheduling of the cascaded hydropower stations on some river in southwest China. Simulations show the algorithm's strong exploration ability in the later evolution stage. After being validated, MGPSO is applied to the comparisons among the different on-grid prices of cascaded hydropower stations in Lancang River Basin.
(2) A complex scheduling model for hydrothermal power systems is established with consideration of water balance of hydro reservoirs, system load balance, and physical limitations of generators and reservoirs. A modified PSO is developed to solve this optimization problem. In the proposed algorithm, a mutation operation acting on the selection of particles' personal best, and a migration operation which regenerates newly diverse population are introduced. The simulations show the modified PSO has fast convergence and the optimal solution obtained is more competitive.
(3) A small population-based PSO (SPPSO) approach is proposed to solve the complex constrained hydrothermal scheduling problem. In this approach, small population (3-5 particles) instead of large population including hundred of particles is adopted in order to avoid more CPU time consumed and lower convergence speed. The operations of mutation, migration and DE-acceleration are incorporated into the SPPSO to enhance its competitive performance. In addition, a special repair procedure instead of penalty function approaches is also employed to handle the complex constraints of hydrothermal scheduling problem. Three hydrothermal test systems from literature are considered to test the proposed algorithm. The results have been reported through the comparisons with those obtained by other population-based evolutionary techniques. It is found that the proposed SPPSO can obtain more competitive solutions within faster convergence and shorter time consumed.
(4) A mathematical model minimizing the total power production cost including both fuel and start-up cost is present for the short-term unit commitment of hydrothermal power systems. Aiming at the solution of this optimization problem, a small population-based hybrid particle swarm optimization (SPHPSO) approach is proposed basing on the improvement and expansion of the SPPSO. The hybrid algorithm employs the binary particle swarm optimization and the SPPSO to handle the variables of discrete unit status and continuous power, respectively. The SPHPSO is used to solve the hydrothermal unit commitment problem after it is validated in thermal unit commitment problem.
(5) Aiming at multi-objective hydrothermal scheduling problems, a mathematical model with consideration of fuel cost, emissions of atmospheric pollutants, transmission losses, energy storage, peaking benefits and ecological impact of cascaded hydropower plants is built. In order to solve the short-term environmental economic hydrothermal scheduling problem, one of the famous multi-objective hydrothermal scheduling problems, two solution approaches are proposed. Multi-objective is coverted into a single objective through weight factors and penalty value factors firstly, and then the SPPSO is adopted to solve the converted optimization problem in one of the solution approaches. Small population-based multi-objective PSO basing on Pareto-domination theory (SPMPSO), which is extended from the above SPPSO, is presented aiming at optimizing the different objective simultaneously in the other solution approach. After it is verified in the famous hydrothermal test systems, the SPMPSO is applied to the multi-objective hydrogenation scheduling of the cascaded hydropower stations among Lancang River Basin.
引文
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