梯级水电站群联合优化调度及其决策方法
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摘要
梯级水电站群是一个复杂非线性巨系统，由于其自身所具有的高维度、多目标、非线性、强耦合、不确定等内在特性，以及约束条件的复杂相关性，其联合优化调度一直以来是学术和工程界研究的前沿和热点问题。并且随着流域梯级水电能源的持续开发，水电站群的规模不断增加，流域上下游和跨流域电站间的水力、电力耦合与作用关系凸显，问题的复杂性与求解方法的局限性之间的矛盾更加突出。传统的优化调度与决策方法局限于孤立电站、单一目标、刚性决策，已不能满足梯级水电系统实际运行管理的需要，亟待发展新的优化理论与决策方法，增强实际问题的求解能力。为此，本文针对梯级水电系统自身特点，引入复杂系统工程理论、群体智能优化技术以及多目标决策理论，对流域梯级联合优化调度及其决策的理论与方法进行了深入研究，取得了一些具有理论意义和实用价值的成果，部分研究成果成功应用于三峡梯调工程实践。主要研究工作及创新成果如下：
(1)针对含有梯级水电站群的水火电优化系统的复杂动力特性，提出一种新的适合于该问题求解的群体智能算法-混合粒子群算法(SPSO)。该算法在克服粒子群算法收敛速度慢、易陷入局部极值缺陷的同时，具有比粒子群算法和混合蛙跳算法更高的优化精度。将该算法应用于水火电系统短期优化调度实例，构建了基于双适应度约束处理方式的SPSO优化求解方法。计算结果表明，SPSO算法在求解该类问题时体现出良好的优化性能，双适应度约束处理方式能够有效的处理复杂系统约束条件，使计算结果快速稳定地收敛到可行空间，为复杂水火电力系统优化问题的求解提供了一种有效手段。
(2)为求解梯级水电系统多目标优化问题，对SPSO算法进一步拓展，提出了多目标混合粒子群算法(MOSPSO)，其核心内容包括基于自适应小生境方法的精英集维护策略以及基于Pareto支配关系的群体排序方式和个体进化过程。典型测试函数的优化结果表明，MOSPSO算法能够很好的处理各种不同特性的复杂多目标优化问题，在解的收敛性和多样性方面与多种代表性多目标进化算法相比具有明显优势。
(3)针对三峡梯级水利枢纽建成后多目标联合调度的新要求，建立了基于MOSPSO算法的三峡梯级多目标发电及防洪调度优化模型，提出了适应于模型求解的编码方式以及约束廊道与罚函数相结合的约束条件处理方法，提高了复杂约束的处理能力和模型的收敛效率。通过不同典型来水条件下的多目标优化计算，揭示出调度目标间的相互影响规律，同时备选方案集也为三峡梯级联合调度提供了可靠的决策依据。
(4)科学、准确的确定最佳调度方案是多目标调度的最终目的。结合熵权法确定多目标权重的客观性和Vague集理论描述模糊信息的准确性，提出一种新的基于改进熵权的Vague集多目标决策方法。该方法克服了熵权法确定指标客观权重时存在的缺陷，同时，备选方案相对理想方案的Vague值描述有助于对其优劣程度进行更为准确的刻画。进一步，以该方法作为数学工具进行了三峡梯级多目标发电及防洪风险决策分析，获得了不同利益趋势和风险偏好下的最佳发电及防洪调度方案，揭示了梯级电站间水力、电力内在耦合关系，优选方案及分析结果有助于三峡梯级联合调度的快速科学决断。
As a complex nonlinear large scale system, the cascade hydropower stations joint optimal dispatch system has the inherent characteristics of high dimension, multi-objective, strong coupling relationship, uncertainty, and complex constraints, and thus is always concerned by researchers. Nowadays, with the continued exploitation of river basin hydropower, the number of hydropower stations is growing up, and the hydraulic and electric relationship between hydropower stations is further enhanced. Hence, the conflict between the complexity of the problem and the limitations of solving methods is more prominent. Traditional optimal dispatch and decision-making methods are confined to isolated hydropower stations, single objective, and rigid decision-making, which has been unable to meet the demands of operation and management of cascade hydropower stations. Therefore, it is needed to develop new optimization theories and decision-making methods, and enhance the ability to solve practical problems. In this paper, aimed at the characteristics of the cascade hydropower system, we deeply study the theories and methods of the cascade joint optimal dispatch and decision-making by adopting the complex systems engineering theory, swarm intelligence algorithm and multi-objective decision-making theory, and obtain a series of conclusions with theoretical and practical value, some of which have been successfully applied to the Three Gorges project. The main research and innovative results are as follows:
(1) Focus on the complex characteristics of hydrothermal power system which contains the cascade hydropower stations, a novel algorithm-Shuffled Particle Swarm Optimization (SPSO) is presented. The SPSO can overcome the shortages of the Particle Swarm Optimization (PSO), such as slow convergence and easily getting rid of local extremum, and it has higher solution accuracy than PSO and Shuffled Frog Leaping Algorithm (SFLA). Furthermore, we apply the algorithm to the complex hydrothermal power system short-term optimization problems, and propose a double fitness constraints treatment approach. The results demonstrate that the SPSO has high calculation accuracy and good convergence in solving the problem. The double fitness constraints treatment approach can effectively handle the complex system constraints and make the calculation results rapidly converge to the feasible space. Thus it can be provided as an effective alternative for solving the complex power system optimization problems.
(2) According to the characteristics of multi-objective problems, a Multi-objective Shuffled Particle Swarm Optimization (MOSPSO) is presented by further expand the proposed SPSO algorithm. The core contents of the MOSPSO include a new archiving strategy based on self-adaptive niche method, and the population sorting approach and memetic evolution process based on the Pareto domination relationship. The numerical experiments indicate that MOSPSO can yield better-spread solutions and converges closer to the true Pareto frontier compared to some representative multi-objective evolutionary algorithms.
(3) In view of the multi-objective joint dispatch requirements with the Three Gorges cascade project completion, we establish the multi-objective power generation dispatch and multi-objective flood control models based on MOSPSO for Three Gorges cascade. Additional, the suitable algorithm coding method and constraints handling approach are proposed based on the constraints corridor and punish functions, which can improve the capacity of constraints treatment and the models convergence efficiency. After multi-objective optimal calculation with the different typical influx, the non-inferior solution sets are obtained, and can be provided as reliable data for the final multi-attribution decision. The non-inferior solution sets also reveal the change rules of objects.
(4) It's the final target of multi-objective dispatch and decision-making to determine the optimal scheme scientifically and reasonably. On the basis of the advantages of entropy method and Vague set theory, a novel Vague set decision-making method based on modified entropy method is proposed. This method overcomes the shortage of basic entropy weight calculation formula, and uses the Vague value referred to the ideal scheme to accurately describe the optimal degree of the alternative solutions. Furthermore, by applying the proposed method to solve the Three Gorges cascade multi-objective power generation and flood control decision-making problem, we obtain the optimal power generation and flood control schemes with different trend of benefits and risk and clarify the hydraulic and electric relationship of the cascade hydropower stations, which are useful to the rapid and scientific decision for the Three Gorges cascade dispatch.
(1)针对含有梯级水电站群的水火电优化系统的复杂动力特性，提出一种新的适合于该问题求解的群体智能算法-混合粒子群算法(SPSO)。该算法在克服粒子群算法收敛速度慢、易陷入局部极值缺陷的同时，具有比粒子群算法和混合蛙跳算法更高的优化精度。将该算法应用于水火电系统短期优化调度实例，构建了基于双适应度约束处理方式的SPSO优化求解方法。计算结果表明，SPSO算法在求解该类问题时体现出良好的优化性能，双适应度约束处理方式能够有效的处理复杂系统约束条件，使计算结果快速稳定地收敛到可行空间，为复杂水火电力系统优化问题的求解提供了一种有效手段。
(2)为求解梯级水电系统多目标优化问题，对SPSO算法进一步拓展，提出了多目标混合粒子群算法(MOSPSO)，其核心内容包括基于自适应小生境方法的精英集维护策略以及基于Pareto支配关系的群体排序方式和个体进化过程。典型测试函数的优化结果表明，MOSPSO算法能够很好的处理各种不同特性的复杂多目标优化问题，在解的收敛性和多样性方面与多种代表性多目标进化算法相比具有明显优势。
(3)针对三峡梯级水利枢纽建成后多目标联合调度的新要求，建立了基于MOSPSO算法的三峡梯级多目标发电及防洪调度优化模型，提出了适应于模型求解的编码方式以及约束廊道与罚函数相结合的约束条件处理方法，提高了复杂约束的处理能力和模型的收敛效率。通过不同典型来水条件下的多目标优化计算，揭示出调度目标间的相互影响规律，同时备选方案集也为三峡梯级联合调度提供了可靠的决策依据。
(4)科学、准确的确定最佳调度方案是多目标调度的最终目的。结合熵权法确定多目标权重的客观性和Vague集理论描述模糊信息的准确性，提出一种新的基于改进熵权的Vague集多目标决策方法。该方法克服了熵权法确定指标客观权重时存在的缺陷，同时，备选方案相对理想方案的Vague值描述有助于对其优劣程度进行更为准确的刻画。进一步，以该方法作为数学工具进行了三峡梯级多目标发电及防洪风险决策分析，获得了不同利益趋势和风险偏好下的最佳发电及防洪调度方案，揭示了梯级电站间水力、电力内在耦合关系，优选方案及分析结果有助于三峡梯级联合调度的快速科学决断。
As a complex nonlinear large scale system, the cascade hydropower stations joint optimal dispatch system has the inherent characteristics of high dimension, multi-objective, strong coupling relationship, uncertainty, and complex constraints, and thus is always concerned by researchers. Nowadays, with the continued exploitation of river basin hydropower, the number of hydropower stations is growing up, and the hydraulic and electric relationship between hydropower stations is further enhanced. Hence, the conflict between the complexity of the problem and the limitations of solving methods is more prominent. Traditional optimal dispatch and decision-making methods are confined to isolated hydropower stations, single objective, and rigid decision-making, which has been unable to meet the demands of operation and management of cascade hydropower stations. Therefore, it is needed to develop new optimization theories and decision-making methods, and enhance the ability to solve practical problems. In this paper, aimed at the characteristics of the cascade hydropower system, we deeply study the theories and methods of the cascade joint optimal dispatch and decision-making by adopting the complex systems engineering theory, swarm intelligence algorithm and multi-objective decision-making theory, and obtain a series of conclusions with theoretical and practical value, some of which have been successfully applied to the Three Gorges project. The main research and innovative results are as follows:
(1) Focus on the complex characteristics of hydrothermal power system which contains the cascade hydropower stations, a novel algorithm-Shuffled Particle Swarm Optimization (SPSO) is presented. The SPSO can overcome the shortages of the Particle Swarm Optimization (PSO), such as slow convergence and easily getting rid of local extremum, and it has higher solution accuracy than PSO and Shuffled Frog Leaping Algorithm (SFLA). Furthermore, we apply the algorithm to the complex hydrothermal power system short-term optimization problems, and propose a double fitness constraints treatment approach. The results demonstrate that the SPSO has high calculation accuracy and good convergence in solving the problem. The double fitness constraints treatment approach can effectively handle the complex system constraints and make the calculation results rapidly converge to the feasible space. Thus it can be provided as an effective alternative for solving the complex power system optimization problems.
(2) According to the characteristics of multi-objective problems, a Multi-objective Shuffled Particle Swarm Optimization (MOSPSO) is presented by further expand the proposed SPSO algorithm. The core contents of the MOSPSO include a new archiving strategy based on self-adaptive niche method, and the population sorting approach and memetic evolution process based on the Pareto domination relationship. The numerical experiments indicate that MOSPSO can yield better-spread solutions and converges closer to the true Pareto frontier compared to some representative multi-objective evolutionary algorithms.
(3) In view of the multi-objective joint dispatch requirements with the Three Gorges cascade project completion, we establish the multi-objective power generation dispatch and multi-objective flood control models based on MOSPSO for Three Gorges cascade. Additional, the suitable algorithm coding method and constraints handling approach are proposed based on the constraints corridor and punish functions, which can improve the capacity of constraints treatment and the models convergence efficiency. After multi-objective optimal calculation with the different typical influx, the non-inferior solution sets are obtained, and can be provided as reliable data for the final multi-attribution decision. The non-inferior solution sets also reveal the change rules of objects.
(4) It's the final target of multi-objective dispatch and decision-making to determine the optimal scheme scientifically and reasonably. On the basis of the advantages of entropy method and Vague set theory, a novel Vague set decision-making method based on modified entropy method is proposed. This method overcomes the shortage of basic entropy weight calculation formula, and uses the Vague value referred to the ideal scheme to accurately describe the optimal degree of the alternative solutions. Furthermore, by applying the proposed method to solve the Three Gorges cascade multi-objective power generation and flood control decision-making problem, we obtain the optimal power generation and flood control schemes with different trend of benefits and risk and clarify the hydraulic and electric relationship of the cascade hydropower stations, which are useful to the rapid and scientific decision for the Three Gorges cascade dispatch.
引文
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