基于分布估计算法的水库群联合优化调度研究
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摘要
流域水库群优化调度一直是学术和工程界研究的热点问题之一。随着我国对水电能源开发进程的加快，众多的大型梯级水电站的建成与投运，梯级间水电站的水力与电力联系日益复杂，电网中多种电源之间的配合需求逐渐增加，给流域水电站群的运行管理带来了巨大的挑战。流域水库群优化调度受径流过程、下游用水需求、发电特性等诸多因素的影响，是一类高维度、非凸、非线性且具有复杂约束的优化问题，如何求解该类问题是研究库群优化调度的关键问题。随着水库群规模的扩大，调度问题的约束条件更加复杂化，传统的优化理论和方法中的如求解精度低、计算时间长等不足更加的凸显。因此，许多学者将智能优化算法引入到求解水库群优化调度的研究中，然而智能优化算法存在陷入局部最优等缺陷，且将其应用于求解水库群优化调度问题时，对复杂约束处理机制的研究较少，仍需研究更有效的优化方法及约束处理策略来求解水库群优化调度问题。文本结合智能优化算法理论、系统工程理论和最优化理论等相关理论，以一种具有较好全局搜索能力的分布估计算法为基础，以水库优化调度、流域水库群优化、水火电联合调度为研究对象，对含有复杂约束的水库群、水火电联合优化问题进行了研究，取得了一些具有理论意义和实际应用价值的成果。本文的研究工作及成果主要如下：
（1）以一种具有较好全局搜索能力的进化算法—分布估计算法为基础，分析了算法在进化过程中的性能和算法中控制参数的作用，针对算法中因控制搜索范围的参数下降过快，导致群体多样性降低从而引起的算法陷入局部最优的问题，提出一种混合单变量边缘分布算法（HUMDA）。该算法采用两阶段参数动态控制策略来控制算法的均值与方差参数，在搜索初期保持群体的多样性，在算法后期提高了算法的局部搜索能力，并引入混沌搜索机制有效提高了算法的搜索精度与效率。通过标准测试函数测试分析了算法的性能、参数的敏感性及局部搜索策略的作用，结果表明HUMDA具有较好的全局搜索能力和较高搜索精度。此外，在HUMDA算法的基础上，将参数控制策略与文化框架相结合提出文化分布估计算法（CEDA），通过文化算法中的知识结构监测群体的信息，控制算法的参数，以提高算法的鲁棒性，通过测试表明CEDA算法具有更好的性能及寻优能力。对分布估计算法的改进研究，有效的提高了算法搜索性能，为求解水库群优化调度问题提供了有效的手段。
（2）通过研究水库优化调度模型解空间的特性，将提出的HUMDA算法应用于求解水库发电优化调度问题，同时针对局部搜索中容易破坏约束导致搜索效率低下的问题，设计一种结合逐步优化思想的混沌局部搜索策略，使得在局部搜索过程中对等式约束的破坏减小，提高了局部搜索的效率。最后通过水库长期调度算例对算法进行测试分析，结果表明算法在求解水库调度问题时具有较好的性能，为后研究具有更复杂约束的水库群调度提供了参考。
（3）通过分析现有的水库群优化调度约束处理方法，针对约束处理容易造成下游水库调整量过大，使得调整的随机性过大，影响算法搜索性能的问题，结合CEDA算法提出一种考虑上下游水力联系的等式约束处理策略。该策略可快速的消除不等式约束对解的影响，提高算法的求解效率。并将其应用于含有10个水库的水库群发电优化调度，得到较好的优化效果。
（4）通过对水火电优化调度中火电机组的特性及火电负荷分配问题的解空间特性进行研究，分析了现有的约束处理策略的特性并针对其影响算法全局搜索能力等问题，提出一种结合CEDA的两阶段约束条件处理策略，测试分析该策略在求解火电机组负荷分配时具有较好的性能。将CEDA结合水电约束处理策略、火电约束处理策略应用于求解水火电联合调度，结果表明结合所提出的约束处理策略的CEDA算法具有较好的求解性能，为求解水火电联合优化调度问题提供一种新的有效方法。
The scheduling of cascaded reservoirs has been one of the hotspot academic andengineering researches. With China's hydropower energy development process to speedup, the large-scale cascade hydropower stations are completed and put into operation.The hydraulic and electrical relationship among cascade reservoirs become morecomplex, with the increasing demand for co-ordination between different power source,which bring great challenges to the management of the cascaded reservoirs. Thecascaded reservoirs scheduling considers runoff process, water supply and powergeneration characteristics, is a kind of high-dimensional, non-convex, nonlinear andcomplex constrained optimization problem. How to solve this kind of problem is a keyissue of the cascade reservoirs scheduling. With the expansion of the scale of thecascaded reservoirs, the constraints of the optimization problem become morecomplicated, the traditional optimization methods can not meet the needs of cascadedreservoirs scheduling development. Therefore, many scholars utilize intelligentoptimization algorithms to solve the optimal scheduling of cascaded reservoirs problems.However, intelligent optimization algorithms are easy trapped into local optimum andlack of constraint handling mechanism, which lead algorithm cannot obtain the globaloptimal solution when solve cascaded reservoirs optimal problems. A more effectiveoptimization method and constraint handling strategy are still needed to deeply researchfor solving cascaded reservoirs optimal problems. In this thesis, combined withintelligent optimization algorithm theory, the theory of systems engineering optimizationtheory and other related theories, a global search algorithm named estimation ofdistribution algorithm is utilized to solve cascaded reservoirs optimal problems andhydrothermal optimal problems. Some conclusions with theoretical and practical valueare obtained. The main achievements are as follows:
(1) Estimation of Distribution Algorithms in a new evolutionary algorithm has betterglobal search capability. The research based on analysis the performance of the algorithmand the effect of control parameters in the evolutionary process. In view of the critical shortcomings such as the scope of the search down too fast which lead to reduceddiversity of the population and caused local convergence, a hybrid Univariate MarginalDistribution Algorithm (HUMDA) was proposed. In the proposed method, a two-stagedynamic parameters control strategy is used to control the mean and variance parametersin order to preserve the diversity of the population at the beginning of algorithm andimprove the local search capability of the algorithm at the end of the execution. Inaddition, the chaotic search strategy is adopted to enhance the precision of solution andsearch efficiency. The HUMDA is tested by standard test functions by analyzing thesensitivity of the parameters and the performance of the local search strategy. The resultsshow that, the algorithm has better global convergence ability and search accuracy.Furthermore, on basis of HUMDA algorithm, a cultured estimation of distributionalgorithm (CEDA) which combines cultural framework control parameters strategy isproposed to improve the robustness of the algorithm. The CEDA has better performanceuse several standard function tests the CEDA, the results show that CEDA has betterperformance and optimization ability. Research on the improvement of the estimation ofdistribution algorithm, effectively improve the search performance of the algorithm andprovide an effective means for solving cascaded reservoirs optimal problems.
(2) By analyzing the characteristics of the solution space in reservoir optimalscheduling model, the proposed HUMDA is applied to solve reservoir optimal schedulingproblems. Considering that local search may violate the constraints which lead to reducethe efficiency of search. A chaotic local search strategy combines with POA algorithm isproposed to improve the efficiency of local search. Applying the strategy to solveLong-term reservoir scheduling problem, the results show that the algorithm has betterperformance in solving reservoir scheduling problem. This research provides a referencefor study cascaded reservoirs optimal scheduling with more complex constraints.
(3) In order to reduce the affect the performance of the algorithm caused byconstraint handling strategy which producing a large randomness variable in solvingcascaded reservoirs optimal scheduling problem. An equality constraints handling strategywhich considers the hydraulic connection between reservoirs is proposed to combine withCEDA to improve the efficiency of the algorithm to solve cascaded reservoirs optimal scheduling problem. Applied to the reservoir system which contains10reservoirs, theresults show that the use proposed algorithm can obtain better solution.
(4) By analyzing the characteristics of the solution space in the hydrothermal optimalscheduling model and the advantages and disadvantages of the existing constrainthandling strategy, a combination of two-stage constraint handling strategy and CEDA isproposed. Using CEDA to solve dynamic economic dispatch in thermal power generationhas better performance. Utilizing CEDA combined constraint handling strategy in thermaland hydropower to solve hydrothermal optimization problems. The results show that theCEDA combined with proposed constraint handling strategy has better performance forsolving hydrothermal optimization problems. The research provides a new effectivemethod for solving hydrothermal optimization problems.
（1）以一种具有较好全局搜索能力的进化算法—分布估计算法为基础，分析了算法在进化过程中的性能和算法中控制参数的作用，针对算法中因控制搜索范围的参数下降过快，导致群体多样性降低从而引起的算法陷入局部最优的问题，提出一种混合单变量边缘分布算法（HUMDA）。该算法采用两阶段参数动态控制策略来控制算法的均值与方差参数，在搜索初期保持群体的多样性，在算法后期提高了算法的局部搜索能力，并引入混沌搜索机制有效提高了算法的搜索精度与效率。通过标准测试函数测试分析了算法的性能、参数的敏感性及局部搜索策略的作用，结果表明HUMDA具有较好的全局搜索能力和较高搜索精度。此外，在HUMDA算法的基础上，将参数控制策略与文化框架相结合提出文化分布估计算法（CEDA），通过文化算法中的知识结构监测群体的信息，控制算法的参数，以提高算法的鲁棒性，通过测试表明CEDA算法具有更好的性能及寻优能力。对分布估计算法的改进研究，有效的提高了算法搜索性能，为求解水库群优化调度问题提供了有效的手段。
（2）通过研究水库优化调度模型解空间的特性，将提出的HUMDA算法应用于求解水库发电优化调度问题，同时针对局部搜索中容易破坏约束导致搜索效率低下的问题，设计一种结合逐步优化思想的混沌局部搜索策略，使得在局部搜索过程中对等式约束的破坏减小，提高了局部搜索的效率。最后通过水库长期调度算例对算法进行测试分析，结果表明算法在求解水库调度问题时具有较好的性能，为后研究具有更复杂约束的水库群调度提供了参考。
（3）通过分析现有的水库群优化调度约束处理方法，针对约束处理容易造成下游水库调整量过大，使得调整的随机性过大，影响算法搜索性能的问题，结合CEDA算法提出一种考虑上下游水力联系的等式约束处理策略。该策略可快速的消除不等式约束对解的影响，提高算法的求解效率。并将其应用于含有10个水库的水库群发电优化调度，得到较好的优化效果。
（4）通过对水火电优化调度中火电机组的特性及火电负荷分配问题的解空间特性进行研究，分析了现有的约束处理策略的特性并针对其影响算法全局搜索能力等问题，提出一种结合CEDA的两阶段约束条件处理策略，测试分析该策略在求解火电机组负荷分配时具有较好的性能。将CEDA结合水电约束处理策略、火电约束处理策略应用于求解水火电联合调度，结果表明结合所提出的约束处理策略的CEDA算法具有较好的求解性能，为求解水火电联合优化调度问题提供一种新的有效方法。
The scheduling of cascaded reservoirs has been one of the hotspot academic andengineering researches. With China's hydropower energy development process to speedup, the large-scale cascade hydropower stations are completed and put into operation.The hydraulic and electrical relationship among cascade reservoirs become morecomplex, with the increasing demand for co-ordination between different power source,which bring great challenges to the management of the cascaded reservoirs. Thecascaded reservoirs scheduling considers runoff process, water supply and powergeneration characteristics, is a kind of high-dimensional, non-convex, nonlinear andcomplex constrained optimization problem. How to solve this kind of problem is a keyissue of the cascade reservoirs scheduling. With the expansion of the scale of thecascaded reservoirs, the constraints of the optimization problem become morecomplicated, the traditional optimization methods can not meet the needs of cascadedreservoirs scheduling development. Therefore, many scholars utilize intelligentoptimization algorithms to solve the optimal scheduling of cascaded reservoirs problems.However, intelligent optimization algorithms are easy trapped into local optimum andlack of constraint handling mechanism, which lead algorithm cannot obtain the globaloptimal solution when solve cascaded reservoirs optimal problems. A more effectiveoptimization method and constraint handling strategy are still needed to deeply researchfor solving cascaded reservoirs optimal problems. In this thesis, combined withintelligent optimization algorithm theory, the theory of systems engineering optimizationtheory and other related theories, a global search algorithm named estimation ofdistribution algorithm is utilized to solve cascaded reservoirs optimal problems andhydrothermal optimal problems. Some conclusions with theoretical and practical valueare obtained. The main achievements are as follows:
(1) Estimation of Distribution Algorithms in a new evolutionary algorithm has betterglobal search capability. The research based on analysis the performance of the algorithmand the effect of control parameters in the evolutionary process. In view of the critical shortcomings such as the scope of the search down too fast which lead to reduceddiversity of the population and caused local convergence, a hybrid Univariate MarginalDistribution Algorithm (HUMDA) was proposed. In the proposed method, a two-stagedynamic parameters control strategy is used to control the mean and variance parametersin order to preserve the diversity of the population at the beginning of algorithm andimprove the local search capability of the algorithm at the end of the execution. Inaddition, the chaotic search strategy is adopted to enhance the precision of solution andsearch efficiency. The HUMDA is tested by standard test functions by analyzing thesensitivity of the parameters and the performance of the local search strategy. The resultsshow that, the algorithm has better global convergence ability and search accuracy.Furthermore, on basis of HUMDA algorithm, a cultured estimation of distributionalgorithm (CEDA) which combines cultural framework control parameters strategy isproposed to improve the robustness of the algorithm. The CEDA has better performanceuse several standard function tests the CEDA, the results show that CEDA has betterperformance and optimization ability. Research on the improvement of the estimation ofdistribution algorithm, effectively improve the search performance of the algorithm andprovide an effective means for solving cascaded reservoirs optimal problems.
(2) By analyzing the characteristics of the solution space in reservoir optimalscheduling model, the proposed HUMDA is applied to solve reservoir optimal schedulingproblems. Considering that local search may violate the constraints which lead to reducethe efficiency of search. A chaotic local search strategy combines with POA algorithm isproposed to improve the efficiency of local search. Applying the strategy to solveLong-term reservoir scheduling problem, the results show that the algorithm has betterperformance in solving reservoir scheduling problem. This research provides a referencefor study cascaded reservoirs optimal scheduling with more complex constraints.
(3) In order to reduce the affect the performance of the algorithm caused byconstraint handling strategy which producing a large randomness variable in solvingcascaded reservoirs optimal scheduling problem. An equality constraints handling strategywhich considers the hydraulic connection between reservoirs is proposed to combine withCEDA to improve the efficiency of the algorithm to solve cascaded reservoirs optimal scheduling problem. Applied to the reservoir system which contains10reservoirs, theresults show that the use proposed algorithm can obtain better solution.
(4) By analyzing the characteristics of the solution space in the hydrothermal optimalscheduling model and the advantages and disadvantages of the existing constrainthandling strategy, a combination of two-stage constraint handling strategy and CEDA isproposed. Using CEDA to solve dynamic economic dispatch in thermal power generationhas better performance. Utilizing CEDA combined constraint handling strategy in thermaland hydropower to solve hydrothermal optimization problems. The results show that theCEDA combined with proposed constraint handling strategy has better performance forsolving hydrothermal optimization problems. The research provides a new effectivemethod for solving hydrothermal optimization problems.
引文
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