基于粒子群算法的水库优化调度模型

基于水库优化调度常用优化方法存在的不足,本文根据水库优化调度的数学模型,将粒子群优化算法运用到水库优化调度中.该算法通过个体间的协作与竞争.实现复杂空间中最优解的搜索分析,具有计算简便,收敛速度快等优点.将混沌优化算法运用到水库调度中,并与其它优化方法比较,获得了较为满意的结果.     

交叉微粒群算法在梯级水电站水库群多目标优化调度中的应用

以发电量和保证出力为目标,建立水库优化调度多目标数学模型,将遗传算法的交叉思想应用到微粒群算法中,尝试应用多目标交叉微粒群算法(multi-objective Hybrid Particle swarm Optimization--multi-objective HPSO)来求解水库调度中的多目标优化模型的不劣解集.通过实例研究计算并与其他算法的优化结果进行比较分析,证明交叉微粒群算法具有灵活和有效性能好的特性.     

基于可行空间搜索遗传算法的梯级水库群调度规则研究

以梯级水库群系统多年平均发电量和旬出力保证率最大为目标函数,以梯级水库群内各水库拐点式调度图为决策变量,建立梯级水库群联合发电调度模型,并采用可行空间搜索遗传算法进行求解。为了避免模型求解过程中对不可行解的过多处理,有针对性地对可行解进行优化。最后,以汉江流域梯级水库群为例,对模型和算法的有效性进行了验证。     

基于可行空间搜索遗传算法的水库调度图优化

本文从调度图概化、调度图模拟模型、目标函数以及优化算法等方面全面介绍了基于可行空间搜索遗传算法的调度图优化模型的原理及建模方法。针对调度图优化问题可行空间搜索困难的特点,在传统遗传算法概念基础上提出了可行空间搜索的概念。根据决策变量间的几何关系,设计了调度图优化问题的编码结构以及信息交互机制,以NSGA-Ⅱ算法为基础实现了优化模型的构建。将研究成果应用于寺坪水库调度图优化,与设计调度图模拟结果相比,发电效益及保证率均得到了明显提高。     

遗传算法在水库(群)优化调度研究中的应用综述

介绍了遗传算法在水库(群)优化调度中的应用背景及算法的收敛性,讨论了水库(群)优化调度中遗传算法的基本应用步骤以及存在的问题,给出了算法的各种改进方法,并对遗传算法的应用前景进行了展望.     

基于状态转移矩阵的梯级水库优化调度确定性离散动态规划方法

基于状态转移矩阵的确定性离散动态规划(DDDP)方法是在考虑水库运行的综合利用的前提下,利用状态转移矩阵寻找出梯级水库优化调度过程中所有的可行策略,基于目标函数逆向搜索可行策略,得出最优解的优化算法.该方法可进行单库优化计算,也可以进行梯级水电站优化计算.文中以锦屏梯级为例,说明该方法可在满足梯级水库上下游生态用水、发电用水等综合利用的前提下,以梯级水电站发电效益最大为目标,求得梯级水库优化调度的较好解.     

改进风驱动优化算法在水库调度中的应用

风驱动算法是一种新兴的基于群体迭代启发式的全局优化算法,与遗传算法、布谷鸟算法等相比,具有明确的物理背景,但该算法避免不了易陷入早熟和收敛效率慢的问题。针对早熟,本文提出了扰动策略,对当前最优适应度值对应的任一元素进行扰动,且随着迭代次数的增加,扰动量逐渐减少。针对收敛效率不高,提出了空间压缩策略,采用奇偶相间的方式,通过计算约束更新解的上下限以保证该解是可行解。将改进的风驱动优化算法运用到某水库的优化调度中,并与粒子群算法和标准风驱动算法进行比较。结果表明改进的风驱动优化算法更为可靠、高效,能以较快速度收敛于最优解,且最优解值更大,为水库优化调度模型求解提出新的解决方案。     

金昌二坝渠调水系统水能水量优化调度研究

针对金昌市二坝渠调水系统中供水水库与受水水库间输水渠有较大落差的特点,为解决输水渠不合理的年调水流量过程造成渠道梯级电站弃水量较大和受水水库供水综合效益低等问题,研究以水库缺水量最小和渠道蕴藏水能资源最大为优化目标,建立了调水系统水能水量联合调度模型。采用基于Pareto强度的多目标遗传算法求解调水流量序列的非劣解集,并基于最优调水流量过程利用"优化-模拟-仿真"的技术框架模拟分析了供水水库与受水水库的调度函数与调度规则。结果表明:优化调度方案合理可行,提高了渠道调水的有效性,增加了受水水库的综合效益,获取调水与发电双赢。     

水电站水库优化调度的改进混沌遗传算法

针对水电站水库优化调度问题,提出了将改进遗传算法和混沌优化相耦合的改进混沌遗传算法。该算法将混沌变量映射到优化变量的取值范围中,对混沌变量进行编码,表示成染色体,然后对其进行选择、交叉和变异,通过增加混沌扰动,不断进化收敛得到最优解。实例计算并与其他方法比较表明,该算法在求解水电站优化调度这样的复杂非线性优化问题时,搜索效率高,收敛性能好,能以较快的速度收敛于全局最优解,为水电站水库优化调度模型求解提供了一种新方法。     

基于耦合随机排序SCE算法的多目标生态调度研究

针对水库优化调度问题的多维性、非线性和强约束性特点,提出了耦合SCE算法与随机排序法的SCE-SR算法。在进行个体比较时综合考虑目标函数值和约束违反量,以实现群体优劣排序。同时,在迭代进化过程中加入精英保留机制,保证优化结果为可行解。将SCE-SR算法应用于三峡水库的优化调度研究,以发电量最大为经济目标及生态保障率最大为优化目标,基于水文学法量化下游河道生态流量适宜区间,并与遗传算法结果进行比较。结果表明:SCE-SR算法相较于遗传算法计算结果更优,枯水年SCE-SR算法所得年发电量比遗传算法增加0.48%,生态保证率增加2.78%。SCE-SR算法在优化过程中简单易行,需要调整的参数少,有机结合确定性方法和随机性方法,能快速定位可行域,兼具全局收敛性与鲁棒性,避免了遗传算法易"早熟"的缺点,所得优化调度方案在对经济效益影响较小的同时能保证生态流量需求,为多约束的水库优化调度问题提供了新思路。     

An Effective Approach to Long-Term Optimal Operation of Large-Scale Reservoir Systems: Case Study of the Three Gorges System

A new approach for optimization of long-term operation of large-scale reservoirs is presented, incorporating Incremental Dynamic Programming (IDP) and Genetic algorithm (GA) . The immense storage capacity of the large scale reservoirs enlarges feasible region of the operational decision variables, which leads to invalidation of traditional random heuristic optimization algorithms. Besides, long term raised problem dimension, which has a negative impact on reservoir operational optimization because of its non-linearity and non-convexity. The hybrid IDP-GA approach proposed exploits the validity of IDP for high dimensional problem with large feasible domain by narrowing the search space with iterations, and also takes the advantage of the efficiency of GA in solving highly non-linear, non-convex problems. IDP is firstly used to narrow down the search space with discrete d variables. Within the sub search space provided by IDP, GA searches the optimal operation scheme with continuous variables to improve the optimization precision. This hybrid IDP-GA approach was applied to daily optimization of the Three Gorges Project-Gezhouba cascaded hydropower system for annual evaluation from the year of 2004 to 2008. Contrast test shows hybrid IDP-GA approach outperforms both the univocal IDP and the classical GA. Another sub search space determined by actual operational data is also compared, and the hybrid IDP-GA approach saves about 10 times of computing resources to obtain similar increments. It is shown that the hybrid IDP GA approach would be a promising approach to dealing with long-term optimization problems of large-scale reservoirs.     

基于MATLAB 7.0的GA工具箱在渡槽优化设计中的应用

针对工程优化问题中的多变量、复杂非线性约束条件，运用GA工具箱，对某渡槽结构进行优化，得出了令人满意的优化结果。研究表明，遗传算法工具箱使用简单方便，且能根据不同的优化指标、计算精度寻找出最佳结果，充分体现了遗传算法的全局搜索性能。本优化方法具有计算可靠，快捷高效和图形结果可视化等特点，并具有广阔的应用前景。     

改进遗传算法在高非线性水质模型参数估值中的应用研究

在标准遗传算法的基础上,提出了一种采用精英保留策略、小生境技术、适应函数调整,同时又能自适应地改变交叉和变异概率的改进遗传算法,使得算法在高维复杂水质模型多参数估值搜索时,不丢失最优解空间和后期有效分辨最优适应度。以测试函数Rastrigin为验证,得到了已知的最优结果;最后,以下水道高非线性水质模型的参数优化估值问题为实例进行验证,将优化后的水质参数代入模型中,模拟所得结果与给定的实测值吻合良好,实现了高维复杂水质模型多参数的同时估值优化功能。该算法对其他高非线性水质模型参数优化问题同样具有较好的适用性。     

基于改进萤火虫算法的梯级水库优化调度研究

梯级水电站水库群联合调度问题具有复杂的约束条件,受到发电、供水、防洪等目标的制约。作为多目标非线性优化调度问题,为了解决传统算法中存在结果受初值参数影响较大、容易陷入局部最优解、收敛速度不理想等问题,首次尝试将萤火虫算法引入梯级水库优化调度研究中。在传统萤火虫算法模仿自然界萤火虫捕食求偶行为的基础上,对其进行优化与改进,引入目标空间中解的Pareto支配关系比较萤火虫荧光亮度,比较其优化解,采用轮盘赌法确定萤火虫每次更新过程中的移动路径,利用精英保留策略建立多目标萤火虫模型。通过典型的梯级水电站进行仿真计算,研究结果表明,改进的多目标萤火虫算法在优化过程中具有较强的寻优能力,能更好地进行全局搜索和局部搜索,计算过程中具有良好的稳定性,并且计算效率较高,优于遗传算法(GA)、粒子群算法(PSO)和蚁群算法(ACO),为多阶段、多约束的梯级水电站水库群中长期优化调度问题提供了新的途径和新方法。     

GA-ILP Method for Optimization of Water Distribution Networks

Optimization of water distribution networks has been of central importance for recent decades. Genetic Algorithms (GA) are the most famous metaheuristics widely used for this purpose with great success. However, the fact that GA basically requires a large number of computations, has led to investigate for faster solvers. In this research, a new approach is proposed in which a simple GA is linked with the Integer-Linear Programming (ILP) method resulting in a hybrid optimization scheme. Using the mathematical method of ILP, the search space is significantly reduced thereby a limited number of evaluations are required to achieve a good solution. The approach is applied to two benchmark pipe-networks in order to show its ability in terms of accuracy and speed. The results are then compared with the previous works. The obtained results indicate that the proposed model is computationally efficient, like classic methods, while is still very promising in finding the global optimum like the nature-inspired metaheuristics.     

Adaptive Genetic Algorithm for Daily Optimal Operation of Cascade Reservoirs and its Improvement Strategy

For the specialty of cascade reservoirs optimization and the premature convergence of GA, several improvement strategies are presented in this paper. Firstly, solution space generation method is found application to generate feasible initial population. Secondly, chaos optimization is adopted to optimize initial population. Thirdly, new selective operators, trigonometric selective operators, are proposed to overcome the fitness requirement of non-negative and to maintain the diversity of population. Fourthly, adaptive probabilities of crossing and mutation are adopted in order to improve the convergence speed of GA. Besides, elitist strategy is used to ensure that the best individual can be remained in each generation. Furthermore, the performance of these proposed improvement strategies was checked against the historical improvement strategies by simulating optimal operation of Three Gorges cascade reservoirs premised on historical hourly inflows, and the comparison yields indications of superior performance. In these proposed improvement strategies, trigonometric selective operators are feasible and effective for optimizing operation of cascade reservoirs. These new selective operators could help GA to find a more excellent solution in the same algebra, and the performance of convergence speed is advanced. Adaptive probabilities of crossing and mutation have better performance than other improvement strategies, such as annealing chaotic mutation and simulated annealing of large probability of mutation, because this method realizes the twin goals of maintaining diversity in the population and advancing the convergence speed of GA.     

Optimization of Multireservoir Systems by Genetic Algorithm

Application of optimization techniques for determining the optimal operating policy of reservoirs is a major issue in water resources planning and management. As an optimization Genetic Algorithm, ruled by evolution techniques, have become popular in diversified fields of science. The main aim of this study is to explore the efficiency and effectiveness of genetic algorithm in optimization of multi-reservoirs. A computer code has been constructed for this purpose and verified by means of a reference problem with a known global optimum. Three reservoirs in the Colorado River Storage Project were optimized for maximization of energy production. Besides, a real-time approach utilizing a blend of online and a posteriori data was proposed. The results obtained were compared to the real operational data and genetic algorithm was found to be effective and can be utilized as an alternative technique to other traditional optimization techniques.     

基于遗传算法的拱坝优化设计

将寻优能力很强、具有全局最优解的遗传算法 ,应用于拱坝坝形的优化设计 .拱坝优化设计实例表明 ,采用遗传算法的优化设计结果优于序列二次规划法     

Large Scale Reservoirs System Operation Optimization: the Interior Search Algorithm (ISA) Approach

Reservoirs are built to provide a powerful tool to control and manage surface water resources in order to cover inconsistency between water resources and demands. Due to finite available water and the increasing demands for water especially in arid and semi-arid regions like Iran, reservoirs must be optimally operated in order to use water in the most efficient way. This study applies the Interior Search Algorithm (ISA) to solve large scale reservoirs system operation optimization problems. The ISA is a meta-heuristic algorithm inspired from a systematic methodology of architecture process and mirror work utilized by Persian designers for decoration. Unlike other meta-heuristic algorithms, the ISA just have one parameter to tune which is a great advantage. In this study the parameter of the ISA tuned automatically using a linear equation. A real-world one-reservoir operation problem (i.e. Karun-4) and two large scale benchmark problems (i.e. four-reservoir and ten-reservoir operation problem) were employed to show the effectiveness of the ISA. The results shows the high ability of the ISA to solve reservoirs system operation problems as it achieved solutions 99.97, 99.99 and 99.95 % of global optimum for Karun-4 reservoir, four-reservoir and ten-reservoir system operation problems, respectively. These results are the best results reported so far in the studied problems. Comparing results of the ISA with those of non-linear programming (NLP), linear programming (LP), genetic algorithm (GA) and other meta-heuristic algorithms indicates fast convergence to global optimum. Considering the results, it can be stated that the ISA is a powerful tool to optimize complex large scale reservoir system operation problems.     

Single Reservoir Operating Policies Using Genetic Algorithm

To obtain optimal operating rules for storage reservoirs, large numbers of simulation and optimization models have been developed over the past several decades, which vary significantly in their mechanisms and applications. As every model has its own limitations, the selection of appropriate model for derivation of reservoir operating rule curves is difficult and most often there is a scope for further improvement as the model selection depends on data available. Hence, evaluation and modifications related to the reservoir operation remain classical. In the present study a Genetic Algorithm model has been developed and applied to Pechiparai reservoir in Tamil Nadu, India to derive the optimal operational strategies. The objective function is set to minimize the annual sum of squared deviation form desired irrigation release and desired storage volume. The decision variables are release for irrigation and other demands (industrial and municipal demands), from the reservoir. Since the rule curves are derived through random search it is found that the releases are same as that of demand requirements. Hence based on the present case study it is concluded that GA model could perform better if applied in real world operation of the reservoir.