乌江流域梯级水电站的经济运行研究

基于梯级水电站各级水库计算期末总蓄能(水)量最大原则，建立了梯级水电站AGC数学模型，并应用二倍体遗传算法进行寻优，能够获得问题的近似最优解。本文为梯级水电站短期发电优化调度问题提供了一种有效的解决方法。     

节能发电调度下梯级水电站短期优化调度研究

随着我国节能发电调度政策的逐步实施,梯级水电站在新的调度方式下如何优化运行已成为当前重要的研究课题.以总蓄能耗用最小为目标,研究了梯级水电站节能运行的短期优化调度模型,采用退火遗传算法进行求解,并对火溪河梯级水电站进行了实例计算.结果表明,该短期优化调度模型能够提高梯级水电站水能利用率,适应节能发电调度的要求.     

改进加速遗传算法在梯级水电站优化调度中的应用

在加速遗传算法中嵌入局部搜索法，以加强算法的全局寻优能力。将改进的加速遗传算法应用到梯级水电站中长期优化调度中，可避免动态规划在解决复杂优化问题时易产生“维数灾”的缺点。建立了以梯级发电量和发电保证率最大为目标函数的梯级水电站中长期优化调度模型，并结合模拟退火思想，构造了罚因子将两目标转化为单目标求解，阐述了用改进加速遗传算法解决问题的方法和步骤。实例表明，与其他几种调度算法比，该方法能够求得较准确合理的解，有一定的实用性。     

基于改进遗传算法的小型水电站短期优化调度

针对小型水电站在丰水期的短期优化调度问题,提出了短期优化调度的数学模型和基于改进遗传算法的工程实现方法,并通过实例仿真及对仿真结果的详细分析,说明了该算法的有效性,对小型水电站短期优化调度有一定的指导意义。     

水电站短期优化调度模型的探讨

在电力市场环境下，水电站短期优化调度作为优化发电计划、指导市场竞价的重要环节，在水电站生产实践中应用广泛。目前，短期优化研究常侧重于提高算法的精度和计算速度，而忽略了短期优化调度模型本身的合理性和准确性。如短期优化调度模型中的出力系数计算问题的研究就很少，而它都是影响短期优化调度计算数度的重要因素。为此，提出了厂内经济运行为短期优化高度提供出力系数的思想。该方法能提高出力系数的精度，从而达到改善短期优化高度的目的，同时它不会增加短期优化调度的计算量，因此具有一定的实用价值。     

梯级水电站短期优化调度算法研究

随着梯级水电站的不断投产,梯级水电站短期优化调度面临的多维性、多目标性、多约束性问题越来越突出。在分时上网电价的基础上,电网峰平谷段出力比约束需着重考虑。为此,以梯级发电收益最大为目标函数研究梯级水电站短期优化调度算法,提出定出力和POA结合算法,将峰平谷出力比离散,先求解定峰平谷出力的调度过程,再用POA优选短期最优调度解。实例验证表明,算法较好地解决了高维数、多约束条件下梯级短期优化调度问题,调度结果实用性、可操作性好。     

双倍体遗传算法求解龙溪河梯级电站长期优化调度问题

本文应用新近提出的双倍体遗传算法，求解龙溪河梯级水电站长期优化调度问题，有效解决了简单遗传算法的不足，优化调度结果令人满意。     

市场环境下梯级水电站短期优化调度研究

市场环境下,梯级水水电站的短期调度竞价报送发电计划阶段,根据预测的边际电价和入库流量,以发电收益最大化为目标制定短期调度计划;电网负荷下达阶段,根据电网下达日内96点负荷和预测的入库流量制定短期调度计划。后者在流域梯级统一调度背景下,若梯级水电站具有流域统一电价,梯级水电站短期调度以耗水量最小为目标进行优化;若梯级水电站具有不同电价,则梯级水电站短期调度以发电收入最大为目标进行优化。以市场环境下不同阶段梯级水电站制定短期优化调度计划为对象,研究梯级水电站短期优化调度模型及其求解算法,构建梯级水电站短期优化调度整体框架。研究表明,市场环境下不同阶段采用不同模型进行短期优化调度,可节约梯级水资源、增加梯级发电收益。     

松江河梯级水电站短期优化调度数学模型分析

应用改进粒子群算法求解松江河梯级水电站短期优化调度问题,建立梯级电站发电量最大和发电效益最大短期优化调度数学模型。针对粒子群算法存在的后期收敛速度慢和易陷入局部最优等缺点,引入收缩因子和基于遗传思想的变异算子对其进行改进。应用改进粒子群算法对松江河梯级水电站进行短期优化调度,分别采用发电量最大和发电效益最大数学模型进行算例分析。结果表明：对梯级电站进行短期优化调度可以提高梯级电站的整体质量和效益;应用改进粒子群算法求解梯级电站短期优化调度问题在求解时间、精度上都取得了满意的效果。     

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。     

模糊动态规划在三峡梯级电站短期优化调度中的应用

在水库的短期优化调度中存在着一些模糊约束条件，如何处理这些约束对于问题的求解至关重要．模糊优化为解决这类问题提供了一条有效的途径，它以模糊集合论为基础，并与常规调度技术、优化调度技术融为一体，互相取长补短，克服了传统方法处理模糊信息及模糊现象所遇到的困难，寻求满意的调度方式、调度方案和调度图，以指导水库运行．本文针对三峡梯级电站的短期优化调度建立了模糊优化模型，通过实例仿真表明，其优化结果是令人满意的，从而说明了该方法的有效性。     

电力系统日发电计划的启发式遗传算法

机组组合是电力系统日发电计划中主要的优化任务，在满足各种约束条件下求得全局最优解是一个比较困难的事情。遗传算法没有充分利用个体基因的有效信息，所以局部搜索能力较弱，而且随机遗传操作产生的大量不可行解，使得遗传算法的收敛能力降低。为了提高算法的全局搜索能力和收敛性，设计了基于局部优化算法的智能变异算子和消除约束冲突的修复算子。结果表明，运用了新算子的启发式遗传算法收敛到最优解的速度有显著提高。     

黑河龙首水电站短期优化调度研究

为了充分发挥黑河龙首水电站的水能利用率，对龙首水电站的短期优化调度进行了研究。通过对5个典型年短期优化调度模型求解结果进行回归分析，获得各月的短期调度规则，对调度规则的验证结果表明相对误差较小，所得到的短期调度规则可以投入实际应用。     

水电站机组优化组合的混合粒子群优化算法

机组组合是水电站短期发电计划中一个非常重要的问题，合理的组合运行能带来显著的经济效益，开展对机组优化组合的可行性和有效性研究有重大的现实意义。建立了该问题的数学模型，并提出了混合粒子群算法（Hybrid Particle Swarm Optimization,HPSO）的工程实现方法，采用量子粒子群算法解决机组方案的确立，并采用粒子群算法求解负荷经济分配。设计了粒子的适应度计算方法和速度更新方法，提出了HPSO算法的求解步骤。仿真分析表明：HPSO算法求解机组优化组合问题是可行和有效的，该算法实现简单，具有更快更好的收敛性能。     

Improved particle swarm optimization algorithm for multi-reservoir system operation

In this paper, a hybrid improved particle swarm optimization (IPSO) algorithm isproposed for the optimization of hydroelectric power scheduling in multi-reservoir systems. The conventional particle swarm optimization (PSO) algorithm is improved in two ways: (1) The linearly decreasing inertia weight coefficient (LDIWC) is replaced by a self-adaptive exponential inertia weight coefficient (SEIWC), which could make the PSO algorithm more balanceable and more effective in both global and local searches. (2) The crossover and mutation idea inspired by the genetic algorithm (GA) is imported into the particle updating method to enhance the diversity of populations. The potential ability of IPSO in nonlinear numerical function optimization was first tested with three classical benchmark functions. Then, a long-term multi-reservoir system operation model based on IPSO was designed and a case study was carried out in the Minjiang Basin in China,where there is a power system consisting of 26 hydroelectric power plants. The scheduling results of the IPSO algorithm were found to outperform PSO and to be comparable with the results of the dynamic programming successive approximation (DPSA) algorithm.     

Short-Term Hydrothermal Scheduling Using a Two-Stage Linear Programming with Special Ordered Sets Method

The short-term hydrothermal scheduling (SHS), typically a complicated nonlinear, nonconvex and non-smooth optimization problem, is very important for the economic operation of power systems. Instead of heuristic algorithms popularly used in previous studies, this paper employs a mathematical approach, where a two-stage linear programming with special ordered sets (TLPSOS) is proposed to solve the SHS problem. The nonlinear thermal cost functions and hydropower output functions are approximated by using the special ordered sets. The TLPSOS involves two stages: solve the linearized model in the first stage, and eliminate the linearization errors in the second. Superior to heuristic algorithms, the TLPSOS does not rely on parameters, and can always give stable results. Applied to a widely used hydrothermal system which consists of four hydroplants and three thermal plants, the present method shows its efficiency and strength in obtaining results better than those of previous studies.     

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.     

Short-term power generation scheduling rules for cascade hydropower stations based on hybrid algorithm

Power generation dispatching is a large complex system problem with multi-dimensional and nonlinear characteristics. A mathematical model was established based on the principle of reservoir operation. A large quantity of optimal scheduling processes were obtained by calculating the daily runoff process within three typical years, and a large number of simulated daily runoff processes were obtained using the progressive optimality algorithm (POA) in combination with the genetic algorithm (GA). After analyzing the optimal scheduling processes, the corresponding scheduling rules were determined, and the practical formulas were obtained. These rules can make full use of the rolling runoff forecast and carry out the rolling scheduling. Compared with the optimized results, the maximum relative difference of the annual power generation obtained by the scheduling rules is no more than 1%. The effectiveness and practical applicability of the scheduling rules are demonstrated by a case study. This study provides a new perspective for formulating the rules of power generation dispatching.     

改进粒子群算法求解水火电系统短期负荷分配问题

粒子群优化（PSO）算法是一种基于群体智能的启发式搜索方法，应用领域很广。文中将PSO算法用于求解水火电系统短期负荷的经济分配，属于高维、强约束工程问题。分析了算法参数设置对解的影响，发现算法的局部开发能力和粒子的多样性是影响解的优劣的关键因素;提出多子群辅助的PSO算法，兼顾了对解空间的全局搜索和局部开发。实际算例证明，改进的算法是有效的。     

Optimization of Pump Scheduling Program in Water Supply Systems Using a Self-Adaptive NSGA-II; a Review of Theory to Real Application

The operation of pumps imposes significant costs on a water distribution system for energy supply and pumps maintenance. To derive an optimum pumps scheduling program, this study presents a multiobjective optimization problem with the objective functions of 1- energy cost and 2- the number of pump switches. The optimization of both objective functions together leads to a multiobjective constrained optimization problem. To solve the problem, the Non-Dominated Sorting Genetic Algorithm, version II, (NSGA-II) is coupled to the EPANET hydraulic simulation model. For constraint handling, some modifications are introduced to the standard NSGA-II to make it self-adaptive through which all constraints of the problem are automatically satisfied. Application of the model to a test example and a real pipe network verifies that the proposed scheme is computationally efficient and reliable. Also, optimization of the real pipe network reveals that by a careful pump scheduling program the total number of pump switches even in optimum operations could be decreased by 69% while the energy cost increases at most by 10%.