实时水库优化调度决策支持系统及其应用

水库是水库运行管理的中心环节，在水库调度中，将数学模型与专家系统结合使用，建立长中短期套接的实时兴利优化调度、防洪实时优化调度及其辅助模型系统；各模型通过逐时段滚动控制与反馈相结合，可以增加水库调度的效益，减少不确定性因素对调度的影响，并提出用专家系统对优化调度的各个环节进行评价，以尽可能降低优化调度方案的风险，提高优化调度方案的可接受程序，所提出的方法已编编制出使用灵活方便的软件，并经实际使用，     

通用型水库调度决策支持系统设计分析

现有的水库调度软件系统一般是针对特定流域建模和编写算法，对其他流域进行软件开发时，需要重新建模、编写算法。文中运用领域工程理论，合理划分水库调度软件系统的稳定部分和易变部分，开发了包含水库、电站、机组等电网调度基本构成要素的标准建模工具和部分通用型的算法。这种面向组件的模块化、标准化的建模方式，提高了水库调度高级应用软件的灵活性，也便于形成系列化的商品软件。     

柘溪水库调度决策支持系统设计与开发

在总结柘溪电站早期建设的决策支持系统经验的基础上，从分析电站特点和业务需求出发，遵循实用性、稳健性、先进性等原则进行了柘溪水库调度决策支持系统的设计和开发。现场实际运行效果表明系统具有较大的实用价值和较强的推广意义。最后总结了系统的特点并分析了综合利用水库调度决策支持系统的发展趋势。     

飞来峡水利枢纽动库容调洪三维决策支持系统

飞来峡水库动库容调洪三维决策支持系统主要包括洪水预报预警、动库调洪计算、淹没分析、计算成果展示等,该文介绍了枢纽动库容调洪三维决策系统所具有的计算分析、演示等功能特点,系统在飞来峡水库防洪调度和南方同类水库防洪调度中得到应用,为枢纽调蓄洪水、安全运行提供了科学依据。     

An Intelligent Decision Support System for Management of Floods

Integrating human knowledge with modeling tools, an intelligent decision support system (DSS) is developed to assist decision makers during different phases of flood management. The DSS is developed as a virtual planning tool and can address both engineering and non-engineering issues related to flood management. Different models (hydrodynamic, forecasting, and economic) that are part of the DSS share data and communicate with each other by providing feedback. The DSS is able to assist in: selecting suitable flood damage reduction options (using an expert system approach); forecasting floods (using artificial neural networks approach); modeling the operation of flood control structures; and describing the impacts (area flooded and damage) of floods in time and space. The proposed DSS is implemented for the Red River Basin in Manitoba, Canada. The results from the test application of DSS for 1997 flood in the Red River Basin are very promising. The DSS is able to predict the peak flows with 2% error and reveals that with revised operating rules the contribution of Assiniboine River to the flooding of Winnipeg city can be significantly reduced. The decision support environment allows a number of “what-if” type questions to be asked and answered, thus, multiple decisions can be tried without having to deal with the real life consequences.     

水电站水库防洪优化调度的模型与方法

水库防洪调度是一种确保水库及下游安全的控制运行方法,通过建立水电站水库优化调度模型,利用动态规划方法,可以计算单一水库或者梯级水电站群的最优调度方案,从而确保水库工程安全,有效利用防洪库容拦蓄洪水,削减洪峰,减免洪水灾害,充分发挥水库的综合效益。通过建立水库防洪调度的数学模型,利用动态规划方法对单一水电站水库的防洪调度进行了研究。     

串联供水库群优化调度的模糊规划模型

针对随机动态规划在解决多个水库联合优化调度时存在“维数灾”问题,尝试基于模糊集理论来解决该优化调度问题。以4个串联供水水库系统为例,目标为各供水片区最小的缺水率最大,将水库的入流过程视为模糊集,而需水过程视为确定性的,建立了模糊规划模型,并引入可靠度和满意度对优化调度结果进行评价。实例分析表明,该模型既可以刻画入流的不确定性,又可以简化问题,具有一定的实用性。     

Folded Dynamic Programming for Optimal Operation of Multireservoir System

Dynamic Programming (DP) is considered as a good technique for optimal reservoir operation due to the sequential decision making and ease in handling non-linear objective functions and constraints. But the application of DP to multireservoir system is not that encouraging due to the problem `curse of dimensionality'. Incremental DP, discrete differential DP, DP with successive approximation, incremental DP with successive approximation are some of the algorithms evolved to tackle this curse of dimensionality for DP. But in all these cases, it is difficult to choose an initial trial trajectory, to get at an optimal solution and there is no control over the number of iterations required for convergence. In this paper, a new algorithm, Folded DP, is proposed, which overcomes these difficulties. Though it is also an iterative process, no initial trial trajectory is required to start with. So, the number of iterations is independent of any initial condition. The developed algorithm is applied to a hypothetical reservoir system, solved by earlier researchers.Operating policy obtained using the present algorithm has compared well with that of the earlier algorithm.     

基于多目标决策技术的水库优化调度

现代水库承担的功用较多,传统的以单一功用为目标的水库调度已不能适应经济社会的发展。多目标决策在多个矛盾的目标下做出最优的均衡决策,有利于发挥水库综合功用。以水库本年发电量最大和下年水库蓄能最大双目标进行水库优化调度。工程实践证明,运用多目标决策技术进行水库调度可行、正确。     

水库最优调度规则的神经网络模型

利用人工神经网络的非线性决策特点,提出了水库（电站）最优调度规则的人工神经网络模型,以四川宝珠寺水电站为例,进行其最优调度规则的模拟,研究结果表明了模型的合理性,可行性及实用性。     

A Neural Networks Approach for Deriving Irrigation Reservoir Operating Rules

A neural networks approach is applied to the derivation of the operating rules of an irrigation supply reservoir. Operating rules are determined as a two step process: first, a dynamic programming technique, which determines the optimal releases byminimizing the sum of squared deficits, assumed as objective function, subject to various constraints is applied. Then, theresulting releases from the reservoir are expressed as a functionof significant variables by neural networks. Neural networks aretrained on a long period, including severe drought events, andthe operation rules so determined are validated on a differentshorter period. The behaviour of different operating rules is assessed by simulating reservoir operation and by computing several performance indices of the reservoir and crop yield through a soil water balance model. Results show that operating rules based on an optimization with constraints resembling real system operation criteria lead to a good performance both in normal and in drought periods, reducing maximum deficits and water spills.     

Optimal Reservoir Operation Using Multi-Objective Evolutionary Algorithm

This paper presents a Multi-objective Evolutionary Algorithm (MOEA) to derive a set of optimal operation policies for a multipurpose reservoir system. One of the main goals in multi-objective optimization is to find a set of well distributed optimal solutions along the Pareto front. Classical optimization methods often fail in attaining a good Pareto front. To overcome the drawbacks faced by the classical methods for Multi-objective Optimization Problems (MOOP), this study employs a population based search evolutionary algorithm namely Multi-objective Genetic Algorithm (MOGA) to generate a Pareto optimal set. The MOGA approach is applied to a realistic reservoir system, namely Bhadra Reservoir system, in India. The reservoir serves multiple purposes irrigation, hydropower generation and downstream water quality requirements. The results obtained using the proposed evolutionary algorithm is able to offer many alternative policies for the reservoir operator, giving flexibility to choose the best out of them. This study demonstrates the usefulness of MOGA for a real life multi-objective optimization problem.     

水库运行管理中优化调度的计算机算法研究

为了发挥江河水资源的更大效益、实现水库统筹兼顾与科学调度,开展水库调度管理研究,提高水库控制运用水平具有十分重要的意义.运用动态规划方法建立了莲花水库优化调度数学模型,并利用可视化编程进行了求解,最后与传统的时历调度方法结果进行了对比分析,结果表明:采用动态规划法简便、快捷,可以提高发电效率.     

基于长期与中长期嵌套的水库优化调度

为解决水库优化调度过程中时间尺度不同而导致的水库综合效益不高的问题,以防洪与兴利为目标,将水库长期优化调度与中长期优化调度进行嵌套,建立水库多目标优化调度嵌套模型。其中,多目标问题通过约束法将防洪目标转换为硬性约束后,再转化为求解发电量最大值的单目标问题。算法方面,综合对比后确定长期优化调度采用动态规划算法求解、中长期优化调度采用遗传算法求解。为验证嵌套模型的优化效果,以澄碧河水库为例,进行水库优化模拟调度。结果表明:在满足防洪目标的前提下,嵌套模型优化调度方案相较于长期优化调度方案和实际调度方案效益均更高,验证了嵌套模型在水库优化调度问题中的优越性。     

Optimal Operation of Reservoir Systems using Simulated Annealing

A stochastic search technique, simulated annealing (SA), is used to optimize the operation of multiple reservoirs. Seminal application of annealing technique in general to multi-period, multiple-reservoir systems, along with problem representation and selection of different parameter values used in the annealing algorithm for specific cases is discussed. The search technique is improved with the help of heuristic rules, problem-specific information and concepts from the field of evolutionary algorithms. The technique is tested for application to a benchmark problem of four-reservoir system previously solved using a linear programming formulation and its ability to replicate the global optimum solution is examined. The technique is also applied to a system of four hydropower generating reservoirs in Manitoba, Canada, to derive optimal operating rules. A limited version of this problem is solved using a mixed integer nonlinear programming and results are compared with those obtained using SA. A better objective function value is obtained using simulated annealing than the value from a mixed integer non-linear programming model developed for the same problem. Results obtained from these applications suggest that simulated annealing can be used for obtaining near-optimal solutions for multi-period reservoir operation problems that are computationally intractable.     

水电站水库群长期运行优化模型和应用

水电站水库群长期运行优化模型及其应用,是根据系统工程的思想,应用线性规划的方法建立起来的.它可进行梯级水电站水库群和跨流域水电站水库群的长期运行的优化.应用这一模型和方法,可大幅度提高水电站 '群的发电效益.     

水库长期优化调度模型探讨

在水库长期优化调度问题中,一般以发电量最大为优化调度模型。考虑到电力市场的影响,综合水电站水库自身约束和电力系统调峰要求,建立以水电站发电效益最大为目标的优化调度模型,并通过实例计算比较这两种模型,发电效益最大模型较发电量最大模型更为符合实际。     

混沌蚁群算法在水库优化调度中的应用

提出一种基于混沌优化算法和蚁群算法相结合的混合算法,在求解水库优化调度问题的方法。根据混沌变量的随机性和遍历性,利用混沌变量进行优化搜索,从而有效地克服了蚁群算法存在的效率低、易于演化停滞及陷入局部最优等问题。又利用蚁群算法信息素正反馈的优点,改善了混沌搜索的盲目性,提高了搜索的效率。通过实例计算,结果表明该算法具有效率高及较强的全局寻优能力。     

高边坡时序位移滚动预测的SVM-Elman模型

基于支持向量机(SVM)和Elman神经网络,提出一种新的高边坡位移时序预测模型——SVM-Elman神经网络预测模型。在对实测数据学习的过程中,寻找最佳学习样本数和最佳测试样本数,利用经粒子群算法优化的SVM模型对边坡位移时间序列进行实时滚动预测;并运用Elman神经网络改进SVM的预测结果,得到SVM-Elman模型预测值,通过比较不同隐含层数的Elman神经网络对预测结果的影响,选择最佳隐含层数的SVM-Elman模型,实现对预测结果的改进。将SVM-Elman模型应用于某混凝土面板堆石坝左岸强卸荷岩体高边坡位移预测分析中,并与传统的SVM预测结果进行比较分析。结果表明,SVM-Elman模型在预测精度上有明显提高,预测结果科学可靠,在岩体高边坡时序位移预测中具有一定的工程应用价值。     

引水工程运行管理决策支持系统设计与实现

引水工程是我国水资源战略调配的重要举措,在我国水资源调配已经或将发挥重要作用,引水工程的信息化建设将促进和提高引水工程运行管理水平。在分析引水工程特点及其运行管理需求的基础上,应用先进的信息采集、通信网络、空间和计算机控制处理技术,对引水工程运行调度管理决策支持系统进行分析设计,以吉林省引嫩入白引水工程为例,进行应用实践。通过应用表明,该系统运行稳定,界面友好,提升了引水工程水利信息化水平,充分发挥了引水工程的效益。