冶峪河流域供水水库优化调度及用水补偿研究

针对梯级水库供水优化调度中模型目标的选择往往只重视综合效益的最大而无法协调各方面的利益,提出梯级水库优化调度与用水补偿相结合的思想,对用水削减方给予一定补偿。以冶峪河流域为研究对象,建立流域可供水量之和最大的多水源联合调度数学模型,采用蚁群算法,对模型进行求解,并根据流域实际分析用水削减方的用水补偿方案。结果表明:上游屯庄水库的运行,影响下游黑松林水库多年平均灌溉水量从1022.7万m3减少到851.2万m3,使黑松林水库灌区供水保证率减小了13.43%;而对下游灌区的用水补偿方案可以设定近期和远期两种补给方案后达到平衡。蚁群算法在研究梯级水库调度中计算速度快、收敛性好;水库调度与用水补偿相结合,可以最大限度地满足各部门用水需求,有效解决流域上下游和不同行政区之间的用水矛盾,为梯级水库供水优化调度提供了一种新的模式。     

面向双保证率的水库供水能力优化计算方法研究

随着城市化进程的推进和生态文明理念的深入,水库供水任务日趋复杂,涵盖城镇供水、农业灌溉、生态保障多类目标,分析水库多目标供水能力对于工程的功能调整等决策十分必要。本文综合考虑水库供水目标的竞争性与保证率差异性,提出面向双保证率的水库供水能力双层优化计算方法,通过调度线控制和城镇供水能力区间迭代实现满足不同保证率的多目标供水能力计算。外层基于粒子群算法以城镇供水能力最大为目标对水库调度线进行优化,协调不同用户的供水关系;内层在给定规则下通过迭代计算满足双保证率要求的城镇供水能力。通过内外层嵌套计算实现对水库调度规则和供水能力的同步优化。以三亚市赤田水库为实例进行研究,在城镇供水保证率为95%、农业灌溉保证率为90%时,通过优化调度规则得出水库总供水能力为9400万m~3。设置不同农业保证率和农业需水情景作分析对比,二者均对城镇供水能力有影响,总供水能力随农业保证率降低而提高。分析表明,双层优化计算方法可以实现对调度线的优化,得出满足城镇、农业双保证率要求的供水能力,且计算方法收敛稳定性较高,优化生成的调度线年内分布合理,可为水库多目标供水能力的计算和相关决策提供参考。     

基于模拟-优化模式的供水水库群联合调度规则研究

提出了基于模拟-优化模式的混联供水水库群联合优化调度规则求解框架。首先,通过构建虚拟聚合水库,编制联合调度图,以做出水库群对各用水户的供水方案;其次,通过优化成员水库供水任务分配因子,并结合供水水库群常规调度规则,实现共同供水任务在水库间的优化分配。采用改进粒子群算法 （NSPSO） 对观音阁-葠窝-汤河水库群联合供水调度模型决策变量 （联合调度图调度线位置和成员水库供水任务分配因子） 进行多目标优化,分析联合供水调度过程中目标之间的竞争关系,检验联合调度规则的合理性与有效性以及NSPSO算法的优化效率。     

基于改进NSGA-Ⅱ的黄河下游水库多目标调度研究

水库兴利、防洪和生态等目标之间既非完全协调也非完全对立,这是多目标间协调的物理基础。本文综合考虑水库不同目标间的矛盾性和统一性,构建考虑生态和兴利的水库多目标优化调度模型,为提高模型求解效率,本文提出了一种基于个体约束和群体约束技术的改进快速非劣排序遗传算法(ICGC-NSGA-Ⅱ)。以黄河下游小浪底-西霞院梯级水库为例进行多目标优化调度,综合考虑下游不同时期各生态功能用水和综合利用需求,建立黄河下游梯级水库多目标调度模型,并采用ICGC-NSGA-Ⅱ求解,探究水库的供水效益、发电效益和生态效益之间的关系。实例研究表明,改进算法能在较短时间内获取一组反映多目标间非劣关系的调度方案集。黄河下游生态、综合供水和发电效益之间存在不对称竞争关系,一方面随发电量增加,生态供水和综合供水保证率均呈下降趋势;另一方面发电和生态供水竞争强烈,而与综合供水竞争关系较弱。研究成果可为制定黄河下游多目标共赢的调度方式提供理论基础和决策依据。     

并联供水水库联合调度规则最优性条件研究Ⅱ：算法开发与实例研究

利用水库调度问题的特征开发高效求解算法是水库优化调度问题算法研究的核心内容。本文以两阶段调度问题与多阶段调度问题应保持最优策略集的一致性作为假设条件,基于本文上篇中并联供水水库两阶段调度问题的最优性条件(K-T条件),设计了该类水库调度问题的优化求解方法,并通过两阶段调度模型的滚动运行,实现并联水库系统长时序优化调度的目的。以辽宁省碧流河水库与英那河水库为例,开展了并联供水工程研究,结果表明:新的算法具有合理性与有效性,较由限制供水规则与参数式规则组合构成的并联供水水库联合调度规则,显著改善了用水户缺水状况,提高了供水保证率。     

Mike Basin水资源模型在水库调度中的应用介绍

Mike Basin是由丹麦水利研究院研发的综合性水资源数学模型软件.介绍了该模型的功能、用途,分析了Mike Basin水资源模型针对水库复杂调度过程中遇到的一些问题,通过自身计算引擎的二次开发,以及使用VBA及C#等程序语言,完成高级水库调度规则程序的设置,解决了水资源模拟过程中的水库下游用水户供水及水库预警泄流等复杂问题.为大尺度、多流域.多用户.多任务的水资源分配及管理的提供了一种科学的模拟解决方法.     

基于模拟-优化模式的供水水库群联合调度规则研究

提出了基于模拟-优化模式的混联供水水库群联合优化调度规则求解框架。首先，通过构建虚拟聚合水库，编制联合调度图，以做出水库群对各用水户的供水方案；其次，通过优化成员水库供水任务分配因子，并结合供水水库群常规调度规则，实现共同供水任务在水库间的优化分配。采用NSPSO算法对观音阁-葠窝-汤河水库群联合供水调度模型决策变量（联合调度图调度线位置和成员水库供水任务分配因子）进行多目标优化，分析联合供水调度过程中目标之间的竞争关系，检验联合调度规则的合理性与有效性以及NSPSO算法的优化效率。     

水库群联合供水方案模拟优化研究

对于由两个或两个以上水库构成的供水系统,将水库群等效为一个聚合水库,建立联合调度供水模式。通过制定合理的供水调度原则,采用优化水库调度图,根据各库实时可供水量实现供水任务合理分配,模拟水库群联合调度供水过程,得到最优供水方案。将该供水方案应用于某市水库群供水调度中,结果表明:各用水户的供水保证率均高于设计保证率,水库群基本满足该地区的用水需求,验证了所提出的水库群联合调度供水方案的可行性与优越性。     

基于遗传算法优化的水库多目标供水能力分析——以岳城水库为例

面对岳城水库供水结构发生的较大改变及漳河上游水资源情势的变化,本文将遗传算法与调度图相结合,以长系列等流量月调节计算为基础,提出了岳城水库多目标供水能力计算方法。按照供水目标优先级逐时段调节计算,通过分析多目标竞争关系,得到了符合相应目标保证率要求下的最大城镇可供水量,为水库多目标供水调度提供参考。结果表明,引入生态供水目标且优先保障,生态用水与城镇用水产生强竞争关系。优先考虑生态供水目标情景下水库城镇供水能力比不考虑生态目标情景下减少了1486万m3。农业灌溉供水保证率低于生态和城镇供水保证率,其竞争关系弱于前者,可通过优化灌溉供水调度线使水库综合供水能力达到最优。当农业灌溉保证率为55%时,岳城水库的综合供水能力达到最优,总供水量13381万m3,其中生态供水1516万m3、城镇供水5802万m3、农业灌溉供水6062万m3。研究成果为水库多目标供水设计提供参考方案。     

水库联合调度供水的探讨

分析水库联合调度的研究现状，针对水库独立供水存在的缺点，根据用水需求提出了水库联合调度供水的方案，通过水库常规调度原则实现两库联合调度的长系列计算，并分析满足供水保证率的前提下提出工程设计规模，实现可充分利用水资源并确定合理工程规模的目标。     

Assessment of Stochastic Operation Optimization for Reservoirs of Contrasting Scales

Deriving optimal release policies for dams and corresponding reservoirs is crucial for the sustainable water resources management of a region as they directly control the distribution of water to several users. Mathematical optimization algorithms can help in finding efficient reservoir operating strategies taking into account complex system constraints and hydrologic uncertainty. The robustness of operation optimization models may be influenced by physical reservoir characteristics such as size and scale and the effectiveness of a model for a particular case study does not always guarantee the same level of success for another application. This research focused on assessing the applicability of an implicit stochastic optimization (ISO) procedure to derive rule curves for two different dams of contrasting reservoir scales in terms of physical and operational characteristics. The results demonstrated the feasibility of the proposed technique for both small- and large-scale systems in view of the lower vulnerability provided by the ISO-derived policies in contrast to operations carried out by the standard reservoir operating policy as well as the proximity of the ISO operations with those by perfect-forecast deterministic optimization. The ISO procedure also provided operating rules similar to, and even less vulnerable than, those derived by stochastic dynamic programming.     

混沌变异粒子群算法在水库供水调度规则制定中的应用

本文针对基于调度图规则的水库供水调度问题,建立了以水库供水保证率高且缺水量少为目标的优化调度模型。同时应用混沌变异减缓粒子群算法收敛速度,当算法进化停滞步数大于停滞步数阀值时,随机选取其中20%的粒子进行混沌变异操作,将原本聚集的粒子群"驱散开来",达到增加种群多样性、避免算法早熟收敛的目的,并将该算法引入到调度图的获取中。并以白石水库为例,得到了满足各项用水保证率的水库调度图,验证了该方法的可行性。     

Robust Methods for Identifying Optimal Reservoir Operation Strategies Using Deterministic and Stochastic Formulations

Water allocation in a competing environment is a major social and economic challenge especially in water stressed semi-arid regions. In developing countries the end users are represented by the water sectors in most parts and conflict over water is resolved at the agency level. In this paper, two reservoir operation optimization models for water allocation to different users are presented. The objective functions of both models are based on the Nash Bargaining Theory which can incorporate the utility functions of the water users and the stakeholders as well as their relative authorities on the water allocation process. The first model is called GA–KNN (Genetic Algorithm–K Nearest Neighborhood) optimization model. In this model, in order to expedite the convergence process of GA, a KNN scheme for estimating initial solutions is used. Also KNN is utilized to develop the operating rules in each month based on the derived optimization results. The second model is called the Bayesian Stochastic GA (BSGA) optimization model. This model considers the joint probability distribution of inflow and its forecast to the reservoir. In this way, the intrinsic and forecast uncertainties of inflow to the reservoir are incorporated. In order to test the proposed models, they are applied to the Satarkhan reservoir system in the north-western part of Iran. The models have unique features in incorporating uncertainties, facilitating the convergence process of GA, and handling finer state variable discretization and utilizing reliability based utility functions for water user sectors. They are compared with the alternative models. Comparisons show the significant value of the proposed models in reservoir operation and supplying the demands of different water users.     

A Fuzzy Stochastic Dynamic Nash Game Analysis of Policies for Managing Water Allocation in a Reservoir System

In this paper a fuzzy dynamic Nash game model of interactions between water users in a reservoir system is presented. The model represents a fuzzy stochastic non-cooperative game in which water users are grouped into four players, where each player in game chooses its individual policies to maximize expected utility. The model is used to present empirical results about a real case water allocation from a reservoir, considering player (water user) non-cooperative behavior and also same level of information availability for individual players. According to the results an optimal allocation policy for each water user can be developed in addition to the optimal policy of the reservoir system. Also the proposed model is compared with two alternative dynamic models of reservoir optimization, namely Stochastic Dynamic Programming (SDP) and Fuzzy-State Stochastic Dynamic programming (FSDP). The proposed modeling procedures can be applied as an appropriate tool for reservoir operation, considering the interaction among the water users as well as the water users and reservoir operator.     

Model-Based Optimization of Downstream Impact during Filling of a New Reservoir: Case Study of Mandaya/Roseires Reservoirs on the Blue Nile River

The aim of this paper is to develop a methodology based on coupled simulation-optimization approach for determining filling rules for the proposed Mandaya Reservoir in Ethiopia with minimum impact on hydropower generation downstream at Roseires Reservoir in Sudan, and ensuring power generation at Mandaya Reservoir in Ethiopia. The Multi-Objective Optimization (MOO) approach for reservoir optimization presented in this paper is a combination of simulation and optimization models, which can assist decision making in water resource planning and management (WRPM). The combined system of reservoirs is set in MIKE BASIN Simulation model, which is then used for simulation of a limited set of feasible filling rules of the Mandaya reservoir according to the current storage level, the inflow, and the time of the year. The same simulation model is then coupled with Multi-Objective optimization Non-dominated Sorting Genetic Algorithm (NSGA-II), which is adopted for determining optimial filling rules of the Mandaya Reservoir. The optimization puts focus on maximization of hydropower generation in both the Mandaya and the Roseires Reservoirs. The results demonstrate that optimal release- (and correspondingly filling-) rules for Mandaya Reservoir which maximize the hydropower generation in both Mandaya and Roseires reservoirs can be found. These rules are determined along the Pareto frontier obtained by the optimization algorithm, which can serve as a decision support tool for choosing the actual filling rule. The results also showed that the NSGA- II is an efficient and powerful tool that could assist decision makers for solving optimization problems in complex water resource systems.     

雅砻江下游梯级水库综合调度规则优化方法

以综合运行水位控制线、防弃水线、防破坏线、防洪限制水位和死水位作为水库调度指导线,并构成水库调度图,围绕该调度图制定水库综合调度规则,进而建立了调度图模拟-优化模型。选取雅砻江下游梯级水库作为研究对象,采用模拟-优化技术对调度图进行优化。采用优化后的调度图指导水库群模拟运行,模拟运行结果验证了该调度图具有良好的可操作性和实用价值。     

基于引水限制调度线的并联水库系统供水优化算法及应用

以实现梯级水库群或者水源连通工程水量补偿式调节为目标,针对并联水库的联合供水特征,采用引水限制调度线控制引水水库可引水量,引入粒子群算法构建了并联水库优化调度模型。通过优化引水调度线提出可以改进并联水库系统的调度规则和调度方法,将优化和常规调度有机结合,并根据并联水库系统实际运行条件设置模型的目标函数、约束条件和参数设置方法。以X市S水库和L水库组成的并联水库系统为实例,采用该模型进行优化模拟。结果表明:并联水库系统经优化年均弃水量比两库单独供水的弃水量减少5 496万m~3,末库容比两库单独供水时提高203万m~3。在95%供水保证率时,并联水库系统供水系统的年供水能力比双库单独供水能力之和提高了5 556万m~3。优化形成的引水限制调度线分布形态合理,并联供水系统经过优化大幅度提高了水库系统的供水能力,证明了基于引水限制调度线的并联水库系统供水优化算法的可行性,该方法对并联水库联合调度具有重要参考价值。     

Multi-reservoir Operation Rules: Multi-swarm PSO-based Optimization Approach

Reservoir operation rules are intended to help an operator so that water releases and storage capacities are in the best interests of the system objectives. In multi-reservoir systems, a large number of feasible operation policies may exist. System engineering and optimization techniques can assist in identifying the most desirable of those feasible operation policies. This paper presents and tests a set of operation rules for a multi-reservoir system, employing a multi-swarm version of particle swarm optimization (MSPSO) in connection with the well-known HEC-ResPRM simulation model in a parameterization–simulation–optimization (parameterization SO) approach. To improve the performance of the standard particle swarm optimization algorithm, this paper incorporates a new strategic mechanism called multi-swarm into the algorithm. Parameters of the rule are estimated by employing a parameterization–simulation–optimization approach, in which a full-scale simulation model evaluates the objective function value for each trial set of parameter values proposed with an efficient version of the particle swarm optimization algorithm. The usefulness of the MSPSO in developing reservoir operation policies is examined by using the existing three-reservoir system of Mica, Libby, and Grand Coulee as part of the Columbia River Basin development. Results of the rule-based reservoir operation are compared with those of HEC-ResPRM. It is shown that the real-time operation of the three reservoir system with the proposed approach may significantly outperform the common implicit stochastic optimization approach.     

基于逐步优化算法的水库预报预泄规则拟定研究

针对水电站水库常规预泄调度运行洪水资源利用率较低等问题,建立了基于逐步优化算法的水电站水库预报预泄优化模型;以优化计算结果及预泄调度的基本原则为基础,分别拟定了水电站水库一般时期和枯汛过渡期的预报预泄运行规则,并构建了相应的数学方程.实例计算表明,按拟定规则模拟水库运行的年发电量及年弃水量与优化计算结果接近.规则具有较强的可操作性,不但能有效保证汛期防洪安全,而且能充分利用水头及减少弃水,进一步提高发电效益,较好地实现洪水的资源化利用.