长江上游水库群多目标优化调度模型及应用研究Ⅱ：水库群调度规则及蓄放次序

水库群联合调度中水库的协同调度规则和蓄放水次序是关键问题。在讨论多目标水库群联合调度的规则制定方法的基础上,依据"长江上游水库群多目标优化调度模型及应用研究(I):模型原理及求解"一文中推荐的多目标协调方案,统计得出长系列调度和多年平均水文条件下的各水库联合调度规则图,并讨论了其适用条件。进一步分析了推荐方案下水库群联合调度的汛前放水和汛末蓄水次序。研究表明,在多年长系列联合优化调度下,长江上游具有调蓄库容和防洪任务的11座多目标混联水库群,其蓄放水次序具有统计规律。出现概率最大的优化放水次序为:三峡—水布垭—锦屏I级—溪洛渡、构皮滩、二滩—紫坪铺、瀑布沟—隔河岩、宝珠寺、向家坝;优化蓄水次序为:锦屏I级—二滩、水布垭—隔河岩—溪洛渡、瀑布沟、紫坪铺、宝珠寺、构皮滩—向家坝—三峡。对于串联梯级水库群,上游水库先放水,下游水库后放水;上游水库先蓄水,下游水库先蓄满。     

考虑鱼类生态需水过程的漓江并联水库群生态调度

水库群联合运行带来巨大的经济和社会效益,同时也对河流生态造成诸多影响。为提升漓江流域水库群联合运行的发电、通航、生态保护和青狮潭水库蓄水综合效益,构建了考虑鱼类生态需水过程的并联水库群多目标调度模型,采用第三代非支配遗传算法（NSGA-Ⅲ）对模型优化求解。结果表明:（1）与常规调度相比,优化调度在保障发电工程效益的同时,实现了通航与生态效益的提升;（2）构建的并联水库群多目标生态调度模型,在枯、平、丰水年的生态流量满足率都维持在47%之上,尤其保障了鱼类越冬、产卵和繁殖期的生态需求;（3）实施漓江并联水库群联合生态调度,青狮潭水库在枯水年和丰水年蓄水量分别增加22.26×106 m3和 24.55×106 m3,缓解了青狮潭水库供水压力。研究可为并联水库群多目标生态调度提供相关参考。     

长江上游梯级水库群多目标联合调度技术研究

长江上游梯级水库是流域治理开发保护的骨干性工程,在保障流域防洪安全、供水安全、生态安全等方面发挥着重要作用.为有效解决长江上游巨型水库群防洪、供水、生态、发电、航运、应急等多目标联合调度关键技术瓶颈,开展了"长江上游梯级水库群多目标联合调度技术"研究攻关.首先重点介绍了长江上游梯级水库群多目标联合调度研究的重点难点问题...     

流域梯级水库群联合调度研究进展

在阐述国内外梯级水库群联合调度研究的基础上,系统分析了现阶段水库调度的新理念,讨论了梯级水库群联合优化调度在防洪和兴利方面的优点,论述了优化调度过程中确定性方法和不确定性方法的优缺点,并指出当前调度管理中存在调度管理信息化建设不够完善、对流域生态环境造成负面影响等问题。建议:1在运行模式方面将水库调度原则和电力市场原则相结合,在调度模型方面构建既符合实际又便于求解的模型,在模型求解方面引进新理论、新技术;2建立包含梯级水库计算机监测控制系统、水情自动测报系统等为一体的水库优化调度管理模式;3将生态调度纳入联合调度统一考虑,积极探索梯级水库群防洪、兴利与生态环境相互协调、统一的水库综合调度方式,将水库群对河流生态和库区水环境造成的负面影响控制在可承受范围内。     

长江上游水库群联合调度中的泥沙问题

长江上游已形成以三峡水库为核心的世界上规模最大的巨型水库群,2012年开始实施上述水库群的联合调度。分析了水库群联合调度中,与水库泥沙密切关联的汛期防洪、汛末蓄水和汛前消落调度方案及其对水库泥沙带来的影响。在较为深入、系统地研究了长江泥沙变化规律及其原因、水库淤积和长江中下游河床冲刷特性的基础上,对水库群联合调度中面临的水库淤积与长期使用问题,以三峡水库为例,提出了精细化的"蓄清排浑"和库尾减淤等泥沙调度方法。对水库群水沙联调,促进流域泥沙合理配置以及水库群联合调度条件下的泥沙问题与应对措施等提出了建议。     

新疆山区水库参与调度对灌区种植业与水库群联合调度影响研究——以玛纳斯河灌区为例

以新疆玛纳斯河灌区为例,通过对比山区肯斯瓦特水库参与调度前后灌区种植业产值与水库群联合调度方案,分析了山区水库参与调度对灌区种植业与水库群联合调度的影响。结果表明,肯斯瓦特水库参与调度可在较大程度上提高灌区水库群蓄水能力以及联合调度灵活性,削减水库群蒸发损失与弃水量,但水库群渗漏损失总量可能会增大,最终减少水库群总水量损失。在增加灌区种植业产值与总种植面积的同时提高水库群年平均蓄水量,有利于灌区种植业经济发展与生态环境的改善。在灌溉季节,应尽可能将水量蓄存在山区水库以减少水量损失并增加水库群联合调度灵活性,但在蓄水期可适当增加平原水库蓄水量以缓解山区水库蓄水能力不够的困难。     

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。     

金沙江下游梯级与三峡梯级枢纽联合蓄放水调度研究

针对流域干支流梯级水库群汛末竞争性蓄水这一工程问题,以金沙江四库和三峡梯级枢纽为研究对象,在保证防洪安全的前提下,将流域水库群蓄水原则与K值判别式法相结合,提出一种新的蓄放水策略来判定流域梯级各水库的蓄水时机及次序,通过对典型年不同频率的来水进行调洪演算模拟,得到各水库不同频率的蓄水控制线,进而绘制蓄水调度图;在此基础上,建立基于蓄水调度图的蓄水优化调度模型,并采用仿电磁学全局优化算法对模型进行求解.优化结果表明,在不减少流域梯级水库群汛末蓄水效益的前提下,与流域库群原规划方案相比,运用本文方法制定出的梯级水库群蓄水优化调度方案在不增加流域防洪风险的同时,可提高梯级水库群蓄水期蓄满度,减少弃水量,并增加发电等兴利效益.     

观音阁水库输水工程兴利调度方案分析

采用离散动态规划理论,分别建立汤河水库和观音阁—葠窝水库群优化调度的多目标动态规划模型,首先建立汤河水库和观音阁—葠窝水库群优化调度的多目标动态规划模型,确定在与汤河、葠窝水库联合调度满足太子河流域下游工农业需水的条件下,求出满足2030水平年本溪市用水时观音阁水库此时的最低水位和观音阁水库水位兴利调度到死水位时,水库可向本溪最大直供工业用水量。     

基于混沌算法的水库群联合优化调度

利用混沌算法全局搜索能力强、求解速度快的特点,将混沌优化算法运用到水库优化调度中,建立了水库群多目标优化模型,并提出交互式的多目标优化模型求解方法.该方法利用混沌优化算法生成满足约束条件的可行集,再采用交互式决策偏好的方法从非劣解集中寻找最佳权衡解;并对滦河下游水库群进行了实例研究.结果表明算法可行,成果合理,能为水库群的联合调度提供技术支持.     

Multi-objective Cultured Differential Evolution for Generating Optimal Trade-offs in Reservoir Flood Control Operation

Reservoir flood control operation (RFCO) is a complex problem because it needs to consider multiple objectives and a large number of constraints. Traditional methods usually convert multiple objectives into a single objective to solve, using weighted methods or constrained methods. In this paper, a new approach named multi-objective cultured differential evolution (MOCDE) is proposed to deal with RFCO. MOCDE takes cultural algorithm as its framework and adopts differential evolution (DE) in its population space. Considering the features of DE and multi-objective optimization, three knowledge structures are defined in belief space to improve the searching efficiency of MOCDE. MOCDE is first tested on several benchmark problems and compared with some well known multi-objective optimization algorithms. On achieving satisfactory performance for test problems, MOCDE is applied to a case study of RFCO. It is found that MOCDE provides decision makers many alternative non-dominated schemes with uniform coverage and convergence to true Pareto optimal solutions in a short time. The results obtained show that MOCDE can be a viable alternative for generating optimal trade-offs in reservoir multi-objective flood control operation.     

含支线调蓄的调水系统水库群联合调度

针对调水工程与其支线调蓄工程所面临的复杂水库群联合调度问题,以陕西省引汉济渭调水工程及其支线调蓄工程焦岩水库、玉带河水库构成的联合调度系统为工程实例,运用拓扑概化方式揭示了各水源之间的水利－水量联系,并由此构建了联合调度模型,采用协同粒子群算法求解了10 a长系列调度,提出了单一水库多目标供水方案,解析了耦合系统补给调水效果。结果表明：支线调蓄工程能够在均衡多目标供水效益的基础上,通过耦合系统有效增加补给调水量,显著改善调水过程。     

考虑蓄水期弃水风险的水库长期发电调度方法

短期径流的大幅波动使得以平均径流为基础制定的水库长期调度方案面临较大弃水风险,是影响水库长期调度决策合理性的重要因素。本文考虑日尺度径流波动影响,提出一种蓄水期弃水风险量化方法,并建立了耦合弃水风险的水库长期发电调度模型。利用长系列日径流资料,结合风险最小蓄水规则,以不蓄弃水流量为指标量化蓄水期各月弃水风险;采用Copula函数构建月均入流与弃水风险的联合分布和条件概率分布,明晰了特定入流条件的风险置信区间;最后,将弃水风险以弃电损失函数融入优化模型,以获得更符合实际的长期调度方案。以澜沧江流域小湾水库为工程背景进行调度模拟分析,结果表明本文方法能够有效降低水库长期优化调度方案的弃水风险,有利于提高优化结果的可操作性,与传统方法相比,能够使多年平均弃水减少约4.76亿m³、发电量增加约1.15亿kW·h。     

基于分段粒子群算法的梯级水库多目标优化调度模型研究

提出了约束破坏向量、分段粒子群算法以及多目标分段粒子群算法,有效解决了在时间步长较小、计算时段数目较多时,传统智能优化算法解水库优化调度问题的寻优效率低下甚至无可行解的问题。该方法基于粒子群算法框架,引入约束破坏向量、分段操作和特殊变异操作来增强进化过程中的种群质量,从而提高算法的计算效率。闽江流域金溪梯级水库多目标优化调度的实例分析表明,在解时间步长较小、计算时段数目较多的水库优化调度问题时,分段粒子群算法、多目标分段粒子群算法相对其他算法具有明显优势。     

基于决策因子选择的梯级水库多目标优化调度规则研究

运用极端随机森林挖掘水库调度信息,考虑各水库相关因子与决策目标的相互关系,提出了梯级水库输入数据选择(Cascade-reservoir Input-variables Selection,CIS)方法,引入Gaussian径向基函数建立水库调度规则,并采用PA-DDS多目标算法对水库调度规则参数进行优化,得到了同时考虑供水和发电的多目标优化调度规则集。汉江上游梯级水库的应用结果表明:CIS方法能优选得到较为稳定的决策因子,且能在充分利用已有优选信息的同时避免决策因子选取上的重复和冗余;经梯级多目标优化调度,优选的决策因子能得到比传统决策因子结果分布更优的非劣解集,在以发电为主要目标进行优化调度时,能有效调整汛期和非汛期的水量利用方式,梯级水库年均发电量和供水量分别提高了0.33%和7.84%。     

Multi-Objective Firefly Integration with the K-Nearest Neighbor to Reduce Simulation Model Calls to Accelerate the Optimal Operation of Multi-Objective Reservoirs

Reservoirs are used as one of the surface water sources for different and often conflicting water supply purposes. Given the complex management policies governing a basin, it is essential to simultaneously consider different goals and cope with the associated trade-off in water resources management. This purpose requires coupling a multi-objective optimization algorithm with a reservoir simulation model, which this approach increases required computational efforts. Various simulation–optimization approaches have been developed and used for solving the related problems. However, they often have complicated methods and certain limitations in real-world applications. In this study, a new multi-objective firefly algorithm—K nearest neighbor (MOFA-KNN) hybrid algorithm is developed which is time-efficient and is not as complicated as previous approaches. The proposed algorithm was evaluated for both benchmark and real problems. The results of the benchmark problem showed that the execution time of the MOFA-KNN hybrid algorithm was up to 99.98% less than that of the multi-objective firefly algorithm (MOFA). In the real problem, the MOFA-KNN algorithm was linked to the 2D hydrodynamic and water quality model, CE-QUAL-W2, to test the developed framework for reservoir operation. The Aidoghmoush reservoir as a case study investigated to minimize the total released dissolved solids (TDS) and the water temperature difference between the inflow and the outflow. The results demonstrated that the MOFA-KNN algorithm significantly reduced the simulation–optimization execution time (>?660 times compared with MOFA). The minimum released TDS from the reservoir was 13.6 mg /l and the minimum temperature difference was 0.005 °C.

     

Multi Objective Simulation-Optimization Approach in Pollution Spill Response Management Model in Reservoirs

In this study, a Pollution Spill Response Management Model (PSRMM) is developed to provide an emergency response on reservoir operation during accidental injection of hazardous material to reservoirs. PSRMM consist of spatial system analyzing (SSA) model, 2D hydrodynamic and water quality simulation model (CE-QUAL-W2), and multi-objective particle swarm optimization (MOPSO) algorithm. CE-QUAL-W2 model is applied for spatial and temporal analysis of water body in simulation routine of PSRMM. Also, in an advanced modeling framework, CE-QUAL-W2 is coupled with MOPSO algorithm to obtain desirable near optimal reservoir operation strategy and/or emergency planning in selective withdrawal framework. The simulation-optimization (SO) routine of PSRMM provides pareto optimum reservoir operation strategy in selective scheme to minimize reservoir cleanup time and to reduce the magnitude and frequency of water quality standard violations. The proposed tool is applied in Ilam reservoir in Iran, as a multipurpose hydraulic project providing water for drinking, irrigation, and flood control during an accident spill of conservative hazardous material. Different scenarios are defined and tested employing the proposed PSRMM for managing accidental spill of conservative pollutant into the reservoir.     

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.     

Flood Control Operation of Reservoir Group Using Yin-Yang Firefly Algorithm

Flood control operation (FCO) of a reservoir is a complex optimization problem with a large number of constraints. With the rapid development of optimization techniques in recent years, more and more research efforts have been devoted to optimizing FCO problems. However, for solving large-scale reservoir group optimization problem, this is still a challenging task. In this work, a reservoir group FCO model is established with minimum flood volume stored in each reservoir and minimum peak flow of downstream control point during the dispatch process. At the same time, a flood forecast model for FCO of a reservoir group is developed by coupling Yin-Yang firefly algorithm (YYFA) with ε constrained method. As a case study, the proposed model is applied to a three-reservoir flood control system in Luanhe River Basin consisting of reservoirs, river channels, and downstream control points. Results show that optimal operation of three reservoirs systems can efficiently reduce the occupied storage capacity for flood control and flood peaks at downstream control point of the basin. The proposed method can be extended to FCO of other reservoir groups with similar conditions.

     

桂林市防洪及漓江补水工程水库群生态设计概述

介绍了桂林市防洪及漓江补水工程设计中的生态设计理念,包括——生态规划——建立水库群联合水资源的多目标体系、生态设计、生态调度——降低低温水影响/优化初期蓄水调度方式。论述了设计中各个层面上如何运用生态措施,实现水资源的合理配置和保护漓江生态环境的目标。