A Memetic Multi-objective Immune Algorithm for Reservoir Flood Control Operation

Reservoir flood control operation (RFCO) is a challenging optimization problem with multiple conflicting decision goals and interdependent decision variables. With the rapid development of multi-objective optimization techniques in recent years, more and more research efforts have been devoted to optimize the conflicting decision goals in RFCO problems simultaneously. However, most of these research works simply employ some existing multi-objective optimization algorithms for solving RFCO problem, few of them considers the characteristics of the RFCO problem itself. In this work, we consider the complexity of the RFCO problem in both objective space and decision space, and develop an immune inspired memetic algorithm, named M-NNIA2, to solve the multi-objective RFCO problem. In the proposed M-NNIA2, a Pareto dominance based local search operator and a differential evolution inspired local search operator are designed for the RFCO problem to guide the search towards the and along the Pareto set respectively. On the basis of inheriting the good diversity preserving in immune inspired optimization algorithm, M-NNIA2 can obtain a representative set of best trade-off scheduling plans that covers the whole Pareto front of the RFCO problem in the objective space. Experimental studies on benchmark problems and RFCO problem instances have illustrated the superiority of the proposed algorithm.     

Optimal Reservoir Operation for Flood Control Using Folded Dynamic Programming

Folded Dynamic Programming (FDP) is adopted for developing optimal reservoir operation policies for flood control. It is applied to a case study of Hirakud Reservoir in Mahanadi basin, India with the objective of deriving optimal policy for flood control. The river flows down to Naraj, the head of delta where a major city is located and finally joins the Bay of Bengal. As Hirakud reservoir is on the upstream side of delta area in the basin, it plays an important role in alleviating the severity of the flood for this area. Data of 68 floods such as peaks of inflow hydrograph, peak of outflow from reservoir during each flood, peak of flow hydrograph at Naraj and d/s catchment contribution are utilized. The combinations of 51, 54, 57 thousand cumecs as peak inflow into reservoir and 25.5, 20, 14 thousand cumecs respectively as peak d/s catchment contribution form the critical combinations for flood situation. It is observed that the combination of 57 thousand cumecs of inflow into reservoir and 14 thousand cumecs for d/s catchment contribution is the most critical among the critical combinations of flow series. The method proposed can be extended to similar situations for deriving reservoir operating policies for flood control.     

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。     

水库多目标优化调度技术比较研究

多目标智能优化算法种类繁多,不断涌现,在水库优化调度中得到了广泛应用,但多目标智能优化技术仍然是目前水库群综合利用优化调度研究中的热点和难点之一。已有的研究算法大多是关于水库优化调度中适用性的应用研究,且实际问题简化多,在算法算子的选择、算法性能的探讨和比较、特别是多目标优化等方面还不够深入。为此,选择应用较为广泛的NSGA-Ⅱ和DEMO算法,从变量规模、约束处理技术等方面,对其在水库多目标优化调度中的应用效果进行初步分析、比较和评估,为水库多目标优化调度算法的选择提供了参考。     

A Parallel Multi-objective Optimization Algorithm Based on Coarse-to-Fine Decomposition for Real-time Large-scale Reservoir Flood Control Operation

Reservoir flood control operation (RFCO) is a multi-objective optimization problem with a long sequence of correlated decision variables. It brings big challenges to large-scale multi-objective optimizers which were generally developed based on the divide-and-conquer strategy. For solving large-scale RFCO problem, a novel coarse-to-fine decomposition method is developed and combined with the algorithmic framework of multi-objective evolutionary algorithm based on decomposition (MOEA/D), giving rise to the proposed pCFD-MOEA/D algorithm. The pCFD-MOEA/D algorithm first divides the original RFCO problem into a sequence of sub-problems from coarse to fine scale with different scheduling time intervals. Then all sub-problems are optimized simultaneously and communicate at set intervals. Experimental results on three typical floods at Ankang reservoir have demonstrated that the proposed pCFD-MOEA/D can successfully obtain the elaborate hourly schedule schemes in real time and outperforms the compared algorithms.

     

水库防洪优化调度风险决策模型及应用

提出一种水库防洪优化调度风险决策模型.通过多目标防洪优化调度获取可行调度方案集.结合洪水预报误差进行风险分析,计算调度方案风险率.在此基础上,建立基于改进熵权的灰色关联分析模型,实现均衡考虑上下游风险的调度方案的灰色优选.将该风险决策模型应用于三峡水库防洪风险决策实例,确定了最佳洪水调度过程.     

水电站水库汛期洪水退水段优化调度研究

有防洪任务的水电站水库,洪水退水段的调度对发电非常重要,退水段调度好坏直接影响水库全年兴利效益。以北方某水电站水库为例,在预测退水段径流过程的条件下,建立了退水段发电量最大模型,并利用遗传算法对该模型进行求解。遗传算法与常规调度结果的对比结果表明,前者能获得更优的调度结果。该方法对指导水电站水库汛期洪水退水段的调度,有较强的实用价值。     

An Improved NSGA-III Algorithm for Reservoir Flood Control Operation

An improved non-dominated sorting genetic algorithm-III (ENSGA-III) is proposed to solve reservoir flood control operation (RFCO) problem. The highest upstream water level and the largest discharge of the dam are considered as two objective functions for the RFCO problem. In the proposed ENSGA-II, there are three aspects of improvements in the original NSGA-III. First, orthogonal design is adopted to generate initial population for making the population more spread and uniform in the search space. Secondly, ε-dominance and constraint violation strategy is designed to find the non-dominated solution set. Thirdly, double populations are updated with three-archive strategy for producing better individuals in evolutionary process. The performance of the proposed ENSGA-III has been tested on the RFCO problem of Three Gorges Reservoir. The simulation results show that the proposed method is able to produce well distributed Pareto optimal solutions for the multi-objective reservoir flood control operation problem in term of solution quality. Compared with the results obtained by other methods, the superiority of the ENSGA-III for solving the RFCO problem is verified.     

Short Term Optimal Operation of Water Supply Reservoir under Flood Control Stress using Model Predictive Control

Reservoir operations require enhanced operating procedures for water systems under stress attributed to growing water demand and consequences of changing hydro-climatic conditions. This study focuses on the management of the Yuvacik Dam Reservoir for water supply and flood mitigation in the Marmara Region of Turkey. We present an improved operating technique for fulfilling the conflicting water supply and flood mitigation objectives. This is accomplished by incorporating the long term water supply objectives into a Guide Curve (GC) whereas the extreme floods are attenuated by means of short-term optimization based on Model Predictive Control (MPC). The reference case implements operating rules with a constant GC at maximum forebay elevation targeting the fulfillment of the water supply objective. We compare the reference with a new time-dependent GC, derived using an Implicit Stochastic Optimization (ISO) approach. This new curve shows nearly the same performance regarding the water supply objectives, but significantly reduces the flooding risk downstream of the dam. Possible flood events observed at the end of the wet season, when the reservoir is at the maximum level to enable water supply for the dry season, can be eliminated by the application of an additional short-term optimization by MPC. The robustness of the approach is demonstrated via hindcasting experiments.     

水库调洪计算方法的应用研究

基于调洪计算的传统方法如试算法、单辅助线法和数值解法如改进欧拉法、四阶龙格-库塔法,分析了不同算法的结果差异和影响因素,并对调洪时段的选取进行了研究。结果表明:1直接求解一阶微分方程的数值解法的优点是无需迭代,可快速得到计算结果,通过迭代试算或反复内插辅助线求解非线性超越方程的计算速度较慢,调洪时段缩短时会出现不稳定、不收敛的情况,同时存在直接内插库容曲线的误差,不推荐采用;2调洪时段较长时,试算法计算的水位偏高,会增大工程投资,调洪时段较小时传统方法结果偏小,对工程安全不利,数值解法随调洪时段缩短精度有所提高,计算水位值增大,有利于工程安全,宜广泛采用;3调洪时段的选取要以能控制入库洪峰流量为前提,时段步长越短,精度越高,精度一定时,四阶龙格-库塔法时段选取的范围较广,对工程的适应性较好。     

Multi-Person Multiobjective Fuzzy Decision-Making Model for Reservoir Flood Control Operation

By analyzing the present situation of reservoir flood control operation, this paper proposes multi-person multiobjective decision-making model for the problems of flood control operation. The model takes the influence of multiobjectives and experience and knowledge of decision-makers into account. In order to find an optimal alternative decided by multiple decision-makers from all the given alternatives, the relative optimal membership degree of every decision-maker to every alternative is first obtained and then the optimal membership degree of every decision-maker to every alternative is obtained which collects group preferences through two stages. The advantages of this model are simple and more adaptable to the practical problem. The model is demonstrated by application in Fengman Reservoir located in Songhua River Basin in China. Of course, this method can also be applied in other science fields.     

Optimal Reservoir Flood Control Operation Using a Hedging Model and Considering the Near-Field Vibrations Induced by Flood Release

Water Resources Management - Several researchers have extensively investigated the flow-induced vibrations caused by flood release because numerous structures have been destroyed by such release....     

梯级水库防洪调度功能需求分析

梯级水库防洪调度系统是实现流域防洪调度的关键和核心.文中从应用角度出发,分析梯级水库防洪调度的业务需求,提出梯级水库防洪调度系统须具有资料管理、仿真学习、调度计算、安全校验、分析会商等功能,总结出较为实用的水库调度方案制定流程为数据录入、分析试算、闸门反推计算、结果安全校验、成果表达,推进了梯级防洪调度系统的实用化、智能化.     

乌鲁瓦提水库调度运用和度汛方案

乌鲁瓦提水利枢纽位于新疆和田河支流喀拉喀什河中游,该河是一条多沙的河流,水库灌溉、发电与汛期排沙的矛盾很突出,为使工程发挥最大的效益,需拟定一个详细的水库调度运行方案．文中从汛期水库调度、灌溉及各业综合用水调度、发电调度等方面说明水库的调度运用和度汛方案。     

石泉水库洪水调度方案研究

将一场洪水划分为上涨、峰顶、退水、拦尾超蓄、发电消落5个阶段,并采用分段优化调度模型对石泉水库4场不同量级的洪水进行了优化调度演算。结果表明:演算结果与实际运行情况相符合;闸门做到了有序开启、均匀启闭;出库流量平缓,为水库下游的防洪创造了条件;期末水位达到预期目标,有利于水库汛后增大发电量。     

南一水库防洪调度模型研究

针对南一水库防洪要求,构建指令调度、水位控制调度和错峰补偿调度等水库防洪调度模型。其中:指令调度模型指定下泄流量,操作简便,侧重于保护水库自身安全;水位控制调度模型能够控制调度期内水库最高水位和调度期末水位;错峰补偿调度模型考虑区间来水进行错峰补偿调度,充分利用河道行洪能力。以2013年"天兔"台风造成的洪水过程作为调度对象进行水库防洪调度,并对不同模型的调度结果进行分析,表明错峰补偿调度模型较另两个模型具有更强的灵活性,能够在保障水库安全的前提下,保证下游防洪参考断面的安全。     

丹江口水库调度系统设计和开发

结合丹江口水库实际工作情况和需要 ,运用现代软件编程和系统集成技术 ,设计开发出先进、实用、可靠和界面友好的应用软件系统 ,并通过了两年的实际运行检验。简要介绍了系统的结构组成和运行情况 ,重点探讨水雨情实时管理、洪水预报、水库防洪发电调度子系统的主要功能和模型。     

水库实时防洪调度风险分析研究

依据水库实时防洪调度流程,分析了水库防洪决策风险源;提出了基于洪量预报误差分布的水库调洪演算风险分析和风险率计算方法;建立了水库实时防洪调度风险交互模拟分析模型,为实时决策中的不确定性分析提供了实用工具.     

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.