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.     

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....     

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.

     

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.     

Technical Communication: Two-Phase Stochastic Dynamic Programming Model for Optimal Operation of Irrigation Reservoir

A two-phase stochastic dynamic programming model is developed for optimal operation of irrigation reservoirs under a multicrop environment. Under a multicrop environment, the crops compete for the available water whenever the water available is less than the irrigation demands. The performance of the reservoir depends on how the deficit is allocated among the competing crops. The proposed model integrates reservoir release decisions with water allocation decisions. The water requirements of crops vary from period to period and are determined from the soil moisture balance equation taking into consideration the contribution of soil moisture and rainfall for the water requirements of the crops. The model is demonstrated over an existing reservoir and the performance of the reservoir under the operating policy derived using the model is evaluated through simulation.     

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。     

Reliability Improved Stochastic Dynamic Programming for Reservoir Operation Optimization

Stochastic Dynamic Programming (SDP) is widely used in reservoir operation problems. Besides its advantages, a few drawbacks have leaded many studies to improve its structure. Handling the infeasible conditions and curse of dimensionality are two major challenges in this method. The main goal of this paper is proposing a new method to avoid infeasible conditions and enhance the solution efficiency with new discretization procedure. For this purpose, an optimization module is incorporated into regular SDP structure, so that, near optimal values of state variables are determined based on the available constraints. The new method (RISDP) employs reliability concept to maximize the reservoir releases to satisfy the downstream demands. Applying the proposed technique improves the reservoir operating policies compared to regular SDP policies with the same assumptions of discretization. Simulation of reservoir operation in a real case study indicates about 15% improvement in objective function value and elimination of infeasible conditions by using RISDP operating policies.     

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.     

Feasibility Improved Stochastic Dynamic Programming for Optimization of Reservoir Operation

Water Resources Management - Stochastic Dynamic Programming (SDP) is a major method for optimizing reservoir operation. Handling non-linear, non-convex and non-differentiable objective functions...     

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.     

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.     

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.     

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。     

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.     

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.     

Optimal Reservoir Operation with Water Supply Enhancement and Flood Mitigation Objectives Using an Optimization-Simulation Approach

Floods are one of the most destructive natural disasters which compensation of their effects inflicts immense costs especially in areas with human developments such as cities. Moreover, supplying water for demands particularly during the drought period is a challenging issue in water resource planning. The aim of this study is to propose a model for optimal operation of a reservoir with enhancing downstream demands supply and flood damage mitigation objectives. The model is developed by coupling MODSIM, river network simulation DSS with the imperialist competitive optimization algorithm (ICA). Gotvand Dam in southwestern Iran is the case of study and amounts of water storage in the reservoir in daily time-steps are the decision variable. Results indicate efficiency of the proposed optimization-simulation approach, suggesting that flood damages can be controlled with proper planning while ensuring that downstream water demands are satisfied. As an instance of results, the optimum reservoir rule curve obtained by the proposed model is able to manage a flood entering to the reservoir 11 times greater than the safe flow in the downstream reach and release it lower than the safe flow rate.     

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。     

A Forecast-Based Operation(FBO) Mode for Reservoir Flood Control Using Forecast Cumulative Net Rainfall

Water Resources Management - With advance of up-to-date hydrology measuring and forecasting system, reservoir operations are no longer required to be as conservative as they once were. The...     

湖南镇水库汛限水位动态控制研究

简要介绍了开展湖南镇水库汛限水位动态控制研究的必要性和可行性,对分期洪水、汛限水位动态控制域、动态控制方法以及洪水回蓄等进行研究,并分析了汛限水位动态控制及洪水回蓄的风险和效益。