Effect of inflow forecast accuracy and operating time horizon in optimizing irrigation releases

In this study, application of Genetic Algorithms (GA) is demonstrated to optimize reservoir release policies to meet irrigation demand and storage requirements. As it is commonly recognized that accuracy of inflow forecast and operating time horizon affects the optimal policies, a trial-and-error approach is suggested to identify the appropriate trade-off between forecast accuracy and operating horizon. The flexibility offered by GA to set up and evaluate objective functions is exploited towards this end. The results are also compared with Linear Programming (LP) model. It is concluded that forecasts models of high accuracy are desirable, particularly when the system is to be operated for periods of high demand. In such cases, the optimization with longer time horizon ensures achievement of the objective more uniformly over the period of operation. The performance of GA is found to be better than LP, when forecast model of higher accuracy and longer period of operating horizon are considered for optimization.     

Application of Intelligent Water Drops Algorithm in Reservoir Operation

Optimum reservoir operation is a challenging problem in water resources systems. In this paper, Intelligent Water Drops (IWD) algorithm is applied in a reservoir operation problem. IWD is a population based algorithm and is initially proposed for solving combinatorial problems. The algorithm mimics the dynamics of river system and the behavior of water drops in the rivers. For this purpose data from Dez reservoir, located in southwestern Iran, has been used to examine the performance of the model. Moreover, due to similarities between IWD and the Ant Colony Optimization (ACO) algorithms, the results are compared with those of the ACO algorithm. Comparison of the results shows that while the IWD algorithm finds relatively better solutions, it is able to overcome the computational time consumption deficiencies inherited in the ACO methods. This is very important in large models with too many decision variables where run time becomes a limiting factor for optimization model applications.     

Optimal Reservoir Release Policy Considering Heterogeneity of Command Area by Elitist Genetic Algorithm

A number of models with conventional optimization techniques have been developed for optimization of reservoir water release policies. However these models are not able to consider the heterogeneity in the command area of the reservoir appropriately, due to non linear nature of the processes involved. The optimization model based on genetic algorithm (GA) can deal with the non linearity due to its inherent ability to consider complex simulation model as evaluation function for optimization. GA based models available in literature generally minimize the water deficits and do not optimize the total net benefits through optimal reservoir release policies. The present study focuses on optimum releases from the reservoir considering heterogeneity of the command area and responses of the command area to the releases instead of minimizing only the reservoir storage volumes. An optimization model has been developed for the reservoir releases based on elitist GA approach considering the heterogeneity of the command area. The developed model was applied to Waghad irrigation project in upper Godavari basin of Maharashtra, India. The results showed that 19% increase in the total net benefits could be possible by adopting the proposed water release policy over the present practice keeping same distribution of area under different crops. The model presented in this study can also optimize the crop area under irrigation. It is found that irrigated area can be increased to 50% of ICA (Irrigable Command Area) from the existing 23% with resulting addition to total net benefits by 31%. The effect of adopting the proposed irrigation schedule and increased irrigation areas would be to increase the net benefits to existing farmers.     

提高水库汛限水位的防洪风险研究

水库在汛期的汛限水位,关系到水库发电效益与水库防洪安全两个方面。从防洪角度,汛限水位越低,意味着预留的水库调洪库容越大,对水库防洪较为有利。本文以江西省最大的水电站万安水库为实例,应用季节性的一阶自回归模型,研究提高水库汛限水位,对水库防洪风险的影响,为决策提供依据。     

Single Reservoir Operating Policies Using Genetic Algorithm

To obtain optimal operating rules for storage reservoirs, large numbers of simulation and optimization models have been developed over the past several decades, which vary significantly in their mechanisms and applications. As every model has its own limitations, the selection of appropriate model for derivation of reservoir operating rule curves is difficult and most often there is a scope for further improvement as the model selection depends on data available. Hence, evaluation and modifications related to the reservoir operation remain classical. In the present study a Genetic Algorithm model has been developed and applied to Pechiparai reservoir in Tamil Nadu, India to derive the optimal operational strategies. The objective function is set to minimize the annual sum of squared deviation form desired irrigation release and desired storage volume. The decision variables are release for irrigation and other demands (industrial and municipal demands), from the reservoir. Since the rule curves are derived through random search it is found that the releases are same as that of demand requirements. Hence based on the present case study it is concluded that GA model could perform better if applied in real world operation of the reservoir.     

Comparison of Policies Derived from Stochastic Dynamic Programming and Genetic Algorithm Models

A comprehensive Genetic Algorithm (GA) model has been developed and applied to derive optimal operational strategies of a multi-purpose reservoir, namely Perunchani Reservoir, in Kodaiyar Basin in Tamil Nadu, India. Most of the water resources problem involves uncertainty, in order to see that the GA model takes care of uncertainty in the input variable, the result of the GA model is compared with the performance of a detailed Stochastic Dynamic Programming (SDP) model. The SDP models are well established and proved that it takes care of uncertainty in-terms of either implicit or explicit approach. In the present study, the objective function of the models is set to minimize the annual sum of squared deviation from desired target release and desired storage volume. In the SDP model the optimal policies are derived by varying the state variables from 3 to 9 representative class intervals, and then the cases are evaluated for their performance using a simulation model for longer length of inflow data, generated using a Thomas–Fiering model. From the performance of the SDP model policies, it is found that the system encountered irrigation deficit, whereas GA model satisfied the demand to a greater extent. The sensitivity analysis of the GA model in selecting optimal population, optimal crossover probability and the optimal number of generations showed the values of 150, 0.76 and 175 respectively. On comparing the performance of SDP model policy with GA model, it is found that GA model has resulted in a lesser irrigation deficit. Thus based on the present case study, it may be concluded that the GA model performs better than the SDP model.     

于桥水库多级汛限水位研究

汛限水位是综合利用水库运行和调度的重要参数之一,汛限水位设置目的是协调水库运行管理中防洪与兴利之间的矛盾。从模糊理论讲,汛限水位在水库中应是一段连续的“抛物线型”水位值,而不是某一特定的固定值,是一个模糊概念。于桥水库是一座综合利用的大型水库,整个汛期采用一个固定汛限水位,蓄水量往往达不到兴利要求。为此,采用汛期描述的模糊集理论,把于桥水库汛期划分为汛前期、汛前过渡期、主汛期、汛后过渡期和汛后期,并确定了不同时节水库的汛限水位。这有利于拦蓄汛末雨洪资源,增加水库蓄水量,减少弃水量。     

Genetic Algorithm for Optimal Operating Policy of a Multipurpose Reservoir

This paper presents a Genetic Algorithm (GA) model for finding the optimal operating policy of a multi-purpose reservoir, located on the river Pagladia, a major tributary of the river Brahmaputra. A synthetic monthly streamflow series of 100 years is used for deriving the operating policy. The policies derived by the GA model are compared with that of the stochastic dynamic programming (SDP) model on the basis of their performance in reservoir simulation for 20 years of historic monthly streamflow. The simulated result shows that GA-derived policies are promising and competitive and can be effectively used for reservoir operation.     

Reservoir flood control operation model incorporating multiple uncontrolled water flows

This study presents a weighted pre‐emptive goal programming model formulation for coordinated reservoir operation, with easy inclusion of uncontrolled water flows. The model is combined with a multiple water inflows forecasting model, and can be used for real time reservoir operation. Water flow routing from various upstream sites is accounted by with a single compact equation. Integration of controlled and uncontrolled water flows in the optimization model simplifies the operation model, resulting in accurate computation of the downstream water flow. Multiple objectives with water storage and flow variables are used to derive optimal regulation for a reservoir system under flood conditions. For real time operations, the model can be used to determine optimal water release rates for a current period, on the basis of an optimal water release schedule for an operating horizon (T). The model is applied to the flood control operation of reservoirs in the Narmada River Basin (India), with three controlled and three uncontrolled water flows affecting the downstream flow at Hoshangabad. Reservoir water storage and downstream control point flows are zoned, with prioritized objectives used to derive the optimal water release rates. Model applications to the 1999 flood event in the Narmada River Basin with observed and forecasted inflows illustrates that, if water inflows were known through a forecasting technique well in advance, the coordinated operation of the reservoirs could substantially reduce the peak water flows at the control points. The study also indicates that uncontrolled channel flows at the damage site were sufficiently high to cause flooding at the damage site.     

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.

     

Optimal and Adaptive Operation of a Hydropower System with Unit Commitment and Water Quality Constraints

Management of water resources has become more complex in recent years as a result of changing attitudes towards sustainability and the attribution of greater attention to environmental issues, especially under a scenario of water scarcity risk introduced by climate changes and anthropogenic pressures. This study addresses the optimal short-term operation of a multi-purpose hydropower system under an environment where objectives are conflicting. New optimization models using mixed integer nonlinear programming (MINLP) with binary variables adopted for incorporating unit commitment constraints and adaptive real-time operations are developed and applied to a real life hydropower reservoir in Brazil, utilizing evolutionary algorithms. These formulations address water quality concerns downstream of the reservoir and optimal operations for power generation in an integrated manner and deal with uncertain future flows due to climate change. Results obtained using genetic algorithm (GA) solvers were superior to gradient based methods, converging to superior optimal solutions especially due to computational intractability problems associated with combinatorial domain of integer variables in the unit commitment formulation. The adaptive operation formulation in conjunction with the solution of turbine unit commitment problem yielded more reliable solutions, reducing forecasting uncertainty and providing more flexible operational rules.     

Reservoir Optimization-Simulation with a Sediment Evacuation Model to Minimize Irrigation Deficits

Importance of existing reservoirs for supplying fresh water has increased significantly due to population increase and enhanced living standards, while the reduced development of new reservoirs in recent decades has made it even more pertinent that the current battery of reservoirs be operated in a sustainable and efficient manner. In order to move a step towards the goal of sustainability, sediment evacuation must be considered when optimizing a reservoir??s operations. The Reservoir Optimization-Simulation with Sediment Evacuation (ROSSE) model is a recently developed tool which internalizes sediment evacuation routines and the simulation module in a newly developed GA-based optimization module. This article applies the ROSSE model with the aim of minimizing irrigation shortages in the Tarbela Reservoir, Pakistan. The article also calculates the suitable values of various GA parameters required to run the model through a sensitivity analysis. Simulation results of three sets of rule curves??one existing and two optimized sets??are compared with each other for parameters like irrigation shortage, power generation, sediment evacuation and flood dis-benefits (damages). It is found that the optimized rule curves of scenario 1 reduce the irrigation shortages by 39?% while the optimized rule curves of scenario 2 can reduce the irrigation shortages by 24?% of that of the shortages by existing rule curves. The optimized rule curves of scenario 2 also ensure the current level of hydropower generation and sediment evacuation for the Tarbela reservoir. The study recommends a change in the reservoir??s existing rule curves in order to reduce irrigation shortages. The incorporation of the sediment evacuation routine and availability of economic and hydro based objective functions in the optimization model will help achieving the goal of sustainability.     

Basin/Reservoir System Integration for Real Time Reservoir Operation

This paper demonstrates the basin/reservoir system integration as a decision support system for short term operation policy of a multipurpose dam. It is desired to re-evaluate and improve the current operational regulation of the reservoir with respect to water supply and flood control especially for real time operation. The most innovative part of this paper is the development of a decision support system (DSS) by the integration of a hydrological (HEC-HMS) and reservoir simulation model (HEC-ResSim) to guide the professional practitioners during the real time operation of a reservoir to meet water elevation and flood protection objectives. In this context, a hybrid operating strategy to retain maximum water elevation is built by shifting between daily and hourly decisions depending on real time runoff forecasts. First, a daily hydro-meteorological rule based reservoir simulation model (HRM) is developed for both water supply and flood control risk. Then, for the possibility of a flood occurrence, hourly flood control rule based reservoir simulation model (FRM) is used. The DSS is applied on Yuvac?k Dam Basin which has a flood potential due to its steep topography, snow potential, mild and rainy climate in Turkey. Numerical weather prediction based runoff forecasts computed by a hydrological model together with developed reservoir operation policy are put into actual practice for real time operation of the reservoir for March – June, 2012. According to the evaluations, proposed DSS is found to be practical and valuable to overcome subjective decisions about reservoir storage.     

《水库调度设计规范》中两级调度图编制方法的改进研究

应用《水库调度设计规范》(GB/T 50587—2010)编制灌溉(供水)结合发电的综合利用两级调度图过程中,经过多次的检验和修改仍不能准确满足设计任务的要求。针对规范中编制方法的问题,提出了一种改进方法。改进方法消除了规范推荐方法的不合理计算和编制问题,准确计算编制了各条基本调度线。实际工程应用结果表明,用改进方法可以提高水库综合调度设计的合理性,改进方法可适用于年调节和多年调节水库,两级调度图可一次性编制成功,无需再反复地检验和修改,能准确地满足灌溉和发电任务的要求。     

Optimal Operation of a Multi-Purpose Reservoir Using Neuro-Fuzzy Technique

Present paper is aimed to develop operation policy for a multi-purpose reservoir using Neuro-Fuzzy technique in an efficient way. Ramganga reservoir behind Ramganga dam, Kalagarh, India has been considered as a study reservoir. The developed policy minimizes the damage due to floods and droughts and determines optimum releases against demands for domestic supply, irrigation and hydropower generation for monsoon and non-monsoon periods. Three Fuzzy Rule Based (FRB) models for monsoon period and three for non monsoon period have been developed and tested. Actual releases have been used to formulate the general operation fuzzy rules. Releases computed from all developed models using Fuzzy Mamdani (FM) and ANFIS (Adaptive Neuro-Fuzzy Interactive System) – Grid and Cluster have been compared and it was found that ANFIS-cluster gives the best results but FM is more users friendly. For any expected inflow, reservoir level and demand, release can be calculated using developed GUI windows of the models.     

A cost allocation approach for irrigation water in Upper Egypt

An approach for estimating irrigation water cost is presented and applied to Upper Egypt. Two cost models are used. The first model is to estimate the cost of irrigation water if controlled with a multi-purpose structure. The model is used in estimating the irrigation cost at the High Aswan Dam. The second model is to allocate the cost of irrigation structures and the cost of irrigation at the multi-purpose structures among the served areas. The two models are applied to estimate the cost of irrigation water at the different regions of Upper Egypt.     

水库防凌调度数学模型的研制与开发

本文着重研究利用水库调节径流减轻冰凌灾害.文中系统分析了影响凌汛的因素、水库防凌调度机理、运用方式；研究了不稳定封冻河段和稳定封冻河段冰下过流能力的经验公式，并依据冰水力学理论、河冰运行规律和水冰两相流连续方程及运动方程，参照黄河流域冰期实测资料，建立了水库防凌调度数学模型.经过模拟黄河内蒙古、下游河段典型年凌汛检验，证明调度模型合理、实用，对减轻凌汛灾害有重要作用，具有较好的应用价值.     

基于API 模型与新安江模型的察尔森水库洪水预报

察尔森水库承担着兴安盟地区的防洪、灌溉和供水等任务,其洪水预报具有重要的研究意义。考虑到下垫面条件和降雨分布特征的影响,单一模型或同组参数在该地区很难取得理想的洪水模拟效果,尝试引入分单元新安江模型与API模型分别对察尔森水库洪水进行预报,并分析总结两模型在该地区的适用性。结果表明:两种模型预报结果都较好,且对于大暴雨洪水预报效果更理想;当前期土壤湿润时,分单元新安江模型预报效果更好;当前期土壤干旱、降雨强度较大时,API模型预报效果优于分单元新安江模型。实际作业时根据需要选择模型进行预报,有利于察尔森水库合理调度,提高水库经济效益。     

佛子岭水库调度方式和效益分析

佛子岭水库是安徽省 2 0世纪 5 0年代初兴建的大型水利水电工程 ,因原设计时缺少水文资料 ,水库防洪库容太小 ,虽经扩建和加固 ,现有防洪标准仍然很低。为保证水库安全 ,汛期限制水位已几次降低。为做好近期水库运行管理工作 ,根据新变化的情况 ,重新研究了水库群灌溉补偿调度、分期兴利调度等问题 ,对汛期和非汛期蓄水位进行了比较。初步分析结果可为水库近期调度参考 ,也可为采取新的工程措施提供依据     

黄河上游水库对宁夏河段防洪防凌及灌溉的影响与对策

黄河上游已建水库特别是龙羊峡、刘家峡、青铜峡３座水库，对宁夏河段的防洪防凌、灌溉有着重要影响。建库后，龙羊峡、刘家峡两水库拦蓄洪水、削减洪峰作用显著；凌期平均封河日期推迟、平均开河日期提前、封冻天数缩短，冰情、冰害有所减轻且主要发生在库区；在一定程度上缓解了宁夏引黄灌区春灌缺水的局面。针对建库后河道冲淤及河势的变化，需加强河道整治和上游水库的调节运用，使水库更好地发挥兴利除弊的作用。