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.     

守口堡水库大坝优化选型

文中主要对拟建的守口堡水库大坝几种坝型结合地形、地质、筑坝材料、施工条件、运行管理及投资等多种因素分析比较,综合考虑后选择了最优化的坝型。     

Intelligent Systems in Optimizing Reservoir Operation Policy: A Review

The paper presents a survey of several optimization techniques, mainly artificial intelligences (AIs) which have been applied to the reservoir operation modelling whether its single or multi-reservoir system. The reservoir system modeling is essential for any nations and the optimal use of it is always asked. The main objective of this review article is to discuss the potentiality of the evolutionary algorithms (EAs) and the ability to integrate with other techniques which can provide the best results. Also the formulation of these types of application has described on the ground of a well known benchmark problem regarding this field. The traditional algorithms got some drawbacks. The study provides a complete understanding to the EA users about next generation optimal search procedure and help to overcome the drawbacks. Though the background of application number of swarm intelligences is less comparatively than the genetic algorithm (GA), it provides a great scope for the researcher for further development. Also comparative results with other popular methods (such as, linear programming, stochastic dynamic programming) are discussed on the basis of past research results. Conclusions and suggestive remarks are made for the help of researchers and the reservoir decision makers as well.     

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.     

Optimizing Hydropower Reservoir Operation Using Hybrid Genetic Algorithm and Chaos

Genetic algorithms (GA) have been widely applied to solve water resources system optimization. With the increase of the complexity and the larger problem scale of water resources system, GAs are most frequently faced with the problems of premature convergence, slow iterations to reach the global optimal solution and getting stuck at a local optimum. A novel chaos genetic algorithm (CGA) based on the chaos optimization algorithm (COA) and genetic algorithm (GA), which makes use of the ergodicity and internal randomness of chaos iterations, is presented to overcome premature local optimum and increase the convergence speed of genetic algorithm. CGA integrates powerful global searching capability of the GA with that of powerful local searching capability of the COA. Two measures are adopted in order to improve the performance of the GA. The first one is the adoption of chaos optimization of the initialization to improve species quality and to maintain the population diversity. The second is the utilization of annealing chaotic mutation operation to replace standard mutation operator in order to avoid the search being trapped in local optimum. The Rosenbrock function and Schaffer function, which are complex and global optimum functions and often used as benchmarks for contemporary optimization algorithms for GAs and Evolutionary computation, are first employed to examine the performance of the GA and CGA. The test results indicate that CGA can improve convergence speed and solution accuracy. Furthermore, the developed model is applied for the monthly operation of a hydropower reservoir with a series of monthly inflow of 38 years. The results show that the long term average annual energy based CGA is the best and its convergent speed not only is faster than dynamic programming largely, but also overpasses the standard GA. Thus, the proposed approach is feasible and effective in optimal operations of complex reservoir systems.     

Operation Policies through Dynamic Programming and Genetic Algorithms,for a Reservoir with Irrigation and Water Supply Uses

Water Resources Management - In this study, operation policies were obtained for a reservoir in Michoacán, Mexico, used for irrigation and domestic water supplies. The main purpose of these...     

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.     

Multi-Objective Optimization of the Reservoir System Operation by Using the Hedging Policy

In the present study the WEAP-NSGA-II coupling model was developed in order to apply the hedging policy in a two-reservoir system, including Gavoshan and Shohada dams, located in the west of Iran. For this purpose after adjusting the input files of WEAP model, it was calibrated and verified for a statistical period of 4 and 2 years respectively (2008 till 2013). Then periods of water shortage were simulated for the next 20 years by defining a reference scenario and applying the operation policy based on the current situation. Finally, the water released from reservoirs was optimized based on the hedging policy and was compared with the reference scenario in coupled models. To ensure the superiority of the proposed method, its results was compared with the results of two well-known multi-objective algorithms called PESA-II and SPEA-II. Results show that NSGA-II algorithm is able to generate a better Pareto front in terms of minimizing the objective functions in compare with PESA-II and SPEA-II algorithms. An improvement of about 20% in the demand site coverage reliability of the optimum scenario was obtained in comparison with the reference scenario for the months with a higher water shortage. In addition, considering the hedging policy, the demand site coverage in the critical months increased about 35% in compared with the reference scenario.     

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...     

Influence of Inflow Nonstationarity on the Multipurpose Optimal Operation of Hydropower Plants Using Nonlinear Programming

Water Resources Management - One of the greatest challenges in the electricity generation sector is to operate hydrothermal plants in view of the randomness of hydrological events and climate...     

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 Genetic Algorithm Parallel Strategy for Optimizing the Operation of Reservoir with Multiple Eco-environmental Objectives

Optimizing the operation of reservoir involving ecological and environmental (eco-environmental) objectives is challenging due to the often competing social-economic objectives. Non-dominated Sorting Genetic Algorithm-II is a popular method for solving multi-objective optimization problems. However, within a complex search space, the NSGA-II population (i.e., a group of candidate solutions) may be trapped in local optima as the population diversity is progressively reduced. This study proposes a computational strategy that operates several parallel populations to maintain the diversity of the candidate solutions. An improved version of the NSGA-II, called c-NSGA-II is implemented by incorporating multiple recombination operators. The parallel strategy is then coupled into the routine of the c-NSGA-II and applied to the operation of the Qingshitan reservoir (Southwest of China) which includes three eco-environmental and two social-economic objectives. Three metrics (convergence, diversity, and hyper volume index) are used for evaluating the optimization performances. The results show that the proposed parallel strategy significantly improves the solution quality in both convergence and diversity. Two characteristic schemes are identified for the operation of the Qingshitan reservoir for trade-off between the eco-environmental and social-economic objectives.     

Improving Optimization Efficiency for Reservoir Operation Using a Search Space Reduction Method

Reservoir operation problems are challenging to efficiently optimize because of their high-dimensionality, stochasticity, and non-linearity. To alleviate the computational burden involved in large-scale and stringent constraint reservoir operation problems, we propose a novel search space reduction method (SSRM) that considers the available equality (e.g., water balance equation) and inequality (e.g., firm output) constraints. The SSRM can effectively narrow down the feasible search space of the decision variables prior to the main optimization process, thus improving the computational efficiency. Based on a hydropower reservoir operation model, we formulate the SSRM for a single reservoir and a multi-reservoir system, respectively. To validate the efficiency of the proposed SSRM, it is individually integrated into two representative optimization techniques: discrete dynamic programming (DDP) and the cuckoo search (CS) algorithm. We use these coupled methods to optimize two real-world operation problems of the Shuibuya reservoir and the Shuibuya-Geheyan-Gaobazhou cascade reservoirs in China. Our results show that: (1) the average computational time of SSRM-DDP is 1.81, 2.50, and 3.07 times less than that of DDP when decision variables are discretized into 50, 100, and 500 intervals, respectively; and (2) SSRM-CS outperforms CS in terms of its capability of finding near-optimal solutions, convergence speed, and stability of optimization results. The SSRM significantly improves the search efficiency of the optimization techniques and can be integrated into almost any optimization or simulation method. Therefore, the proposed method is useful when dealing with large-scale and complex reservoir operation problems in water resources planning and management.     

Optimization of Exclusive Release Policies for Hydropower Reservoir Operation by Using Genetic Algorithm

The efficient utilization of hydropower resources play an important role in the economic sector of power systems, where the hydroelectric plants constitute a significant portion of the installed capacity. Determination of daily optimal hydroelectric generation scheduling is a crucial task in water resource management. By utilizing the limited water resource, the purpose of hydroelectric generation scheduling is to specify the amount of water releases from a reservoir in order to produce maximum power, while the various physical and operational constraints are satisfied. Hence, new forms of release policies namely, BSOPHP, CSOPHP, and SHPHP are proposed and tested in this research. These policies could only use in hydropower reservoir systems. Meanwhile, to determine the optimal operation of each policy, real coded genetic algorithm is applied as an optimization technique and maximizing the total power generation over the operational periods is chosen as an objective function. The developed models have been applied to the Cameron Highland hydropower system, Malaysia. The results declared that by using optimal release policies, the output of power generation is increased, while these policies also increase the stability of reservoir system. In order to compare the efficiency of these policies, some reservoir performance indices such as reliability, resilience, vulnerability, and sustainability are used. The results demonstrated that SHPHP policy had the highest performance among the tested release policies.     

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.     

Prediction of Hydropower Energy Using ANN for the Feasibility of Hydropower Plant Installation to an Existing Irrigation Dam

Recently, artificial neural networks (ANNs) have been used successfully for many engineering problems. This paper presents a practical way of predicting the hydropower energy potential using ANNs for the feasibility of adding a hydropower plant unit to an existing irrigation dam. Because the cost of energy has risen considerably in recent decades, addition of a suitable capacity hydropower plant (HPP) to the end of the pressure conduit of an existing irrigation dam may become economically feasible. First, a computer program to realistically calculate all local, frictional, and total head losses (THL) throughout any pressure conduit in detail is coded, whose end-product enables determination of the C coefficient of the highly significant model for total losses as: THL = C·Q ². Next, a computer program to determine the hydroelectric energies produced at monthly periods, the present worth (PW) of their monetary gains, and the annual average energy by a HPP is coded, which utilizes this simple but precise model for quantification of total energy losses from the inlet to the turbine. Inflows series, irrigation water requirements, evaporation rates, turbine running time ratios, and the C coefficient are the input data of this program. This model is applied to randomly chosen 10 irrigation dams in Turkey, and the selected input variables are gross head and reservoir capacity of the dams, recorded monthly inflows and irrigation releases for the prediction of hydropower energy. A single hidden-layered feed forward neural network using Levenberg–Marquardt algorithm is developed with a detailed analysis of model design of those factors affecting successful implementation of the model, which provides for a realistic prediction of the annual average hydroelectric energy from an irrigation dam in a quick-cut manner without the excessive operation studies needed conventionally. Estimation of the average annual energy with the help of this model should be useful for reconnaissance studies.     

Seasonal Inflow Forecasts Using Gridded Precipitation and Soil Moisture Information: Implications for Reservoir Operation

Water Resources Management - Reservoir inflow forecasts are important for guiding reservoir operation. This study proposes an integrated framework of incorporating different forms of seasonal...     

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...     

Implementation and Comparison of Reservoir Operation Strategies for the Hoa Binh Reservoir, Vietnam using the Mike 11 Model

Hoa Binh is the largest reservoir in Vietnam. It has been operated since 1990 with the main purposes of flood control in the Red River basin and hydropower generation. Because these different purposes always cause conflicts and disputes during the flood season, it is desirable to improve the current operational regulations of the reservoir. In this paper, the operation rules of the reservoir are analysed by applying the Mike 11 river modelling tool. The model set up includes the main rivers and tributaries of the Red River basin and a logical decision tree defining the reservoir regulation. These strategies define the reservoir release as function of the time of the year, the actual reservoir stage, and the water level forecast at Hanoi. A data set consisting of twenty years of flood season data was used to evaluate the control strategies with respect to flood control and hydropower generation. The reservoir operation using the complete control system and the current as well as alternative regulation strategies has been evaluated and compared to the actual operation practice. Results showed that the implemented control system performs better than the actual operation. In addition, lowering the target downstream water level for flood control improves the operation with respect to both flood protection and hydropower generation. An alternative strategy where the target water level in the reservoir is increased can improve hydropower generation but at the expense of a reduced flood protection.     

Simulation-Based Inexact Rough-Interval Programming for Agricultural Irrigation Management: A Case Study in the Yongxin County, China

A simulation-based inexact rough-interval programming approach is proposed for agricultural irrigation management in a China??s rural region. The conjunctive use of multiple water sources is examined under a set of land-area, water-availability, environmental-standard, capital, and technical constraints. The formulated model presents capability in interpreting implication of various water-supply means on agricultural water-allocation plans, as well as handling highly-uncertain parameters existing in many real-world practices. A case study in the central-south China demonstrates the applicability of the proposed model. The modelling inputs of economy-related parameters are identified as conventional intervals based on the statistical data, while those of water availability are characterized as rough intervals by converting the predicted values from a distributed hydrological model. Scenarios of groundwater supplementation rates being 0, 20, 30 and 40?%, respectively are considered to generate optimal irrigation plans. Reasonable results are obtained, which are then used to analyze the impact of water-supplier variation on planning sustainable development strategies.