Multi-Objective Optimization of Fusegates System under Hydrologic Uncertainties

Fusegates are independent units held only by the gravity installed on the free spillway of existing dams in order to increase reservoir storage and/or discharge capacity. Increasing reservoir storage in many dams can partly sacrifice dam’s reliabilities. So considering the failure risk of a dam together with the amount of increase in the reservoir capacity can prevent selecting fusegates which seriously endanger a dam safety. However, Lack of accurate information on various damage functions and difficulty in quantifying failure consequences are among principal limitations that hinder practical application of conventional approaches which account failure risk in real world hydrosystems problems. This study develops two effective multi-objective frameworks to optimize fusegates’ configuration in order to eliminate the need for such hard-to-get mathematical damage functions and provide valuable information on the failure risk, total cost, and increased water volume of a reservoir. The proposed models find trade-off solutions between two sets of conflicting objectives. The first competing objectives is investment cost and water storage and the second conflicting goals are water storage and failure probability under the inherent and parameter hydrologic uncertainties. Complicated flood routing phenomenon within a reservoir equipped with fusegates is explicitly taken into account to attain a more cost-effective and reliable design without jeopardizing the dam safety. Applicability and performance of the developed optimization schemes are discussed and demonstrated on a real life case study. The multi-objective optimization results represented as an ensemble of diverse trade-off solutions provide decision makers with more insight and understanding of system behavior and different design alternatives.     

Fractional Stochastic Interval Programming for Optimal Low Impact Development Facility Category Selection under Uncertainty

Uncertainties in nature and human society influence low impact development (LID) facility category selection during LID facility optimization distribution, however the investigation of this area is seldom. There are still two problems with uncertainty which influence LID facility distribution 1) how uncertainty factors affect LID facility selection and 2) in the case of a number of LID facilities of multiple categories are to be set, how to construct the LID facility optimization distribution model for LID facility category selection under uncertainty. To handle the problems, this study develops a fractional stochastic interval programming model to process LID facility category selection under the influence of uncertainty. The model can either process multiple objectives via objective maximization and minimization or process the stochastic uncertainty and interval uncertainty. The study shows that the uncertainties which influence LID facility category selection exist in rainfall, infiltration rate, release coefficient, unit price and budget. and the study reveal that the key constraint of LID facility category selection is the uncertainty parameter characteristic of the LID facility, in which different parameters lead to various LID facility optimization schemes. Results of the model include a series of LID facility optimization distribution schemes in multiple scenarios.Results also provide a series of feasible schemes for decision makers, and the manager can select the most appropriate scheme according to water processing level or budget. The developed model could 1) identifying the uncertainty which impact the LID facility distribution. 2) processing the LID facility category selection under interval uncertainty and stochastic uncertainty during LID facility optimization distribution. The method can also be used to estimate the rationality of the LID facility optimization scheme. Moreover, the proposed method is universal and could be extended to other cases of LID facility category selection under uncertainty.

     

Many-Objective Multi-Scenario Algorithm for Optimal Reservoir Operation Under Future Uncertainties

An increase in greenhouse gases in future can exacerbate the climate change phenomenon and may have negative consequences on different elements of hydrologic system, including rainfall, temperature, and streamflow. Since the reservoir operation is highly dependent on the timing and magnitude of inflow, the impact of potential climate change on inflow sequences should be considered in deriving the system operation rule. Nevertheless, existing algorithms are only able to optimize the operation policy for a single predetermined climate scenario. Thus, the derived operation rule would not work well if the scenario changes. This paper proposes an algorithm which is able to handle simultaneously multiple scenarios in finding optimum system operation rule. Thus, it can overcome drawbacks caused by uncertainties in the occurrence of future scenarios. The proposed algorithm is used to optimize reservoir operation policy considering various climate change scenarios (RCPs). To evaluate the performance of the proposed algorithm, a five-reservoir system within Tehran region with several objectives including municipal, agricultural, environmental, and hydropower demands is employed as the case study. Results show that in all cases the multi-scenario rule derived by the proposed method performs as good as the operation rule derived for any specific scenario using a powerful optimization algorithm when evaluated for that scenario. While, in all other models as the future scenario changes to the one other than that used in deriving the operation rule, the model performance declines as compared to the proposed model.     

Developing Water Cycle Algorithm for Optimal Operation in Multi-reservoirs Hydrologic System

Water Resources Management - Optimal operation of multi-objective reservoirs is one of the complex and, sometimes nonlinear, issues in multi-objective hydrologic system optimization. Meta-heuristic...     

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 Short-term Reservoir Operation with Integrated Long-term Goals

A methodology to incorporate long-term goals within the short-term reservoir operation optimization model is proposed. Two conflicting objectives for the management of hydropower generation in two different power plants are incorporated. A chance-constrained optimization model is used to derive long-term (annual) operation strategies. With the time horizon of operation for the short-term optimization model kept equal to a single time-step of the long-term optimization model, the optimum end storages derived from the long-term model are incorporated as constraints (storage lower bounds) within the short-term model. The long-term benefits accrued from such an operation model are illustrated for a small reservoir, in South India. The solutions are compared with the historic operation. These are also compared with the solutions of a short-term optimal operation model ignoring long-term goals. The optimization model is solved using a multi-objective genetic algorithm.     

VFP与Excel交互编程在水情数据库中的应用

本文探讨了在水情数据库中，怎样应用VFP与Excel交互编程实现库表调用，满足防汛工作的需要。     

Optimal Operation of Reservoir Systems using Simulated Annealing

A stochastic search technique, simulated annealing (SA), is used to optimize the operation of multiple reservoirs. Seminal application of annealing technique in general to multi-period, multiple-reservoir systems, along with problem representation and selection of different parameter values used in the annealing algorithm for specific cases is discussed. The search technique is improved with the help of heuristic rules, problem-specific information and concepts from the field of evolutionary algorithms. The technique is tested for application to a benchmark problem of four-reservoir system previously solved using a linear programming formulation and its ability to replicate the global optimum solution is examined. The technique is also applied to a system of four hydropower generating reservoirs in Manitoba, Canada, to derive optimal operating rules. A limited version of this problem is solved using a mixed integer nonlinear programming and results are compared with those obtained using SA. A better objective function value is obtained using simulated annealing than the value from a mixed integer non-linear programming model developed for the same problem. Results obtained from these applications suggest that simulated annealing can be used for obtaining near-optimal solutions for multi-period reservoir operation problems that are computationally intractable.     

神经网络技术在水文系列中长期预报中的应用

运用人工神经网络技术的基本原理，以降雨量作为基本影响因子，建立了流域年径流量的神经网络预报模型。在建模过程中，为保证计算快速收敛，重新定义了权重增量的计算公式。从两个流域的应用实例资料验证表明，模型基本合理、可靠，具有较好的适应性和预报精度。由模型计算结果可以看出，将人工神经网络技术应用于流域年径流量的预报研究，是以系统的观点将降雨与径流作为输入和输出联系起来，它可为流域径流的中长期变化预测提供一条崭新而有效的途径。     

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.     

金沙江干流梯级水电站水库群长期优化调度

以金沙江干流梯级水电站水库群为研究对象,建立了金沙江梯级水电站水库群兼顾保证出力要求的以发电量最大为目标的长期优化调度模型;考虑水电系统的调峰要求,建立了金沙江梯级发电量最大长期优化调度模型.均用逐次逼近动态规划法求解,结果表明,调峰系数不同对梯级水电站发电效益及运行方式有明显影响.     

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.     

Coupled Operating Rules for Optimal Operation of Multi-Reservoir Systems

Due to the complexity of multi-reservoir system operation problems, researchers usually prefer to assume lumped demands located downstream of such systems. Consequently, distributed local demands through the system are neglected or supplied completely (e.g. using Standard operating policy, SOP), in order to simplify the problem. In this study, Coupled Operating Rules (COR) as a simple and suitable operating policy is applied for optimal operation of multi-reservoir systems with local demands. The applied policy includes two types of linear rules, which are defined to determine total releases and local water allocations in decision points. This policy is adopted within a simulation-optimization approach to optimally operate a three-reservoir system in the Karkheh river basin. Obtained results indicate that the proposed strategy reduces the intensity of demand deficits and distributes the occurred shortages throughout the system properly. Moreover, the system losses are managed appropriately and big unbalanced local shortages are prevented. Although COR strategy decreases the reliability of local demands compared to SOP, it is a reasonable operating policy for systems with distributed local demand sites. Moreover, in this study an effective Improved Melody Search (IMeS) algorithm is proposed to achieve the optimum values of operating rules’ parameters. The efficiency of the optimization method is compared to the results achieved by other selected well-known heuristic search methods. Based on the experimental results, it is revealed that the proposed algorithm is more effective in finding precise solutions over a long-term period, comparing with the other conventional algorithms.     

Rules for Optimal Operation of Reservoir-River-Groundwater Systems Considering Water Quality Targets: Application of M5P Model

The aim of this paper is to develop rules for optimal reservoir operation and water withdrawal from river and aquifer considering water supply and pollution control targets. The general approach is making use of an integrated water quantity-quality management (IWQM) modeling in conjunction with accurate data mining techniques. The IWQM model generates data, including; optimal releases and water withdrawal from river and aquifer for different conditions, and M5P and Support Vector Regression (SVR) data mining models utilize the results of the IWQM model for the derivation of rules. The IWQM model minimizes the deviation from water supply and water quality targets during the planning horizon. This method for derivation of operating rules is applied to a real world case study, Zayandehrood system, in Iran, with serious water supply and water pollution problems. The IWQM model is analyzed for different hydrologic and water demands scenarios with total dissolved solids (TDS) as the water quality indicator. Results show that an integrated approach to reservoir-river-aquifer operation in the study area can reduce the TDS by 43 % in the downstream river.     

Water Resources and Farmland Management in the Songhua River Watershed under Interval and Fuzzy Uncertainties

The Songhua River Watershed (SHRW) in Northeastern China has been challenged by water scarcity, water contamination, and soil erosion for decades. These problems will remain or even worsen in the following decades, threatening regional eco-environmental quality and socio-economic development. Mitigation of these problems through integrated water resources and farmland management (WRFM) is desired but is challenged by multiple system complexities, e.g. interrelations of diverse system components. To fill this gap, an interval fuzzy water resources and farmland programming (IFWRFP) approach is developed in this study for eliminating the potential problems in the SHRW, leading to increased reliability of the decision support process. A series of systematic WRFM measures are proposed for enabling harmonious development of ecological environment and social economy in the SHRW. For instance, planting should always be the priority due to the major contribution of agriculture to the regional economy. As the primary commercial crop, rice cultivation should be allocated the most irrigation water, followed by corn, potato and soybean. Potato yield should be increased to compensate for reduced productivity of the other crops since 2019. It is also revealed that economic benefits are proportional to water environmental pollution in the SHRW. Therefore, decision-makers should adopt the most reasonable suggested schemes after fully balancing the trade-off of environment and economy. Most importantly, a variety of supporting policies are required for enabling sufficient implementation of these measures across the SHRW. For instance, individual farmers can be encouraged to follow the overall crop cultivation plan by the alteration of subsidiaries, taxes, and prices on crop-related activities. The modeling solutions show that the IFWRFP approach can systematically optimize allocations of water resources and cultivation patterns and thus potentially eliminate the problems of water scarcity, water contamination, and soil erosion in the SHRW.     

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。     

Optimal Operation of Multi-reservoir Systems Considering Time-lags of Flood Routing

Operations of multi-reservoir systems are nonlinear and high-dimensional problems, which are difficult to find the optimal or near-optimal solution owing to the heavy computation burden. This study focuses on flood control operation of multi-reservoir systems considering time-lags caused by Muskingum flood routing of river channels. An optimal model is established to jointly minimize the flood peak on the downstream flood control station for the multi-reservoir systems. A hybrid algorithm, Progressive Optimality Algorithm and Successive Approximation (POA-SA), is improved to solve the multi-reservoir operation model by modifying the POA. The POA-SA uses the DPSA to reduce the spatial dimensionality due to the multiple reservoirs, and adopts an improved POA to alleviate the temporal dimensionality caused by the time-lags of the Muskingum flood routing. Linear programming is then implemented to verify the solution of the POA-SA method with a linear approximation of the discharge capacity curve. The multi-reservoir systems of China’s Xijiang River is selected for a case study. Results show that the flood peak of Wuzhou station can be averagely decreased by 6730 m³/s (12.8 %) for the 100-year return period floods, indicating that the proposed method is efficient to operate the multi-reservoir systems and resolve the time-lags issues.     

Genetic Algorithms for Optimal Operation of Soil Aquifer Treatment Systems

The genetic algorithm (GA) is a nonconventional search technique which is patterned after the biological processes of natural selection and evolution. It has the ability to search large and complex decision spaces and handle nonconvexities. In this paper, the genetic algorithm is investigated and applied to solve the optimal operation problem of soil aquifer treatment (SAT) systems. This problem involves finding optimal water application time and drying time which maximize infiltration for a predetermined starting influent rate of waste water and subject to various physical and operational constraints. A new scaling method is developed and some improvements on the evolution procedure are presented. A comprehensive GA–SAT computer model was developed and applied to an example SAT problem. The results are encouraging, when compared with using the successive approximation linear quadratic regulator algorithm. It was found that genetic algorithms are easy to program and interface with large complicated simulators.     

Optimal Operation of Hydropower Reservoir Systems Using Weed Optimization Algorithm

The optimal hydropower operation of reservoir systems is known as a complex nonlinear nonconvex optimization problem. This paper presents the application of invasive weed optimization (IWO) algorithm, which is a novel evolutionary algorithm inspired from colonizing weeds, for optimal operation of hydropower reservoir systems. The IWO algorithm is used to optimally solve the hydropower operation problems for both cases of single reservoir and multi reservoir systems, over short, medium and long term operation periods, and the results are compared with the existing results obtained by the two most commonly used evolutionary algorithms, namely, particle swam optimization (PSO) and genetic algorithm (GA). The results show that the IWO is more efficient and effective than PSO and GA for both single reservoir and multi reservoir hydropower operation problems.     

New Methodology for Optimal Operation of Soil Aquifer Treatment Systems

A new methodology is presented in thisarticle for computing the optimal operation of soilaquifer treatment systems. The mathematical problemis stated as a discrete-time optimal control problemto maximize infiltration subject to various physicaland operation constraints. The methodology is basedupon solving the discrete-time optimal control problemusing a successive approximation linear quadraticregulator interfaced with a simulator. Theunsaturated flow model HYDRUS is modified to simulatethe water content distribution, the infiltrationprocess, and the draining process. A penalty functionmethod is used to treat the bound constraints on thewater content and the cycle time. Sample problems aregiven to illustrate the capability of the model tosolve the optimal operation of soil aquifer treatment systems.