水库调度性能风险评价方法研究

运用水库常规调度和优化调度模型,分别确定水库调度策略,从水电站发电和下游生态需水的可靠性、可恢复性、脆弱性和防洪调度权转移风险出发,建立基于综合利用水库调度模型的调度性能风险评价指标体系。以新安江水库调度为例,对1960—2009年旬径流系列的常规与优化调度结果进行风险评价,结果表明:水库优化调度方法比常规调度方法的发电效益、可靠性、可恢复性更高,但其发电脆弱性和防洪调度权转移风险更高。水库调度性能指标全面地评价了不同调度方法对水库调度结果的影响,该研究为综合利用水库在防洪安全、供水安全、生态与环境安全等方面的决策管理提供了一种新的评价思路。     

Improved Reservoir Operation Using Hybrid Genetic Algorithm and Neurofuzzy Computing

A hybrid genetic and neurofuzzy computing algorithm was developed to enhance efficiency of water management for a multipurpose reservoir system. The genetic algorithm was applied to search for the optimal input combination of a neurofuzzy system. The optimal model structure is modified using the selection index (SI) criterion expressed as the weighted combination of normalized values of root mean square error (RMSE) and maximum absolute percentage of error (MAPE). The hybrid learning algorithm combines the gradient descent and the least-square methods to train the genetic-based neurofuzzy network by adjusting the parameters of the neurofuzzy system. The applicability of this modeling approach is demonstrated through an operational study of the Pasak Jolasid Reservoir in Pasak River Basin, Thailand. The optimal reservoir releases are determined based on the reservoir inflow, storage stage, sideflow, diversion flow from the adjoining basin, and the water demand. Reliability, vulnerability and resiliency are used as indicators to evaluate the model performance in meeting objectives of satisfying water demand and maximizing flood prevention. Results of the performance evaluation indicate that the releases predicted by the genetic-based neurofuzzy model gave higher reliability for water supply and flood protection compared to the actual operation, the releases based on simulation following the current rule curve, and the predicted releases based on other approaches such as the fuzzy rule-based model and the neurofuzzy model. Also the predicted releases based on the newly developed approach result in the lowest amount of deficit and spill indicating that the developed modeling approach would assist in improved operation of Pasak Jolasid Reservoir.     

Evaluation of Real-Time Operation Rules in Reservoir Systems Operation

Reservoir operation rules are logical or mathematical equations that take into account system variables to calculate water release from a reservoir based on inflow and storage volume values. In fact, previous experiences of the system are used to balance reservoir system parameters in each operational period. Commonly, reservoir operation rules have been considered to be linear decision rules (LDRs) and constant coefficients developed by using various optimization procedures. This paper addresses the application of real-time operation rules on a reservoir system whose purpose is to supply total downstream demand. Those rules include standard operation policy (SOP), stochastic dynamic programming (SDP), LDR, and nonlinear decision rule (NLDR) with various orders of inflow and reservoir storage volume. Also, a multi-attribute decision method, elimination and choice expressing reality (ELECTRE)-I, with a combination of indices, objective functions, and reservoir performance criteria (reliability, resiliency, and vulnerability) are used to rank the aforementioned rules. The ranking method employs two combinations of indices: (1) performance criteria and (2) objective function and performance criteria by using the same weights for all criteria. Results show that the NLDR gives an appropriate rule for real-time operation. Moreover, NLDR validation is presented by testing predefined curves for dry, normal, and wet years.     

Effect of Hedging-Integrated Rule Curves on the Performance of the Pong Reservoir (India) During Scenario-Neutral Climate Change Perturbations

This study has evaluated the effects of improved, hedging-integrated reservoir rule curves on the current and climate-change-perturbed future performances of the Pong reservoir, India. The Pong reservoir was formed by impounding the snow- and glacial-dominated Beas River in Himachal Pradesh. Simulated historic and climate-change runoff series by the HYSIM rainfall-runoff model formed the basis of the analysis. The climate perturbations used delta changes in temperature (from 0° to +2 °C) and rainfall (from ?10 to +10 % of annual rainfall). Reservoir simulations were then carried out, forced with the simulated runoff scenarios, guided by rule curves derived by a coupled sequent peak algorithm and genetic algorithms optimiser. Reservoir performance was summarised in terms of reliability, resilience, vulnerability and sustainability. The results show that the historic vulnerability reduced from 61 % (no hedging) to 20 % (with hedging), i.e., better than the 25 % vulnerability often assumed tolerable for most water consumers. Climate change perturbations in the rainfall produced the expected outcomes for the runoff, with higher rainfall resulting in more runoff inflow and vice-versa. Reduced runoff caused the vulnerability to worsen to 66 % without hedging; this was improved to 26 % with hedging. The fact that improved operational practices involving hedging can effectively eliminate the impacts of water shortage caused by climate change is a significant outcome of this study.     

Optimisation of Multiple Hydropower Reservoir Operation Using Artificial Bee Colony Algorithm

In this study, the Artificial Bee Colony (ABC) algorithm was developed to solve the Chenderoh Reservoir operation optimisation problem which located in the state of Perak, Malaysia. The proposed algorithm aimed to minimise the water deficit in the operating system and examine its performance impact based on monthly and weekly data input. Due to its capability to identify different possible events occurring in the reservoir, the ABC algorithm provides promising and comparable solutions for optimum release curves. The optimal release curves were then used to stimulate the reservoir release under different operating times under different inflow scenarios. To investigate the performance of both the monthly and weekly ABC optimisation employed in the reservoir, the well-known reliability, resilience and vulnerability indices were used for performance assessment. The indices tests revealed that weekly ABC optimisation outperformed in terms of reliability and vulnerability leading to the development of a better release policy for optimal operation.     

Simulation and Optimization of Multi-Reservoir Operation in Inter-Basin Water Transfer System

Inter-basin water transfer projects are usually considered as one of the most effective facilities to balance the non-uniform temporal and spatial distribution of water resources and water demands by diverting water from surplus to deficient area. However, the operation of these projects are always daunting, especially for projects with multi-donor reservoirs but only one recipient reservoir. In this study, a set of water transfer rule curves are firstly proposed to determine when, where and how much water should be diverted from each donor reservoir. In addition, a simulation-optimization model with the objective to minimize both water shortage risk and vulnerability is established to derive the optimal operation rule curves. Following that, the new transfer rules are applied to provide guidelines for the operation of a water transfer-supply project with two donor reservoirs in central China. The effects of water diversion on each reservoir are evaluated under different scenarios including no diversion, diversion from the donor reservoir with relatively sufficient water, diversion from the donor reservoir with relatively limited water, and diversion from both donor reservoirs. The results show the advantages of improving the performance of whole water diversion system and demonstrate the feasibility of the proposed approach.     

Reliability analysis for reservoir water supply due to uncertainties in hydrological factors,rainfall-runoff routing and operating rule curves

This study aims at developing a reliability-analysis model (RA_WS_RES) to quantify the effect of variations in uncertainties factors regarding the reservoir inflow and outflow at various 10-day periods on the reliability of water supply from the reservoir. The uncertainty factors considered are classified into three types: the hydrological factors (rainfall, baseflow, and initial water level of the reservoir), the reservoir operation rules for the water-allocation model (RIBASIM), and the parameters of rainfall-runoff model (i.e. Sacramento Soil Moisture Accounting, SAC-SMA). In the proposed RA_WS_RES model, the reliability of the water supply attributed to the uncertainty factors considered can be quantified by collaborating the multivariate Monte Carlo simulation (MMCS) methods and uncertainty-risk (advanced first order and second moment, AFOSM) analysis. Shihmen Reservoir watershed in Northern Taiwan is selected as the study area and four associated demand nodes are regarded as the study nodes; the hourly rainfall data from 1987 to 2014 and other hydrological data (i.e. rainfall, baseflow, and initial water level of the reservoir) as well as the operation rule curves are adopted in the model development and application. The results from the numerical experiences indicate that among the uncertainty factors concerned, the average rainfall depth at the current 10-day period and the range between the lower and critical levels are more sensitive to the estimation of water supply from Shihmen Reservoir. Additionally, the impact of variation in baseflow on the reliability of the water supply from Shihmen Reservoir should be taken into account, especially in the dry season. In addition to rainfall and baseflow, the initial water level should be an important source for the water supply, and its effect gradually reduces with the 10-day period. Furthermore, the reliability of water supply is obviously impacted by the uncertainty in the range between the lower and critical rule level, especially in the dry season, due to its range being adversely related with the water supply. Eventually, the proposed RA_WS_RES model can effectively and reasonably quantify the reliability of water supply attributed to variations in uncertainty factors at different 10-day period under the consideration of climate change.     

Modelling Intelligent Water Resources Allocation for Multi-users

This study proposes intelligent water resources allocation strategies for multiple users through hybrid artificial intelligence techniques implemented for reservoir operation optimization and water shortage rate estimation. A two-fold scheme is developed for (1) knowledge acquisition through searching input–output patterns of optimal reservoir operation by optimization methods and (2) the inference system through mapping the current input pattern to estimate the water shortage rate by artificial neural networks (ANNs). The Shihmen Reservoir in northern Taiwan is the study case. We first design nine possible water demand conditions by investigating the changes in historical water supply. With the nine designed conditions and 44-year historical 10-day reservoir inflow data collected during the growth season (3 months) of the first paddy crop, we first conduct the optimization search of reservoir operation by using the non-dominated sorting genetic algorithm-II (NSGA-II) in consideration of agricultural and public water demands simultaneously. The simulation method is used as a comparative model to the NSGA-II. Results demonstrate that the NSGA-II can suitably search the optimal water allocation series and obtain much lower seasonal water shortage rates than those of the simulation method. Then seasonal water shortage rates in response to future water demands for both sectors are estimated by using the adaptive network fuzzy inference system (ANFIS). The back-propagation neural network (BPNN) is adopted as a comparative model to the ANFIS. During model construction, future water demands, predicted monthly inflows (or seasonal inflow) of the reservoir in the next coming quarter and historical initial reservoir storages configure the input patterns while the optimal seasonal water shortage rates obtained from the NSGA-II serve as output targets (training targets) for both neural networks. Results indicate that the ANFIS and the BPNN models produce almost equally good performance in estimating water shortage rates, yet the ANFIS model produces even better stability. The reliability of the proposed scheme is further examined by scenario analysis. The scenario analysis indicates that an increase in public water demand or a decrease in agricultural water demand would bring more impacts of water supply on agricultural sectors than public sectors. Similarly, a bigger decrease in inflow amount would obviously bring more influence on agricultural sectors than public one. Consequently, given predicted inflow, decision makers can pre-experience the possible outcomes in response to competing water demands through the estimation models in order to determine adequate water supply as well as preparedness measures, if needed, for drought mitigation.     

并联供水水库联合调度规则最优性条件研究Ⅱ：算法开发与实例研究

利用水库调度问题的特征开发高效求解算法是水库优化调度问题算法研究的核心内容。本文以两阶段调度问题与多阶段调度问题应保持最优策略集的一致性作为假设条件,基于本文上篇中并联供水水库两阶段调度问题的最优性条件(K-T条件),设计了该类水库调度问题的优化求解方法,并通过两阶段调度模型的滚动运行,实现并联水库系统长时序优化调度的目的。以辽宁省碧流河水库与英那河水库为例,开展了并联供水工程研究,结果表明:新的算法具有合理性与有效性,较由限制供水规则与参数式规则组合构成的并联供水水库联合调度规则,显著改善了用水户缺水状况,提高了供水保证率。     

Real-Time Operation of Reservoir System by Genetic Programming

Reservoir operation policy depends on specific values of deterministic variables and predictable actions as well as stochastic variables, in which small differences affect water release and reservoir operation efficiency. Operational rule curves of reservoir are policies which relate water release to the deterministic and stochastic variables such as storage volume and inflow. To operate a reservoir system in real time, a prediction model may be coupled with rule curves to estimate inflow as a stochastic variable. Inappropriate selection of this prediction model increases calculations and impacts the reservoir operation efficiency. Thus, extraction of an operational policy simultaneously with inflow prediction helps the operator to make an appropriate decision to calculate how much water to release from the reservoir without employing a prediction model. This paper addresses the use of genetic programming (GP) to develop a reservoir operation policy simultaneously with inflow prediction. To determine a water release policy, two operational rule curves are considered in each period by using (1) inflow and storage volume at the beginning of each period and (2) inflow of the 1^st, 2^nd, 12^th previous periods and storage volume at the beginning of each period. The obtained objective functions of those rules have only 4.86 and 0.44?% difference in the training and testing data sets. These results indicate that the proposed rule based on deterministic variables is effective in determining optimal rule curves simultaneously with inflow prediction for reservoirs.     

黄河三门峡以下流域水资源调配研究

黄河三门峡以下河段水资源供需紧张、断流频繁。本文针对此问题，采用月径流量延时曲线和超越机率曲线、系统仿真方法研究黄河三门峡以下流域在不同来流条件下水资源的供需情势，研究结果表明：(1)小浪底水库的兴建，大大改善了三门峡以下水资源的供需状况，但在来水较枯的年份，水资源供给仍然紧张；(2)在枯水年份，三门峡以下即使再增加水库或加大库容，也不能缓解水资源的供需紧张状况，要解决缺水状况主要依靠上游水库调节水量或采取节约用水的措施。     

Development and application of a simulation model for reservoir management

The success of irrigation system operation and planning depends on accurate quantification of supply and demand, and an equitable distribution of available water. The ultimate aim of this study was to determine how to meet the irrigation water demands if possible or to minimize the gap between the water supply and the demand. Most of the irrigation literature focuses only on the demand and the distribution aspects of this issue Irrigation projects that receive water from reservoirs, however, can be challenging to manage because the annual fluctuations in available water release from a reservoir can have a considerable impact on the irrigation management strategy. In real‐world situations, the reservoir operating rules guide reservoir operators in making actual water release decisions. This study develops a water balance simulation model for reservoir management, as well as testing it for Kangsabati Reservoir, West Bengal, India. Two rule curves for deciding irrigation water available from the reservoir were generated by taking the average and minimum stage values on a daily basis for a 16‐year period (1988–2003). Maintaining a minimum stage of 120.4 m throughout the year served as another rule to decide the release water available for irrigation. The minimum allowable stage of reservoir corresponding to a particular date of the year can be determined from those reservoir specific rule curves generated for irrigation purposes. The maximum permissible water release/outflow for irrigation from the test reservoir was taken as the volume of water available above the minimum allowable stage corresponding to the selected rule curve. The saturated hydraulic conductivity value (K_S) was calibrated to be 4.31 mm day^?1 for Kangsabati Reservoir.     

Multiobjective water resources systems analysis using genetic algorithms--application to Chou-Shui River Basin, Taiwan.

Multipurpose operation is adopted by most reservoirs in Taiwan in order to maximize the benefits of power generation, water supply, irrigation and recreational purposes. A multiobjective approach can be used to obtain trade-off curves among these multipurpose targets. The weighting method, in which different weighting factors are used for different purposes, was used in this research work. In Taiwan, most major reservoirs are operated by rule curves. Genetic algorithms with characteristics of artificial intelligence were applied to obtain the optimal rule curves of the multireservoir system under multipurpose operation in Chou-Shui River Basin in central Taiwan. The model results reveal that different shapes of rule curves under different weighting factors on targets can be efficiently obtained by genetic algorithms. Pareto optimal solutions for a trade-off between water supply and hydropower were obtained and analyzed.     

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.     

Development of an Optimal Reservoir Operation Scheme Using Extended Evolutionary Computing Algorithms Based on Conflict Resolution Approach: A Case Study

Optimal reservoir operation and water allocation are critical issues in sustainable water resource management due to increasing water demand. Multiplicity of stockholders with different objectives and utilities makes reservoir operation a complicated problem with a variety of constraints and objectives to be considered. In this case, the conflict resolution models can be efficiently used to determine the optimal water allocation scheme considering the utility and relative authority of different stakeholders. In this study, the Nash product is used for formulation of the objective function of a reservoir water allocation model. The Analytic Hierarchy Process (AHP) is used to determine the importance of each stockholder in bargaining for water. The Particle Swarm Optimization algorithm (PSO) and the Imperialism Competitive Algorithm (ICA) are applied to solve the proposed optimization model. System performance indices including reliability, resiliency and vulnerability are used to evaluate the performance of optimization algorithms. The simplest and most often-used reservoir policy (Standard Operating Policy, SOP) is also used in order to evaluate the performance of the proposed models. The proposed model is applied to the Karkheh River-Reservoir system located in south western part of Iran as a case study. Results show the significance of the application of conflict resolution models, such as the Nash theory and proposed optimization algorithms, for water allocation in the regional scale especially in complicated water supply systems.     

Derivation of Optimal Hedging Rules for a Water-supply Reservoir through Compromise Programming

This study derives optimal hedging rules for simultaneously minimizing short- and long-term shortage characteristics for a water-supply reservoir. Hedging is an effective measure to reduce a high-percentage single period shortage, but at a cost of more frequent small shortages. Thus simultaneously minimizing the maximum monthly shortage and the shortage ratio (defined as the ratio of total shortages to total demands) over the analysis horizon is the operation goal of a water-supply reservoir to derive optimal hedging rules. Two types of hedging are explored in this study: the first uses water availability defined as storage plus inflow, while the second depends on the potential shortage conditions within a specific future lead-time period. The compromise programming is employed to solve this conflicting multiobjective problem. The optimal hedging rules under given reservoir inflow are derived first. Because future inflow cannot be known exactly in advance, the monthly decile inflows are suggested as a surrogate for forecast of future inflows in hedging rules for real-time reservoir operations. The results show that the suggested method can effectively achieve the reservoir operation goal. The merits of the proposed methodology are demonstrated with an application to the Shihmen reservoir in Taiwan.     

供水水库优化模拟风险调度模式的研究

针对水库调度过程中的风险问题,引入了供水水库调度的优化模拟风险管理模式.首先建立了供水水库群的优化调度模型,然后对度量风险的可靠性、脆弱性、回弹性、事故周期等指标作了论述.在此基础上,模拟水库调度过程中存在的风险,并通过模拟与优化的反馈机制,建立了供水水库优化模拟风险调度的总体模式.     

Derivation of Aggregation-Based Joint Operating Rule Curves for Cascade Hydropower Reservoirs

Operating rule curves have been widely applied to reservoir operation, due to their ease of implementation. However, these curves are generally used for single reservoirs and have rarely been applied to cascade reservoirs. This study was conducted to derive joint operating rule curves for cascade hydropower reservoirs. Steps in the proposed methodology include: (1) determining the optimal release schedule using dynamic programming to solve a deterministic long-term operation model, (2) identifying the forms of operating rule curves suitable for cascade hydropower reservoirs based on the optimal release schedule, (3) constructing a simulation-based optimization model and then using the non-dominated sorting genetic algorithm-II (NSGA-II) to identify the key points of the operating rule curves, (4) testing and verifying the efficiency of the generated joint operating rule curves using synthetic inflow series. China’s Qing River cascade hydropower reservoirs (the Shuibuya, Geheyan and Gaobazhou reservoirs) were selected for a case study. When compared with the conventional operating rule curves, the annual power generation can be increased by 2.62% (from 7.27 to 7.46 billion kWh) using the observed inflow from 1951 to 2005, as well as by about 1.77% and 2.52% using the synthetic inflows generated from two alternative hydrologic simulation methods. Linear operating rules were also implemented to simulate coordinated operation of the Qing River cascade hydropower reservoirs. The joint operating rule curves were more efficient and reliable than conventional operating rule curves and linear operating rules, indicating that the proposed method can greatly improve hydropower generation and work stability.     

Multi-Objective Operating Rules for Danjiangkou Reservoir Under Climate Change

The natural variations of climatic system, as well as the potential influence of human activity on global warming, have changed the hydrologic cycle and threatened current water resources management. And the conflicts between different objectives in reservoir operation may become more and more challenging because of the impact of climate change. This study aims at deriving multi-objective operating rules to adapt to climate change and alleviate the conflicts. By combining the reservoir operation function and operating rule curves, an adaptive multi-objective operation model was proposed and developed. The optimal operating rules derived both by dynamic programming and NSGA-II method were compared and discussed. The projection pursuit method was used to select the best operating rules. The results demonstrate that the reservoir operating rules obtained by NSGA-II can increase the power generation and water supply yield and reliability, and the rules focusing on water supply can significantly increase the reservoir annual water supply yield (by 18.7 %). It is shown that the proposed model would be effective in reservoir operation under climate change.