PA-DDS算法在水库多目标优化调度中的应用

Pareto存档动态维度搜索（Pareto-Archived Dynamically Dimensioned Search,PA-DDS）算法是一种求解多目标问题的随机搜索启发式算法。本文将PA-DDS算法引入考虑供水和发电的多目标优化模型优化水库调度图,与非支配排序遗传算法（NSGA-II）和多目标粒子群算法（MOPSO）对比了收敛性,并在求得非劣解分布的均匀性和与理论Pareto前端的相似性方面与NSGA-II进行比较,分析该算法求解水库多目标调度问题的优化性能,对比分析不同目标下的优化调度图。结果表明：PA-DDS算法能够得到更高质量的非劣解集,优化调度图与原设计调度图相比,能更有效协调供水和发电的矛盾,在小幅降低（0.96%）发电量的前提下显著提高（8.07%）水库供水量,平均每年增加经济效益0.55亿元。     

基于决策因子选择的梯级水库多目标优化调度规则研究

运用极端随机森林挖掘水库调度信息,考虑各水库相关因子与决策目标的相互关系,提出了梯级水库输入数据选择(Cascade-reservoir Input-variables Selection,CIS)方法,引入Gaussian径向基函数建立水库调度规则,并采用PA-DDS多目标算法对水库调度规则参数进行优化,得到了同时考虑供水和发电的多目标优化调度规则集。汉江上游梯级水库的应用结果表明:CIS方法能优选得到较为稳定的决策因子,且能在充分利用已有优选信息的同时避免决策因子选取上的重复和冗余;经梯级多目标优化调度,优选的决策因子能得到比传统决策因子结果分布更优的非劣解集,在以发电为主要目标进行优化调度时,能有效调整汛期和非汛期的水量利用方式,梯级水库年均发电量和供水量分别提高了0.33%和7.84%。     

Operating rules of irrigation reservoir under climate change and its application for the Dongwushi Reservoir in China

Since agriculture development would be affected by climate change, the reservoir operation for agricultural irrigation should be adjusted. However, there are to date few literatures addressing how to design adaptive operating rules for an irrigation reservoir. This study aims to analyze the adaption of fixed operating rules and to derive adaptive operating rules under climate change. The deterministic optimization model is established with the solving method of two-dimensional dynamic programming (TDDP), and its optimal trajectory is supplied to derive reservoir operating rules at time intervals of crop growth periods. Then, two alternative operating rules, including fixed operating rules based on historical data and adaptive operating rules based on climate change data, are extracted using the fitting method with the multiple linear regression model. The alteration of reservoir inflow under climate change is calculated by the Budyko formula. A case study of the China’s Dongwushi Reservoir shows that: (1) fixed operating rules are unable to adapt climate change in the future scenario. Thus, adaptive operating rules should be established, (2) adaptive operating rules can reduce profits loss resulting from climate change, and improve field soil water storages, and (3) precipitation reduction by 7%/40a is the major cause for agricultural profits loss, whereas, the decrement of agricultural profits is less than that of precipitation, which indicates agricultural crops have the resilience to resist the adverse influence from precipitation decrease. These findings are helpful for adaptive operation of irrigation reservoirs under climate change.     

Adapting Multireservoir Operation to Shifting Patterns of Water Supply and Demand

The aim of this study is to determine whether dam reoperation (the adjustment of reservoir operating rules) is an effective adaptation strategy to reduce the potential impacts of climate change and regional socio-economic developments. The Xinanjiang-Fuchunjiang reservoir cascade, located in Hangzhou Region (China), is selected as case study. We use a scenario-based approach to explore the effects of various likely degrees of water stress for the future period between 2011 and 2040, which are compared to the control period from 1971 to 2000. The scenario impacts are simulated with the WEAP water allocation model, which is interlinked with the NSGA-II metaheuristic algorithm in order to derive optimal operating rules adapted to each scenario. Reservoir performance is measured with the Shortage Index (SI) and Mean Annual Energy Production (MAEP). For the investigated scenarios, adapted operating rules on average reduce the SI with 84 % and increase the MAEP with 6.4 % (compared to the projected future performance of conventional operation). Based on the optimization results, we conclude that for the studied case dam reoperation is an effective adaptation strategy to reduce the impact of changing patterns of water supply and demand, even though it is insufficient to completely restore system performance to that of the control period.     

基于仿真规则与智能优化的水库多目标调控模型及其应用

为解决多个用户的用水竞争矛盾,改变经济社会用水长期挤占生态环境用水的不合理现状,把水库下游的河流生态系统作为一个独立用水户,进而按照水库用水户的优先级,制定水库分区供水调度仿真规则;为解决水库调度方案的最优性与可操作性的统一问题,构建多用户供水保证率最大化且尽可能均衡的水库优化调度模型目标函数;在此基础上建立了基于仿真规则与智能优化的水库多目标调控模型。以潘家口水库为例,计算了现状供水规模下的水库调度方案,绘制了水库多用户分区供水调度图。结果表明:本文模型有效,提出的水库分区调度方案较之现行调度,可在不降低兴利和防洪效益的前提下,显著提高水库下游河道生态系统的供水保证率。     

含支线调蓄的调水系统水库群联合调度

针对调水工程与其支线调蓄工程所面临的复杂水库群联合调度问题,以陕西省引汉济渭调水工程及其支线调蓄工程焦岩水库、玉带河水库构成的联合调度系统为工程实例,运用拓扑概化方式揭示了各水源之间的水利－水量联系,并由此构建了联合调度模型,采用协同粒子群算法求解了10 a长系列调度,提出了单一水库多目标供水方案,解析了耦合系统补给调水效果。结果表明：支线调蓄工程能够在均衡多目标供水效益的基础上,通过耦合系统有效增加补给调水量,显著改善调水过程。     

考虑生态流量的小流域水库群联合调度研究

在小流域开展考虑生态流量需求的水库群联合调度,成为改善小流域河道生态环境的重要途径。采用水库群模拟优化方法建立了考虑下游河道生态需水的水库群多目标联合调度模型。模型依据流域内水库群的调节能力和调度目标,将水库群划分为3类;以供水目标为优先原则,确立水库群的调度方式和调度图。在此基础上,采用多目标遗传算法（NSGA-II）求解水库群联合调度的调度图。最后以重庆市龙溪河流域为研究实例,对不同生态需水流量下的水库群多目标调度效益进行对比分析。结果表明,生态流量的增加对流域内的农业用水影响最为明显,水库群多目标调度成为平衡各目标之间竞争性的重要方式。     

Streamflow Forecast and Reservoir Operation Performance Assessment Under Climate Change

This study attempts to investigate potential impacts of future climate change on streamflow and reservoir operation performance in a Northern American Prairie watershed. System Dynamics is employed as an effective methodology to organize and integrate existing information available on climate change scenarios, watershed hydrologic processes, reservoir operation and water resource assessment system. The second version of the Canadian Centre for Climate Modelling and Analysis Coupled Global Climate Model is selected to generate the climate change scenarios with daily climatic data series for hydrologic modeling. Watershed-based hydrologic and reservoir water dynamics modeling focuses on dynamic processes of both streamflow generation driven by climatic conditions, and the reservoir water dynamics based on reservoir operation rules. The reliability measure describes the effectiveness of present reservoir operation rules to meet various demands which are assumed to remain constant for the next 100 years in order to focus the study on the understanding of the structure and the behaviour of the water supply. Simulation results demonstrate that future climate variation and change may bring more high-peak-streamflow occurrences and more abundant water resources. Current reservoir operation rules can provide a high reliability in drought protection and flood control.     

Multi-Objective Optimal Model for Conjunctive Use Management Using SGAs and NSGA-II Models

The widespread investigations on water resources management has become an essential issue because due to lack of sufficient research and inattention to planning and management of conjunctive use of surface and groundwater. The conjunctive management is a suitable alternative for imbalanced water resources distribution and related constraints in using of surface water. In this paper, a multi-objective model is developed to maximize the minimum reliability of system as well as minimize the costs due to water supply, aquifer reclamation and violation of the reservoir capacity in operation and allocation priority. The non-dominated sorting genetic algorithm (NSGA-II) is used to present the optimal trade-off between the objectives. The sequential genetic algorithms is also applied (SGA) in order to be compared with the NSGA-II model. The results show that the NSGA-II model can considerably reduce the computation burden of the conjunctive use models in comparison with the SGA optimization model. The obtained trade-off curve shows that a little increase in reliability leads to much more system costs. The weighted single objective SGA model results verify optimal trade-off obtained from NSGA-II model and show the optimality of allocated discharges.     

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.     

A Neural Networks Approach for Deriving Irrigation Reservoir Operating Rules

A neural networks approach is applied to the derivation of the operating rules of an irrigation supply reservoir. Operating rules are determined as a two step process: first, a dynamic programming technique, which determines the optimal releases byminimizing the sum of squared deficits, assumed as objective function, subject to various constraints is applied. Then, theresulting releases from the reservoir are expressed as a functionof significant variables by neural networks. Neural networks aretrained on a long period, including severe drought events, andthe operation rules so determined are validated on a differentshorter period. The behaviour of different operating rules is assessed by simulating reservoir operation and by computing several performance indices of the reservoir and crop yield through a soil water balance model. Results show that operating rules based on an optimization with constraints resembling real system operation criteria lead to a good performance both in normal and in drought periods, reducing maximum deficits and water spills.     

变化环境下水库适应性调度研究进展与展望

在气候变化与人类活动共同驱动的变化环境下,依赖于一致性水文条件所设计的传统水库调度运行策略,将难以满足决策者的需求。为了保障水资源安全与高效利用,水库管理者需对传统水库调度运行策略进行适应性调整。因此,变化环境下水库适应性调度作为当前水库调度领域的一项前沿课题,国内外专家学者已经开展了大量卓越的工作。本文旨在于总结近年来的水库适应性调度研究进展,包括变化环境下水库入库径流预测、水库调度规则编制以及耦合变化环境-入库径流-调度规则的框架等方面,并归纳当前相关研究中存在的问题及不足。进而,未来水库适应性调度研究发展方向,建议更多关注考虑自然-人工互馈影响的入库径流预测、水库调度运行策略的静态衔接与动态调整。     

面向双保证率的水库供水能力优化计算方法研究

随着城市化进程的推进和生态文明理念的深入,水库供水任务日趋复杂,涵盖城镇供水、农业灌溉、生态保障多类目标,分析水库多目标供水能力对于工程的功能调整等决策十分必要。本文综合考虑水库供水目标的竞争性与保证率差异性,提出面向双保证率的水库供水能力双层优化计算方法,通过调度线控制和城镇供水能力区间迭代实现满足不同保证率的多目标供水能力计算。外层基于粒子群算法以城镇供水能力最大为目标对水库调度线进行优化,协调不同用户的供水关系;内层在给定规则下通过迭代计算满足双保证率要求的城镇供水能力。通过内外层嵌套计算实现对水库调度规则和供水能力的同步优化。以三亚市赤田水库为实例进行研究,在城镇供水保证率为95%、农业灌溉保证率为90%时,通过优化调度规则得出水库总供水能力为9400万m~3。设置不同农业保证率和农业需水情景作分析对比,二者均对城镇供水能力有影响,总供水能力随农业保证率降低而提高。分析表明,双层优化计算方法可以实现对调度线的优化,得出满足城镇、农业双保证率要求的供水能力,且计算方法收敛稳定性较高,优化生成的调度线年内分布合理,可为水库多目标供水能力的计算和相关决策提供参考。     

Multi-Objective Optimization of the Proposed Multi-Reservoir Operating Policy Using Improved NSPSO

Severe water shortage is unacceptable for water-supply reservoir operation. For avoiding single periods of catastrophic water shortage, this paper proposes a multi-reservoir operating policy for water supply by combining parametric rule with hedging rule. In this method, the roles of parametric rule and hedging rule can be played at the same time, which are reducing the number of decision variables and adopting an active reduction of water supply during droughts in advance. In order to maintain the diversity of the non-dominated solutions for multi-objective optimization problem and make them get closer to the optimal trade-off surfaces, the multi-population mechanism is incorporated into the non-dominated sorting particle swarm optimization (NSPSO) algorithm in this study to develop an improved NSPSO algorithm (I-NSPSO). The performance of the I-NSPSO on two benchmark test functions shows that it has a good ability in finding the Pareto optimal set. The water-supply multi-reservoir system located at Taize River basin in China is employed as a case study to verify the effect of the proposed operating policy and the efficiency of the I-NSPSO. The operation results indicate that the proposed operating policy is suitable to handle the multi-reservoir operation problem, especially for the periods of droughts. And the I-NSPSO also shows a good performance in multi-objective optimization of the proposed operating policy.     

Structural and Non-Structural Climate Change Adaptation Strategies for the Péribonka Water Resource System

This paper discusses the possibility for a privately managed hydro-power system to adapt to a projected increase in water flow in their central-Québec watersheds by adding power generation potential. Runoffs simulated by a lumped rainfall-runoff model were fed into a stochastic dynamic programming (SDP) routine to generate reservoir operating rules. These rules were optimized for maximum power generation under maximal and minimal reservoir level constraints. With these optimized rules, a power generation simulator was used to predict the amount of generated hydropower. The same steps, excluding calibration, were performed on 60 climate projections (from 23 general circulation models and 3 greenhouse gas emission scenarios) for future horizons 2036–2065 and 2071–2100. Reservoir operation rules were optimized for every climate change projection for the 3 power plants in the system. From these simulations, it was possible to determine hydropower numbers for both horizons. The same steps were performed under a modified system in which an additional turbine was added to each power plant. Results show that both the non-structural (optimizing reservoir rules) and structural (adding turbines) adaptation measures allow for increased power production, but that adapting operating rules is sufficient to reap the most of the benefits of increased water availability.     

官厅水库多目标调度规律与方案研究

为探究水文变异和外调水源补给对水电站发电和生态的影响,以官厅水库下游减水河段为例,在计算水文变异下生态流量的基础上,定义了减水河段的生态保证程度,建立了水库发电和生态保证程度多目标优化调度模型,采用非支配遗传算法(NSGA-II)求解多种情景方案。研究结果表明：官厅水库入库径流分别于1971年和1983年发生变异,水文变异后发电和平均生态保证程度显著降低;水电站发电和生态之间多目标关系显著,发电量减少37.4%可增加67.8%的生态效益;外调水源补给后发电量和平均生态保证程度均显著提高,特别是枯水年,分别提高56.9%和40.3%。     

Development of a Demand Driven Hydro-climatic Model for Drought Planning

In recent years, droughts with increasing severity and frequency have been experienced around the world due to climate change effects. Water planning and management during droughts needs to deal with water demand variability, uncertainties in streamflow prediction, conflicts over water resources allocation, and the absence of necessary emergency schemes in drought situations. Reservoirs could play an important role in drought mitigation; therefore, development of an algorithm for operation of reservoirs in drought periods could help to mitigate the drought impacts by reducing the expected water shortages. For this purpose, the probable drought’s characteristics and their variations in response to factors such as climate change should be incorporated. This study aims at developing a contingency planning scheme for operation of reservoirs in drought periods using hedging rules with the objective of decreasing the maximum water deficit. The case study for evaluation of the performance of the proposed algorithm is the Sattarkhan reservoir in the Aharchay watershed, located in the northwestern part of Iran. The trend evaluations of the hydro-climatic variables show that the climate change has already affected streamflow in the region and has increased water scarcity and drought severity. To incorporate the climate change study in reservoir planning; streamflow should be simulated under climate change impacts. For this purpose, the climatic variables including temperature and precipitation in the future under climate change impacts are simulated using downscaled GCM (General Circulation Model) outputs to derive scenarios for possible future drought events. Then a hydrological model is developed to simulate the river streamflow, based on the downscaled data. The results show that the proposed methodology leads to less water deficit and decreases the drought damages in the study area.     

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.     

洪泽湖多目标水量调度决策模型及其应用

针对洪泽湖水量利用与生态水位维持这一矛盾开展多目标水量调度决策方法研究,构建贴近适宜生态水位（生态效益）、缺水率最小、引水量最小、入湖水量改变度最小等目标的多目标调度模型,考虑生态效益目标与水资源利用目标的不可共度性及决策者偏好模糊性特征,采用多目标模糊决策法从非劣解集中筛选最适宜调度方案。结果表明：多目标模型解集反映生态效益和经济社会效益的置换关系,贴近适宜生态水位的调控方式在一定程度上降低洪泽湖调蓄能力,与水资源利用形成矛盾关系;模糊决策法筛选的均衡调度策略可以有效反映决策者的偏好情况,并提供适用于不同情景的优化调度方式;生态目标优先方案通过减少供水、增加引水量、调节入湖水量等方式有效补充生态用水,可将生态水位偏离差降低至0.30 m,可为生态优先原则下的适宜调度策略制定提供参考。     

Developing a Multi-Objective Conflict-Resolution Model for Optimal Groundwater Management Based on Fallback Bargaining Models and Social Choice Rules: a Case Study

Conflict-resolution models can be used as practical approaches to consider the contradictions and trade-offs between the involved stakeholders in integrated water resource management. These models are utilized to reach an optimal solution considering agents interactions. In this paper, a new methodology is developed based on multi-objective optimization model (NSGA-II), groundwater simulation model, M5P model tree, fallback bargaining procedures and social choice rules to determine the optimal groundwater management policies with an emphasis on resolving conflicts between stakeholders. By incorporating the multi-objective simulation-optimization model and bargaining methods, the optimal groundwater allocation policies are determined and the preferences of the stakeholders as well as social criteria such as justice are also considered. The obtained data set, based on Monte Carlo analysis of calibrated MODFLOW model, is used for training and validating the M5P meta-models. The validated M5P meta-models are linked with NSGA-II to determine the trade-off curve (Pareto front) for the objectives. Social choice rule and fallback bargaining methods, as conflict-resolution models, are applied to determine the best socio-optimal solution among stakeholders, and their results are compared. The effectiveness of the proposed methodology is verified in a case study of Darian aquifer, Fars province, Iran. Results indicated that the solutions obtained by the proposed conflict-resolution approaches have an appropriate applicability. Total groundwater withdrawal, after applying the optimal groundwater allocations, reduced to 20.85 MCM, resulting in a 4.62 m increase in the mean groundwater level throughout the aquifer.