An Inexact Two-Stage Water Resources Allocation Model for Sustainable Development and Management Under Uncertainty

In this study, an inexact two-stage water resources allocation (ITWR) model is put forward for supporting sustainable development and management of water resources in Sanjiang Plain, China, which is in such a situation, with multi-water source, multi-water supply subarea, multi-water user and multi-planning goal. The costs of net system, water supply and recourse are analyzed. The developed ITWR model, which shows a strong ability in tacking with various uncertain factors in probability distributions and discrete interval numbers, mixes the techniques of interval-parameter programming (IPP) and two-stage stochastic programming (TSP) within a general optimization framework. And it also has formed an effective link in such a conflict between the policy scenarios and the associated various levels of economic penalties, when the pre-allocation targets of water resources are violated. Based on this model, a series of scenarios under different levels of pre-allocation water is done and different degrees of water surplus and shortage are obtained correspondingly. The results indicate that the reasonable distribution plans with maximum system benefit and minimum system-failure risk have been generated. And these results are valuable for saving water resources to realize its sustainable development and mitigating the penalty to gain economic benefits maximum, and thus some desired results are provided for decision makers in tackling with a complex and uncertain water-resource system.     

A Multi-Objective and Equilibrium Scheduling Model Based on Water Resources Macro Allocation Scheme

Water Resources Management - A multi-objective and equilibrium scheduling model is established based on water resources macro allocation (WRMAA) scheme to describe the scheduling process accurately...     

An Inexact Two-stage Fuzzy-stochastic Programming Model for Water Resources Management

An inexact two-stage fuzzy-stochastic programming (ITFSP) method is developed for water resources management under uncertainty. Fuzzy sets theory is introduced to represent various punishment policies under different water availability conditions. As an extension of conventional two-stage stochastic programming (TSP) method, two special characteristics of the proposed approach make it unique compared with existing approaches. One is it could handle flexible penalty rates, which are much reasonable for both of the authorities and users, and have seldom been considered in the TSP framework. The other is uncertain information expressed as discrete intervals and probability distribution functions can be effectively reflected in the optimization processes and solutions. After formulating the model, a hypothetical case is employed for demonstrating its applicability under two scenarios, where the inflow is divided into four and eight intervals, respectively. The results indicate that reasonable solutions have been obtained. They provide desired allocation patterns with maximized system benefit under two feasibility levels. The solutions present as stable intervals with different risk levels in violating the water demands, and can be used for generating decision alternatives. Comparisons of the solution from the ITFSP with that from the ITSP (inexact two-stage stochastic programming) and TSP approach are also undertaken. It shows that the ITFSP could produce more system benefit than existing methods and deal with flexible penalty policies for better water management and utilization.     

基于量与质的多目标水资源配置模型

根据水资源配置的目标建立了水量分析、水质分析、经济分析、生态环境分析等子模型，并在此基础上，根据大系统理论和多目标决策理论建立了基于量与质的面向经济发展和生态环境保护的多目标协调配置模型，用以解决目前水资源短缺和用水竞争性的问题，从而为流域或区域的水资源可持续开发与社会经济的协调发展提供参考。     

An Inexact Chance-constrained Quadratic Programming Model for Stream Water Quality Management

Water quality management is complicated with a variety of uncertainties and nonlinearities. This leads to difficulties in formulating and solving the resulting inexact nonlinear optimization problems. In this study, an inexact chance-constrained quadratic programming (ICCQP) model was developed for stream water quality management. A multi-segment stream water quality (MSWQ) simulation model was provided for establishing the relationship between environmental responses and pollution-control actions. The relationship was described by transformation matrices and vectors that could be used directly in a multi-point-source waste reduction (MWR) optimization model as water-quality constraints. The interval quadratic polynomials were employed to reflect the nonlinearities and uncertainties associated with wastewater treatment costs. Uncertainties associated with the water-quality parameters were projected into the transformation matrices and vectors through Monte Carlo simulation. Uncertainties derived from water quality standards were characterized as random variables with normal probability distributions. The proposed ICCQP model was applied to a water quality management problem in the Changsha section of the Xiangjiang River in China. The results demonstrated that the proposed optimization model could effectively communicate uncertainties into the optimization process, and generate inexact solutions containing a spectrum of wastewater treatment options. Decision alternatives could then be obtained by adjusting different combinations of the decision variables within their solution intervals. Solutions from the ICCQP model could be used to analyze tradeoffs between the wastewater treatment cost and system-failure risk due to inherent uncertainties. The results are valuable for supporting decision makers in seeking cost-effective water management strategies.     

基于协同学原理的流域水资源合理配置模型和方法研究

水资源的有限性和稀缺性导致水资源配置过程中经济、社会和生态环境目标相互冲突。本文在识别水资源配置系统协同特征基础上，根据协同学理论中有序度概念和支配原理，分别在水资源配置社会、经济和生态环境子系统中设置序参量，并结合信息熵原理，构建了一种基于协同学原理的流域水资源配置模型，部分程度上有效解决了水资源合理配置系统中多目标、多维数求解问题。将此理论运用于我国南方丰水地区－东江流域水资源合理配置，得出了满意结果。     

A Model for Optimal Allocation of Water to Competing Demands

The present study develops a simple interactive integrated water allocation model (IWAM), which can assist the planners and decision makers in optimal allocation of limited water from a storage reservoir to different user sectors, considering socio-economic, environmental and technical aspects. IWAM comprises three modules—a reservoir operation module (ROM), an economic analysis module (EAM) and a water allocation module (WAM). The model can optimize the water allocation with any of two different objectives or two objectives together. The two individual objectives included in the model are the maximization of satisfaction and the maximization of net economic benefit by the demand sectors. Weighting technique (WT) or simultaneous compromise constraint (SICCON) technique is used to convert the multi-objective decision-making problem into a single objective function. The single objective functions are optimized using linear programming. The model applicability is demonstrated for various cases with a hypothetical example.     

IFTSQP: An Inexact Optimization Model for Water Resources Management under Uncertainty

Abstract

An interval-fuzzy two-stage quadratic programming (IFTSQP) method is developed for water resources management under uncertainty. The methodincorporates techniques of interval-parameter programming, two-stage stochastic programming, and fuzzy quadratic programming within a general optimization framework to tackle multiple uncertainties presented as intervals, fuzzy sets and probability distributions. In the model formulation, multiple control variables are adopted to handle independent uncertainties in the model's right-hand sides; fuzzy quadratic terms are used in the objective function to minimize the variation in satisfaction degrees among the constraints. Moreover, the method can support the analysis of policy scenarios that are associated with economic penalties when the promised targets are violated. The developed method is then applied to a case study of water resources management planning. The results indicate that reasonable solutions have been obtained. They can help provide bases for identifying desired water-allocation plans with a maximized system benefit and a minimized constraint-violation risk.     

区域性联合供水系统水量模拟调度模式研究

陕西省关中地区水资源总量不足，时空分布不均，供需矛盾突出，省会西安严重缺水，因此提出建设对西安城市供水为重点、以黑河引水工程为主体的区域性联合供水系统的构想，并开展了多水我工程联网模拟调度的研究与实践，根据对西安市城市供水系统的构成以及可利用水量及需水量的分析，按照以中近期为主、充分利用地表水、合理开采地下水、统一考虑生活、生产、生态用水及多方案调度的水量调配原则，采用多水源逐日模拟调度方式建立模     

HEC-ResSim在固原市水资源优化配置中的应用

固原市原州区位于宁夏南部,水资源量少质差,其天然禀赋条件不能满足日益增长的社会经济发展对水资源的需求,故需对水资源进行合理高效配置,打破水资源瓶颈制约。通过建立HEC-Res Sim多水源多用户模型,对当地水、引调水及再生水等多水源进行合理配置,充分考虑生产、生活、生态等不同用水需求,进行规划水平年的供需平衡分析,明确供需缺口并提出相应的解决办法,为实现固原市原州区经济社会和生态环境的可持续发展提供技术支撑。     

水资源分配模型及模拟求解技术

建立了以供水满足程度最大化为目标,供水系统的供水能力、输水系统的输水能力、用水系统的供需变化和非负约束为约束条件的水资源分配模型;提出了模型的模拟求解技术;最后以焦作市为例,求解了2020年规划年75%来水频率下焦作市水资源的分配量.计算结果满足用水户的要求,体现了水源的时空分配过程.     

基于模糊优选理论的区域工业合理配水量研究

工业配水量的合理确定是区域水资源配置中的难点。根据多个可行方案，应用区域模糊优选工业配水方案模型，确定各方案隶属度大小和优次排序，进而结合区域的具体情况，选定最佳配水量。并以江苏省某县规划水平年2010年工业配水情况为实例进行研究，从权重和隶属度的确定、方案的排序及最终方案的制定可以看出，工业配水的特点已隐含在推荐的方案中，结论更合理、客观。     

A Nonlinear Interval Model for Water and Waste Load Allocation in River Basins

In this paper, a new methodology is proposed for simultaneous allocation of water and waste load in river basins. A nonlinear interval number optimization model is used to incorporate the uncertainties of model inputs and parameters. In this methodology, the bounds of the uncertain inputs are only required, not necessarily knowing their probability density or fuzzy membership functions. In the proposed model, the existing uncertainties in water demands and monthly available water are considered in the optimization model. Also the economic and environmental impacts of water allocation to the agricultural water users are taken into account. To have an equitable water and waste load allocation, benefits are reallocated to water users using some solution concepts of the cooperative game theory. Results of applying the methodology to the Dez river system in south-western part of Iran show its effectiveness and applicability for water and waste load allocation in an uncertain environment.     

区域水资源优化配置模型研究

结合已开工的南水北调东线工程山东段和胶东调水工程,建立了山东省水资源优化配置模型,按照现状年、2010年和2030年共3个水平年的6个方案进行了计算机模拟计算,探讨了山东省当地水、黄河水和长江水联合运用和优化配置。为新时期山东省水资源开发利用决策提供了科学依据。     

基于遗传算法的灌区渠系优化配水模型研究

为了使灌区配水获得更好的效果,通过改进传统"定流量,变历时"渠系运行方式的轮灌优化配水模型,以灌区配水渠道输水时间最短和各个轮灌组合持续引水时间差异最小建立了双目标函数,采用层次分析法确定各目标函数权重系数,然后根据各目标函数权重系数把双目标函数转化为单目标函数,最后通过遗传算法对单目标函数进行求解,确定了灌区最优轮灌组组合。实例计算结果表明:采用渠系优化配水方法进行轮灌组合优化,缩短了配水渠道输水时间,提高了水资源利用率,取得了较好的配水效果。     

基于水权初始配置的区域利益博弈与优化模型

明晰水权初始配置是协调流域内各区域之间水资源综合效益的重要途径。针对流域水权初始配置过程,首先,结合流域内各区域的社会经济发展目标,构建水权初始配置指标体系,采用理想解法,获得水权初始配置初步方案,确定各区域的水权配置量初始值;其次,结合流域内各区域的利益诉求,基于动态博弈理论,分析各区域之间的动态博弈机理,构建动态博弈模型和利益补偿函数,实现水权增加利益主体对水权减少利益主体的利益补偿;然后,确定各区域的水权配置量优化值和流域内各区域水资源综合效益的优化值,并获得水权初始配置优化方案;最后,通过案例分析验证了利益博弈与优化模型在水权初始配置过程中具有较好的适用性。研究表明,水权初始配置本质上是对流域内各区域之间进行利益博弈的过程,通过水权增加利益主体对水权减少利益主体进行利益补偿,可进一步优化各区域的利益和流域水资源综合效益。     

基于混合算子遗传算法的水资源优化配置

构建了以经济效益、社会效益、生态效益为多目标的水资源配置模型,针对模型中存在的多目标多约束优化问题,对基本遗传算法进行改造,设计了一个混合算子遗传算法,并用算例进行验证,最后将水资源优化配置模型和算法应用于东江流域。实例计算结果表明,基于混合算子遗传算法的多目标水资源优化配置模型配置结果合理可行,可作为研究流域水资源配置的决策依据。遗传算法作为新型智能算法,可应用于水资源配置领域。     

A Multiobjective Stochastic Programming Model for Hydropower Hedging Operations under Inexact Information

This study develops a multiobjective stochastic programming model for informing hedging decisions for hydropower operations under an electricity market environment considering the benefit from selling energy production and the cost of penalizing energy shortfall. Aiming to determine the optimal strategy that hedges the risk of energy shortfall while keeping a high level of direct revenue from energy production under uncertain streamflows and inexact penalizing price conditions, competing objectives of minimizing energy shortfall percentage and maximizing direct revenue from energy production are analyzed. The conflict is resolved by determining the optimal level of energy shortfall percentage such that the net benefit of the hydropower system is maximized. The first-order optimality condition of maximized system net revenue is derived, which states that the marginal benefit of hedging equals the marginal cost of hedging at optimality. The tradeoff ratio between the competing objectives serves as the marginal cost of hedging and the penalizing price of energy shortfall represents the marginal benefit of hedging. Using the optimality condition, sensitivity tests are conducted for investigating the influence of different ranges of penalizing prices and reservoir initial storages on hedging decisions. The proposed method is evaluated on the operations of the Three Gorges cascade hydropower system during the drawdown season. Results show that: (1) minimizing the energy shortfall percentage adversely affects the maximization in system direct revenue from energy production, and the conflicting results are related to the depletion strategies of reservoir storage; (2) to reduce the energy shortfall percentage to the lowest level could result in significant reduction in total energy production and the direct revenue, especially when reservoir initial storages are low; and (3) the optimal level of energy shortfall percentage would decrease as penalizing price increases, when the influence of penalizing cost from energy shortfall gradually dominates the influence of energy production on the net revenue. The model framework and the implications could be applied to rationalize hedging decisions for hydropower operations under inexact information upon streamflow and penalizing prices.     

A Stochastic Non-linear Programming Model for a Multi-period Water Resource Allocation with Multiple Objectives

A rapid increase in demand and severe droughts in recent years has increased the pressure on water supplies throughout most parts of Australia. This has resulted in the need for tools to allocate limited water across users in different regions, and explore scenarios so as to achieve economic, social and environmental benefits. A major challenge in water resource allocation is dealing with the uncertainty in the system, particularly with respect to reservoir inflow. Stochastic non-linear programming is applied to water resource allocation to accommodate this uncertainty across the time periods of the planning horizon. A large range of solutions is produced representing the distributions of uncertainty in reservoir inflow. These solutions are used in a Monte Carlo simulation to estimate the trade-off in amounts of water allocated versus risk of not achieving minimal reservoir levels. The methodology is applied to a case study in South East Queensland in Australia, a region which is currently facing a severe water shortage over the next 3 years. A new water supply initiative that the Queensland State Government is considering to overcome the water crisis is assessed using the methodology.