水库供水发电多目标优化调度模型及应用研究

提出了一种研究确定水库最优供水量的多目标水库优化调度数学模型,此模型以供水量最大和发电量最大为目标函数,考虑水量平衡、防洪、发电、航运及水库综合利用要求约束条件.提出一种交互式的求解方法对模型进行解算,此方法首先采用约束法,通过松驰供水量最大目标将多目标模型转换成多个单目标模型,在单目标模型中引入2个参数,用于调整模型的计算结果.对单目标模型,采用动态规划法求解,求得多目标模型的不劣解集.提出一个用于选择多目标最优解的决策偏好系数,对多目标模型的不劣解集进行对比分析,从而确定多目标模型的最优解.应用所提出的方法研究了广东省白盆珠水库的调度方案,提出了白盆珠水库的最优供水方案.     

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.     

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.     

Generation of operating rules with stochastic dynamic programming and multiple objectives

Conclusion Several models were presented for finding Pareto-optimal solution for a reservoir operating problem. By simply changing the weights of the objectives, as many operating rules as is desired can be generated. Each result yields an operating rule which is a compromise solution between several noncommensurable objective functions. These results should be further analyzed through simulation on a real-time basis and application of multi-objective techniques for final selection of an optimal operating rule.     

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。     

Single Reservoir Operating Policies Using Genetic Algorithm

To obtain optimal operating rules for storage reservoirs, large numbers of simulation and optimization models have been developed over the past several decades, which vary significantly in their mechanisms and applications. As every model has its own limitations, the selection of appropriate model for derivation of reservoir operating rule curves is difficult and most often there is a scope for further improvement as the model selection depends on data available. Hence, evaluation and modifications related to the reservoir operation remain classical. In the present study a Genetic Algorithm model has been developed and applied to Pechiparai reservoir in Tamil Nadu, India to derive the optimal operational strategies. The objective function is set to minimize the annual sum of squared deviation form desired irrigation release and desired storage volume. The decision variables are release for irrigation and other demands (industrial and municipal demands), from the reservoir. Since the rule curves are derived through random search it is found that the releases are same as that of demand requirements. Hence based on the present case study it is concluded that GA model could perform better if applied in real world operation of the reservoir.     

Modelling an overdeveloped irrigation system in south India

An optimization model for irrigation planning is developed based on the experience gained from an overdeveloped irrigation system in South India. This model helps the decision maker in choosing the appropriate policy decisions under conditions of shortage of the available water potential to meet the demand of already overgrown irrigation systems. The objective of the model is to maximize the net benefits from crops in the commands of the irrigation projects considered. The constraints of the model include total land limitations of each project, subregional land limitations; reservoir balance, storage capacity, beginning‐year storage constraints for each reservoir; range of possible downstream riparian release policies; sociological constraints regarding essential food crop policy and commercial crop limitations.     

Optimizing Reservoir Operation Policy Using Chance Constraint Nonlinear Programming for Koga Irrigation Dam,Ethiopia

One of typical problems in water resources system modeling is derivation of optimal operating policy for reservoir to ensure water is used more efficiently. This paper introduces optimization analysis to determine monthly reservoir operating policies for five scenarios of predetermined cropping patterns for Koga irrigation scheme, Ethiopia. The objective function of the model was set to minimize the sum of squared deviation (SSD) from the desired targeted supply. Reservoir operation under different water availability and thresholds of irrigation demands has been analyzed by running a chance constraint nonlinear programming model based on uncertain inflow data. The model was optimized using Microsoft Excel Solver. The lowest SSD and vulnerability, and the highest volumetric reliability were gained at irrigation deficit thresholds of 20 % under scenario I, 30 % under scenario II, III and V, and at 40 % under scenario IV when compensation release is permitted for downstream environment. These thresholds of deficits could be reduced by 10 % for all scenarios if compensation release is not permitted. In conclusion the reservoir water is not sufficient enough to meet 100 % irrigation demand for design command areas of 7,000 ha. The developed model could be used for real time reservoir operation decision making for similar reservoir irrigation systems. In this specific case study system, attempt should be made to evaluate the technical performance of the scheme and introduce a regulated deficit irrigation application.     

Optimal allocation of water resources in large river basins: II. Application to Yellow River Basin

In this paper, the theoretical approach presented in Part I is demonstrated by means of case study on the optimal allocation water resources for Yellow River Basin of China. The object of the case study was to find the optimization of water allocation among subregions and trunk streams of the basin in order to achieve the maximum national economic benefits and the optimal reservoir storage required to maintain the long-term balance of water resources.The optimal allocation of water resources pattern caters for irrigation, hydropower generation, navigation, water supply and other sectors, depending to a large extent on the objective economic benefits obtained from the whole valley and on the objective of reservoir storage. Other factors incapable of being expressed in terms of the two objectives considered in the section concerning constrained conditions. This research is applicable widely and suitable for the solution of complicated, multi-objective large-system problems involving non-linearity, numerous variables and various constrained conditions. Finally, the results of optimal allocation of water resources for Yellow River Basin (OAWRYRB) have been selected on an optimum basis in accordance with the multi-objective method.The application of our optimization techniques to the Yellow River basic showed that the total water of the Yellow River over a year can be allocated by optimization. The maximum national economic benefits and the optimal reservoir storage required to maintain the long-term balance of water resources can also be obtained by the optimal techniques.     

Optimal reservoir operation using Nash bargaining solution and evolutionary algorithms

Optimizing reservoir operation is critical to ongoing sustainable water resources management. However, different stakeholders in reservoir management often have different interests and resource competition may provoke conflicts. Resource competition warrants the use of bargaining solution approaches to develop an optimal operational scheme. In this study, the Nash bargaining solution method was used to formulate an objective function for water allocation in a reservoir. Additionally, the genetic and ant colony optimization algorithms were used to achieve optimal solutions of the objective function. The Mahabad Dam in West Azerbaijan, Iran, was used as a case study site due to its complex water allocation requirements for multiple stakeholders, including agricultural, domestic, industrial, and environmental sectors. The relative weights of different sectors in the objective function were determined using a discrete kernel based on the priorities stipulated by the government (the Lake Urmia National Restoration Program). According to the policies for the agricultural sector, water allocation optimization for different sectors was carried out using three scenarios: (1) the current situation, (2) optimization of the cultivation pattern, and (3) changes to the irrigation system. The results showed that the objective function and the Nash bargaining solution method led to a water utility for all stakeholders of 98%. Furthermore, the two optimization algorithms were used to achieve the global optimal solution of the objective function, and reduced the failure of the domestic sector by 10% while meeting the required objective in water-limited periods. As the conflicts among stakeholders may become more common with a changing climate and an increase in water demand, these results have implications for reservoir operation and associated policies.     

Application of the modified fibonacci search-based search beam method to derive a nondominated release policy for a multi-purpose reservoir

One of the most serious problems of water resources development planning is the lack of acceptable procedures to deal with conflicts among different purposes at the early conceptual planning stage of any water resources system. As far as the consideration of formulating planning strategies is concerned, a new interactive multi-objective decision making (MODM) is approach, the search beam method (SBM) has been conceived. This model is expected to provide a reliable systematic procedure to search for the nondominated solutions along prespecified lines representing hypothetical search beams (SB) in the objective space. The proposed SBM consists of two major parts, an optimization phase and a subsequent evaluation phase. Incremental dynamic programming (IDP), one of the best known approximative techniques of dynamic programming (DP), is applied in the optimization phase. The modified Fibonacci search is heuristically used to seek, in a successive search for the intersection with the Pareto optimal surface along each SB. The viability of the SBM is demonstrated by applying it to an existing single unit reservoir system. This reservoir is assumed to accommodate two conflicting objectives: maximizing hydropower generation and maximizing water surface elevation to sustain accessibility of the navigation structures and to enable low head pumping of irrigation water.     

Optimization of Water Resources Utilization by PSO-GA

The objective of this paper is to present an optimal model to address the water resources utilization of the Tao River basin in China. The Tao River water diversion project has been proposed to alleviate the problem of water shortages in Gansu Province in China. A multi reservoir system is under consideration with multiple objectives including water diversion, ecological water demand, irrigation, hydropower generation, industrial requirements, and domestic uses in the Tao River basin. A multi-objective model for the minimization of water shortages and the maximization of hydro-power production is proposed to manage the utilization of Tao River water resources. An adjustable PSO-GA (particle swarm optimization – genetic algorithm) hybrid algorithm is proposed that combines the strengths of PSO and GA to balance natural selection and good knowledge sharing to enable a robust and efficient search of the solution space. Two driving parameters are used in the adjustable hybrid model to optimize the performance of the PSO-GA hybrid algorithm by assigning a preference to either PSO or GA. The results show that the proposed hybrid algorithm can simultaneously obtain a promising solution and speed up the convergence.     

Managing Water Supply and Demand to Achieve Economic and Environmental Objectives: Application of Mathematical Programming and ANFIS Models

The integrated management of water supply and demand has been considered by many policymakers; due to its complexity the decision makers have faced many challenges so far. In this study, we proposed an efficient framework for managing water supply and demand in line with the economic and environmental objectives of the basin. To design this framework, a combination of ANFIS and multi-objective augmented ε-constraint programming models and TOPSIS were used. First, using hydrological data from 2001 to 2017, the rate of water release from the dam reservoir was estimated with the ANFIS model; afterwards, its allocation to agricultural areas was performed by combining multi-objective augmented ε-constraint models and TOPSIS. To prove the reliability of the proposed model, the southern Karkheh basin in Khuzestan province, Iran, was considered as a case study. The results have showed that this model is able to reduce irrigation water consumption and to improve its economic productivity in the basin.

     

水库多目标调度方案优选决策方法

针对水库多目标调度模型生成的众多Pareto可行解,决策者往往面临方案优选决策困难的问题。提出利用可视化展示工具,结合边际效益分析及模糊优选方法,对可行方案进行逐步决策的优选决策方法。结果表明,利用不同的分析工具与决策方法,使众多可行方案得到形象可视化展示的同时,能够融入决策者的主观偏好等信息,得到具有不同决策需求的满意方案。通过不同决策方法的层层决策,逐渐减少备选方案的数量,降低了多目标决策问题的复杂性,为决策者的方案优选提供一定的参考。     

Effect of inflow forecast accuracy and operating time horizon in optimizing irrigation releases

In this study, application of Genetic Algorithms (GA) is demonstrated to optimize reservoir release policies to meet irrigation demand and storage requirements. As it is commonly recognized that accuracy of inflow forecast and operating time horizon affects the optimal policies, a trial-and-error approach is suggested to identify the appropriate trade-off between forecast accuracy and operating horizon. The flexibility offered by GA to set up and evaluate objective functions is exploited towards this end. The results are also compared with Linear Programming (LP) model. It is concluded that forecasts models of high accuracy are desirable, particularly when the system is to be operated for periods of high demand. In such cases, the optimization with longer time horizon ensures achievement of the objective more uniformly over the period of operation. The performance of GA is found to be better than LP, when forecast model of higher accuracy and longer period of operating horizon are considered for optimization.     

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.     

基于多目标差分进化算法的水库多目标防洪调度研究

为求解水库多目标防洪优化调度问题,提出一种基于自适应柯西变异的多目标差分进化算法,克服了早熟收敛问题,提高了收敛精度;同时,根据多目标优化的特点对差分算子进行修正,并引入外部档案技术,提高了算法的收敛速度。本文以坝前最高水位最低、最大下泄流量最小和汛末水位最接近汛限水位为目标,对三峡水库展开多目标防洪调度研究,结果表明,该算法可在较短时间内生成大量在各目标分布均匀、分布范围广的非劣调度方案供决策者评价优选,为水库多目标防洪调度决策提供了一种新的调度方案生成方法。     

Socially-Optimal and Nash Pareto-Based Alternatives for Water Allocation under Uncertainty: an Approach and Application

This study has proposed a methodology by enhancing an interactive algorithm to multi-objective optimization problems with interval parameters, in an attempt to reach the tradeoff between quality and reliability of the resultant optimum solutions. The earlier algorithm could turn into a prolonged procedure that deals with several players with different aspirations at different reliability, or risk, levels under non-deterministic conditions. Hence, it is not a pertinent approach to solve problems of water allocation between competing parties. The enhanced methodology aims to alleviate the burdens of the procedure and generate a unique set of solutions (i.e., near-Pareto-optimal alternatives), instead of a myriad of compromise solution sets. We have investigated a real-world hydro-environmental problem, the allocation of water between Dorudzan-Korbal irrigation networks and Bakhtegan Lake in Fars Province, Iran to assess feasibility of this methodology. In order to reach a consensus concerning the stakeholders’ individual preferences, we identified the compromise alternatives from the obtained sets of non-dominated solutions by taking advantage of various social choice rules and the Nash bargaining model. The results demonstrated that the developed methodology could incorporate the risk of system constraints violations (i.e., planning reliability under uncertainty) into the process of approximating the optimal tradeoff set of solutions. It also gave policymakers a chance to acquire perception into the potentially best compromise for land and water allocation schemes regarding the preference profiles of the involved interest groups.     

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。     

面向梯级水库多目标优化调度的进化算法研究

实际工程中以梯级水库多目标优化调度为代表的大规模高维多目标优化问题,其优化难度是一般方法所难以应对的。为此本文提出一种新型的多目标粒子群算法LMPSO,其包含了基于超体积指标I_h^k的适应值分配方法与基于问题变换的搜索空间降维策略,以有效处理问题的高维目标向量与大规模决策变量。将该算法应用于溪洛渡-向家坝梯级水库的中长期多目标优化调度中,并与4种知名算法的计算结果进行对比分析,验证LMPSO在求解该类问题上的卓越性能。由此为多目标优化调度高质量非劣解集的获取提供一种可靠的方法,并为下一步的多目标调度决策提供有力的数据支持。