水库长期优化调度模型探讨

在水库长期优化调度问题中,一般以发电量最大为优化调度模型。考虑到电力市场的影响,综合水电站水库自身约束和电力系统调峰要求,建立以水电站发电效益最大为目标的优化调度模型,并通过实例计算比较这两种模型,发电效益最大模型较发电量最大模型更为符合实际。     

Assessment of Stochastic Operation Optimization for Reservoirs of Contrasting Scales

Deriving optimal release policies for dams and corresponding reservoirs is crucial for the sustainable water resources management of a region as they directly control the distribution of water to several users. Mathematical optimization algorithms can help in finding efficient reservoir operating strategies taking into account complex system constraints and hydrologic uncertainty. The robustness of operation optimization models may be influenced by physical reservoir characteristics such as size and scale and the effectiveness of a model for a particular case study does not always guarantee the same level of success for another application. This research focused on assessing the applicability of an implicit stochastic optimization (ISO) procedure to derive rule curves for two different dams of contrasting reservoir scales in terms of physical and operational characteristics. The results demonstrated the feasibility of the proposed technique for both small- and large-scale systems in view of the lower vulnerability provided by the ISO-derived policies in contrast to operations carried out by the standard reservoir operating policy as well as the proximity of the ISO operations with those by perfect-forecast deterministic optimization. The ISO procedure also provided operating rules similar to, and even less vulnerable than, those derived by stochastic dynamic programming.     

Robust Methods for Identifying Optimal Reservoir Operation Strategies Using Deterministic and Stochastic Formulations

Water allocation in a competing environment is a major social and economic challenge especially in water stressed semi-arid regions. In developing countries the end users are represented by the water sectors in most parts and conflict over water is resolved at the agency level. In this paper, two reservoir operation optimization models for water allocation to different users are presented. The objective functions of both models are based on the Nash Bargaining Theory which can incorporate the utility functions of the water users and the stakeholders as well as their relative authorities on the water allocation process. The first model is called GA–KNN (Genetic Algorithm–K Nearest Neighborhood) optimization model. In this model, in order to expedite the convergence process of GA, a KNN scheme for estimating initial solutions is used. Also KNN is utilized to develop the operating rules in each month based on the derived optimization results. The second model is called the Bayesian Stochastic GA (BSGA) optimization model. This model considers the joint probability distribution of inflow and its forecast to the reservoir. In this way, the intrinsic and forecast uncertainties of inflow to the reservoir are incorporated. In order to test the proposed models, they are applied to the Satarkhan reservoir system in the north-western part of Iran. The models have unique features in incorporating uncertainties, facilitating the convergence process of GA, and handling finer state variable discretization and utilizing reliability based utility functions for water user sectors. They are compared with the alternative models. Comparisons show the significant value of the proposed models in reservoir operation and supplying the demands of different water users.     

Optimal Reservoir Release Policy Considering Heterogeneity of Command Area by Elitist Genetic Algorithm

A number of models with conventional optimization techniques have been developed for optimization of reservoir water release policies. However these models are not able to consider the heterogeneity in the command area of the reservoir appropriately, due to non linear nature of the processes involved. The optimization model based on genetic algorithm (GA) can deal with the non linearity due to its inherent ability to consider complex simulation model as evaluation function for optimization. GA based models available in literature generally minimize the water deficits and do not optimize the total net benefits through optimal reservoir release policies. The present study focuses on optimum releases from the reservoir considering heterogeneity of the command area and responses of the command area to the releases instead of minimizing only the reservoir storage volumes. An optimization model has been developed for the reservoir releases based on elitist GA approach considering the heterogeneity of the command area. The developed model was applied to Waghad irrigation project in upper Godavari basin of Maharashtra, India. The results showed that 19% increase in the total net benefits could be possible by adopting the proposed water release policy over the present practice keeping same distribution of area under different crops. The model presented in this study can also optimize the crop area under irrigation. It is found that irrigated area can be increased to 50% of ICA (Irrigable Command Area) from the existing 23% with resulting addition to total net benefits by 31%. The effect of adopting the proposed irrigation schedule and increased irrigation areas would be to increase the net benefits to existing farmers.     

An alternative approach to the operation of multinational reservoir systems: Application to the Amistad &; Falcon system (Lower Rio Grande/Río Bravo)

An optimization approach for the operation of international multi-reservoir systems is presented. The approach uses Stochastic Dynamic Programming (SDP) algorithms – both steady-state and real-time – to develop two models. In the first model, the reservoirs and flows of the system are aggregated to yield an equivalent reservoir, and the obtained operating policies are disaggregated using a non-linear optimization procedure for each reservoir and for each nation's water balance. In the second model a multi-reservoir approach is applied, disaggregating the releases for each country's water share in each reservoir. The non-linear disaggregation algorithm uses SDP-derived operating policies as boundary conditions for a local time-step optimization. Finally, the performance of the different approaches and methods is compared. These models are applied to the Amistad-Falcon International Reservoir System as part of a binational dynamic modeling effort to develop a decision support system tool for a better management of the water resources in the Lower Rio Grande Basin, currently enduring a severe drought.     

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.     

跨流域引水后受水水库引水与供水联合调度研究

针对跨流域引水工程中受水水库引水与供水联合调度问题，建立了供水量最大与引水效率最高的多目标联合调度模型，并将其分解成两个单目标调度模型，应用长系列模拟优化的方法， 求解受水水库引水与供水联合调度图及其调度规则。以大伙房水库输水应急入连工程规划为基础，采用本文建立的模型方法对其受水水库碧流河水库进行实例研究，先后求解引水后水库最大可供水量以及如何高效引水的问题。结果表明，进行联合优化调度后，可提高跨流域引水的有效性，从而增加受水水库的综合效益。     

A Fuzzy Stochastic Dynamic Nash Game Analysis of Policies for Managing Water Allocation in a Reservoir System

In this paper a fuzzy dynamic Nash game model of interactions between water users in a reservoir system is presented. The model represents a fuzzy stochastic non-cooperative game in which water users are grouped into four players, where each player in game chooses its individual policies to maximize expected utility. The model is used to present empirical results about a real case water allocation from a reservoir, considering player (water user) non-cooperative behavior and also same level of information availability for individual players. According to the results an optimal allocation policy for each water user can be developed in addition to the optimal policy of the reservoir system. Also the proposed model is compared with two alternative dynamic models of reservoir optimization, namely Stochastic Dynamic Programming (SDP) and Fuzzy-State Stochastic Dynamic programming (FSDP). The proposed modeling procedures can be applied as an appropriate tool for reservoir operation, considering the interaction among the water users as well as the water users and reservoir operator.     

抽水蓄能电站运行优化的动态规划模型

按照电站抽水-发电循环效益最大化，满足电力电量平衡条件，满足库容、发电出力及抽水功率等限制条件，将抽水蓄能电站的运行优化表达为基于日或周运行优化的多阶段优化决策过程，即抽水蓄能电站运行优化的动态规划模型，并通过算例对算法及应用做了进一步说明。     

实时水库优化调度决策支持系统及其应用

水库是水库运行管理的中心环节，在水库调度中，将数学模型与专家系统结合使用，建立长中短期套接的实时兴利优化调度、防洪实时优化调度及其辅助模型系统；各模型通过逐时段滚动控制与反馈相结合，可以增加水库调度的效益，减少不确定性因素对调度的影响，并提出用专家系统对优化调度的各个环节进行评价，以尽可能降低优化调度方案的风险，提高优化调度方案的可接受程序，所提出的方法已编编制出使用灵活方便的软件，并经实际使用，     

Reservoir Optimization in Water Resources: a Review

This paper reviews current optimization technique developed to solve reservoir operation problems in water resources. The application of conventional, especially evolutionary computation, combination of simulation-optimization and multi objectives optimization in reservoir operation will be discussed and investigated. Furthermore, new optimization algorithm from other applications will be presented by focusing on Artificial Bee Colony (ABC) and Gravitational Search Algorithm (GSA) as alternative methods that can be explored by researchers in water resources field. Finally this paper looks into the challenges and issues of climate change in reservoir optimization.     

Ant Colony Optimization for Multi-Purpose Reservoir Operation

In this paper a metaheuristic technique called Ant Colony Optimization (ACO) is proposed to derive operating policies for a multi-purpose reservoir system. Most of the real world problems often involve non-linear optimization in their solution with high dimensionality and large number of equality and inequality constraints. Often the conventional techniques fail to yield global optimal solutions. The recently proposed evolutionary algorithms are also facing problems, while solving large-scale problems. In this study, it is intended to test the usefulness of ACO in solving such type of problems. To formulate the ACO model for reservoir operation, the problem is approached by considering a finite time series of inflows, classifying the reservoir volume into several class intervals, and determining the reservoir release for each period with respect to a predefined optimality criterion. The ACO technique is applied to a case study of Hirakud reservoir, which is a multi-purpose reservoir system located in India. The multiple objectives comprise of minimizing flood risks, minimizing irrigation deficits and maximizing hydropower production in that order of priority. The developed model is applied for monthly operation, and consists of two models viz., for short-time horizon operation and for long-time horizon operation. To evaluate the performance of ACO, the developed models are also solved using real coded Genetic Algorithm (GA). The results of the two models indicate that ACO model performs better, in terms of higher annual power production, while satisfying irrigation demands and flood control restrictions, compared to those obtained by GA. Finally it is found that ACO model outperforms GA model, especially in the case of long-time horizon reservoir operation.     

基于GWO-LSTM的丹江口水库入库径流预测

入库径流预测对丹江口水库调度及水资源利用具有重要的指示意义。基于灰狼优化算法（GWO）构建不同的预测模型,开展丹江口水库月入库径流预测研究,并探讨网络结构超参数的选取及验证GWO全局遍历性、收敛快的特点。结果表明:灰狼优化的长短期记忆模型（GWO-LSTM）的预测精度和泛化性能优于灰狼优化的人工神经网络模型（GWO-BP）和逐步回归模型,其验证期的纳什效率系数平均达到0.969,整体趋势预测较好,峰值捕捉略有不足,可适用于丹江口水库月入库径流预测;模型超参数依据经验取值时,其预测结果不如GWO优化,验证期的纳什效率系数不足0.5,未达到可接受范围,而且带有一定的偶然性,建议选用具有全局优化特性的优化算法进行超参数选取;验证了GWO算法全局遍历性和收敛快的特点,平均在3次迭代后可达到收敛状态。     

New Approach: Integrated Risk-Stochastic Dynamic Model for Dam and Reservoir Optimization

Multiple studies have developed management models to identify optimal operating policies for reservoirs in the last four decades. In an uncertain environment, in which climatic factors such as stream flow are stochastic, the economic returns from reservoir releases that are based on policy are uncertain. Furthermore, the consequences of reservoir release are not fully realized until it occurs. Rather than explicitly recognizing the full spectrum of consequences that are possible within an uncertain environment, the existing optimization models have focused on addressing these uncertainties by identifying the release policies that optimize the summative metric of the risks that are associated with release decisions. This technique has limitations for representing risks that are associated with release policy decisions. In fact, the approach of these techniques may conflict with the actual attitudes of the decision-makers regarding the risk aspects of release policies. The risk aspects of these decisions affect the design and operation of multi-purpose reservoirs. A method is needed to completely represent and evaluate potential consequences that are associated with release decisions. In this study, these techniques were reviewed from the stochastic model and risk analysis perspectives. Therefore, previously developed optimization models for operating dams and reservoirs were reviewed based on their advantages and disadvantages. Specifically, optimal release decisions that use the stochastic variable impacts and the levels of risk that are associated with decisions were evaluated regarding model performance. In addition, a new approach was introduced to develop an optimization model that is capable of replicating the manner in which reservoir release decision risks are perceived and interpreted. This model is based on the Neural Network (NN) theory and enables a more complete representation of the risk function that occurs from particular reservoir release decisions.     

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.     

Near Real-Time Optimization of Multi-Reservoir during Flood Season in the Fengman Basin of China

This study aims to develop a multi-objective optimization model in a multi-reservoir system during flood season using Numerical Weather Predictions (NWPs) outputs (short forecast). The optimization model was coupled with the Water and Energy Budget-based Distributed Hydrological Model that was used to forecast the reservoir inflows. The model was forced by 8-day lead time global deterministic NWPs by Japan Meteorological Agency. The reservoir objective function was established by considering the reservoir and upstream safety, downstream safety and future water use. The model was applied to the Baishan-Fengman multi-reservoir system of Northeast China. The results have demonstrated the model with high efficiency in optimizing reservoir objectives for all of the reservoirs. The sensitivity of the system to lead time and decision time were investigated. With the decreasing of lead time, the dam release peaks decrease and the end water levels increase. This is mainly due to the fact that the model with longer lead time needs to keep storage capacity for detected floods during long lead time period. The variation amplitude of dam releases and water levels decrease with the increasing of decision time due to the smoothing of floods and dam releases during long decision period. The model is easy to operate and is able to be coupled with other hydrological models or earth system models.     

Improving Optimization Efficiency for Reservoir Operation Using a Search Space Reduction Method

Reservoir operation problems are challenging to efficiently optimize because of their high-dimensionality, stochasticity, and non-linearity. To alleviate the computational burden involved in large-scale and stringent constraint reservoir operation problems, we propose a novel search space reduction method (SSRM) that considers the available equality (e.g., water balance equation) and inequality (e.g., firm output) constraints. The SSRM can effectively narrow down the feasible search space of the decision variables prior to the main optimization process, thus improving the computational efficiency. Based on a hydropower reservoir operation model, we formulate the SSRM for a single reservoir and a multi-reservoir system, respectively. To validate the efficiency of the proposed SSRM, it is individually integrated into two representative optimization techniques: discrete dynamic programming (DDP) and the cuckoo search (CS) algorithm. We use these coupled methods to optimize two real-world operation problems of the Shuibuya reservoir and the Shuibuya-Geheyan-Gaobazhou cascade reservoirs in China. Our results show that: (1) the average computational time of SSRM-DDP is 1.81, 2.50, and 3.07 times less than that of DDP when decision variables are discretized into 50, 100, and 500 intervals, respectively; and (2) SSRM-CS outperforms CS in terms of its capability of finding near-optimal solutions, convergence speed, and stability of optimization results. The SSRM significantly improves the search efficiency of the optimization techniques and can be integrated into almost any optimization or simulation method. Therefore, the proposed method is useful when dealing with large-scale and complex reservoir operation problems in water resources planning and management.     

Modeling Evaporation-Seepage Losses for Reservoir Water Balance in Semi-arid Regions

In the water balance of reservoir system, evaporation plays a crucial role particularly so for the reservoir systems of smaller size located in the semi-arid or arid regions. Such regions are most often characterized by significant seepage losses from reservoirs, besides evaporation losses. Usually, in the optimization of a reservoir system, it is a common practice to assume evaporation loss either as some constant value or as negligible. Such assumptions, however, may affect the results of reservoir optimization. This is demonstrated in this study by a case study in the optimal scheduling of Pilavakkal reservoir system in Vaipar basin of Tamilnadu, India. For modeling reservoir losses, many models are available, of which, Penman combination model is most commonly used. In this study, an alternative approach based on Genetic Programming (GP) is proposed. The results of GP and Penman model for both evaporation loss estimation and reservoir scheduling are compared. It is found that while GP and Penman combination model performs equally well for estimating evaporation losses, GP is also able to model seepage losses (or other losses from reservoir) to a much better degree. It is also shown the reservoir scheduling does get influenced based on how the reservoir losses are modeled in the reservoir water balance equation.     

基于DPSA 的梯级水库群优化调度

动态规划法是一种求解多阶段决策优化问题的常用方法,在水库优化调度计算中应用广泛。该方法最大的缺陷就是用于水库群优化调度时易出现"维数灾"问题。逐次逼近动态规划法(DPSA)可以有效克服这一问题,它采用逐次迭代逼近的思想,将一个多维问题分解为多个一维问题求解。本文以水库运行模拟模型为基础,建立了基于DPSA的梯级水库群中长期优化调度模型,以汉江上游梯级水库群为研究对象,选取发电量最大为目标,对各水库库容进行离散,从而求解水库优化运行过程,其结果对于水库优化调度运行具有指导意义。     

东武仕水库兴利调度

本文结合东武仕水库的优化调度原则,以水库水量利用率最大作为目标函数,以水量平衡、用水量、水库库容、水库水位等作为约束条件,建立数学模型,通过优化调度模型求解运行期的出库水量.按动态规划顺序递推计算,寻找最优转移路径,得到了东武仕水库兴利调度最优方案.