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.     

The application of the aggregative approach in simulation modeling of water resources systems

This is a discussion about constructing simulation models for complex water resources systems (WRS). They are based on the piecewise linear aggregates theory developed in the U.S.S.R. The method allows us to lay down general principles for the construction of such simulation models and automate water resources calculations. Water resources systems simulation models incorporate balance equations, describe water resources inputs into and outputs from sites and reservoirs, and algorithms for both reservoir control and water distribution at sites. Water reservoir control is based on rule curves. The aggregative approach is especially efficient in constructing such algorithms for coordinated control of reservoirs.     

集约型灌溉农业区供水系统优化调度技术研究

本文针对干旱地区集约型灌溉农业的供水特点，基于大系统分解协调技术中的目标规划法，用模拟技术生成子系统非劣解集，提出目标规划模拟方法，建立多水库供水系统目标规划模拟模型．在优化调度的基础上研究实时调度方法，并编制出操作性强的交互式实时调度软件．     

库区滑坡涌浪数值模拟方法研究进展

对近年来库区滑坡涌浪涉及的边坡失稳、滑动、涌浪产生和传播这4个阶段研究中采用的数值模拟方法的研究进展和发展趋势进行了综述,重点归纳总结了边坡滑动过程以及涌浪产生过程的数值模拟方法的研究成果,指出滑坡涌浪从库区边坡失稳到涌浪传播是一个完整连续的过程,今后需要在目前各个阶段独立研究的基础上融合固体力学和流体力学两类学科中的相关研究方法,建立库区滑坡涌浪全程数值模拟方法,实现滑坡涌浪的全过程模拟。     

Technical Communication: Two-Phase Stochastic Dynamic Programming Model for Optimal Operation of Irrigation Reservoir

A two-phase stochastic dynamic programming model is developed for optimal operation of irrigation reservoirs under a multicrop environment. Under a multicrop environment, the crops compete for the available water whenever the water available is less than the irrigation demands. The performance of the reservoir depends on how the deficit is allocated among the competing crops. The proposed model integrates reservoir release decisions with water allocation decisions. The water requirements of crops vary from period to period and are determined from the soil moisture balance equation taking into consideration the contribution of soil moisture and rainfall for the water requirements of the crops. The model is demonstrated over an existing reservoir and the performance of the reservoir under the operating policy derived using the model is evaluated through simulation.     

Nominees as Candidates for the Executive Board 1989–1991

ABSTRACT

Principles of sustainable development are inviting a change in the approach used for reservoir management. Instead of designing and planning new reservoirs, more emphasis will be placed on management of existing reservoirs. Reservoir storage reallocation and reassessment of reservoir operational rules are considered to be the two main problems to be addressed by reservoir research in the near future. Appropriate techniques for solving these two problems are necessary An attempt at developing a sound methodology for a reservoir reassessment has been presented here. A simulation approach, based on the four probability criteria, has been used in this context for storage analysis. Reservoir management strategies have been reassessed using an implicit stochastic optimization model. The methodology has been applied to Wonogiri Reservoir located in Central Java, Indonesia. Analysis of the storage capacity indicated that the current capacity is larger than that required for the irrigation of an area of 25,319 ha, corresponding to the Wonogiri reservoir final development phase. Thus, the present size of the reservoir allows for additional multipurpose use of the reservoir storage. Optimization of the reservoir yield demonstrated that the reservoir can provide for municipal and industrial water supply and hydropower generation in addition to the current use af reservoir storage for irrigation water supply.     

Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System

It is remarkable that several hydrological parameters have a significant effect on the reservoir operation. Therefore, operating the reservoir system is complex issue due to existing the nonlinearity hydrological variables. Hence, determining modern model has high ability in handling reservoir operation is crucial. The present study developed artificial intelligence model, called Shark Machine Learning Algorithm (SMLA) to provide optimal operational rules. The major objective for the proposed model is minimizing the deficit volume between water releases and the irrigation water demand. The current study compared the performance of the SML model with popular evolutionary computing methods, namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The proposed models have been utilized of finding the optimal policies to operate Timah Tasoh Dam, which is located in Malaysia. The study utilized considerable statistical indicators to explore the efficiency of the models. The simulation period showed that SMLA approach outperforms both of conventional algorithms. The SMLA attained high Reliability and Resilience (Rel. = 0.98%, Res. = 50%) and minimum Vulnerability (Vul. = 21.9 of demand). It is demonstrated that shark machine learning algorithm would be a promising tool in handling the long-term optimization problem in operation a reservoir system.     

河道型水库溢油费伊模型与油粒子模型的对比研究

针对河道型水库溢油模拟研究匮乏的现状,以龙溪口库区河道突发溢油事故的预测模拟为例,分别建立了传统费伊(FAY)模型及油粒子模型,力图探明这两种主流模型在求解狭长河段溢油扩散时的特性。研究结果表明:FAY模型侧重于油膜扩展阶段中油膜厚度的精细模拟,但在水文情势复杂的河道溢油模拟过程中没有有效考虑流场、风场、地形对油膜漂移、扩散的影响;油粒子MIKE21-OS模型则克服了上述不足,对于河道溢油的模拟具备一定精度。模拟结果可为龙溪口库区突发溢油事故的预测预警提供参考。     

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.     

A combined stochastic dynamic programming-statistical disaggregation approach applied to multiple reservoir systems

The stochastic dynamic programming (SDP) method faces computational difficulties when used to determine the optimal operation of multiple storages. A new approach, a combined SDP-statistical disaggregation approach is introduced to determine releases for a special situation relating to multiple reservoir systems, that is, for a system of multiple storages where operational data are available. The approach consists of defining an equivalent single reservoir which represents the system of multiple reservoirs. The optimal releases from the equivalent single reservoir are derived by the use of SDP. Disaggregation of the optimal releases from the equivalent single reservoir, to produce the releases from the individual storages is based on historical operational data. The Melbourne (Australia) water supply system is considered as the example. The releases derived from the combined SDP-statistical disaggregation approach are tested by operating a simulation model, and the conclusion is made that the approach produces satisfactory releases for a system of multiple reservoirs where operational data are available. The method cannot be applied to existing systems where insufficient or no operational data are available, or to proposed systems where operational data are not available. The method uses a small amount of computer time.     

汛限水位动态控制的防洪极限风险分析

综合考虑水文、水力不确定性因素对汛限水位控制下的水库防洪极限风险进行研究，采用随机模拟方法计算极限防洪风险率。应用一阶季节性自回归模型模拟多场入库洪水序列，考虑水力不确定性对泄洪能力的影响，在给定调洪规则下对不同汛限水位方案进行调洪，得到水库最高调洪水位和防洪极限风险率。实例结果表明：水文因素的随机性和防洪调度规则是水库防洪风险的主要影响因素，水力因素对防洪风险影响不大，同时得出了水库面临汛限水位所能承受的极限风险率，为决策者安全度汛提供一种参考依据。     

Optimization and risk analyses for rule curves of reservoir operation: application to Tien-Hua-Hu Reservoir in Taiwan.

Tien-Hua-Hu Reservoir is currently under planning by the Water Resources Agency, Taiwan to meet the increasing water demands of central Taiwan arising from rapid growth of domestic water supply, and high-tech industrial parks. This study develops a simulation model for the ten-day period reservoir operation to calculate the ten-day water shortage index under varying rule curves. A genetic algorithm is coupled to the simulation model to find the optimal rule curves using the minimum ten-day water shortage index as an objective function. This study generates many sets of synthetic streamflows for risk, reliability, resiliency, and vulnerability analyses of reservoir operation. ARMA and disaggregation models are developed and applied to the synthetic streamflow generation. The optimal rule curves obtained from this study perform better in the ten-day shortage index when compared to the originally designed rule curves from a previous study. The optimal rule curves are also superior to the originally designed rule curves in terms of vulnerability. However, in terms of reliability and resiliency, the optimal rule curves are inferior to the those originally designed. Results from this study have provided in general a set of improved rule curves for operation of the Tien-Hua-Hu Reservoir. Furthermore, results from reliability, resiliency and vulnerability analyses offer much useful information for decision making in reservoir operation.     

基于自由搜索的水库入库含沙量预测模型

水库泥沙淤积问题直接关系到水库的规模、寿命以及综合经济效益的发挥。入库水流含沙量是影响水库调度运行以及水库泥沙淤积的主要因素,目前国内结构简单、实用性较强的含沙量预测模型不多。以水库的入库站流量及上游站至入库站的区间流量为研究对象,提出了基于两个水文站流量的水库入库含沙量的预测模型,采用改进的自由搜索算法率定模型参数,并将模型应用于龚嘴水库的入库含沙量的预测。研究结果表明,构建的模型结构简单,参数率定便捷,预测含沙量的精度较高,为水库入库含沙量预测提供了一种简便适用的方法。     

Modelling Watershed Scale Soil Loss Prediction and Sediment Yield Estimation

Computer simulation models are becoming increasingly popular in predicting soil loss for various land use and management practices. A GIS-based system, GeoUSLE, was developed in this study for soil loss prediction and sediment yield estimation in the watershed scale. The Universal Soil Loss Equation (USLE) and watershed analysis models are incorporated in the system. The required watershed information and USLE factors are derived from digital elevation models (DEMs) and remote sensing data. The GIS-based system can flexibly delineate drainage networks and watersheds and rapidly query the sediment yield at any point or watershed outlet via the point-and-click interface. The study presents an example application of the system to an agricultural reservoir watershed in central Taiwan. The estimated result shows that the developed system scales up USLE applications from the slope to the watershed, which can be used to assess the erosion hot spots in a watershed for the management decision making.     

跨流域补偿调节的梯级水电站主要参数优选

本文应用非线性规划,优选规划阶段的水电站群的主要设计参数,并提出了相应的数学模型和算法。这一模型能较好地适应水电系统大规模和非线性的特点,在考虑水电站群跨流域补偿调节的条件下,同时优选各个水库的正常蓄水位和死水位,同时优选各水电站的最佳装机规模及其装机程序,使规划期内全电力系统的总费用现值最小。 本文曾结合某电力系统,对分布在四个不同流域上的五座具有不同调节性能的水库,和十一个水电站所组成的系统,对待建水电站进行了参数优选,得到了比较满意的结果,已提供有关部门在规划阶段中参考。     

典型综合利用水库水环境问题及污染控制措施——以张峰水库为例

提出了“典型综合利用水库”的概念;论述了典型综合利用水库在水环境方面的主要特点;以张峰水库为例,研究了污染源、上游来流、库区及下游河段存在的主要水环境问题;结合2个已建水库-屯绛水库和后湾水库,论述了其水环境保护的经验;最后,对典型综合利用水库需采取的污染控制措施进行了探讨,并指出了落实保护中应注意的问题。本文的成果及观点对于典型综合利用水库的水环境保护管理及工程设计等方面将具有参考价值。     

基于1D CNN-LSTM模型的红旗水库水位序列模拟

为了提升水库水位模拟的精度,通过1D CNN-LSTM模型与五种常用的机器学习模型对安徽省红旗水库历史水位数据和降雨量数据实现未来7天的水位模拟并进行对比验证。CNN和LSTM能够表现出比较好的模拟性能,结合两种模型的优势能够更加显著的提升模型的模拟效果;1D CNN-LSTM具有较高鲁棒性,对于未来3天以内水位模拟都有较好的预测效果和精度,虽然3天以后的模拟效果有明显下降,但对未来第7天的模拟NSE和KGE依然能够达到0.8以上,在不发生极端天气的情况下,模型对于水位趋势的模拟依然具有相当的参考价值。1D CNN-LSTM模型对于红旗水库的水位模拟优于其他五种传统的机器学习模型,并具有相当高的精度。     

Integrated Artificial Neural Network (ANN) and Stochastic Dynamic Programming (SDP) Model for Optimal Release Policy

Complexicity in reservoir operation poses serious challenges to water resources planners and managers. These challenges of water reservoir operation are illustrated using a simulation to aid the development of an optimal operation policy for dam and reservoir. To achieve this, a Comprehensive Stochastic Dynamic Programming with Artificial Neural Network (SDP-ANN) model were developed and tested at Sg. Langat Reservoir in Malaysia. The nonlinearity of the natural physical processes was a major problem in determining the simulation of the reservoir parameters (elevation, surface-area, storage). To overcome water shortages resulting from uncertainty, the SDP-ANN model was used to evaluate the input variable and the performance outcome of the Model were compared with the Stochastic Dynamic Programming integrated with auto-regression (SDP-AR) model. The objective function of the models was set to minimize the sum of squared deviation from the desired targeted supply. Comparison result on the performance between SDP-AR model policy with SDP-ANN model found that the SDP-ANN model is a reliable and resilience model with a lesser supply deficit. The study concludes that the SDP-ANN model performs better than the SDP-AR model in deriving an optimal operating policy for the reservoir.