Performance Evaluation Model for Multipurpose Multireservoir System Operation

This paper describes the development of a performance evaluation based model for the operation of multipurpose multireservoir in a river basin system. The methodology developed in the present study has been evolved for (1) allocation of releases for multi-purpose from each reservoir, (2) fair allocation of mandatory flow releases in the river, and (3) the assessment of the system capability for multipurpose operation. The System Performance Index (SYSPI) has been introduced as a measure of the overall performance of the system. SYSPI is defined as a function of the performance indicator indices which are developed to measure each of the objectives of the multireservoir system, namely, reliability of water supply, hydropower production, revenue income, and spill prevention. The SYSPI is maximized using a search algorithm which is linked to the simulation module. The application of the developed methodology is demonstrated for the reservoirs on the Narmada River System, India. The application of the methodology should enable increasing the hydropower generation within the existing framework.     

Development of 3-D Optimal Surface for Operation Policies of a Multireservoir in Fuzzy Environment Using Genetic Algorithm for River Basin Development and Management

A Multi objective, Multireservoir operation model for maximization of irrigation releases and maximization of hydropower production is proposed using Genetic Algorithm. These objectives are fuzzified and are simultaneously maximized by defining and then maximizing level of satisfaction (λ). In the present study a multireservoir system in Godavari River sub basin in Maharashtra State, India is considered. Problem is formulated with four reservoirs and a barrage. A monthly Multi Objective Genetic Algorithm Fuzzy Optimization (MOGAFUOPT) model for the present study is developed in ‘C’ Language. The optimal operation policy for maximization of irrigation releases, maximization of hydropower production and maximization of level of satisfaction is presented for existing demand in command area. The entire range of optimal operation policies, for different levels of satisfaction i.e. λ (ranging from 0 to 1), are determined. From the relationships developed amongst irrigation releases, hydropower production and level of satisfaction, a three dimensional (3-D) surface covering the whole range of policies has been developed. This solution surface can be the basis for decision makers for implementing the policies. Considering the future requirements in the command area, both the irrigation and hydropower demands are increased by 10 and 20%. The optimal operation policy for maximization of irrigation releases, maximization of hydropower production and maximization of level of satisfaction is also presented for these cases. The 3-D solution surface is also developed in these cases.     

Multiobjective water resources systems analysis using genetic algorithms--application to Chou-Shui River Basin, Taiwan.

Multipurpose operation is adopted by most reservoirs in Taiwan in order to maximize the benefits of power generation, water supply, irrigation and recreational purposes. A multiobjective approach can be used to obtain trade-off curves among these multipurpose targets. The weighting method, in which different weighting factors are used for different purposes, was used in this research work. In Taiwan, most major reservoirs are operated by rule curves. Genetic algorithms with characteristics of artificial intelligence were applied to obtain the optimal rule curves of the multireservoir system under multipurpose operation in Chou-Shui River Basin in central Taiwan. The model results reveal that different shapes of rule curves under different weighting factors on targets can be efficiently obtained by genetic algorithms. Pareto optimal solutions for a trade-off between water supply and hydropower were obtained and analyzed.     

A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam

River flow forecasting is an essential procedure that is necessary for proper reservoir operation. Accurate forecasting results in good control of water availability, refined operation of reservoirs and improved hydropower generation. Therefore, it becomes crucial to develop forecasting models for river inflow. Several approaches have been proposed over the past few years based on stochastic modeling or artificial intelligence (AI) techniques. In this article, an adaptive neuro-fuzzy inference system (ANFIS) model is proposed to forecast the inflow for the Nile River at Aswan High Dam (AHD) on monthly basis. A major advantage of the fuzzy system is its ability to deal with imprecision and vagueness in inflow database. The ANFIS model divides the input space into fuzzy sub-spaces and maps the output using a set of linear functions. A historical database of monthly inflows at AHD recorded over the past 130 years is used to train the ANFIS model and test its performance. The performance of the ANFIS model is compared to a recently developed artificial neural networks (ANN) model. The results show that the ANFIS model was capable of providing higher inflow forecasting accuracy specially at extreme inflow events compared with that of the ANN model. It is concluded that the ANFIS model can be quite beneficial in water management of Lake Nasser reservoir at AHD.     

Reservoir Operation Incorporating Hedging Rules and Operational Inflow Forecasts

Reservoir operation incorporating a naïve hedging strategy and operational inflow forecasting is studied in this paper. Gridded precipitation forecasts from climate model, ECHAM4.5, are used as potential predictors for reservoir inflow forecasting. In building a statistical predicting model, principal component analysis (PCA) is used to reduce the dimension of the regression model. Performance evaluation indices, including water supply satisfaction ratio, environmental flow satisfaction ratio, end-of-month storage satisfaction ratio and flood prevention capacity index, are defined. Three scenarios where a naïve hedging operation rule under different set of reservoir inflow are investigated. These are evaluated for a water supply reservoir, Falls Lake Reservoir, at Neuse River in the southeast United State. Reservoir simulation with monthly average inflow serves as a benchmark. The utility of operational inflow forecasts is quantified by the improvements of performance indices. Results show that reservoir operation under perfect inflow forecasting has the highest values for most indices. Compared to climatology, operational inflow forecasts result in higher index values. Among all the performance indices, end-of-month storage satisfaction ratio is the most sensitive index to inflow information. Limitation of this study and further work is also discussed.     

A multiobjective fuzzy linear programming model for sustainable integrated operation of a multireservoir system

Irrigated agriculture is the largest consumer of water, with high significance in India as the majority of its people depend on it. Rainfall uncertainties, and uneven distribution of utilizable water over space and time, have presented a serious challenge to irrigation water management and crop production. Surface water reservoirs play a major role in supplying irrigation water and also addressing uneven water distribution to some degree. Varying water requirements for different crops under multiple canals, however, requires that planners develop an optimal crop planning for the efficient operation of a multireservoir irrigation system. The spatial and temporal water transfer within a multireservoir irrigation system necessitates integrated operation of all the involved reservoirs. Thus, integrated operation of a multireservoir system is a fundamental requirement for efficient irrigation water management, and also become a key issue for sustainable agricultural development. This study derived an optimal crop plan for the integrated operation of a complex multireservoir system with intrabasin water transfer, using a multiobjective fuzzy linear programming (MOFLP) approach. The MOFLP model resulted in a level of satisfaction of 0.46, with an irrigation intensity of 102.18%, and a total crop area of 149 232.10 ha. The optimal policies were furthermore assessed with various statistical indicators with a simulation model, indicating they are performing well, with high reliability and resilience for longer periods.     

A Bayesian Stochastic Optimization Model for a Multi-Reservoir Hydropower System

This paper presents the development of an operating policy model for a multi-reservoir system for hydropower generation by addressing forecast uncertainty along with inflow uncertainty. The stochastic optimization tool adopted is the Bayesian Stochastic Dynamic Programming (BSDP), which incorporates a Bayesian approach within the classical Stochastic Dynamic Programming (SDP) formulation. The BSDP model developed in this study considers, the storages of individual reservoirs at the beginning of period t, aggregate inflow to the system during period t and forecast for aggregate inflow to the system for the next time period t + 1, as state variables. The randomness of the inflow is addressed through a posterior flow transition probability, and the uncertainty in flow forecasts is addressed through both the posterior flow transition probability and the predictive probability of forecasts. The system performance measure used in the BSDP model is the square of the deviation of the total power generated from the total firm power committed and the objective function is to minimize the expected value of the system performance measure. The model application is demonstrated through a case study of the Kalinadi Hydroelectric Project (KHEP) Stage I, in Karnataka state, India.     

Derivation of Aggregation-Based Joint Operating Rule Curves for Cascade Hydropower Reservoirs

Operating rule curves have been widely applied to reservoir operation, due to their ease of implementation. However, these curves are generally used for single reservoirs and have rarely been applied to cascade reservoirs. This study was conducted to derive joint operating rule curves for cascade hydropower reservoirs. Steps in the proposed methodology include: (1) determining the optimal release schedule using dynamic programming to solve a deterministic long-term operation model, (2) identifying the forms of operating rule curves suitable for cascade hydropower reservoirs based on the optimal release schedule, (3) constructing a simulation-based optimization model and then using the non-dominated sorting genetic algorithm-II (NSGA-II) to identify the key points of the operating rule curves, (4) testing and verifying the efficiency of the generated joint operating rule curves using synthetic inflow series. China’s Qing River cascade hydropower reservoirs (the Shuibuya, Geheyan and Gaobazhou reservoirs) were selected for a case study. When compared with the conventional operating rule curves, the annual power generation can be increased by 2.62% (from 7.27 to 7.46 billion kWh) using the observed inflow from 1951 to 2005, as well as by about 1.77% and 2.52% using the synthetic inflows generated from two alternative hydrologic simulation methods. Linear operating rules were also implemented to simulate coordinated operation of the Qing River cascade hydropower reservoirs. The joint operating rule curves were more efficient and reliable than conventional operating rule curves and linear operating rules, indicating that the proposed method can greatly improve hydropower generation and work stability.     

Joint Operation and Dynamic Control of Flood Limiting Water Levels for Cascade Reservoirs

Reservoirs are among the most effective tools for integrated water resources development and management. The dynamic control of reservoir flood limiting water level (FLWL) is a valuable and effective method to compromise the flood control and conservation for reservoir operation during the flood season. This paper focuses on joint operation and dynamic control of FLWL for cascade reservoirs. A composition and decomposition-based model that consists of an aggregation module, a storage decomposition module and a simulation operation module was developed. The model was applied to the Qingjiang basin in south of China using the 3-hour inflow data series for representative hydrological years. Application results indicate that the proposed model can make an effective tradeoff between the flood control and hydropower generation. Joint operation and dynamic control of FLWL can increase power production by 4.51 % (1.79?×?10⁸?kWh) and increase water use rate by 2.73 %. It can enhance benefits of the Qingjiang cascade reservoirs without compromising flood prevention objectives.     

考虑蓄水期弃水风险的水库长期发电调度方法

短期径流的大幅波动使得以平均径流为基础制定的水库长期调度方案面临较大弃水风险,是影响水库长期调度决策合理性的重要因素。本文考虑日尺度径流波动影响,提出一种蓄水期弃水风险量化方法,并建立了耦合弃水风险的水库长期发电调度模型。利用长系列日径流资料,结合风险最小蓄水规则,以不蓄弃水流量为指标量化蓄水期各月弃水风险;采用Copula函数构建月均入流与弃水风险的联合分布和条件概率分布,明晰了特定入流条件的风险置信区间;最后,将弃水风险以弃电损失函数融入优化模型,以获得更符合实际的长期调度方案。以澜沧江流域小湾水库为工程背景进行调度模拟分析,结果表明本文方法能够有效降低水库长期优化调度方案的弃水风险,有利于提高优化结果的可操作性,与传统方法相比,能够使多年平均弃水减少约4.76亿m³、发电量增加约1.15亿kW·h。     

Effects of reservoir operation methods on downstream ecological disturbance and economic benefits

The natural flow regime can sustain the ecological integrity of riverine ecosystems. Different reservoir operation polices differ in their effects on the degree of alteration of natural flow regimes. Dynamic programming plays an important role in developing operation policies. When using dynamic programming models to develop operation policies, the discrete number of storage states (DNSS), which is a key factor affecting the reservoirs operation policies, is always been determined based on computational efficiency and economic benefits. Little consideration has been given to the ecological disturbance caused by different DNSS‐based operation policies. To analyze the impact of DNSS, we built a deterministic dynamic programming model to explore the relationship among DNSS, the flow regime alteration (ecological disturbance), and the cumulative annual power generation (economic benefits) by setting a range of DNSS scenarios. We used three reservoirs with different storage coefficients (ratios of usable storage to annual average runoff) as examples and used the range of variability approach to assess the ecological disturbance under these scenarios. We compared the results with those of a stochastic dynamic programming (SDP) model and a Bayesian SDP (BSDP) model. We found that when DNSS is low, increasing DNSS improves economic benefits but causes a more severe ecological disturbance; when DNSS is high, increasing DNSS improves the economic benefits only slightly, without exacerbating the ecological disturbance; for a given DNSS, the BSDP model provides higher economic benefits than the SDP model and a similar disturbance of the riverine ecosystem; and larger reservoirs more often cause more severe disturbance of riverine ecosystems because monthly mean flows and annual extreme flows change more drastically. Our results will help to protect the riverine ecosystems and improve economic benefits if reservoir operation managers consider DNSS using dynamic programming models.     

Impacts of Inflow Variations on the Long Term Operation of a Multi-Hydropower-Reservoir System and a Strategy for Determining the Adaptable Operation Rule

Obvious inflow variations resulting from changing environments bring big challenges to the operations of hydropower reservoirs. This study reveals the impacts of average annual inflow volume (AAIV) variations on the long term operation of a multi-hydropower-reservoir (MHR) system, and presents a strategy for determining the adaptable operation rule. The strategy includes two parts. One part is making different inflow scenarios based on the change points of AAIVs. Another part is applying the principle of cross validation to select the adaptable rule from the formulated operation rules in various inflow scenarios. Specifically, the change points of AAIVs are identified by three statistical methods. An optimization operation model of an MHR system is built, and three evolutionary and meta-heuristic algorithms are applied to resolve the model in different inflow scenarios. Based on the optimal operation results, two machine learning algorithms are employed to formulate operation rules in each inflow scenario. The MHR system at the upstream of Yellow River basin is taken as a case study. The results show that (1) the long term operation of an MHR system is sensitive to the AAIV variations; and (2) the presented strategy is feasible in determining the adaptable operation rule for an MHR system under the AAIV variations. The findings of the study are helpful for the long term operation of an MHR system under the AAIV variations.

     

Interstate multivalley multireservoir simulation: A case study

Most of the water resources systems in India are operated with conventional approaches formulated several decades ago without much knowledge about the system behaviour. In the present study, the HEC‐3 simulation model has been used to derive the operating policies for an interstate, multivalley‐multireservoir system in India, to achieve optimal operational policies. Four different HEC‐3 policies were analysed together with the standard operating policy. The results of the simulation runs were analysed and a suitable optimal operating policy for a multivalley‐multireservoir system operation has been evolved. Trade‐off analysis between the conflicting objectives of irrigation releases and hydropower production and the probability distribution for the annual average hydropower production were also arrived at to estimate the reliability of the system.     

Reliability-based Operation of Reservoirs Using Combined Monte Carlo Simulation Model and a Novel Nature-inspired Algorithm

One of the critical issues in surface water resources management is the optimal operation of dam reservoirs. In recent decades, meta-heuristics algorithms have gained attention as a powerful tool for finding the optimal program for the dam reservoir operation. Increasing demand due to population growth and lack of precipitation for reasons such as climate change has caused uncertainties in the affecting parameters on the planning of reservoirs, which invalidates the operational plans of these reservoirs. In this study, a novel optimization algorithm with the combination of genetic algorithm (GA) and multi-verse optimizer (MVO) called multi-verse genetic algorithm (MVGA) has been developed to solve the optimal dam reservoir operation issue under influence of the joint uncertainties of inflow, evaporation and demand. After validating the performance of MVGA by solving several benchmark functions, MVGA was used to find the optimal operation program of the Amirkabir Dam reservoir in 132 months, in both deterministic and probabilistic states. Minimizing the deficit between downstream demand and release from the reservoir during the operation period was considered as the objective function. Also, the limitations of the reservoir continuity equation, storage volume, and reservoir release equation were applied to the objective function. For modeling the effect of uncertainty, Monte Carlo simulation (MCS) is coupled to MVGA. The results of model implementations showed that the MVGA-MCS model with the best value of the objective function equal to 26 in the 1st rank and MVGA, MVO, and GA, with 15%, 34%, and 46% increase in the value of the objective function compared to the MVGA-MCS stood in the second to fourth ranks, respectively. Also, the results of the resiliency, and vulnerability indices of the reservoir operation showed that MVGA-MCS and MVGA models have better performance than other models.

     

Adaptive Reservoir Management by Reforming the Zone-based Hedging Rules against Multi-year Droughts

In this study, the zone-based hedging rule, which is the main operating policy adopted from multipurpose reservoirs in Korea is adjusted to reflect the multi-year droughts caused by climate change. Annual synthetic inflow series with different magnitudes of long memory were generated using the autoregressive fractional integrated moving average (ARFIMA) model. The generated inflow series were then disaggregated into 10-day series and utilized as input variables to derive the alternative hedging rules. The alternative hedging rules from this study were used in adaptive reservoir management by newly updated information. Finally, the performance of the suggested policy is measured in terms of frequency and magnitude under the historical inflow series. As a result, adaptive reservoir management demonstrated improvements in the following terms of the frequency of critical failures (water deficit ratio greater than 30%): 6.14% of the simulation period in the status quo (SQ) policy, and 2.99% in the adaptive management. However, the overall reliability of the reservoir during the simulation horizon was better when operated with the SQ policy (41.19%) than the results from adaptive management (26.42%). Because this result is in a good agreement with the original objective of the hedging rules, the adaptive policy suggested in this study holds promise and may be utilized in further reservoir management with an increase of potential drought risk from climate change.

     

Risk Evaluation of Water Shortage in Source Area of Middle Route Project for South-to-North Water Transfer in China

Water diversion causes changes in the downstream flow regime, which may intensify the crisis of water shortage. The effect of the diversion on water shortage depends on the volumes of water transferred and water demand of source area, the upstream inflow and the way the reservoir is operated. This paper reports the findings of a study to assess the impact of water diversion from Danjiangkou reservoir on middle and lower Hanjiang River, part of the source area of South-to-North Water Transfer Project, China. The risk evaluation model consists of four parts, including inflow generation, water demand, simulation, and performance evaluation. Thomas?CFiering model and Mont-Carlo method are utilized to simulate monthly reservoir inflow data and a 12-dimensional random vector is used to describe the 12-month water demand in middle and lower Hanjiang River. A reservoir simulation model is established for optimum operation of Danjiangkou reservoir. Several scenarios including different water diversion scales are run by the risk evaluation model, whose outputs provide valuable information for decision making.     

Three Level Rule Curve for Optimum Operation of a Multipurpose Reservoir using Genetic Algorithms

Finding optimal policies for real-life reservoir systems operation (RSO) is a challenging task as the available analytical methods cannot handle the arbitrary functions of the problem. Most of the methods employed are numerical or iterative type and are computer dependent. Since the computer resources in terms of memory and CPU time are limited efficient algorithms are necessary to deal with the RSO problems. In this paper we present a Genetic Algorithms (GA) optimized rule curve (RC) model for monthly operation of a multipurpose reservoir which maximizes hydropower produced while meeting the irrigation demands with a given reliability. Instead of the usual single target storage for each period the proposed model considers three sets of target storages, namely dry, normal, and wet storages, based on the beginning of the period storage level. The reservoir considered is Bhadra Multipurpose Reservoir, in the state of Karnataka, India, which supplies water to irrigation fields through two canals while generating hydropower with turbines installed at each of the canal heads and at the river bed. Optimization ability and robustness of GA-RC approach are ascertained through simulation with a different inflow sequence for which global optimum is computed using Dynamic Programming. Further, a 15 year real-time simulation of the reservoir using historical inflows and demands showed significant improvement in the benefit, i.e. power produced, without compromising on the irrigation demands throughout the operation period.

     

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Ihk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。     

Use of a Multi-Objective Correlation Index to Analyze the Power Generation,Water Supply and Ecological Flow Mutual Feedback Relationship of a Reservoir

Hitherto, there has been insufficient work to quantify the degree of mutual feedback among the different objectives of multi-objective reservoir operation. The purpose of this study is to use a Multi-objective Correlation Index (MCI) to quantitatively analyze the complicated relationship of a multi-objective reservoir operation under changing inflow and water demands. First, an improved maximum probability density function method has been used to generate the adaptive ecological flow thresholds in long-term, wet-year, normal-year and dry-year inflow scenarios. Based on these ecological flow thresholds, a multi-objective reservoir optimal operation including the three objectives of power generation, water supply and ecology is constructed to research the tradeoff relationship among the objectives. Moreover, using the optimal solution set as determined by the Progressive Optimality Algorithm-Particle Swarm Optimization (POA-PSO) algorithm, the MCI is used to quantify the degree of the tradeoffs and their trends in responding to the changing conditions. The results show that the synergistic degree between the water supply and ecological objective decreases when the climatic condition changes from wet years to dry years, while the conflicting degree of power generation with respect to water supply or ecological objectives increases. Furthermore, the major degree of tradeoffs changes from the power generation-ecological flow objective pair to the power generation-water supply objective pair. In general, the MCI is able to quantify the extent and characteristics of the tradeoffs between different objectives. Hence, this index is useful for managers to make more informed and transparent decisions.

     

Value of Medium-range Precipitation Forecasts in Inflow Prediction and Hydropower Optimization

This paper presents an inflow-forecasting model and a Piecewise Stochastic Dynamic Programming model (PSDP) to investigate the value of the Quantitative Precipitation Forecasts (QPFs) comprehensively. Recently medium-range quantitative precipitation forecasts are addressed to improve inflow forecasts accuracy. Revising the Ertan operation, a simple hydrological model is proposed to predict 10-day average inflow into the Ertan dam using GFS-QPFs of 10-day total precipitation during wet season firstly. Results show that the reduction of average absolute errors (ABE) is of the order of 15% and the improvement in other statistics is similar, compared with those from the currently used AR model. Then an improved PSDP is proposed to generate monthly or 10-day operating policies to incorporate forecasts with various lead-times as hydrologic state variables. Finally performance of the PSDP is compared with alternative SDP models to evaluate the value of the GFS-QPFs in hydropower generation. The simulation results demonstrate that including the GFS-QPFs is beneficial to the Ertan reservoir inflow forecasting and hydropower generation dispatch.