Real-Time Operation of Reservoir System by Genetic Programming

Reservoir operation policy depends on specific values of deterministic variables and predictable actions as well as stochastic variables, in which small differences affect water release and reservoir operation efficiency. Operational rule curves of reservoir are policies which relate water release to the deterministic and stochastic variables such as storage volume and inflow. To operate a reservoir system in real time, a prediction model may be coupled with rule curves to estimate inflow as a stochastic variable. Inappropriate selection of this prediction model increases calculations and impacts the reservoir operation efficiency. Thus, extraction of an operational policy simultaneously with inflow prediction helps the operator to make an appropriate decision to calculate how much water to release from the reservoir without employing a prediction model. This paper addresses the use of genetic programming (GP) to develop a reservoir operation policy simultaneously with inflow prediction. To determine a water release policy, two operational rule curves are considered in each period by using (1) inflow and storage volume at the beginning of each period and (2) inflow of the 1^st, 2^nd, 12^th previous periods and storage volume at the beginning of each period. The obtained objective functions of those rules have only 4.86 and 0.44?% difference in the training and testing data sets. These results indicate that the proposed rule based on deterministic variables is effective in determining optimal rule curves simultaneously with inflow prediction for reservoirs.     

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.     

串联供水库群优化调度的模糊规划模型

针对随机动态规划在解决多个水库联合优化调度时存在“维数灾”问题,尝试基于模糊集理论来解决该优化调度问题。以4个串联供水水库系统为例,目标为各供水片区最小的缺水率最大,将水库的入流过程视为模糊集,而需水过程视为确定性的,建立了模糊规划模型,并引入可靠度和满意度对优化调度结果进行评价。实例分析表明,该模型既可以刻画入流的不确定性,又可以简化问题,具有一定的实用性。     

Reservoir operation policies for optimizing energy generation at the Shiroro Dam

Reservoir water release policies are computed for the Shiroro Dam hydroelectric power scheme in Northern Nigeria, using a probabilistic dynamic programming model. The state variable is the reservoir storage volume, while the uncertain nature of the inflow process is accounted for in the model by considering different possible inflow volumes and their inflow probabilities. Simulation of the reservoir operations with the derived policies show that on the average the hydrosystem has acceptable reliability when two units are in use, at 45% design power plant factor. At 70% power plant factor, which is the desired optimum for the power system in Nigeria, system failures are frequent and, in most cases, severe. For normal operation of the Shiroro Dam hydroelectric power system, two or three generating units, running at 40–50% power plant factor is recommended.     

随机约束线性规划在水库综合利用中的应用

由于水库入流的不确定性，各用水目标的基本要求（目标放水量）将体现在年内各时期水库放水的随机约束上，配合水库线民生蓄泄水决策规则，将全部随机约束进行确定性等效转换，得到线性规划模型，经多次解析，就可得到水主加容量一定情况下的最优运行规则，针对大型水资源工程综合利用的多目标要求，研究建立了随机约束线性规划模型，以求解水库最优运行规划的方法，并以西南地区某大型综合利用水库为例，对模型进行求解，该方法随机     

An Interactive Management System for Operational Control of Kirazdere Reservoir (Turkey)

This article presents a prototype, user-interactive computing system for the operational management of the Kirazdere reservoir in Turkey. The aim is to make best use of the full storage capacity of the reservoir for meeting the contractual water-supply obligations as consistently as possible, having regard to the possible risk of downstream flooding. In determining the amount to be released for supply purposes in any one month, the system takes account of the current storage level and predicted inflow. In doing so, the system incorporates the prevailing situation with water-release rules previously derived using a combination of optimisation and simulation-based scenarios analyses, thereby providing a comprehensive analytical capability which is easy to manipulate. For each month, the system provides the operator with a proposed release schedule as well as the opportunity to explore the consequences of alternatives, in terms of the reservoir performance criteria. An important feature of the system is the ability to quickly evaluate a priori, the impacts of different release decisions over an extended period of time for a variety of possible inflow scenarios, including extreme drought and flood periods, helping the operator to establish the robustness of a particular operating policy. Whilst the system has been specifically designed to support the operation of the Kirazdere reservoir, its extension to include a more general class of reservoir management problems would be comparatively simple.     

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。     

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 flood control operation model incorporating multiple uncontrolled water flows

This study presents a weighted pre‐emptive goal programming model formulation for coordinated reservoir operation, with easy inclusion of uncontrolled water flows. The model is combined with a multiple water inflows forecasting model, and can be used for real time reservoir operation. Water flow routing from various upstream sites is accounted by with a single compact equation. Integration of controlled and uncontrolled water flows in the optimization model simplifies the operation model, resulting in accurate computation of the downstream water flow. Multiple objectives with water storage and flow variables are used to derive optimal regulation for a reservoir system under flood conditions. For real time operations, the model can be used to determine optimal water release rates for a current period, on the basis of an optimal water release schedule for an operating horizon (T). The model is applied to the flood control operation of reservoirs in the Narmada River Basin (India), with three controlled and three uncontrolled water flows affecting the downstream flow at Hoshangabad. Reservoir water storage and downstream control point flows are zoned, with prioritized objectives used to derive the optimal water release rates. Model applications to the 1999 flood event in the Narmada River Basin with observed and forecasted inflows illustrates that, if water inflows were known through a forecasting technique well in advance, the coordinated operation of the reservoirs could substantially reduce the peak water flows at the control points. The study also indicates that uncontrolled channel flows at the damage site were sufficiently high to cause flooding at the damage site.     

刘家峡水库对水沙变异影响分析

利用刘家峡水库运行前后进出库控制站的水文资料,对水库运行以来的径流泥沙特征进行了分析。研究表明:(1)水库运行对进出库年径流量影响很小,对年内分配影响大;(2)单库运行期水库蓄水拦沙,出库洪峰流量削减,年内径流量分配发生变化,泥沙量大幅度减少;(3)龙羊峡水库运行后的联合运行期,洪峰流量略有削减,年内蓄水过程改变后仍有一定的拦沙作用,出库含沙量较单库运行期增加;(4)水库运行减小了出库含沙量,并没有改变出库含沙量与入库含沙量的线性关系。     

Optimal Operation of a Multi-Purpose Reservoir Using Neuro-Fuzzy Technique

Present paper is aimed to develop operation policy for a multi-purpose reservoir using Neuro-Fuzzy technique in an efficient way. Ramganga reservoir behind Ramganga dam, Kalagarh, India has been considered as a study reservoir. The developed policy minimizes the damage due to floods and droughts and determines optimum releases against demands for domestic supply, irrigation and hydropower generation for monsoon and non-monsoon periods. Three Fuzzy Rule Based (FRB) models for monsoon period and three for non monsoon period have been developed and tested. Actual releases have been used to formulate the general operation fuzzy rules. Releases computed from all developed models using Fuzzy Mamdani (FM) and ANFIS (Adaptive Neuro-Fuzzy Interactive System) – Grid and Cluster have been compared and it was found that ANFIS-cluster gives the best results but FM is more users friendly. For any expected inflow, reservoir level and demand, release can be calculated using developed GUI windows of the models.     

Improving Implicit Stochastic Reservoir Optimization Models with Long-Term Mean Inflow Forecast

This technical note introduces a reservoir operation model based on implicit stochastic optimization (ISO) in which the release policy is guided by the forecast of the mean inflow for a given future horizon rather than by the prediction of the current-month inflow, such as in typical ISO models. The model also does not require the forecast of all inflows for the future horizon and shows to be more efficient in finding less vulnerable release policies when compared to several other explicit and implicit stochastic procedures.     

Evaluation of Real-Time Operation Rules in Reservoir Systems Operation

Reservoir operation rules are logical or mathematical equations that take into account system variables to calculate water release from a reservoir based on inflow and storage volume values. In fact, previous experiences of the system are used to balance reservoir system parameters in each operational period. Commonly, reservoir operation rules have been considered to be linear decision rules (LDRs) and constant coefficients developed by using various optimization procedures. This paper addresses the application of real-time operation rules on a reservoir system whose purpose is to supply total downstream demand. Those rules include standard operation policy (SOP), stochastic dynamic programming (SDP), LDR, and nonlinear decision rule (NLDR) with various orders of inflow and reservoir storage volume. Also, a multi-attribute decision method, elimination and choice expressing reality (ELECTRE)-I, with a combination of indices, objective functions, and reservoir performance criteria (reliability, resiliency, and vulnerability) are used to rank the aforementioned rules. The ranking method employs two combinations of indices: (1) performance criteria and (2) objective function and performance criteria by using the same weights for all criteria. Results show that the NLDR gives an appropriate rule for real-time operation. Moreover, NLDR validation is presented by testing predefined curves for dry, normal, and wet years.     

Artificial Neural Network and Support Vector Machine Models for Inflow Prediction of Dam Reservoir (Case Study: Zayandehroud Dam Reservoir)

Inflow prediction of reservoirs is of considerable importance due to its application in water resources management related to downstream water release planning and flood protection. Therefore, in this research, different new input patterns for predicting inflow to Zayandehroud dam reservoir is proposed employing artificial neural network (ANN) and support vector machine (SVM) models. Nine different models with different patterns of input data such as inflow to the dam reservoir considering time duration lags, time index, and monthly rainfall of Ghaleh-Shahrokh station have been proposed to predict the inflow to the dam reservoir. Comparison of the results indicates that the ninth proposed model has the least error for inflow prediction in which the results of SVM model outperform those of ANN model. That is, the least error has been obtained using the ninth SVM (ANN) model with correlation coefficient (R) values of 0.8962 (0.89296), 0.9303 (0.92983) and 0.9622 (0.95333) and root mean squared error (RMSE) values of 47.9346 (48.5441), 42.69093 (43.748) and 23.56193 (28.5125) for training, validation and test data, respectively.

     

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.

     

Modeling the Yield–Evaporation–Spill in the Reservoir Storage Process: The Regulation Triangle Diagram

From the dimensionless reservoir water budget equation, a graphical method to model the yield–spill–evaporation loss trade-off in the reservoir storage process was built. The reservoir inflows were transformed into three parts that sum to the total mean inflow for long-term operation: evaporation, spill and yield. A regulation triangle diagram (RTD) has been proposed to provide a better understanding of the reservoir storage process as a function of reservoir capacity, hydrological river regime, evaporation and reservoir morphology. The inflows were assumed to be serially uncorrelated and to originate from a Gamma probability distribution function. The diagrams were developed using the Monte Carlo method, while the graphics were developed for intermittent rivers with a coefficient of variation of annual inflows that ranges from 0.6 to 1.6. In the model, the reservoir is a single over-year system, and the values are referenced to the steady state conditions.     

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。     

水库群供水调度预警系统研究及应用

针对水库供水调度的实时性和不确定性,通过分析入库径流超越机率,以缺水率最小为目标函数,建立水库群供水调度模型,并应用基于免疫进化的蚁群算法对模型进行求解,绘制水库群供水调度操作规线;应用模糊数学、信息熵等原理,确定现状水库供水指标D、未来供水水情指标S以及水库供水预警指标和应变措施,建立了水库群供水调度预警系统。实例计算表明,本文建立的水库供水预警系统能够合理、有效地为调度操作人员提供正确、及时的决策。     

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.     

An Optimal Operation Model for Hydropower Stations Considering Inflow Forecasts with Different Lead-Times

To make full use of inflow forecasts with different lead times, a new reservoir operation model that considers the long-, medium- and short-term inflow forecasts (LMS-BSDP) for the real-time operation of hydropower stations is presented in this paper. First, a hybrid model, including a multiple linear regression model and the Xinanjiang model, is developed to obtain the 10-day inflow forecasts, and ANN models with the circulation indexes as inputs are developed to obtain the seasonal inflow forecasts. Then, the 10-day inflow forecast is divided into two segments, the first 5 days and the second 5 days, and the seasonal inflow forecast is deemed as the long-term forecast. Next, the three inflow forecasts are coupled using the Bayesian theory to develop LMS-BSDP model and the operation policies are obtained. Finally, the decision processes for the first 5 days and the entire 10 days are made according to their operation policies and the three inflow forecasts, respectively. The newly developed model is tested with the Huanren hydropower station located in China and compared with three other stochastic dynamic programming models. The simulation results demonstrate that LMS-BSDP performs best with higher power generation due to its employment of the long-term runoff forecast. The novelties of the present study lies in that it develops a new reservoir operation model that can use the long-, medium- and short-term inflow forecasts, which is a further study about the combined use of the inflow forecasts with different lead times based on the existed achievements.