Optimization of Exclusive Release Policies for Hydropower Reservoir Operation by Using Genetic Algorithm

The efficient utilization of hydropower resources play an important role in the economic sector of power systems, where the hydroelectric plants constitute a significant portion of the installed capacity. Determination of daily optimal hydroelectric generation scheduling is a crucial task in water resource management. By utilizing the limited water resource, the purpose of hydroelectric generation scheduling is to specify the amount of water releases from a reservoir in order to produce maximum power, while the various physical and operational constraints are satisfied. Hence, new forms of release policies namely, BSOPHP, CSOPHP, and SHPHP are proposed and tested in this research. These policies could only use in hydropower reservoir systems. Meanwhile, to determine the optimal operation of each policy, real coded genetic algorithm is applied as an optimization technique and maximizing the total power generation over the operational periods is chosen as an objective function. The developed models have been applied to the Cameron Highland hydropower system, Malaysia. The results declared that by using optimal release policies, the output of power generation is increased, while these policies also increase the stability of reservoir system. In order to compare the efficiency of these policies, some reservoir performance indices such as reliability, resilience, vulnerability, and sustainability are used. The results demonstrated that SHPHP policy had the highest performance among the tested release policies.     

Performance of the Equivalent Reservoir Modelling Technique for Multi-Reservoir Hydropower Systems

This paper investigates the validity of a simplified equivalent reservoir representation of a multi-reservoir hydroelectric system for modelling its optimal operation for power maximization. This simplification, proposed by Arvanitidis and Rosing (IEEE Trans Power Appar Syst 89(2):319–325, 1970), imputes a potential energy equivalent reservoir with energy inflows and outflows. The hydroelectric system is also modelled for power maximization considering individual reservoir characteristics without simplifications. Both optimization models employed MINOS package for solution of the non-linear programming problems. A comparison between total optimized power generation over the planning horizon by the two methods shows that the equivalent reservoir is capable of producing satisfactory power estimates with less than 6% underestimation. The generation and total reservoir storage trajectories along the planning horizon obtained by equivalent reservoir method, however, presented significant discrepancies as compared to those found in the detailed modelling. This study is motivated by the fact that Brazilian generation system operations are based on the equivalent reservoir method as part of the power dispatch procedures. The potential energy equivalent reservoir is an alternative which eliminates problems with the dimensionality of state variables in a dynamic programming model.     

Risk assessment of optimal firm water and energy supply from hydroelectric plants

When developing optimal reservoir operating policies it is desired to specify beneficial and reliable supply levels. The presented pre‐contract study of the operation of a hydroelectric plant concerns the estimation of optimal annual firm water and energy supply levels such that there is a tolerable risk or probability that the contract will be violated and shortages will occur. Control over operational reliability is successfully accomplished by incorporating into the optimization problems stochastic analysis techniques. The stochastic mathematical model employed was successfully applied to the reservoir system of the Palialona hydroelectric plant on the Aliakmon river in northwestern Greece, improving the estimation of the optimal annual release policy.     

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.     

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.

     

Enhancing Inflow Forecasting Model at Aswan High Dam Utilizing Radial Basis Neural Network and Upstream Monitoring Stations Measurements

The Nile River is considered the main life artery for so many African countries especially Egypt. Therefore, it is of the essence to preserve its water and utilize it very efficiently. Developing inflow-forecasting model is considered the technical way to effectively achieve such preservation. The hydrological system of the Nile River under consideration has several dams and barrages that are equipped with control gates. The improvement of these hydraulic structures’ criteria for operation can be assessed if reliable forecasts of inflows to the reservoir are available. Recently, the authors developed a forecasting model for the natural inflow at Aswan High Dam (AHD) based on Artificial Intelligence (AI). This model was developed based on the historical inflow data of the AHD and successfully provided accurate inflow forecasts with error less than 10%. However, having several forecasting models based on different types of data increase the level of confidences of the water resources planners and AHD operators. In this study, two forecasting model approach based on Radial Basis Function Neural Network (RBFNN) method for the natural inflow at AHD utilizing the stream flow data of the monitoring stations upstream the AHD is developed. Natural inflow data collected over the last 30 years at four monitoring stations upstream AHD were used to develop the model and examine its performance. Inclusive data analysis through examining cross-correlation sequences, water traveling time, and physical characteristics of the stream flow data have been developed to help reach the most suitable RBFNN model architecture. The Forecasting Error (FE) value of the error and the distribution of the error are the two statistical performance indices used to evaluate the model accuracy. In addition, comprehensive comparison analysis is carried out to evaluate the performance of the proposed model over those recently developed for forecasting the inflow at AHD. The results of the current study showed that the proposed model improved the forecasting accuracy by 50% for the low inflow season, while keep the forecasting accuracy in the same range for the high inflow season.     

基于MB80 PLC的水电厂油泵控制系统

介绍了水电厂油泵控制系统的原理、组成及具体控制方式,同时通过工程实践证明了将油泵控制系统纳入机组可编程逻辑控制器(PLC)控制中的可行性.此方案对中小型水电厂实现类似系统的控制有一定参考价值.     

Two-step daily reservoir inflow prediction using ARIMA-machine learning and ensemble models

The reservoirs play a crucial role in the development of civilisation as they facilitate the storage of water for multiple purposes like hydroelectric power generation, flood control, irrigation, and drinking water etc. In order to effectively meet these multiple purposes, the knowledge of the inflow in the reservoir is essential. Apart from the historical data, future prediction of the inflows is also necessary specially in context of climate change. A two-step algorithm for the prediction of reservoir inflow to enable meticulous planning and execution of daily reservoir operation keeping the historical variation of inflow in account has been proposed. The developed algorithm takes into account the patterns in the historic inflow data using the time series analysis along with the variability in the climatic patterns using the different predictors in the machine learning model. The first step uses time series model, ARIMA method to forecast the monthly inflows, which are then used as the targets in the second step for the month-wise daily forecasting of the inflows using the two types of ensemble models, namely, averaging and boosting models in machine learning. The test results show that for both the monthly models and daily models the NRMSE and NMAE values were low for the monsoon periods compared to the non-monsoon periods. The averaging ensemble models were found to perform better than the boosting ensemble models for maximum number of months. The yearly results show an error of less than 5% between actual and predicted values for all the test cases, showing the precision in the developed algorithm. Further, the uncertainty analysis shows that the prediction done using the weighted average of the different inflow scenarios performs better than the prediction against the single inflow scenario.     

上墅阮水电站的技术改造

针对上墅阮水电站水轮发电机组设备老化、效率低、输出功率不足等问题,经分析评价确定电站技术改造方案为更换水轮机转轮和发电机线圈,并考虑汛期弃水适当增加机组容量.通过技改,1#机组水轮机效率提高约10%,机组容量从160kW提高到200kW;2#机组水轮机效率提高约2%,机组容量从90kW提高到100kW.电站改造后运行效果良好,1#水轮发电机组输出功率可达到210kW,2#水轮发电机组输出功率可达到115kW,发电量比改造前增加了20%.     

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.     

Simulating Reservoir Management under the Threat of Sedimentation: The Case of Tarbela Dam on the River Indus

The useful life of Tarbela reservoir, on the River Indus, is threatened by a sediment delta which is approaching the dam'sintake tunnels; these lead to a hydroelectric power station and are used for irrigation releases. This article describes thesimulated system, involving Tarbela Dam, with Ghazi Barotha hydropower scheme downstream, and the planned construction of Basha Dam upstream. This study formed an innovative approachwhich enabled the relationship between demands and supply in the basin to be studied, under a range of development and operating scenarios, and to different time horizons. A computer software package, 'Hydro', was used to perform systemsimulation modelling of Tarbela Dam and the Upper Indus Basin,Pakistan. The results enabled estimation of the economic benefits of several potential future operating strategies forTarbela to be tested and compared. Employing the results of associated sediment modelling, projected storage/elevation curves were used to predict the irrigation and power benefitsavailable to Pakistan over the coming sixty years. It wassubsequently demonstrated that the most beneficial remedial measures are an underwater dike or dam to protect the intakes,and low-level flushing facilities.     

Implementation of Strategies for the Management of Dams with Sedimented Reservoirs

Dams accumulate sediment by interrupting the continuity of rivers, resulting in a loss of reservoir water storage capacity and decreased productive life. These issues raise a growing concern about the decreasing benefits of projects. This paper contributes to the implementation of sediment transit strategies and operating rules of reservoirs to reduce overflows and recover the technical–economic viability of sedimented reservoirs by maintaining ecological flow. The main difficulty lies in the fact that sedimentation of the reservoir limits the mobility of dredging equipment and blocks the intake. To regain the viability of the reservoir, the commonly used strategies to manage water resources and reservoir sedimentation were analyzed. To control reservoir sedimentation and restore the generation capacity, different sediment management strategies were implemented and evaluated at the entrance, body of the reservoir and intake; these strategies included reduction of the entry of sediments, restoration of the storage capacity, clearing of the water intake for the turbines to restore power generation, trash rack cleaning during the power generation process and modification of the hydroelectric power plant operating rules to optimize the economic income. The implemented strategies successfully reduced overflows from 88 to 40% in 3 years and stabilized the reservoir storage capacity by balancing the inflow and removal of sediments. Although the water intake for the turbines was cleaned, accumulation increased in other areas of the reservoir. Finally, root cause analysis (RCA) was employed, and solutions were proposed to increase the capacity of the reservoir and reduce overflows to 15%.

     

金沙江下游至三峡-葛洲坝梯级水库群 发电联合调度增益机制分析

金沙江下游溪洛渡、向家坝至三峡、葛洲坝四库梯级为长江流域水能资源调控重点枢纽工程,开展四库发电联合优化调度效益显著。研究建立四库系统独立发电、联合两种模式下优化调度模型,采用长系列径流系列计算发电效益增益,统计增益的时间、年型、空间分布特征。提出基于全微分法的增益占比析因方法辨识发电增益受发电水量、水头影响贡献占比,归纳增益产生机制。结果表明:(1)消落期发电增益集中产生在5月份;向家坝、葛洲坝为主要受益水库。(2)联合增益的产生原因在于联合调度过程中溪洛渡、三峡均化了5月、6月放水过程,降低向家坝、葛洲坝的弃水量,抬升两库水头,增加发电效益。(3)向家坝的补偿增益受发电水量、水头贡献占比相当,葛洲坝的补偿增益主要以发电水头抬升的水头效益为主,溪洛渡、三峡的效益变化主要受联合调度过程中水头变化影响。     

Corrected correlations between the capacity and levels of the Ust'-Khantaika hydroelectric power plant

Using the Ust'-Khantaika hydraulic power system as an example, results are presented from investigations to obtain corrected relationships between the capacity and water levels of the reserve by taking into account the increase in the capacity as a result of deformation of the bed of the reservoir and banks associated with defrosting of permafrost rock. A precise curve expressing the correlation V=f(z) of the reservoir during its lifetime is needed to arrive at a proper selection of the optimal operating mode of the hydroelectric power plant. Recommendations are presented for further investigations intended to verify the precise dependences of the capacity on the levels of the reservoir to correct the hydroeconomic and hydropower indicators of the Ust'-Khantaika reservoir.Translated from Gidrotekhnicheskaya Stroitel'stvo, No. 3, pp. 19–23, March, 1995.     

A Study on the Large-scale System Decomposition—Coordination Method Used in Optimal Operation of a Hydroelectric System

Abstract

The optimal operation problem of multiple hydroelectric reservoir systems is very complex because of the large dimensions. Large-scale system decomposition-coordination methods, which can simplify complex problems into several interrelated sub-problems to avoid the “curse of dimensionality” and to obtain the global optimum on the global through coordination among sub-systems, is particularly well suited for optimizing large-scale, multi-reservoir systems. Applying this kind of theory and method, this paper studies and analyzes the problems of optimal operation of multiple hydroelectric reservoir systems in series, and sets up the optimal operation model of hydroelectric reservoir systems in series. On this basis, a practical example of two hydroelectric reservoirs in series on the upper reaches of the Yellow River in China is calculated and analyzed and the results are satisfactory. It is believed that applying this model can cut down the dimensions of the problem notably and that the theory and method are effective for real time operation.     

Evaluation and selection of hedging policies using stochastic reservoir simulation

A hedging policy is characterized by three parameters, namely, starting water availability (SWA), ending water availability (EWA) and hedging factor (HF). The effects of these three parameters on the reservoir performance indicators have been evaluated and discussed for a southwest monsoon-dependent within-year reservoir system in southern India. For the performance evaluation, synthetically generated periodic inflow sequences from a periodic autoregressive model have been used. Quite a number of the 1800 hedging policies considered for the reservoir system, yield a better overall performance compared to the standard operating policy (SOP). Reliability, Resilience and vulnerability are found to increase with SWA for a specified EWA. On the other hand, all these performance indicators are found to decrease with EWA for a specified SWA. Hence, it is desirable to start the hedging at reasonably high SWA. All performance indicators remain practically constant at higher ranges of EWA for a given SWA. If hedging is started when there is enough water in storage, reliability, resilience and average deficit increase with degree of hedging, whereas vulnerability decreases significantly up to a hedging factor of 0.3. An interactive computer program has been developed for the selection of compromising hedging policies, and its usefulness has been discussed.     

A numerical study of pumping effects on flow velocity distributions in Mosul Dam reservoir using the HEC‐RAS model

Water flow direction and velocity affect and controls erosion, transport and deposition of sediment in rivers, reservoirs and different hydraulic structures. One of the main structures affected is pumping stations within the dams wherein the velocity distribution near the station intake is disturbed. The two‐dimensional (2‐D) HEC‐RAS 5.01 model was utilized to study, analyse and evaluate the effects of pumping rates and flow depth on the flow velocity distribution, flow stream power and their effects in the Mosul Dam reservoir. The pumping station was considered as a case study. The station is suffering from sediment accumulation around, and in, its intake and suction pipes. The main inflow sources to the reservoir are the Tigris River and run‐off from the valleys within its basin. The reservoir was divided into two parts for the present study, including the upper part near the pumping station (analysed as a two‐dimensional zone), while the lower part was analysed as a one‐dimensional flow to reduce the simulation period computation time (1986–2011). Different operation plans (i.e. pumping rate and water depth) were considered. The results of the depth‐averaged velocity model indicated that when the pumping station was working at a range from the designed full capacity (100% to 25% of its full capacity), the maximum flow velocity increased from 75 to 4 times the normal velocity when there is no pumping dependent on pumping rate and flow depth. For the same operation plans, the flow stream power varied from around zero values to 400 times at full pumping capacity and low flow depth. For sediment routing along the reservoir, the considered statistical criteria indicated the model performance in estimating the total sediment load deposition and invert bed level is much better than in the case of erosion and deposition areas for different considered bed sections of the reservoir.     

Applying instream flow incremental method for the spawning habitat protection of Chinese sturgeon (Acipenser sinensis)

The Chinese sturgeon, Acipenser sinensis, is an anadromous species that spawns in the Yangtze River and Pearl River of China. Its population has declined dramatically since the construction of the Gezhouba Dam (GD) in 1981 and then with the impoundment of the Three Gorges Dam (TGD) upstream of the GD in 2003. This paper presents a quantitative method based on the instream flow incremental method to explore the relationship between the fish spawning habitat and the operations of the GD and TGD, aiming to find a solution for conservation of the species. A two‐dimensional hydrodynamic model was built with the River2D to simulate the hydraulic behaviour of the stream below the GD. Habitat suitability index was determined by the biological data of the fish collected in the field. The two parts were then integrated through a geographical information system developed via ArcGIS to outline the fish habitat area variation with flows. The decision support system is applied to set up a habitat time series for validating the assumption that more habitats have the potential to support more fish. The fish habitat results for alternative instream flow schemes are then compared with one another for defining the optimal flow requirements and evaluating effects of reservoir operation alternatives in order to improve the operation management for the GD and TGD projects. The results show that the optimal flow for spawning of the fish is about 7000–13000 m³/s and the optimal inlets combination is where the inflow comes from two power plants. Copyright © 2009 John Wiley & Sons, Ltd.     

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。     

Long-Term Planning of Water Systems in the Context of Climate Non-Stationarity with Deterministic and Stochastic Optimization

Seasonal inflow variability, climate non-stationarity and climate change are matters of concern for water system planning and management. This study presents optimization methods for long-term planning of water systems in the context of a non-stationary climate with two levels of inflow variability: seasonal and inter-annual. Deterministic and stochastic optimization models with either one time-step (intra-annual) or two time-steps (intra-annual and inter-annual) were compared by using three water system optimization models. The first model used one time-step sampling stochastic dynamic programming (SSDP). The other models with two time-steps are long-term deterministic dynamic programming (LT-DDP) and long-term sampling stochastic dynamic programming (LT-SSDP). The study area is the Manicouagan water system located in Quebec, Canada. The results show that there will be an increase of inflow to hydropower plants in the future climate with an increase of inflow uncertainty. The stochastic optimization with two time-steps was the most suitable for handling climate non-stationarity. The LT-DDP performed better in terms of reservoir storage, release and system efficiency but with high uncertainty. The SSDP had the lowest performance. The SSDP was not able to deal with the non-stationary climate and seasonal variability at the same time. The LT-SSDP generated operating policies with smaller uncertainty compared to LT-DDP, and it was therefore a more appropriate approach for water system planning and management in a non-stationary climate characterized by high inflow variability.