Optimal Reservoir Operation Research Articles

Extraction and application of energy storage operation chart in Yangtze River cascade reservoirs

Abstract Reservoir operation charts have been widely researched and applied to reservoir operation. However, these achievements are generally used for a single reservoir and have rarely been applied to cascade reservoirs. Considering the requirements of flood control and water supply, this paper studied the extraction and application of energy storage operation chart (ESOC) for cascade reservoirs. Steps in the methodology mainly include: (1) model building of cascade reservoirs operation optimization (CROO), (2) extracting ESOC based on discriminant coefficient method (DCM) and CROO model, (3) simulation operation of ESOC based on DCM, (4) choosing the optimal ESOC and verifying its efficiency through the results. Cascade reservoirs in the Yangtze River of China were selected for a case study. Compared with the conventional operation method, the simulation results show that the ESOC presents better performance in terms of power generation, guaranteed output and assurance rate. In detail, the annual power generation of ESOC can be increased by 0.9%, the total guaranteed output can be increased by 3.4% and the assurance rate can be increased by 9.6%, which indicates that the proposed ESOC method can greatly improve the hydropower energy efficiency and reliability of cascade reservoirs’ power supply.

The Improvement of Particle Swarm Optimization: a Case Study of Optimal Operation in Goupitan Reservoir

Due to the weakness in holding diversity and reaching global optimum, the standard particle swarm optimization has not performed well in reservoir optimal operation. To solve this problem, this paper introduces downhill simplex method to work together with the standard particle swarm optimization. The application of this approach in Goupitan reservoir optimal operation proves that the improved method had better accuracy and higher reliability with small investment.

Optimizing Irrigation Deficit of Multipurpose Cascade Reservoirs

Reservoirs play a strategic role in the rapid monetary growth of the world by providing numerous benefits. However, the reduction in appropriate sites along with environmental and social apprehensions has resulted in curtailment of new reservoirs around the world in twenty-first century. There is a potential of benefits available from existing reservoirs which can be best capitalized through their optimized operation. Reservoirs Operation Optimization considering Sediment Evacuation (RESOOSE), recently developed model which combines multiple reservoirs operation and sediment evacuation with Genetic Algorithm based optimization module, has been used in the study. The objective of the study was to optimize the irrigation deficit through cascade reservoirs with consideration to hydropower, sediment evacuation and flood damages reduction benefits. The RESOOSE model was applied to optimize the irrigation deficits of Tarbela and Diamer Basha Reservoirs in Pakistan using developed objective function. The article computed and compared the benefits of optimized and existing rule curves. The hydropower benefits of 36.92 Billion Kw, sediment evacuation benefits of 21.534 Million m3 and flood damages of 616.19 Million US$ due to existing rule curves were considered as minimum benefits for achieving the optimized rule curves to minimize irrigation deficits. The developed optimized rule curves reduced the irrigation shortages of case study reservoirs from 6.9 to 5.8 Billion m3 (16% enhancement) annually as compared to existing rule curves. The optimized rule curves minimized the irrigation deficits by maintaining the existing benefits and without lowering the minimum operating levels of case study reservoirs. The study suggests change in existing rule curves of Tarbela and Diamer Basha Reservoirs due to less irrigation shortages. The RESOOSE model can be applied to other cascade reservoirs for optimizing the rule curves.

Experimental study on the critical initiation of reservoir fluid mud motions

Reservoir fluid mud is the stagnant or near-stagnant suspension distributed at the bottom of a reservoir after variable-density flows. It consists of fine particles that flocculate easily, and its thickness can be maintained unchanged over a long time period. Because the formation and the movement of reservoir fluid mud are affected by various factors, and given that measured data relevant to the dynamic conditions of the reservoir fluid mud are lacking, current results can hardly explain scientific questions that have to be resolved. These include the development of reservoir fluid mud and its response mechanisms to subsequent floods. In this study, a pressurized sealed water flume is employed to simulate deep-water conditions and to facilitate the conduct of experiments on the initiation of reservoir fluid mud motions. The results demonstrate that the critical shear stress required for the initiation of motion of fluid mud increases exponentially as a function of the volumetric weight of fluid mud and water depth. The critical shear stress is much smaller than the Bingham yield stress and the two are associated according to a power-function relationship. The findings provide technical support for the utilization of the reservoir fluid mud and the optimization of reservoir operations.

Joint optimal flood control operation of large scale mixed reservoirs

With the reservoir construction gradually completed, joint operation of reservoir groups is an important measure to realize reservoir flood control potential,but when the river basin is large, the flood channel routing and time-lag cannot be simplified, in addition, the curse of dimensionality is very difficult for model solving With the expansion of the number of reservoirs. These factors restrict the application of joint operation of large scale mixed reservoirs.In this study, The DP-POA cyclic iterative algorithm which is based on large scale system decomposed-coordinating method was proposed to solve the optimal problem considering the flood routing and time-lag.The upper Yangtze River is selected as a case study. 6 reservoirs, including the Three Gorges Reservoir(TGR)、Xiangjiaba Reservoir(XJB)、Xiluodu Reservoir (XLD)、 Pubugou Reservoir (PBG)、Goupitan Reservoir (GPT) and Tingzikou Reservoir (TZK)are taken into accounts.Compared with current operation of reservoirs independent,results demonstrate that the method can effectively reduce the maximum operating water level of TGR and flood diversion in the lower reaches of the TGR. Therefore, the safety of the flood control points along the river has been largely improved based on the proposed method.

Optimization of Water Reservoir Operation to Minimize the Economic Losses Caused by Pollution

The pollution of river water as a result of an accident can cause different types of serious damages to people, aquatic life, agricultural land, plants, and animal ecosystems. Apart from tragic events such as deaths or injuries, the accidental water pollution causes important economic losses. One effective measure is to reduce the concentration of the pollutant in the river water by adding clean water released from reservoirs placed on tributaries of the river. In this paper, we focus on finding an optimal operation of the water reservoirs (such as opening/closing time of the gates) with the goal of minimizing the total cost of the economic damages. Two components of the total cost are defined and quantified: the cost of the water used to achieve dilution and the losses caused by the existence of the pollutant in a certain concentration in the water. This problem is formalized as a non-linear multi-objective simulation-optimization model, subject to constraints. The solution is evaluated for a real use case (Jijia River, respectively, Dracsani and Halceni reservoirs in North–East Romania). The results are presented for two experiments. They show that by using the proposed optimization solution, the total cost of economic losses may be reduced by almost six times as compared with an empirical operation of the reservoirs and by almost $10\times $ if no dilution takes place.

Application of Hybrid Particle Swarm Optimization Algorithm in the Optimal Water Operation of Cascade Reservoirs in Dry Season

In this paper, a hybrid particle swarm optimization (HPSO) algorithm is proposed to solve the problem of optimal water operation of cascade reservoirs in dry season. Based on the basic particle swarm optimization (PSO) algorithm, chaos algorithm is introduced to traverse the search space to generate the initial population and improve the global searching ability of the algorithm. A self-adaptive inertial weighting method based on optimized inertial weighting coefficient is adopted to improve the ability of particle individual search and avoid local optimum. The proposed algorithm is applied to the optimal water operation in dry season of cascade reservoirs on the mainstream of Xijiang River. The results show that the HPSO algorithm can effectively improve the guarantee degree of ecological flow and suppressing salinity flow in the control reach of Wuzhou station under different typical dry year scenarios.

Comparative Study of Optimization Algorithms for The Optimal Reservoir Operation

Apart from traditional optimization techniques, e.g. progressive optimality algorithm (POA), modern intelligence algorithms, like genetic algorithms, differential evolution have been widely used to solve optimization problems. This paper deals with comparative analysis of POA, GA and DE and their applications in a reservoir operation problem. The results show that both GA and DES are feasible to reservoir operation optimization, but they display different features. GA and DE have many parameters and are difficult in determination of these parameter values. For simple problems with mall number of decision variables, GA and DE are better than POA when adopting appropriate parameter values and constraint handling methods. But for complex problem with large number of variables, POA combined with simplex method are much superior to GA and DE in time-assuming and quality of optimal solutions. This study helps to select proper optimization algorithms and parameter values in reservoir operation.

Studying Operation Rules of Cascade Reservoirs Based on Multi-Dimensional Dynamics Programming

Although many optimization models and methods are applied to the optimization of reservoir operation at present, the optimal operation decision that is made through these models and methods is just a retrospective review. Due to the limitation of hydrological prediction accuracy, it is practical and feasible to obtain the suboptimal or satisfactory solution by the established operation rules in the actual reservoir operation, especially for the mid- and long-term operation. In order to obtain the optimized sample data with global optimality; and make the extracted operation rules more reasonable and reliable, this paper presents the multi-dimensional dynamic programming model of the optimal joint operation of cascade reservoirs and provides the corresponding recursive equation and the specific solving steps. Taking Li Xianjiang cascade reservoirs as a case study, seven uncertain problems in the whole operation period of the cascade reservoirs are summarized after a detailed analysis to the obtained optimal sample data, and two sub-models are put forward to solve these uncertain problems. Finally, by dividing the whole operation period into four characteristic sections, this paper extracts the operation rules of each reservoir for each section respectively. When compared the simulation results of the extracted operation rules with the conventional joint operation method; the result indicates that the power generation of the obtained rules has a certain degree of improvement both in inspection years and typical years (i.e., wet year; normal year and dry year). So, the rationality and effectiveness of the extracted operation rules are verified by the comparative analysis.

Optimal reservoir operation using soil and water assessment tool and genetic algorithm

Recent research and literature in the area of water resources demonstrated applications of modern tool of operation research and system analysis for assisting in development, planning, and management of water resources project. This study aims to derive equitable management of water resources from study area to fulfill various demands in command area. GIS-integrated SWAT model has been used to calculate the inflows for case study. The performance of model is evaluated by NS, R2, and RSR which are found to be 0.77, 0.79, 0.48 for calibration and 0.82, 0.83, 0.42 for validation, respectively, using SWAT-CUP. Further the multi-objective reservoir operation model is developed. The reservoir operation model considers two objectives: maximization of releases for irrigation and maximization of releases for hydropower generation. The applicability of model is demonstrated through the case study of Jayakwadi reservoir stage I, Maharashtra State, India. The multipurpose reservoir operation model is solved under MATLAB environment using multi-objective optimization of genetic algorithm solver and optimal Pareto front is generated. Total 33 solution sets are obtained. From these solution sets, decision-maker can choose operating policy as per requirement and acceptance level. In present approach, decision-maker is not required to give any objective function preference.

New Constraint-Handling Technique for Evolutionary Optimization of Reservoir Operation

AbstractEvolutionary optimization of reservoir operation is subject to complex physical and operational constraints. Constraint-handling techniques (CHTs) in this field are predominantly problem-sp...

Stochastic optimal operation of reservoirs based on copula functions

Stochastic dynamic programming (SDP) has been widely used to derive operating policies for reservoirs considering streamflow uncertainties. In SDP, there is a need to calculate the transition probability matrix more accurately and efficiently in order to improve the economic benefit of reservoir operation. In this study, we proposed a stochastic optimization model for hydropower generation reservoirs, in which 1) the transition probability matrix was calculated based on copula functions; and 2) the value function of the last period was calculated by stepwise iteration. Firstly, the marginal distribution of stochastic inflow in each period was built and the joint distributions of adjacent periods were obtained using the three members of the Archimedean copulas, based on which the conditional probability formula was derived. Then, the value in the last period was calculated by a simple recursive equation with the proposed stepwise iteration method and the value function was fitted with a linear regression model. These improvements were incorporated into the classic SDP and applied to the case study in Ertan reservoir, China. The results show that the transition probability matrix can be more easily and accurately obtained by the proposed copula function based method than conventional methods based on the observed or synthetic streamflow series, and the reservoir operation benefit can also be increased.

A Novel Parallel Cellular Automata Algorithm for Multi-Objective Reservoir Operation Optimization

In this paper, a novel Parallel Cellular Automata (PCA) approach is presented for multi-objective reservoir operation optimization. The problem considers the multi-objective operation of a single reservoir with the two conflicting objectives of water supply and energy production. The water supply objective is defined as the squared deviation of the monthly release from the downstream demand while the hydropower objective is defined as the squared deficit of the monthly power production from the installed capacity. The proposed method uses two parallel cellular automata methods each searching for the solution of a single objective problem starting from an initial random solution. Each CA, however, is randomly seeded with the solution provided by the other CA method at each CA iteration. Two different version of the proposed PCA is considered based on the way the CAs are seeded. In the first method referred to as PCA1, a fixed value of 0.5 is used for the probability of exchange while in the second method, referred to as PCA2, a temperature-based variable probability of exchange is used for seeding the CAs. The proposed methods are used for bi-objective operation of Dez reservoir in Iran. Various operation periods of 60, 120, 240 and 480 months are considered to illustrate the efficiency and effectiveness of the proposed PCA methods for problems of different scales. In addition, Non-dominated Sorting Genetic Algorithm (NSGAII), is also used to solve the problems and the results are presented and compared. The results indicate that Pareto solutions obtained by the proposed temperature based method PCA2 are well-scattered over the front and in particular toward the end points compared to those of NSGAII requiring much less computational time. The superiority of the proposed method to that of NSGAII is shown to increase with increasing scale of the problem.

Evaluation of contemporary evolutionary algorithms for optimization in reservoir operation and water supply

This study evaluates three contemporary evolutionary algorithms, namely, shark, genetic, and particle swarm algorithms, for optimization in reservoir operation and water supply. The Klang gate dam in Malaysia is selected as the case study to optimize reservoir operation. The key objective of this study is the minimization of water deficits based on demands and released water. The global solution of the problem is computed based on software Lingo and the average solution of the shark algorithm is able to attain 99% of global solution. As well, the shark algorithm can furnish demand values at a faster convergence rate than both genetic and particle swarm algorithms. The reliability index and resiliency index, as useful indices in water resource management, are used and the values of these indices have the highest percent for the shark algorithm, indicating its superiority over other evolutionary algorithms.

Application of Cat Swarm Optimization Algorithm for Optimal Reservoir Operation

AbstractThe scarcity of water resources throughout the world has caused many complexities in meeting water demands, which in turn has created a tendency toward developing more efficient and effecti...

Discussion of “Application of the Firefly Algorithm to Optimal Operation of Reservoirs with the Purpose of Irrigation Supply and Hydropower Production” by Irene Garousi-Nejad, Omid Bozorg-Haddad, Hugo A. Loáiciga, and Miguel A. Mariño

Discussion of “Application of the Firefly Algorithm to Optimal Operation of Reservoirs with the Purpose of Irrigation Supply and Hydropower Production” by Irene Garousi-Nejad, Omid Bozorg-Haddad, Hugo A. Loáiciga, and Miguel A. Mariño

Discussion of “Application of the Firefly Algorithm to Optimal Operation of Reservoirs with the Purpose of Irrigation Supply and Hydropower Production” by Irene Garousi-Nejad, Omid Bozorg-Haddad, Hugo A. Loáiciga, and Miguel A. Mariño

Discussion of “Application of the Firefly Algorithm to Optimal Operation of Reservoirs with the Purpose of Irrigation Supply and Hydropower Production” by Irene Garousi-Nejad, Omid Bozorg-Haddad, Hugo A. Loáiciga, and Miguel A. Mariño

Closure to “Application of the Firefly Algorithm to Optimal Operation of Reservoirs with the Purpose of Irrigation Supply and Hydropower Production” by Irene Garousi-Nejad, Omid Bozorg-Haddad, Hugo A. Loáiciga, and Miguel A. Mariño

Closure to “Application of the Firefly Algorithm to Optimal Operation of Reservoirs with the Purpose of Irrigation Supply and Hydropower Production” by Irene Garousi-Nejad, Omid Bozorg-Haddad, Hugo A. Loáiciga, and Miguel A. Mariño

Design and development of bio-inspired framework for reservoir operation optimization

Abstract Frameworks for optimal reservoir operation play an important role in the management of water resources and delivery of economic benefits. Effective utilization and conservation of water from reservoirs helps to manage water deficit periods. The main challenge in reservoir optimization is to design operating rules that can be used to inform real-time decisions on reservoir release. We develop a bio-inspired framework for the optimization of reservoir release to satisfy the diverse needs of various stakeholders. In this work, single-objective optimization and multiobjective optimization problems are formulated using an algorithm known as “strawberry optimization” and tested with actual reservoir data. Results indicate that well planned reservoir operations lead to efficient deployment of the reservoir water with the help of optimal release patterns.

Maximizing the Firm Energy Yield Preserving Total Energy Generation Via an Optimal Reservoir Operation

As specified from the marketing standpoint, firm hydro-electrical energy must be available on an assured basis. To achieve the firm energy yield at maximum level, simultaneous optimization of operational variables is utilized. The variables include reservoir releases over historical stream-inflow records as well as the plant factor. Here, the plant factor denotes the percentage of day for operating a peak-time power plant at full capacity. To this end, a differential evaluation (DE) algorithm equipped with an indirect constraint handling is employed, in which an adaptive penalty system imposes the desirable reliability and preserves the total energy generation. To implement the model for a real example, a reservoir dam named Karun4, in southwest of Iran, is applied. The experimental results show the firm energy produced by the current and developed method are 49.2 and 127.5 (109 watt hours), respectively. Also, the results reveal the maximum capability of generating the firm energy yield with target reliability whereas the total energy generation is preserving.