scholarly journals Optimizing multi-reservoir operation rules: an improved HBMO approach

2010 ◽  
Vol 13 (1) ◽  
pp. 121-139 ◽  
Author(s):  
Abbas Afshar ◽  
Mahyar Shafii ◽  
Omid Bozorg Haddad
Keyword(s):  
Reservoir Operation ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Case Examples ◽  
Rule Curves ◽  
Operating Rules ◽  
Operation Rules ◽  
Real Coded Genetic Algorithm ◽  
Reservoir Systems ◽  
Improved Model

We present an improved version of Honey Bees Mating Optimization (HBMO) algorithm to develop operating rules for multi-reservoir systems. The performance of the proposed model is tested through sensitivity analysis and comparing the result with those of a real-coded Genetic Algorithm (GA) for a 60-month period single-reservoir operation problem. The improved model is subsequently employed to derive release rule and storage balancing functions which form operating policy for a multi-reservoir system along two case examples: (i) water supply and (ii) hydropower generation. The obtained operating rule curves can be used to guide the system operators in decision-making. These rule curves provide the operator with the opportunity to systematically look at the system and to make proper decisions. The obtained results showed that the optimization technique proposed in this study is capable of solving complex multi-reservoir systems operation problems. Moreover, the proposed structure properly handled the tight constraints defining the parallel reservoirs operation in such a way that all the generated solutions were feasible after a particular set of iterations. The proposed optimization algorithm of this study can be developed more in future to solve multi-modal optimization problems, and also to define operation policies for highly complex multi-reservoir systems.

scholarly journals Self-optimization system dynamics simulation of reservoir operating rules

2018 ◽  
Vol 246 ◽  
pp. 01013 ◽  
Author(s):  
Benjun Jia ◽  
Jianzhong Zhou ◽  
Lu Chen ◽  
Zhongzheng He ◽  
Liu Yuan ◽  
...  
Keyword(s):  
Power Generation ◽  
System Dynamics ◽  
Reservoir Operation ◽  
Goodness Of Fit ◽  
Dynamics Simulation ◽  
Term Operation ◽  
Optimization System ◽  
Operating Rules ◽  
Operation Rules ◽  
Fitting Method

Operating rules have been used widely in the reservoir long-term operation duo to its characteristics of coping with inflow uncertainty and easy implementation. And implicit stochastic optimization (ISO) has been widely applied to derive reservoir operation rules, based on linear regression or nonlinear fitting method. However, the maximum goodness-of-fit criterion of fitting method may be unreliable to determine the effective rules. Therefore, this paper develops a self-optimization system dynamics (SD) simulation of reservoir operation for optimizing the operating rules, by taking advantages of feedback loops in SD simulation. A deterministic optimization operation model is firstly established, and then resolved using dynamic programming (DP). Simultaneously, the initial operating rules (IOR) are derived using the linear fitting method. Finally, the refined optimal operating rules (OOR) are obtained by improving the IOR based on the self-optimization SD simulation. China’s Three Gorges Reservoir is used as a case study. The results show that the SD simulation is competent in simulating a complicated hydropower system with feedback and causal loops. Moreover, it makes a contribution to improve the IOR derived by fitting method within an ISO frame. And the OOR improve effectively the guarantee rate of power generation on the premise of ensuring power generation.

Dynamic programming with the principle of progressive optimality for searching rule curves

2007 ◽  
Vol 34 (2) ◽  
pp. 170-176 ◽  
Author(s):  
C Chaleeraktrakoon ◽  
A Kangrang
Keyword(s):  
Dynamic Programming ◽  
Reservoir Operation ◽  
Water Shortage ◽  
Continuous Type ◽  
Rule Curves ◽  
Multi Stage ◽  
Long Run ◽  
Reservoir Systems ◽  
Feasible Solutions ◽  
Illustrative Application

Rule curves are monthly reservoir-operation guidelines for meeting the minimum of water shortage over the long run. This paper proposes a dynamic programming (DP) approach for finding the optimal rule curves of single- and multi-reservoir systems. The proposed DP approach uses a traditional DP technique conditionally and applies the principle of progressive optimality (PPO) to search its optimal solutions. The proposed DP–PPO approach is suitable because of the multi-stage, nonlinear, and continuous-type characteristics of the rule curve search. Its dimensionality is relatively small, as compared with that of the traditional one. Results of an illustrative application to a multi-reservoir system under two different initial feasible solutions (i.e., new or existing reservoirs) have demonstrated that the DP–PPO approach is generally fast and robust. Its convergence varies only slightly, according to the initial conditions.Key words: rule curves, principle of progressive optimality, dynamic programming (DP), monthly reservoir operation.

A New Functional Approach for Searching Optimal Reservoir Rule Curves

2014 ◽  
Vol 915-916 ◽  
pp. 1452-1455 ◽  
Author(s):  
Yi Fan Ding ◽  
De Shan Tang ◽  
Zhen Zhu Meng
Keyword(s):  
Reservoir Operation ◽  
Optimization Technique ◽  
Water Shortage ◽  
Functional Approach ◽  
New Approach ◽  
Term Operation ◽  
Optimal Rule ◽  
Rule Curves ◽  
Reservoir Rule Curves

Rule curves are guidelines for long term reservoir operation. An efficient optimization technique is required to find the optimal rule curves that can mitigate water shortage in long-term operation. A new functional approach was proposed to search the optimal rule curves of reservoir. The results indicated that the situations of water shortage and excess release water of using the new approach are smaller than the situations of using the existing rule curves.

scholarly journals Development of multi-objective reservoir operation rules for integrated water resources management

2009 ◽  
Vol 12 (2) ◽  
pp. 185-200 ◽  
Author(s):  
T. S. Cheong ◽  
I. Ko ◽  
J. W. Labadie
Keyword(s):  
Water Resources ◽  
Water Resources Management ◽  
Reservoir Operation ◽  
Integrated Water Resources Management ◽  
Optimization Models ◽  
Resources Management ◽  
Operating Rules ◽  
Operation Rules ◽  
Optimal Network ◽  
Reservoir Conditions

Real-time monitoring, databases, optimization models and visualization tools have been integrated into a Decision Support System (DSS) for optimal water resources management of two water supply reservoirs, the Daechung Reservoir and the Yongdam Reservoir of the Geum River basin, Daejeon, Korea. The KModSim as a DSS has been designed to provide information on current reservoir conditions to operational staff and to help in making decisions for short- and long-term management. For the physical calibration, the network simulations in seasonal water allocation of both reservoirs are performed for 23 years from January 1 1983 to June 30 2006. Linear and nonlinear operating rules are developed by using the actual reservoir operation data obtained from both reservoirs which are then used in KModSim by the hydrologic state method to estimate optimized target storages of both reservoirs. For validation of hydrologic states in KModSim and scenario testing for the management simulations, the optimal network simulation for the seasonal water allocations from October 1 2002 to June 30 2006 were also performed. The results' simulation by new rules fit the measured actual reservoir storage and represent well the various outflow discharge curves measured at the gauging stations of Geum River. The developed operating rules are proven to be superior in explaining actual reservoir operation as compared to the simulated target storages by existing optimization models.

Reliability optimization in stochastic domain via genetic algorithm

2014 ◽  
Vol 31 (6) ◽  
pp. 698-717 ◽  
Author(s):  
Laxminarayan Sahoo ◽  
Asoke Kumar Bhunia ◽  
Dilip Roy
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Mixed Integer ◽  
Reliability Optimization ◽  
Programming Technique ◽  
Content Type ◽  
Real Coded Genetic Algorithm ◽  
Interval Valued

Purpose – The purpose of this paper is to formulate the reliability optimization problem in stochastic and interval domain and also to solve the same under different stochastic set up. Design/methodology/approach – Stochastic programming technique has been used to convert the chance constraints into deterministic form and the corresponding problem is transformed to mixed-integer constrained optimization problem with interval objective. Then the reduced problem has been converted to unconstrained optimization problem with interval objective by Big-M penalty technique. The resulting problem has been solved by advanced real coded genetic algorithm with interval fitness, tournament selection, intermediate crossover and one-neighbourhood mutation. Findings – A new optimization technique has been developed in stochastic domain and the concept of interval valued parameters has been integrated with the stochastic setup so as to increase the applicability of the resultant solution to the interval valued nonlinear optimization problems. Practical implications – The concept of probability distribution with interval valued parameters has been introduced. This concept will motivate the researchers to carry out the research in this new direction. Originality/value – The application of genetic algorithm is extended to solve the reliability optimization problem in stochastic and interval domain.

Identifying the key water levels in reservoir operation on ecological objectives

2014 ◽  
Vol 14 (6) ◽  
pp. 1160-1167 ◽  
Author(s):  
Haixia Wang ◽  
Jinggang Chu ◽  
Chi Zhang ◽  
Huicheng Zhou
Keyword(s):  
Water Supply ◽  
Water Level ◽  
Reservoir Operation ◽  
Water Levels ◽  
Rule Curves ◽  
Operation Rules ◽  
Operation Rule ◽  
Water Level Variations ◽  
Time Periods ◽  
Ecological Objectives

This paper investigates the influences of reservoir water level variations of the operation rule curves on different objectives, especially on ecological objectives. Five representative ecological objectives are selected besides industrial and domestic (I&D) and agricultural water supply objectives. They can reflect the impacts of reservoir operation on different attributes of ecological flow regime in the Biliuhe Reservoir case study, Northeastern China. Sensitivities of water supply and ecological objectives caused by water level variations of I&D and agricultural operation rule curves are analyzed by using a variance-based sensitivity analysis method – Sobol's method. Results show that impacts of individual water levels and their interactions on ecological objectives are very different. Also the ecological objectives do not always conflict with water supply objectives. This study provides new insights for reservoir managers to improve downstream aquatic ecosystem by adjusting water levels not only at individual time periods but also at some interacting time periods. Furthermore, it helps us better understand the influence mechanism of water level changes on different objectives, and provides guidance for the development of reservoir operation rules.

scholarly journals Combined Aggregated Sampling Stochastic Dynamic Programming and Simulation-Optimization to Derive Operation Rules for Large-Scale Hydropower System

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 625
Author(s):  
Xinyu Wu ◽  
Rui Guo ◽  
Xilong Cheng ◽  
Chuntian Cheng
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Large Scale ◽  
Simulation Optimization ◽  
Optimal Operation ◽  
Stochastic Dynamic ◽  
Operation Model ◽  
Two Stage ◽  
Operation Rules ◽  
Reservoir Systems

Simulation-optimization methods are often used to derive operation rules for large-scale hydropower reservoir systems. The solution of the simulation-optimization models is complex and time-consuming, for many interconnected variables need to be optimized, and the objective functions need to be computed through simulation in many periods. Since global solutions are seldom obtained, the initial solutions are important to the solution quality. In this paper, a two-stage method is proposed to derive operation rules for large-scale hydropower systems. In the first stage, the optimal operation model is simplified and solved using sampling stochastic dynamic programming (SSDP). In the second stage, the optimal operation model is solved by using a genetic algorithm, taking the SSDP solution as an individual in the initial population. The proposed method is applied to a hydropower system in Southwest China, composed of cascaded reservoir systems of Hongshui River, Lancang River, and Wu River. The numerical result shows that the two-stage method can significantly improve the solution in an acceptable solution time.

scholarly journals A Reservoir Operation Policy Using Inter-Basin Water Transfer for Maximizing Hydroelectric Benefits in Brazil

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2564 ◽  
Author(s):  
Anderson Passos de Aragão ◽  
Patrícia Teixeira Leite Asano ◽  
Ricardo de Andrade Lira Rabêlo
Keyword(s):  
Reservoir Operation ◽  
Network Flow ◽  
Water Transfer ◽  
Energy Deficit ◽  
Generation System ◽  
Operation Rules ◽  
First Case ◽  
Basin Water ◽  
Hydroelectric Plants

The Hydrothermal Coordination problem consists of determining an operation policy for hydroelectric and thermoelectric plants within a given planning horizon. In systems with a predominance of hydraulic generation, the operation policy to be adopted should specify the operation of hydroelectric plants, so that hydroelectric resources are used economically and reliably. This work proposes the implementation of reservoir operation rules, using inter-basin water transfer through an optimization model based on Network Flow and Particle Swarm Optimization (PSO). The proposed algorithm aims to obtain an optimized operation policy of power generation reservoirs and consequently to maximize the hydroelectric benefits of the hydrothermal generation system, to reduce the use of thermoelectric plants, the importation and/or energy deficit and to reduce the cost associated with meeting the demand and reduce CO2 emissions from combustion of fossil fuels used by thermoelectric plants. In order to illustrate the efficiency and effectiveness of the proposed approach, it was evaluated by optimizing two case studies using a system with four hydroelectric plants. The first case study does not consider transfer and water and the second case study uses water transfer between rivers. The obtained results illustrate that the proposed model allowed to maximize the hydroelectric resources of a hydrothermal generation system with economy and reliability.

Genetic algorithm- and finite element-based design and analysis of nonprismatic piezolaminated beam for optimal vibration energy harvesting

2015 ◽  
Vol 230 (14) ◽  
pp. 2532-2548 ◽  
Author(s):  
Alok Ranjan Biswal ◽  
Tarapada Roy ◽  
Rabindra Kumar Behera
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Energy Harvesting ◽  
Output Power ◽  
Governing Equation ◽  
Optimization Technique ◽  
Vibration Energy ◽  
Fe Model ◽  
Vibration Energy Harvesting ◽  
Real Coded Genetic Algorithm

The current article deals with finite element (FE)- and genetic algorithm (GA)-based vibration energy harvesting from a tapered piezolaminated cantilever beam. Euler–Bernoulli beam theory is used for modeling the various cross sections of the beam. The governing equation of motion is derived by using the Hamilton's principle. Two noded beam elements with two degrees of freedom at each node have been considered in order to solve the governing equation. The effect of structural damping has also been incorporated in the FE model. An electric interface is assumed to be connected to measure the voltage and output power in piezoelectric patch due to charge accumulation caused by vibration. The effects of taper (both in the width and height directions) on output power for three cases of shape variation (such as linear, parabolic and cubic) along with frequency and voltage are analyzed. A real-coded genetic algorithm-based constrained (such as ultimate stress and breakdown voltage) optimization technique has been formulated to determine the best possible design variables for optimal harvesting power. A comparative study is also carried out for output power by varying the cross section of the beam, and genetic algorithm-based optimization scheme shows the better results than that of available conventional trial and error methods.

Sign in / Sign up

Export Citation Format

Share Document