scholarly journals Optimal operation of multi-reservoir systems for increasing power generation using a seagull optimization algorithm and heading policy

2021 ◽  
Vol 7 ◽  
pp. 3703-3725
Author(s):  
Mohammad Ehteram ◽  
Fatemeh Barzegari Banadkooki ◽  
Chow Ming Fai ◽  
Mohsen Moslemzadeh ◽  
Michelle Sapitang ◽  
...  
Keyword(s):  
Power Generation ◽  
Optimization Algorithm ◽  
Optimal Operation ◽  
Reservoir Systems

scholarly journals Introducing modified version of penguins search optimization algorithm (PeSOA) and its application in optimal operation of reservoir systems

2017 ◽  
Vol 18 (4) ◽  
pp. 1484-1496 ◽  
Author(s):  
Afshin Mansouri ◽  
Babak Aminnejad ◽  
Hassan Ahmadi
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Real World ◽  
Global Optimum ◽  
Optimal Operation ◽  
The Other ◽  
Reservoir System ◽  
Search Optimization ◽  
Real World Problem ◽  
Reservoir Systems

Abstract In the current study, modified version of the penguins search optimization algorithm (PeSOA) was introduced, and its usage was assessed in the water resources field. In the modified version (MPeSOA), the Gaussian exploration was added to the algorithm. The MPeSOA performance was evaluated in optimal operation of a hypothetical four-reservoir system and Karun-4 reservoir as a real world problem. Also, genetic algorithm (GA) was used as a criterion for evaluating the performance of PeSOA and MPeSOA. The results revealed that in a four-reservoir system problem, the PeSOA performance was much weaker than the GA; but on the other hand, the MPeSOA had better performance than the GA. In the mentioned problem, PeSOA, GA, and MPeSOA reached 78.43, 97.46, and 98.30% of the global optimum, respectively. In the operation of Karun-4 reservoir, although PeSOA performance had less difference with the two other algorithms than four-reservoir problem, its performance was not acceptable. The average values of objective function in this case were equal to 26.49, 23.84, and 21.48 for PeSOA, GA, and MPeSOA, respectively. According to the results obtained in the operation of Karun-4 reservoir, the algorithms including MPeSOA, GA, and PeSOA were situated in ranks one to three in terms of efficiency, respectively.

CHP Microgrid Optimized Operation Based on Bacterial Foraging Optimization Algorithm

2014 ◽  
Vol 981 ◽  
pp. 668-672
Author(s):  
Rui Li ◽  
Peng Li
Keyword(s):  
Power Generation ◽  
Energy Storage ◽  
Optimization Algorithm ◽  
Storage System ◽  
Energy Storage System ◽  
Optimal Operation ◽  
Bacterial Foraging Optimization ◽  
Distributed Power ◽  
Bacterial Foraging ◽  
Micro Grid

CHP system with Energy saving, environmental protection, economic and other characteristics,have good prospects for the development and application value.This paper directe a micro grid system consisted by photovoltaic cells, wind turbines, fuel cells, microturbines, auxiliary boilers, thermal energy storage systems and batteries and heat load and electrical load.Considering various distributed power generation costs, environmental costs and micro-grid equipment maintenance costs,To meet the constraints of micro-grid operation, optimization of the different micro-grid distributed power and energy storage system power output, make the system's total operating costs are minimized.This paper analyzes the economic and environmental of micro-grid optimal operation characteristics, given a model of CHP micro-grid.For the cost of power generation and emissions of different weights, using bacterial foraging optimization(BFO) algorithm,through a numerical example verified the Correctness and effectiveness of mathematical model and optimization algorithm .

scholarly journals Comparative analysis of some evolutionary-based models in optimization of dam reservoirs operation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohammad Reza Sharifi ◽  
Saeid Akbarifard ◽  
Kourosh Qaderi ◽  
Mohamad Reza Madadi
Keyword(s):  
Objective Function ◽  
Optimization Algorithm ◽  
Optimal Operation ◽  
Engineering Problem ◽  
Performance Criteria ◽  
Reliable Technique ◽  
Reservoir System ◽  
Dam Reservoirs ◽  
Reservoir Systems ◽  
Swarm Algorithm

AbstractDeriving optimal operation policies for multi-reservoir systems is a complex engineering problem. It is necessary to employ a reliable technique to efficiently solving such complex problems. In this study, five recently-introduced robust evolutionary algorithms (EAs) of Harris hawks optimization algorithm (HHO), seagull optimization algorithm (SOA), sooty tern optimization algorithm (STOA), tunicate swarm algorithm (TSA) and moth swarm algorithm (MSA) were employed, for the first time, to optimal operation of Halilrood multi-reservoir system. This system includes three dams with parallel and series arrangements simultaneously. The results of mentioned algorithms were compared with two well-known methods of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The objective function of the optimization model was defined as the minimization of total deficit over 223 months of reservoirs operation. Four performance criteria of reliability, resilience, vulnerability and sustainability were used to compare the algorithms’ efficiency in optimization of this multi-reservoir operation. It was observed that the MSA algorithm with the best value of objective function (6.96), the shortest CPU run-time (6738 s) and the fastest convergence rate (< 2000 iterations) was the superior algorithm, and the HHO algorithm placed in the next rank. The GA, and the PSO were placed in the middle ranks and the SOA, and the STOA placed in the lowest ranks. Furthermore, the comparison of utilized algorithms in terms of sustainability index indicated the higher performance of the MSA in generating the best operation scenarios for the Halilrood multi-reservoir system. The application of robust EAs, notably the MSA algorithm, to improve the operation policies of multi-reservoir systems is strongly recommended to water resources managers and decision-makers.

Optimal Operation of Hydropower Reservoir Systems Using Weed Optimization Algorithm

2016 ◽  
Vol 30 (11) ◽  
pp. 3995-4009 ◽  
Author(s):  
Mohammad Azizipour ◽  
Vahid Ghalenoei ◽  
M. H. Afshar ◽  
S. S. Solis
Keyword(s):  
Optimization Algorithm ◽  
Optimal Operation ◽  
Reservoir Systems ◽  
Hydropower Reservoir

Biogeography-Based Optimization Algorithm for Optimal Operation of Reservoir Systems

2016 ◽  
Vol 142 (1) ◽  
pp. 04015034 ◽  
Author(s):  
Omid Bozorg Haddad ◽  
Seyed-Mohammad Hosseini-Moghari ◽  
Hugo A. Loáiciga
Keyword(s):  
Optimization Algorithm ◽  
Optimal Operation ◽  
Reservoir Systems

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 hybrid constrained coral reefs optimization algorithm with machine learning for optimizing multi-reservoir systems operation

2021 ◽  
Vol 286 ◽  
pp. 112250
Author(s):  
Mohammad Emami ◽  
Sara Nazif ◽  
Sayed-Farhad Mousavi ◽  
Hojat Karami ◽  
Andre Daccache
Keyword(s):  
Machine Learning ◽  
Coral Reefs ◽  
Optimization Algorithm ◽  
Reservoir Systems ◽  
Coral Reefs Optimization Algorithm

Coupled Operating Rules for Optimal Operation of Multi-Reservoir Systems

2017 ◽  
Vol 31 (14) ◽  
pp. 4505-4520 ◽  
Author(s):  
Seyed Mohammad Ashrafi ◽  
Alireza Borhani Dariane
Keyword(s):  
Optimal Operation ◽  
Operating Rules ◽  
Reservoir Systems

Investigation Into Optimal Operation of BCHP Systems in the Air-Conditioning Season

2006 ◽  
Author(s):  
Jiacong Cao ◽  
Hong Fang
Keyword(s):  
Power Generation ◽  
Energy Conservation ◽  
Performance Optimization ◽  
Optimization Problems ◽  
Economic Effect ◽  
Numerical Data ◽  
Computer Time ◽  
Optimal Operation ◽  
Exergetic Efficiency ◽  
Lower Efficiency

Building cooling, heating and power generation (BCHP) is important for the sustainable energy strategy in China because of its contribution to energy conservation and the reduction of CO2 emissions. The number of BCHP or small-scaled combined cooling, heating and power generation systems that have been put to use or are in the course of construction is steadily increasing in China. However, in many cases the performance of BCHP systems is not good enough, i.e., the average real exergetic efficiency of whole system is much lower than expected and the economic effect is not satisfactory. This is a problem that perplexes designers and plant owners and need be investigated so as to increase the knowledge of optimizing the operation of BCHP systems. In this paper the performance of a typical BCHP system is investigated using thermodynamic and thermoeconomic analyses based on the simulating results of off-design operation and the solution of performance optimization of the system. With the help of a great number of real running data of the system and the master data supplied by manufacturers, a model of the system operation is developed to simulate the whole domain of operation on off-design conditions. In order to shorten computer time the operation domain is described by a set of functions obtained by curve fitting using the numerical data from the simulation. Two models of optimization, of which the objective functions are the exergetic efficiency and gross benefit of the whole BCHP system separately, are established in virtue of these fitted functions. The simulation of off-design operation and the solution of the optimization problems supply a great number of useful data that form various graphs, which are to be the references to energy conservation and economic operation of the systems. The investigation indicates that there are some differences between the optimum working conditions obtained by the two optimization models, whereas it is inevitable that the system runs with some lower efficiency and less gross benefit when working at high cooling or heating load factors. By analyzing the data some significant conclusions are obtained, which will be helpful for the BCHP industry in China.

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