Optimal Operation of Reservoir Power Generation Based on Improved Grey Wolf Algorithm

2021 ◽  
pp. 987-996
Author(s):  
Ran-Fan Chen ◽  
Kuo-Chi Chang ◽  
Kai-Chun Chu ◽  
Fu-Hsiang Chang ◽  
Hsiao-Chuan Wang ◽  
...  
Keyword(s):  
Power Generation ◽  
Optimal Operation ◽  
Grey Wolf ◽  
Grey Wolf Algorithm

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 A hybridized model based on neural network and swarm intelligence-grey wolf algorithm for spatial prediction of urban flood-inundation

2021 ◽  
pp. 126854
Author(s):  
Hamid Darabi ◽  
Ali Torabi Haghighi ◽  
Omid Rahmati ◽  
Abolfazl Jalali Shahrood ◽  
Sajad Rouzbeh ◽  
...  
Keyword(s):  
Neural Network ◽  
Swarm Intelligence ◽  
Spatial Prediction ◽  
Flood Inundation ◽  
Grey Wolf ◽  
Urban Flood ◽  
Model Based ◽  
Grey Wolf Algorithm

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.

scholarly journals A Novel Coverage Optimization Strategy Based on Grey Wolf Algorithm Optimized by Simulated Annealing for Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yong Zhang ◽  
Li Cao ◽  
Yinggao Yue ◽  
Yong Cai ◽  
Bo Hang
Keyword(s):  
Wireless Sensor Networks ◽  
Simulated Annealing ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Coverage Optimization ◽  
Grey Wolf Algorithm

The coverage optimization problem of wireless sensor network has become one of the hot topics in the current field. Through the research on the problem of coverage optimization, the coverage of the network can be improved, the distribution redundancy of the sensor nodes can be reduced, the energy consumption can be reduced, and the network life cycle can be prolonged, thereby ensuring the stability of the entire network. In this paper, a novel grey wolf algorithm optimized by simulated annealing is proposed according to the problem that the sensor nodes have high aggregation degree and low coverage rate when they are deployed randomly. Firstly, the mathematical model of the coverage optimization of wireless sensor networks is established. Secondly, in the process of grey wolf optimization algorithm, the simulated annealing algorithm is embedded into the grey wolf after the siege behavior ends and before the grey wolf is updated to enhance the global optimization ability of the grey wolf algorithm and at the same time improve the convergence rate of the grey wolf algorithm. Simulation experiments show that the improved grey wolf algorithm optimized by simulated annealing is applied to the coverage optimization of wireless sensor networks. It has better effect than particle swarm optimization algorithm and standard grey wolf optimization algorithm, has faster optimization speed, improves the coverage of the network, reduces the energy consumption of the nodes, and prolongs the network life cycle.

scholarly journals Multi-Objective Joint Optimal Operation of Reservoir System and Analysis of Objectives Competition Mechanism: A Case Study in the Upper Reach of the Yangtze River

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2542 ◽  
Author(s):  
Mufeng Chen ◽  
Zengchuan Dong ◽  
Wenhao Jia ◽  
Xiaokuan Ni ◽  
Hongyi Yao
Keyword(s):  
Power Generation ◽  
Water Supply ◽  
Yangtze River ◽  
Water Resource Management ◽  
Flood Control ◽  
Optimal Operation ◽  
Total Power ◽  
The Yangtze River ◽  
Multi Objective ◽  
Reservoir System

The multi-objective optimal operation and the joint scheduling of giant-scale reservoir systems are of great significance for water resource management; the interactions and mechanisms between the objectives are the key points. Taking the reservoir system composed of 30 reservoirs in the upper reaches of the Yangtze River as the research object, this paper constructs a multi-objective optimal operation model integrating four objectives of power generation, ecology, water supply, and shipping under the constraints of flood control to analyze the inside interaction mechanisms among the objectives. The results are as follows. (1) Compared with single power generation optimization, multi-objective optimization improves the benefits of the system. The total power generation is reduced by only 4.09% at most, but the water supply, ecology, and shipping targets are increased by 98.52%, 35.09%, and 100% at most under different inflow conditions, respectively. (2) The competition between power generation and the other targets is the most obvious; the relationship between water supply and ecology depends on the magnitude of flow required by the control section for both targets, and the restriction effect of the shipping target is limited. (3) Joint operation has greatly increased the overall benefits. Compared with the separate operation of each basin, the benefits of power generation, water supply, ecology, and shipping increased by 5.50%, 45.99%, 98.49%, and 100.00% respectively in the equilibrium scheme. This study provides a widely used method to analyze the multi-objective relationship mechanism, and can be used to guide the actual scheduling rules.

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