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Sizing and optimization of on-grid hybrid renewable energy systems considering hydroelectric energy storage

Authors :
Jihua Han
Jian Yang
Hao Zhang
Lixia Shang
Tong Wu
Source :
Journal of Intelligent & Fuzzy Systems. 40:1521-1536
Publication Year :
2021
Publisher :
IOS Press, 2021.

Abstract

The economic development of any country is closely linked with the consumption of energy. Therefore, international policies encourage increasing penetration of renewable energy sources (RES) into the electrical grid in order to reduce CO2 emissions and cover ever-increasing demands. However, high variance of RES complicates their integration into power systems and complicates their transition from central to distributed energy sources. On the other hand, increasing the penetration of RES in electrical networks stimulates the demand for large capacity for energy storage. This paper presents a new approach to optimize the size of on-grid renewable energy systems integrated to pumped storage system using Salp Swarm Algorithm (SSA). This approach allows the examination of various energy sources and their combination to handle the optimal configuration of the hybrid system. The simulation and optimization process of the studied system have been carried out by MATLAB programming. The impact of the system under study on the grid is examined according to the power exchange values between the system and the grid. Moreover, different scenarios have been introduced for optimal operation. The simulation results indicate that these hybrid systems can reduce power exchange with the grid and ensure that the proposed system is economically and environmentally feasible. Furthermore, the results indicate the technical feasibility of seawater hydroelectric power plants in increasing the capacity of the electric grid to allow for high penetration of RES. Finally, the results showed that the best minimum value of the objective function is 3.9113 and showed that CO2 emission can be reduced about 29.65% per year compared to the conventional power plants.

Details

ISSN :
18758967 and 10641246
Volume :
40
Database :
OpenAIRE
Journal :
Journal of Intelligent & Fuzzy Systems
Accession number :
edsair.doi...........697374e0333aeaeb85cd05d44ff5f36f