Grid-connected multi-microgrid system operational scheduling optimization: A hierarchical improved marine predators algorithm

Li, L.-L., Ji, B.-X., Liu, G.-C., Yuan, J.-P., Lim, M. K. and Tseng, M.-L. (2024) Grid-connected multi-microgrid system operational scheduling optimization: A hierarchical improved marine predators algorithm. Energy, 294, 130905. (doi: 10.1016/j.energy.2024.130905)

Abstract

This study deals with grid-connected multi-microgrid system (MMGS) operation scheduling optimization problem using improved marine predators algorithm. Studies are lacking a system operation strategy designed to involve the internal power interaction and load demand response in the scheduling manipulation is short. This study involves power interaction and demand response to achieve the MMGS operational economy and reliability. Each MGS is combined to main grid with the continuous use of renewable energy and MMGS rapid development. This study proposes the analytical steps as follows. (1) the MMGS construction model includes wind energy and photovoltaic; (2) the optimization objective function so as to minimize the cost is constructed according to the characteristics of the equipment in the system; (3) a hierarchical improved marine predators algorithm is assumed to boost the optimal effect; (4) the test of the enhanced algorithm is completed and the optimization problem of the total running cost is solved; and; (5) a system operation strategy is designed considering the internal power interaction and load demand response in the MMGS scheduling operation. A different algorithms comparative analysis is performed. The proposed scheduling optimization approach is testified among analysis of specific examples, and the total operating cost is reduced by 11.19%.

• Item Type: Articles
• Additional Information: This work was supported by the National Ministry of Education's ‘Chunhui Plan’ Cooperative Research Project [Project No. HZKY20220242].
• Keywords: grid-connected multi-microgrid system, renewable energy, hierarchical improved marine predators algorithm, scheduling optimization, demand response.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Lim, Professor Ming
• Authors: Li, L.-L., Ji, B.-X., Liu, G.-C., Yuan, J.-P., Lim, M. K., and Tseng, M.-L.
• College/School: College of Social Sciences > Adam Smith Business School > Management
• Journal Name: Energy
• Publisher: Elsevier
• ISSN: 0360-5442
• ISSN (Online): 1873-6785
• Published Online: 16 March 2024