Capacity Optimization of Independent Microgrid with Electric Vehicles Based on Improved Pelican Optimization Algorithm

Jiyong Li, Ran Chen (), Chengye Liu, Xiaoshuai Xu and Yasai Wang
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Jiyong Li: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Ran Chen: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Chengye Liu: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Xiaoshuai Xu: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Yasai Wang: School of Electrical Engineering, Guangxi University, Nanning 530004, China

Energies, 2023, vol. 16, issue 6, 1-23

Abstract: In order to reduce the comprehensive power cost of the independent microgrid and to improve environmental protection and power supply reliability, a two-layer power capacity optimization model of a microgrid with electric vehicles (EVs) was established that considered uncertainty and demand response. Based on the load and energy storage characteristics of electric vehicles, the classification of electric vehicles was proposed, and their mathematical models were established. The idea of robust optimization was adopted to construct the uncertain scenario set. Considering the incentive demand response, a two-layer power capacity optimization model of a microgrid was constructed. The improved pelican optimization algorithm (IPOA) was proposed as the two-layer model. In view of the slow convergence rate of the pelican optimization algorithm (POA) and its tendency to fall into the local optimum, methods such as elite reverse learning were proposed to generate the initial population, set disturbance inhibitors, and introduce Lévy flight to improve the initial population of the algorithm and enhance its global search ability. Finally, an independent microgrid was used as an example to verify the effectiveness of the proposed model and the improved algorithm. Considering that the total power capacity optimization cost of the microgrid after addition of electric vehicles was reduced by CNY 139,600, the total power capacity optimization cost of the microgrid after IOPA optimization was reduced by CNY 49,600 compared with that after POA optimization.

Keywords: uncertainty; electric vehicles; independent microgrid; capacity optimization; improved pelican optimization algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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