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Collaborative capacity planning method of wind-photovoltaic-storage equipment in microgrids considering different energy selling incomes

Authors :
Lingyu Liang
Xiangyu Zhao
Wenqi Huang
Liming Sun
Ziyao Wang
Fengsheng Chen
Source :
Frontiers in Energy Research, Vol 11 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

A microgrid is a promising small-scale power generation and distribution system. The selling prices of wind turbine equipment (WT), photovoltaic generation equipment (PV), and battery energy storage equipment (BES) have a significant impact on microgrid profits, which, in turn, affects the planning capacity of renewable energy. However, existing research has not yet conducted in-depth modeling and analysis for different kinds of energy generation electricity prices. This paper proposes an optimal capacity planning method for wind-photovoltaic-storage equipment, considering different energy selling incomes in microgrids. Stochastic characteristics of renewable energy (WT and PV), selling prices of different types of energy, and timing coupling characteristic are considered in the proposed model. In addition, the configuration capacities of WT, PV, and BES are modeled as discrete decision variables, according to the type of specific equipment. The comprehensive life cycle cost (LCC) is considered an objective function. It can be found that the proposed collaborative capacity planning model is a mathematical programming problem with complex nonlinear constraints and integer variables. To solve this problem, a cultural gray wolf optimization algorithm (CGWO) is applied in this paper. The proposed method’s efficiency, convergence, superiority, and effectiveness are verified through a case study. Moreover, the impact of different new energy sales prices on capacity planning results is also revealed in the article.

Details

Language :
English
ISSN :
2296598X
Volume :
11
Database :
Directory of Open Access Journals
Journal :
Frontiers in Energy Research
Publication Type :
Academic Journal
Accession number :
edsdoj.66591d603f0d43dfba98af4714e74907
Document Type :
article
Full Text :
https://doi.org/10.3389/fenrg.2023.1264986