Robust optimal capacity planning of grid-connected microgrid considering energy management under multi-dimensional uncertainties

Xianqing Chen, Wei Dong and Qiang Yang

Applied Energy, 2022, vol. 323, issue C, No S0306261922009436

Abstract: Microgrid is considered an efficient paradigm for managing the massive number of distributed renewable generation and storage facilities. The optimal microgrid capacity planning is a non-trivial task due to the impact of randomness and uncertainties of renewable generation sources, and the adopted energy management strategies. In this paper, an optimal capacity planning model for the grid-connected microgrid is developed fully considering the renewable generation uncertainties through efficient scenario generation and reduction based on the deep convolutional generative adversarial network (DCGAN) and improved k-medoids clustering algorithm, as well as the microgrid energy management strategy. The proposed solution optimizes the capacity planning for the maximization of renewable energy utilization efficiency, and minimizes the economic cost and carbon emissions. The proposed solution is assessed using a case study of a microgrid (MG) project in northern China through a comparative study and the numerical results confirm the cost-effectiveness of the proposed solution.

Keywords: Scenario generation; DCGAN; Scenario reduction; k-medoids; Microgrid planning; Capacity configuration; Carbon emission; Multi-objective particle swarm optimization (MOPSO) (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (12)

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DOI: 10.1016/j.apenergy.2022.119642

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