Optimal energy management for grid-connected microgrids via expected-scenario-oriented robust optimization

Junjie Zhu, Shengjun Huang, Yajie Liu, Hongtao Lei and Bo Sang

Energy, 2021, vol. 216, issue C

Abstract: In order to facilitate the integration of distributed energy resources (DERs), grid-connected microgrids (MGs) have been rapidly deployed over the last decade. However, their potential negative impact on the security of utility grids cannot be ignored. For the existence of various uncertainties, uncertainty quantification method, e.g., robust optimization (RO), has been proposed for the energy management of MGs. Targeting the conservatism problem presented by the traditional two-stage RO method, which is oriented towards the worst-case scenario, this paper proposes a novel method targeting the expected scenario. Furthermore, in the proposed model, the decision variables representing the charging/discharging states of energy storage system (ESS) is considered in the re-dispatch stage, in contrast to existing models that putting them in the pre-dispatch stage, to increase the operational flexibility of ESS for energy management. Notably, this shift in the treatment of ESS translates the proposed two-stage RO model into a mixed integer programming (MIP) model with recourse. Therefore, the nested column-and-constraint generation (N–C&CG) algorithm is adopted. Results of numerical experiments illustrated the superiorities of the proposed model in minimizing the cost of system operation and reducing the negative impact on the utility grid.

Keywords: Grid-connected microgrid; Energy management; Robust optimization; Expected-scenario-oriented; Two-stage decision process; Column-and-constraint generation algorithm (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:216:y:2021:i:c:s0360544220323318

DOI: 10.1016/j.energy.2020.119224

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