Optimal Scheduling of Distribution Network Incorporating Topology Reconfiguration, Battery Energy System and Load Response

Distributed generation (DG) is becoming increasingly important due to the serious environmental pollution caused by conventional fossil-energy-based generation and the depletion of non-renewable energy. As the flexible resources in the active distribution network (ADN), battery energy system (BES) a...

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Bibliographic Details
Main Authors: Cao, Y. (Author), Jiang, L. (Author), Li, Y. (Author), Luo, Y. (Author), Qiao, X. (Author), Shao, X. (Author), Tan, Y. (Author), Xiao, J. (Author), Xu, J. (Author)
Format: Article
Language:English
Published: China Electric Power Research Institute 2022
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Online Access:View Fulltext in Publisher
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Summary:Distributed generation (DG) is becoming increasingly important due to the serious environmental pollution caused by conventional fossil-energy-based generation and the depletion of non-renewable energy. As the flexible resources in the active distribution network (ADN), battery energy system (BES) and responsive load (RL) are all able to assist renewable DG integration in day-ahead dispatch. In addition, the security and economic level can be significantly improved by adjusting network topology. Therefore, in this paper, a coordinated day-ahead scheduling method incorporating topology reconfiguration, BES optimization and load response is presented to minimize the total day-ahead operational costs in the ADN. Linearized current injection models are presented for renewable DG, RL and BES based on the linear power flow model, and an extensible linear switching operations calculation (ELSOC) method is proposed to address the network reconfiguration. Thus, a mixed integer linear programming (MILP) model is proposed for optimal coordinated operation of an ADN. The correctness and effectiveness of the proposed method are demonstrated by simulations on a modified test system. In addition, the combined scenario and Monte-Carlo method is used to handle the uncertainties of loads and DGs, and the results of different uncertainties can further verify the feasibility of the proposed model. © 2015 CSEE.
ISBN:20960042 (ISSN)
DOI:10.17775/CSEEJPES.2019.02300