Open AccessDRO‐MPC‐based data‐driven approach to real‐time economic dispatch for islanded microgrids
Author(s) -
Lyu Cheng,
Jia Youwei,
Xu Zhao
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2020.0849
Subject(s) - microgrid , computer science , scalability , mathematical optimization , ambiguity , constraint (computer aided design) , economic dispatch , wind power , energy storage , electric power system , power (physics) , control (management) , engineering , artificial intelligence , mathematics , mechanical engineering , physics , quantum mechanics , database , programming language , electrical engineering
Rechargeable battery banks have been widely utilised in islanded microgrids as energy storage systems to complement the instant power imbalance in real‐time. However, the cycle degradation becomes an unavoidable concern of the battery energy storage systems (BESSs) in achieving microgrid economic dispatch (ED). In this study, a novel degradation cost model based on an online auction algorithm is proposed for real‐time management of BESS. To settle the intermittent distributed sources in real‐time operation, a Wasserstein ambiguity set is adopted to characterise the uncertainties. Meanwhile, the authors newly reformulate the real‐time microgrid ED as a two‐stage distributionally robust optimisation (DRO) problem. To improve the tractability and scalability of the DRO problem, a model predictive control (MPC)‐based data‐driven approach is proposed, in which a novel affine policy namely extended event‐wise affine adaption is properly employed. Through extensive case studies, the numerical results demonstrate the effectiveness of the proposed approach.