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Cooperative MPC-Based Energy Management for Networked Microgrids
Univ Manchester, Sch Elect & Elect Engn, Manchester M13 9PL, Lancs, England..ORCID iD: 0000-0001-8633-1641
Berlin Univ Technol, SENSE Lab, D-10587 Berlin, Germany..
VTT Tech Res Ctr Finland, Espoo 02044, Finland..
VTT Tech Res Ctr Finland, Espoo 02044, Finland..
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2017 (English)In: IEEE Transactions on Smart Grid, ISSN 1949-3053, E-ISSN 1949-3061, Vol. 8, no 6, p. 3066-3074Article in journal (Refereed) Published
Abstract [en]

Microgrids are subsystems of the distribution grid operating as a single controllable system either connected or isolated from the grid. In this paper, a novel cooperative model predictive control (MPC) framework is proposed for urban districts comprising multiple microgrids sharing certain distributed energy resources (DERs). The operation of the microgrids, along with the shared DER, are coordinated such that the available flexibility sources are optimised and a common goal is achieved, e.g., minimizing energy exchanged with the distribution grid and the overall energy costs. Each microgrid is equipped with an MPC-based energy management system, responsible for optimally controlling flexible loads, heating systems, and local generation devices based on end-user preferences, weather-dependent generation and demand forecasts, energy prices, and technical and operational constraints. The proposed coordination algorithm is distributed and guarantees constraints satisfaction, cooperation among microgrids and fairness in the use of the shared resources, while addressing the issue of scalability of energy management in an urban district. Furthermore, the proposed framework guarantees an agreed cost saving to each microgrid. The described method is implemented and evaluated in a virtual testing environment that integrates accurate simulators of the microgrids. Numerical experiments show the feasibility, the computational benefits, and the effectiveness of the proposed approach.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. Vol. 8, no 6, p. 3066-3074
Keywords [en]
Model predictive control, flexibility services, energy management systems, demand response, distributed optimization, mixed integer linear programming
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-243568DOI: 10.1109/TSG.2017.2726941ISI: 000413244600052Scopus ID: 2-s2.0-85029007511OAI: oai:DiVA.org:kth-243568DiVA, id: diva2:1286324
Funder
Swedish Energy AgencyVINNOVAKnut and Alice Wallenberg Foundation
Note

QC 20190206

Available from: 2019-02-06 Created: 2019-02-06 Last updated: 2019-02-06Bibliographically approved

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