A combined optimisation and decision-making approach for battery-supported HMGS

Abstract

Hybrid micro-grid systems (HMGS) are gaining increasing attention worldwide. The balance between electricity load and generation based on fluctuating renewable energy sources is a main challenge in the operation and design of HMGS. Battery energy storage systems are considered essential components for integrating high shares of renewable energy into a HMGS. Currently, there are very few studies in the field of mathematical optimisation and multi-criteria decision analysis that focus on the evaluation of different battery technologies and their impact on the HMGS design. The model proposed in this paper aims at optimising three different criteria: minimising electricity costs, reducing the loss of load probability, and maximising the use of locally available renewable energy. The model is applied in a case study in southern Germany. The optimisation is carried out using the C-DEEPSO algorithm. Its results are used as input for an AHP-TOPSIS model to identify the most suitable alternative out of five different battery technologies using expert weights. Lithium batteries are considered the best solution with regard to the given group preferences and the optimisation results.

Publication DOI: https://doi.org/10.1080/01605682.2019.1582590
Divisions: College of Engineering & Physical Sciences
Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis Group in Journal of the Operational Research Society on 20 April 2019, available online at: http://www.tandfonline.com/10.1080/01605682.2019.1582590
Uncontrolled Keywords: battery energy storage systems,decision theory,evolutionary optimisation,renewable energy,Smart grids,Management Information Systems,Strategy and Management,Management Science and Operations Research,Marketing
Publication ISSN: 1476-9360
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.tan ... 82.2019.1582590 (Publisher URL)
PURE Output Type: Article
Published Date: 2020
Published Online Date: 2019-04-20
Accepted Date: 2019-01-28
Authors: Marcelino, Carolina
Baumann, Manuel
Carvalho, Leonel
Chibeles-Martins, Nelson
Weil, Marcel
Almeida, Paulo
Wanner, Elizabeth (ORCID Profile 0000-0001-6450-3043)

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