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.
| Original language | English |
|---|---|
| Journal | Journal of the Operational Research Society |
| DOIs | |
| Publication status | Published - Apr 2019 |
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Keywords
- battery energy storage systems
- decision theory
- evolutionary optimisation
- renewable energy
- Smart grids
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A combined optimisation and decision-making approach for battery-supported HMGS. / Marcelino, Carolina; Baumann, Manuel; Carvalho, Leonel; Chibeles-Martins, Nelson; Weil, Marcel; Almeida, Paulo; Wanner, Elizabeth.
In: Journal of the Operational Research Society, 04.2019.Research output: Contribution to journal › Article
TY - JOUR
T1 - A combined optimisation and decision-making approach for battery-supported HMGS
AU - Marcelino, Carolina
AU - Baumann, Manuel
AU - Carvalho, Leonel
AU - Chibeles-Martins, Nelson
AU - Weil, Marcel
AU - Almeida, Paulo
AU - Wanner, Elizabeth
N1 - The authors would like to thank Brazilian research foundations: CAPES, CNPq, FAPEMIG and FAPERJ for the financial support. This work was financed by the BE MUNDUS Project and the Helmholtz-Project Energy System 2050.
PY - 2019/4
Y1 - 2019/4
N2 - 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.
AB - 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.
KW - battery energy storage systems
KW - decision theory
KW - evolutionary optimisation
KW - renewable energy
KW - Smart grids
UR - http://www.scopus.com/inward/record.url?scp=85064670271&partnerID=8YFLogxK
U2 - 10.1080/01605682.2019.1582590
DO - 10.1080/01605682.2019.1582590
M3 - Article
JO - Journal of the Operational Research Society
JF - Journal of the Operational Research Society
SN - 0160-5682
ER -