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To evaluate system cost and dependability, optimizing the size of microgrid system elements, including energy storage systems connected with the principal network, is crucial. In this line, a study has already been performed using a uni-objective optimization approach for the techno-economic sizing of a microgrid. It was noted that, despite the economic criterion, the environmental criterion can have a considerable impact on the elements constructing the microgrid system. In this paper, two multi-objective optimization approaches are proposed, including a non-dominated sorting genetic algorithm (NSGA-II) and the Pareto Search algorithm (PS) for the eco-environmental design of a microgrid system. The k-means clustering of the non-dominated point on the Pareto front has delivered three categories of scenarios: best economic, best environmental, and trade-off. Energy management, considering the three cases, has been applied to the microgrid over a period of 24\u00a0h to evaluate the impact of system design on the energy production system\u2019s behavior.<\/jats:p>","DOI":"10.1007\/978-3-031-53036-4_23","type":"book-chapter","created":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T07:02:28Z","timestamp":1706857348000},"page":"326-342","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-objective Optimal Sizing of\u00a0an\u00a0AC\/DC Grid Connected Microgrid System"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8811-0823","authenticated-orcid":false,"given":"Yahia","family":"Amoura","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7082-115X","authenticated-orcid":false,"given":"Andr\u00e9","family":"Pedroso","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1912-2556","authenticated-orcid":false,"given":"\u00c2ngela","family":"Ferreira","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7902-1207","authenticated-orcid":false,"given":"Jos\u00e9","family":"Lima","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3155-5039","authenticated-orcid":false,"given":"Santiago","family":"Torres","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3803-2043","authenticated-orcid":false,"given":"Ana I.","family":"Pereira","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,3]]},"reference":[{"issue":"7875","key":"23_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3233\/his-230004","volume":"19","author":"Y Amoura","year":"2023","unstructured":"Amoura, Y., Torres, S., Lima, J., Pereira, A.I.: Hybrid optimisation and machine learning models for wind and solar data prediction. 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