AC/DC optimal power flow and techno-economic assessment for hybrid microgrids: TIGON CEDER demonstrator
In recent years, the interest in electric direct current (DC) technologies (such as converters, batteries, and electric vehicles) has increased due to their potential in energy efficiency and sustainability. However, the vast majority of electric systems and networks are based on alternating current...
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Veröffentlicht in: | Frontiers in energy research 2024-08, Vol.12 |
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Sprache: | eng |
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Zusammenfassung: | In recent years, the interest in electric direct current (DC) technologies (such as converters, batteries, and electric vehicles) has increased due to their potential in energy efficiency and sustainability. However, the vast majority of electric systems and networks are based on alternating current (AC) as they also have certain advantages regarding cost-effective transport and robustness. In this paper, an AC/DC optimal power flow method for hybrid microgrids and several key performance indicators (KPIs) for its techno-economic assessment are presented. The combination of both calculations allows users to determine the viability of their hybrid microgrids. AC/DC networks have been modeled considering their most common elements. For the power flow method, polynomial optimization is formulated considering four different objective functions: the minimization of energy losses, voltage deviation, and operational costs and the maximization of the microgrid generation. The power flow method and the techno–economic analysis are implemented in Python and validated in the Centro de Desarrollo de Energías Renovables (CEDER) demonstrator for TIGON. The results show that the calculated power flow variables and those measured at CEDER are practically the same. In addition, the KPIs are obtained and compared for four operating scenarios: baseline, no battery, battery flexibility, and virtual battery (VB) flexibility. The last scenario results in the most profitable option. |
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ISSN: | 2296-598X 2296-598X |
DOI: | 10.3389/fenrg.2024.1399114 |