AC/DC optimal power flow and techno-economic assessment for hybrid microgrids: TIGON CEDER demonstrator
<|reference_start|>AC/DC optimal power flow and techno-economic assessment for hybrid microgrids: TIGON CEDER demonstrator: In the recent years, the interest in electric direct current (DC) technologies (such as converters, batteries, electric vehicles, etc.) is increasing due to its potential on 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 clearly determine the viability of their hybrid microgrids. AC/DC networks have been modelled considering their most common elements. For the power flow method, a polynomial optimisation is formulated considering four different objective functions: the minimisation of energy losses, voltage deviation and operational costs, and also the maximisation of the microgrid generation. The power flow method and the techno-economic analysis have been implemented in Python and validated in the Centro de Desarrollo de Energ\'ias Renovables (CEDER) demonstrator for TIGON. The results show that the calculated power flow variables and the ones measured at CEDER are practically the same. In addition, the KPIs have been obtained and compared for four operating scenarios: baseline, no battery, battery flexibility and virtual battery (VB) flexibility. The last one result in the most profitable option.<|reference_end|>
@article{martín-crespo2024ac/dc,
    title={AC/DC optimal power flow and techno-economic assessment for hybrid
  microgrids: TIGON CEDER demonstrator},
    author={Alejandro Mart'in-Crespo, Alejandro Hern'andez-Serrano, 'Oscar
  Izquierdo-Monge, Paula Pe~na-Carro, 'Angel Hern'andez-Jim'enez, Fernando
  Frechoso-Escudero, Enrique Baeyens},
    journal={arXiv preprint arXiv:2403.06625},
    year={2024},
    doi={10.3389/fenrg.2024.1399114},
    archivePrefix={arXiv},
    eprint={2403.06625},
    primaryClass={eess.SY cs.SY}
}