Efficiency optimisation and converterless PV integration by applying a dynamic voltage on an LVDC backbone

Hakim Azaioud, Arash Farnam, Jos Knockaert, Lieven Vandevelde and Jan Desmet

Applied Energy, 2024, vol. 356, issue C, No S0306261923017804

Abstract: A low-voltage DC (LVDC) backbone with a battery energy storage system (BESS) and distributed photovoltaics (PV) is proven to be a more efficient alternative compared to the traditional AC architecture. However, previous research has also proven the fact that the benefit strongly depends on the operating voltage level. In this study, a dynamic backbone voltage on architectures with and without distributed maximum power point trackers (MPPT) is investigated. The dynamic voltage is driven by a multi-objective optimisation algorithm that will minimise the cable and converter loss. Applying the dynamic voltage with MPPT leads to a massive decrease of the loss compared to a static voltage with MPPT. Although, if the optimisation objective is extended with the minimisation of the PV curtailment loss, the MPPT can be eliminated without causing a considerable curtailment loss while maintaining a high efficiency. The sensitivity analysis showed that even when the cable is longer, the orientations of the PV distributed systems are different or the PV system is exposed to dynamic shading, dynamic voltage without maximum power point is still competitive compared to the alternatives. However, heavy static shading conditions could cause a notable shift of the MPP making it very hard to track this operating point by the algorithm. The elimination of the MPPT leads to a simpler design and installation, lower investment costs and a reduction of the raw material use. The proposed strategies and architectures are hence favourable to be applied in LVDC backbones massively fed by roof-mounted or building-integrated PV systems. This study provides the general approach which could be extended with distributed electric vehicle chargers.

Keywords: LVDC; Dynamic voltage; PV systems; Energy efficiency; Multi-objective optimisation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2023.122416

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