Enhancing PV-EV hosting capacities in residential distribution systems considering thermal limits of substation and feeders
Abstract
This paper presents a methodology for enhancing the hosting capacity (HC) of photovoltaic (PV) generation and electric vehicle (EV) demand in residential unbalanced distribution systems using the Jellyfish search (JS) algorithm. The primary role of the JS algorithm is to determine the maximum number of PVs and EVs that can be hosted in the distribution systems while satisfying operational and system constraints. The constraints include the voltage profile, the utility transformer’s thermal limit, and the system feeders’ thermal limit. Four case studies on the hosting capacity of PVs and EVs, with and without reactive power support from their inverters. The first and second case studies simulate the EV-HC without and with reactive power support from the EV inverters. The third and fourth cases are dedicated to EV-PV-HC without and with reactive power support. The JS algorithm optimizes the HC of 207 and 225 EVs for the first and second case studies, respectively. The EV-HC has risen to 3,201 and 3,540 EVs with PV generation in the third and fourth case studies, respectively. In addition, the PV-HC reaches 2,988 and 3,084 PVs in the last two cases, respectively. Moreover, the results show that the PV-HC with EV demand reduces power loss by 47.39 % compared to hosting EVs alone. Furthermore, the power loss is reduced by 66.64 % through reactive power assistance from the regulation of PV and EV inverters. Equally, reactive power loss decreases by 57.38 % when using PV-HC compared to EV-HC alone. Furthermore, reactive power loss is reduced by 70.39 % with inverter reactive power support. The increased hosting capacity for PV and EV into distribution systems indicates a more robust and sustainable energy management approach, marked by reduced energy losses and potentially improved efficiency.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI, and Machine Learning Tools: None of the AI or ML tools is used.
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Conflict of interest: The author states no conflict of interest.
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Research funding: No funding is provided for this research.
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Data availability: Data can be provided upon request.
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