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Probabilistic energy management of renewable-based charging stations hub for urban electric public transportation fleets

Year: 2026
Type: article
Abstract: Urban public transport electrification increasingly couples charging demand with variable renewable supply, creating operating conditions that deterministic station scheduling does not represent well. This work addresses that limitation through a probabilistic energy-management framework for a grid-connected charging station supplied by photovoltaic (PV) and wind generation. Solar irradiance and wind speed were represented by Beta and Weibull distributions, while fleet-side uncertainty was modeled through the initial state of charge and price-responsive charging or discharging behavior. Monte Carlo simulation generated stochastic realizations, and the Coati Optimization Algorithm (COA) determined 24-hour grid exchange and fleet power decisions subject to power-balance and operating constraints. The same scheduling problem was solved under deterministic and uncertainty-aware scenarios and benchmarked against four metaheuristic methods. Uncertainty raised the COA operating cost from 190.97 USD to 197.84 USD, indicating the economic effect of simultaneous supply- and demand-side variability. Even so, COA retained the lowest cost among the tested methods; under uncertainty, its cost was 15.70% below that obtained by particle swarm optimization. The stochastic results also preserved hourly power balance while capturing changes in renewable output and bidirectional fleet demand. The framework therefore provides a tractable basis for uncertainty-aware scheduling of renewable-supported public-transport charging stations and for assessing optimizer behavior under coupled generation and charging variability.
DOI: 10.1038/s41598-026-80343-1
Language: English
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7225804333
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