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Multi-time scaling optimization for electric station considering uncertainties of renewable energy and EVs
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Multi-time scaling optimization for electric station considering uncertainties of renewable energy and EVs
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Journal Article
Multi-time scaling optimization for electric station considering uncertainties of renewable energy and EVs
2025
Overview
The development of new energy vehicles, particularly electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs), represents a strategic initiative to address climate change and foster sustainable development. Integrating PV with hydrogen production into hybrid electricity-hydrogen energy stations enhances land and energy efficiency but introduces scheduling challenges due to uncertainties. A multi-time scale scheduling framework, which includes day-ahead and intraday optimization, is established using fuzzy chance-constrained programming to minimize costs while considering the uncertainties of PV generation and charging/refueling demand. Correspondingly, trapezoidal membership function and triangular membership function are used for the fuzzy quantification of day-ahead and intraday predictions of photovoltaic power generation and load demands. The system achieves 29.37% lower carbon emissions and 17.73% reduced annualized costs compared to day-ahead-only scheduling. This is enabled by real-time tracking of PV/load fluctuations and optimized electrolyzer/fuel cell operations, maximizing renewable energy utilization. The proposed multi-time scale framework dynamically addresses short-term fluctuations in PV generation and load demand induced by weather variability and temporal dynamics. By characterizing PV/load uncertainties through fuzzy methods, it enables formulation of chance-constrained programming models for operational risk quantification. The confidence level – reflecting decision-makers’ reliability expectations – progressively increases with refined temporal resolution, balancing economic efficiency and operational reliability.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
