A Systematic Review of Uncertainty Handling Approaches for Electric Grids Considering Electrical Vehicles

Auza, Anna; Asadi, Ehsan; Chenari, Behrang; da Silva, Manuel Gameiro · 2023 · Crossref

DOI: 10.3390/en16134983

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Summary

This systematic review addresses the critical challenge of managing uncertainty in electric grids integrating electric vehicles (EVs) through vehicle-to-grid (V2G) technology. As renewable energy sources increase grid volatility, V2G integration offers a mechanism to smooth residual loads by discharging EV batteries during peak demand. However, the flexible nature of EV integration introduces significant uncertainties regarding load patterns, state of charge, and market dynamics. The authors identify a gap in existing literature, noting that while general power system uncertainty methods have been reviewed, no specific systematic reviews exist for V2G-related uncertainties. The study aims to characterize these uncertainties, evaluate the prevalence of various handling techniques, and analyze how method selection correlates with geographical, economic, and technical factors. The methodology employs a systematic search of the Scopus database using keywords related to uncertainty, electric vehicles, and demand-side management. From an initial pool of 406 articles, the authors applied strict exclusion criteria, removing studies lacking V2G integration, explicit uncertainty handling, or primary research status. This process resulted in a final dataset of 87 articles, with 30 high-impact papers selected for in-depth analysis. The review categorizes uncertainty sources into basic random variables (e.g., load values, wind speed), initial conditions (e.g., battery state of charge), and model discrepancies. It evaluates five primary uncertainty handling approaches: Monte Carlo simulation, probabilistic scenarios, stochastic optimization, point estimate methods (PEM), and robust optimization, alongside secondary methods like possibilistic and interval analysis. The findings reveal that probabilistic techniques are the most widely applied methods for handling EV-related uncertainties. Specifically, Monte Carlo simulation is used in 19% of cases, followed by scenario analysis at 15%, and both robust optimization and stochastic approaches at 10% each. The study observes a temporal shift in methodology: early research favored robust optimization due to a lack of historical data, whereas recent studies increasingly adopt Monte Carlo simulations. The choice of uncertainty handling technique is significantly influenced by the type of uncertainty being modeled and the availability of human resources, with PEM showing distinct usage patterns relative to EV accumulation and researcher share. Conversely, the selection of methods shows no significant correlation with the type of energy generation source. The significance of this review lies in its comprehensive mapping of the current state of uncertainty modeling in V2G systems, providing researchers with guidance on method selection based on data availability and problem complexity. By highlighting the dominance of probabilistic methods and the evolving trend toward Monte Carlo simulations, the paper underscores the importance of historical data in modern grid modeling. The findings suggest that while robust optimization remains valuable for scenarios with severe uncertainty or insufficient data, the field is moving toward more data-intensive probabilistic approaches. This synthesis aids in identifying future research directions, particularly in addressing the computational costs and implementation complexities associated with these advanced modeling techniques.

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