Model Identification and Transferability Analysis for Vehicle-to-Grid Aggregate Available Capacity Prediction Based on Origin–Destination Mobility Data

Luca Patanè, Francesca Sapuppo (), Gabriele Rinaldi, Antonio Comi, Giuseppe Napoli and Maria Gabriella Xibilia
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Luca Patanè: Department of Engineering, University of Messina, 98166 Messina, Italy
Francesca Sapuppo: Department of Engineering, University of Messina, 98166 Messina, Italy
Gabriele Rinaldi: Department of Engineering, University of Messina, 98166 Messina, Italy
Antonio Comi: Department of Enterprise Engineering, University of Rome Tor Vergata, 00133 Rome, Italy
Giuseppe Napoli: National Research Council of Italy, Institute of Advanced Technologies for Energy, 98126 Messina, Italy
Maria Gabriella Xibilia: Department of Engineering, University of Messina, 98166 Messina, Italy

Energies, 2024, vol. 17, issue 24, 1-22

Abstract: Vehicle-to-grid (V2G) technology is emerging as an innovative paradigm for improving the electricity grid in terms of stabilization and demand response, through the integration of electric vehicles (EVs). A cornerstone in this field is the estimation of the aggregated available capacity (AAC) of EVs based on available data such as origin–destination mobility data, traffic and time of day. This paper considers a real case study, consisting of two aggregation points, identified in the city of Padua (Italy). As a result, this study presents a new method to identify potential applications of V2G by analyzing floating car data (FCD), which allows planners to infer the available AAC obtained from private vehicles. Specifically, the proposed method takes advantage of the opportunity provided by FCD to find private car users who may be interested in participating in V2G schemes, as telematics and location-based applications allow vehicles to be continuously tracked in time and space. Linear and nonlinear dynamic models with different input variables were developed to analyze their relevance for the estimation in one-step- and multiple-step-ahead prediction. The best results were obtained by using traffic data as exogenous input and nonlinear dynamic models implemented by multilayer perceptrons and long short-term memory (LSTM) networks. Both structures achieved an R 2 of 0.95 and 0.87 for the three-step-ahead AAC prediction in the two hubs considered, compared to the values of 0.88 and 0.72 obtained with the linear autoregressive model. In addition, the transferability of the obtained models from one aggregation point to another was analyzed to address the problem of data scarcity in these applications. In this case, the LSTM showed the best performance when the fine-tuning strategy was considered, achieving an R 2 of 0.80 and 0.89 for the two hubs considered.

Keywords: vehicle-to-grid; available aggregate capacity; model identification; predictive model; data-driven model; floating car data (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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