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2168. Management of electric vehicles to provide energy flexibility under uncertainty
Invited abstract in session TB-22: Optimization for electric vehicles, stream Energy Management.
Tuesday, 10:30-12:00Room: 81 (building: 116)
Authors (first author is the speaker)
| 1. | Antoni Bosch Pons
|
| Energy Systems Analytics Research Group, Institut de Recerca en Energia de Catalunya | |
| 2. | Fernando García
|
| IREC | |
| 3. | Ferran Pinsach Batet
|
| Energy Systems Analytics Research Group, Institut de Recerca en Energia de Catalunya | |
| 4. | Lucia Igualada
|
| Energy Systems Analytics Research Group, Intitut de Recerca en Energia de Catalunya |
Abstract
In recent years, the adoption of Electric Vehicles (EVs) has increased, driven by government initiatives to require them to reduce greenhouse gas emissions. This increased adoption, while environmentally beneficial, has also led to an increase in electricity consumption. This paper analyses the advantages of bidirectional vehicle-to-grid (V2G) chargers for Charging Station Operators (CSOs): Electric Vehicles energy flexibility could be a valuable resource during grid congestion episodes. We analyze this flexibility by proposing a two-stage stochastic programming model under uncertainty, regarding the presence of EVs at charging stations. We utilize a mixed Long Short-Term Memory (LSTM) neural network to generate scenarios for each charger and then blend and reduce them into the most representative ones. We propose a large-scale optimization approach for solving the two-stage stochastic model. The use of a large-scale optimization approach helps to reduce the time for executing the optimal solution, which is beneficial to ensure management of grid variability. The results demonstrate that electric vehicles can provide demand flexibility, resulting in potential cost reductions and contributing to the prevention of power grid congestion during periods of low generation or high demand. Also, the developed solutions help to integrate renewable resources into the grid and reduce greenhouse gas emissions.
Keywords
- Stochastic Optimization
- OR in Energy
- Large Scale Optimization
Status: accepted
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