Optimizing EV Charging in Real-Time With a Distributed Game-Theoretic Framework
In light of the difficulties posed by optimizing and scheduling the integration of large-scale electric vehicles (EVs) into the power grid, this study introduces a real-time optimization approach rooted in dynamic non-cooperative game theory. An equivalent model of the EV cluster is constructed, and...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11080423/ |
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| Summary: | In light of the difficulties posed by optimizing and scheduling the integration of large-scale electric vehicles (EVs) into the power grid, this study introduces a real-time optimization approach rooted in dynamic non-cooperative game theory. An equivalent model of the EV cluster is constructed, and the unique Nash equilibrium of the game model is proven using complete potential game theory. A distributed real-time optimization approach is then implemented through the alternating direction method of multipliers (ADMM). Simulations are conducted across three scenarios: disordered charging, orderly charging, and orderly charging with energy storage. The results indicate that disordered charging increases the grid load peak and exacerbates the peak-valley difference, while orderly charging reduces the peak-valley difference by 15.35%. When energy storage is optimally configured, the peak-valley difference is reduced by up to 20.65%. Additionally, in the orderly charging scenario, the average electricity purchasing cost for EVs drops by 8.72%, and the peak cost decreases by 10.4%. The system demonstrates high computational efficiency with response times in the millisecond range for different EVA (Electric Vehicle Aggregator) charging strategies. The probabilistic model used for predicting EV charging loads achieves an average error rate of less than 5%, ensuring accurate and stable scheduling. These findings show that the proposed method not only reduces grid load fluctuations and achieves peak shaving and valley filling but also lowers charging aggregators’ (EVA) purchasing costs, offering stability in optimization results and excellent performance in computation time and user privacy protection. |
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| ISSN: | 2169-3536 |