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The transition to Electric Vehicles (EVs) for reducing urban greenhouse gas emissions is hindered by the lack of public charging infrastructure, particularly fast-charging stations. Given that electric vehicle fast charging stations (EVFCS) can burden the electricity grid, it is crucial for EVFCS to adopt sustainable energy supply methods while accommodating the growing demands of EVs. Despite recent research efforts to optimize the placement of renewable-powered EV charging stations, current planning methods face challenges when applied to a complex city scale and integrating with renewable energy resources. This study thus introduces a robust decision-making model for optimal EVFCS placement planning integrated with solar power supply in a large and complex urban environment (e.g., Chicago), utilizing an advantage actor-critic (A2C) deep reinforcement learning (DRL) approach. The model balances traffic demand with energy supply, strategically placing charging stations in areas with high traffic density and solar potential. As a result, the model is used to optimally place 1,000 charging stations with a random starting search approach, achieving total reward values of 74.30 %, and estimated the capacities of potential EVFCS. This study can inform the identification of suitable locations to advance the microgrid-based charging infrastructure systems in large urban environments.
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Stochastic Model Predictive Control-Based Electric Taxi Fleet Coordination under Solar Power Uncertainty
As electric vehicles (EVs) gradually replace fuel vehicles and provide transportation services in cities, e.g., electric taxi fleets, solar-powered charging stations with energy storage systems have been deployed to provide charging services for EV fleets. The mixture of solar-powered and traditional charging stations brings efficiency challenges to charging stations and reliability challenges to power systems. In this article, we explore e-taxis’ mobility and charging demand flexibility to co-optimize service quality of e-taxi fleets and system cost of charging infrastructures, e.g., solar power under-utilization and reliability issues of power distribution networks due to reverse power flow. We propose SAC, an e-taxi coordination framework to dispatch e-taxis for charging or serving passengers under spatial-temporal dynamics of renewable energy and passenger mobility, which integrates the renewable power generation estimation from a forecast system. Moreover, we extend our design to a stochastic Model Predictive Control problem to handle the uncertainty of solar power generation, aiming to fully utilize generated solar power. Our data-driven evaluation shows that SAC significantly outperforms existing solutions, enhancing the usage rate of solar power by up to 172.6%, while maintaining e-taxi service quality with very small overhead, i.e., reducing the supply-demand ratio by 2.2%.
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- Award ID(s):
- 2431552
- PAR ID:
- 10654009
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Transactions on Cyber-Physical Systems
- Volume:
- 9
- Issue:
- 4
- ISSN:
- 2378-962X
- Page Range / eLocation ID:
- 1 to 22
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1145/3744748
