On optimal charging scheduling for electric vehicles with wind power generation
Junjie Wu1,2, Qing-Shan Jia1
1Center for Intelligent and Networked Systems (CFINS), Department of Automation, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China.
Fundamental Research
|August 19, 2024
Summary
We developed efficient algorithms for scheduling electric vehicle (EV) charging with renewable energy. Our methods ensure self-sustained charging or optimal management during power shortages, outperforming existing approaches.
Area of Science:
- Electrical Engineering
- Computer Science
- Renewable Energy Systems
Background:
- Increasing electric vehicle (EV) adoption and renewable energy integration necessitate efficient charging strategies.
- Optimizing EV charging is complex due to large action spaces, multi-stage decisions, and high uncertainty.
- Current methods are often time-consuming for large-scale systems, requiring practical and efficient solutions.
Purpose of the Study:
- To develop efficient algorithms for scheduling electric vehicle charging integrated with renewable power generation.
- To provide a sufficient condition for self-sustained EV charging using distributed generation.
- To investigate optimal charging policies under renewable energy deficits and propose a near-optimal general solution.
Main Methods:
- Derivation of a sufficient condition for self-sustained charging.
- Development of an optimal charging policy algorithm when the condition holds.
- Proof of an optimal policy using the modified least laxity and longer remaining processing time first (mLLLP) rule for deterministic renewable generation.
- Proposal of an adaptive rule-based algorithm for near-optimal charging in general scenarios.
Main Results:
- A sufficient condition for self-sustained EV charging via distributed generation was identified.
- An optimal charging policy was derived for scenarios meeting the sufficient condition.
- An optimal policy based on the mLLLP rule was proven for deterministic renewable generation deficits.
- The proposed adaptive rule-based algorithm demonstrated superior performance compared to existing methods in numerical experiments.
Conclusions:
- Efficient EV charging scheduling is crucial for integrating EVs and renewable energy.
- The developed algorithms provide practical and efficient solutions for both self-sustained and deficit scenarios.
- The adaptive rule-based algorithm offers a near-optimal and efficient approach for general EV charging management.
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