Summary of A Deep Q-learning Based Smart Scheduling Of Evs For Demand Response in Smart Grids, by Viorica Rozina Chifu et al.
A Deep Q-Learning based Smart Scheduling of EVs for Demand Response in Smart Grids
by Viorica Rozina Chifu, Tudor Cioara, Cristina Bianca Pop, Horia Rusu, Ionut Anghel
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a model-free solution for scheduling electric vehicle (EV) charging and discharging within a microgrid to align with a target energy profile. The approach leverages Deep Q-Learning, adapting the Bellman Equation to assess state values based on rewards for EV scheduling actions. A neural network estimates Q-values, while the epsilon-greedy algorithm balances exploration and exploitation to meet the target profile. Results show that the proposed solution effectively schedules EVs charging and discharging with a Person coefficient of 0.99. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Electric vehicles are getting cleaner alternatives to combustion engine vehicles, but they have some negative impacts on microgrid equipment and energy balance. This paper finds a way to use electric vehicle scheduling flexibility to help local networks by joining demand response programs. They came up with a model-free solution that uses Deep Q-Learning to schedule EV charging and discharging within a microgrid to match the target energy profile given by the distribution system operator. |
Keywords
* Artificial intelligence * Neural network
