Computationally Efficient Joint Coordination of Multiple Electric Vehicle Charging Points Using Reinforcement Learning
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Advanced optimization- or machine-learning-based schedulers require extensive computational resources and forecasting precision, making them less accessible to DSOs seeking near-term deployment.
“Second, advanced optimization-or machine-learning-based schedulers (for example, those discussed in [12,13]) require extensive computational resources and forecasting precision, making them less accessible to DSOs seeking near-term deployment.”
From A Multi-Agent, Laxity-Based Aggregation Strategy for Cost-Effective Electric Vehicle Charging and Local Transformer Overload Prevention (2025) · cites this paper as [12]
The Abstract says computational challenges could limit deployment of an earlier reinforcement-learning approach, and the Introduction says model-based demand-response algorithms face model uncertainty and scalability limits. The paper also presents a more computationally efficient scheduler, so it does not establish that advanced schedulers generally require extensive resources. It does not establish a need for forecasting precision or discuss near-term accessibility to DSOs. Page numbers are unavailable in our copy. (LLM, conf=0.87)
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Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning
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Cited by
Sorted by citations
Christensen, K., Jørgensen, B. N., & Ma, Z. G. (2025) · Sustainability · doi:10.3390/su17093847 · Cited by 8 · see citing works
- Authors
- Lahariya, M., Sadeghianpourhamami, N., & Develder, C.
- Affiliations
- Ghent University (BE)
- Corresponding author
- Lahariya, Manu
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2022
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W4293219589
- Cited by
- 6
- References
- 1
- Citations by year
- 2025: 3 · 2024: 1 · 2023: 2
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- European Commission (HORIZON2020, 957816, 957845)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
- Keywords
- electric vehicle charging, demand response, coordinated charging, reinforcement learning, Markov decision process, computational efficiency, peak shaving, grid flexibility
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