Reinforcement learning for demand response: A review of algorithms and modeling techniques
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Techniques such as deep RL, agent-based models, online optimization, and dynamic programming can help maximize profits for decentralized storage, demand, and generation, given real-time prices.
“Techniques such as (deep) RL [291,292,810,881], agent-based models [173,662,663,863], online optimization [99], and dynamic programming [704] can then help maximize profits for decentralized storage, demand, and generation, given real-time prices.”
From Tackling climate change with machine learning (2023) · cites this paper as [810]
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Integrating active demand into the distribution system using metaheuristic techniques
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- Authors
- Vázquez-Canteli, J. R., & Nagy, Z.
- Affiliations
- The University of Texas at Austin (US)
- Corresponding author
- Nagy, Zoltán
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 235
- Pages
- 1072-1089
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2901645090
- Cited by
- 883
- References
- 159
- Citations by year
- 2026: 155 · 2025: 144 · 2024: 122 · 2023: 111 · 2022: 115 · 2021: 111 · 2020: 95 · 2019: 29 · 2018: 1
- FWCI
- 22.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Applied Energy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Keywords
- demand response, demand-side management, reinforcement learning, smart grid, demand-side flexibility, peak demand shifting, multi-agent systems, HVAC control, electric vehicle charging, battery energy storage, renewable energy integration, building energy consumption
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