mlsanityData to 14 Oct 2026

Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems

arXiv 2406.19825 ↗First posted 28 Jun 2024cs.LGcited by 3

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

The ongoing energy transition drives the development of decentralised renewable energy sources, which are heterogeneous and weather-dependent, complicating their integration into energy systems. This study tackles this issue by introducing a novel reinforcement learning (RL) framework tailored for the co-optimisation of design and control in energy systems. Traditionally, the integration of renewable sources in the energy sector has relied on complex mathematical modelling and sequential processes. By leveraging RL's model-free capabilities, the framework eliminates the need for explicit system modelling. By optimising both control and design policies jointly, the framework enhances the integration of renewable sources and improves system efficiency. This contribution paves the way for advanced RL applications in energy management, leading to more efficient and effective use of renewable energy sources.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 12 of its 33 references that are in the map. First posted on arXiv 28 Jun 2024.

Builds on

12 of 33 references.

References · 12

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 672
  3. 2018An Introduction to Deep Reinforcement Learningcited by 206
  4. 2018Understanding the impact of entropy on policy optimizationcited by 176
  5. 2018Reinforcement Learning for Improving Agent Designcited by 56
  6. 2018Jointly Learning to Construct and Control Agents using Deep Reinforcement Learningcited by 55
  7. 2022Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robotscited by 47
  8. 2019Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learningcited by 37
  9. 2020Hardware as Policy: Mechanical and Computational Co-Optimization using Deep Reinforcement Learningcited by 32
  10. 2021Integrating process design and control using reinforcement learningcited by 6
  11. 2020Jointly Learning Environments and Control Policies with Projected Stochastic Gradient Ascentcited by 2
  12. 2023Reinforcement Learning for Joint Design and Control of Battery-PV Systemscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Trustworthy and Explainable Deep Reinforcement Learning for Safe and Energy-Efficient Process Control: A Use Case in Industrial Compressed Air Systemscited by 0
  2. 2026A Reinforcement Learning-based Transmission Expansion Framework Considering Strategic Bidding in Electricity Marketscited by 0
  3. 2026Environment Parameter Gradient Theorem for Co-Design in Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources
  2. 2025Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges
  3. 2024Learning a local trading strategy: deep reinforcement learning for grid-scale renewable energy integration
  4. 2025A Reinforcement Learning Approach for Optimal Control in Microgrids
  5. 2025RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations
  6. 2025Wide-Area Feedback Control for Renewables-Heavy Power Systems: A Comparative Study of Reinforcement Learning and Lyapunov-Based Design
  7. 2024Reinforcement Learning for Sustainable Energy: A Survey
  8. 2025Data-Driven Policy Mapping for Safe RL-based Energy Management Systems