paper-with-me

Papers

Reinforcement Learning for Electricity Network Operation

2020-03-16 · Adrian Kelly, Aidan O'Sullivan, Patrick de Mars, Antoine Marot

This paper presents the background material required for the Learning to Run Power Networks Challenge. The challenge is focused on using Reinforcement Learning to train an agent to manage the real-time operations of a power grid, balancing power flows and making interventions to maintain stability. We present an introduction to power systems targeted at the machine learning community and an introduction to reinforcement learning targeted at the power systems community. This is to enable and encourage broader participation in the challenge and collaboration between these two communities.

📄 PDF Abstract BibTeX arXiv:2003.07339

Code (0)

등록된 구현이 없습니다.

Tasks

BIG-bench Machine Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)

Similar Papers 제목 키워드 기반

Bilevel Model for Electricity Market Mechanism Optimisation via Quantum Computing Enhanced Reinforcement Learning

2024-10-28 · Shuyang Zhu, Ziqing Zhu

In response to the increasing complexity of electricity markets due to low-carbon requirements and the integration of sustainable energy sources, this paper proposes a dynamic quantum computing enhanced bilevel optimizat…

Bilevel OptimizationReinforcement Learning (RL)

A Deep Reinforcement Learning Approach to Battery Management in Dairy Farming via Proximal Policy Optimization

2024-07-01 · Nawazish Ali, Rachael Shaw, Karl Mason

Dairy farms consume a significant amount of electricity for their operations, and this research focuses on enhancing energy efficiency and minimizing the impact on the environment in the sector by maximizing the utilizat…

Deep Reinforcement Learningenergy managementManagementQ-Learning+2

Reinforcement Learning-Based Co-Design and Operation of Chiller and Thermal Energy Storage for Cost-Optimal HVAC Systems

2026-01-30 · Tanay Raghunandan Srinivasa, Vivek Deulkar, Aviruch Bhatia, Vishal Garg arxiv

We study the joint operation and sizing of cooling infrastructure for commercial HVAC systems using reinforcement learning, with the objective of minimizing life-cycle cost over a 30-year horizon. The cooling system cons…

Reinforcement Learning

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage

2025-06-06 · Manuel Sage, Khalil Al Handawi, Yaoyao Fiona Zhao

Power-to-Gas (P2G) technologies gain recognition for enabling the integration of intermittent renewables, such as wind and solar, into electricity grids. However, determining the most cost-effective operation of these sy…

Deep Reinforcement Learning

Pareto local search for a multi-objective demand response problem in residential areas with heat pumps and electric vehicles

2024-07-16 · Thomas Dengiz, Andrea Raith, Max Kleinebrahm, Jonathan Vogl 외

In future energy systems characterized by significant shares of fluctuating renewable energy sources, there is a need for a fundamental change in electricity consumption. The energy system requires the ability to adapt t…

Evolutionary AlgorithmsHeuristic Searchreinforcement-learningReinforcement Learning