Comparison and Evaluation of Methods for a Predict+Optimize Problem in Renewable Energy

no code implementations • 21 Dec 2022 • Christoph Bergmeir, Frits de Nijs, Abishek Sriramulu, Mahdi Abolghasemi, Richard Bean, John Betts, Quang Bui, Nam Trong Dinh, Nils Einecke, Rasul Esmaeilbeigi, Scott Ferraro, Priya Galketiya, Evgenii Genov, Robert Glasgow, Rakshitha Godahewa, Yanfei Kang, Steffen Limmer, Luis Magdalena, Pablo Montero-Manso, Daniel Peralta, Yogesh Pipada Sunil Kumar, Alejandro Rosales-Pérez, Julian Ruddick, Akylas Stratigakos, Peter Stuckey, Guido Tack, Isaac Triguero, Rui Yuan

As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area.

Scheduling

Proximal Policy Optimization Based Reinforcement Learning for Joint Bidding in Energy and Frequency Regulation Markets

no code implementations • 13 Dec 2022 • Muhammad Anwar, Changlong Wang, Frits de Nijs, Hao Wang

Driven by the global decarbonization effort, the rapid integration of renewable energy into the conventional electricity grid presents new challenges and opportunities for the battery energy storage system (BESS) participating in the energy market.

Model-Free Approach to Fair Solar PV Curtailment Using Reinforcement Learning

no code implementations • 13 Dec 2022 • Zhuo Wei, Frits de Nijs, Jinhao Li, Hao Wang

This paper investigates reinforcement learning, which gradually optimizes a fair PV curtailment strategy by interacting with the system.

Fairness reinforcement-learning +1

Reinforcement Learning for Strategic Recommendations

no code implementations • 15 Sep 2020 • Georgios Theocharous, Yash Chandak, Philip S. Thomas, Frits de Nijs

Strategic recommendations (SR) refer to the problem where an intelligent agent observes the sequential behaviors and activities of users and decides when and how to interact with them to optimize some long-term objectives, both for the user and the business.

reinforcement-learning Reinforcement Learning (RL)