most citedScalable multi-agent reinforcement learning for distributed control of residential energy flexibility

77 citations · 79 across the 4 of their papers we have counts for

collaborators

4 papers

eess.SY2023

Home Electricity Data Generator (HEDGE): An open-access tool for the generation of electric vehicle, residential demand, and PV generation profiles

Flora Charbonnier, Thomas Morstyn, Malcolm McCulloch

In this paper, we present the Home Electricity Data Generator (HEDGE), an open-access tool for the random generation of realistic residential energy data. HEDGE generates realistic…

eess.SY2023

Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility

Flora Charbonnier, Bei Peng, Thomas Morstyn +1

This paper investigates how deep multi-agent reinforcement learning can enable the scalable and privacy-preserving coordination of residential energy flexibility. The coordination…

eess.SY2022★ 77 cited

Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility

Flora Charbonnier, Thomas Morstyn, Malcolm D. McCulloch

This paper proposes a novel scalable type of multi-agent reinforcement learning-based coordination for distributed residential energy. Cooperating agents learn to control the flexi…

eess.SY2022★ 2 cited

Coordination of resources at the edge of the electricity grid: systematic review and taxonomy

Flora Charbonnier, Thomas Morstyn, Malcolm McCulloch

This paper proposes a novel taxonomy of coordination strategies for distributed energy resources at the edge of the electricity grid, based on a systematic analysis of key literatu…