Article

Understanding Reinforcement Learning Control in Cyber-Physical Energy Systems

Maurizio RepettoPolytechnic University of Turin,Energy Department,10129 Turin,ItalyFrancesco MoraglioPolytechnic University of Turin,Energy Department,10129 Turin,ItalyLorenti, GianmarcoPolytechnic University of Turin,Energy Department,10129 Turin,Italy

2022en

ABI

Abstract

The possibility of modeling a renewable energy system as a Cyber-Physical Energy System (CPES) offers new possibilities in terms of control. More precisely, this document discusses the applicability of Reinforcement Learning (RL) techniques to CPES. By considering a benchmark algorithm, we focus on conceptual and implementation details and on how such details relate to the problem of interest. In this case, we simulate how a RL model can optimize the energy storage control in order to reduce energy costs. The work also discusses the issues that arise in RL models and the possible approaches to these difficulties. Specifically, we propose investigating a better exploitation of the memory mechanism.

Topics

Reinforcement Learning in Robotics Smart Grid Energy Management Smart Grid Security and Resilience

Identifiers

DOI: 10.1109/mscpes55116.2022.9770172

Citations and references

Cited by 014 references

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