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AAMAS 2025 Tutorial:
Multi-Agent and AI Techniques for Decentralised Energy Systems

Summary Description

Energy systems are undergoing fundamental changes, having to deal with increasing uncertainty, digitalization, and decentralization. This makes them a natural area for multi-agent systems research, given the increasingly decentralized way in which they are planned and operated. Decentralized AI-based solutions are playing an increasingly important role in energy systems management and control, yet building successful AI applications for this domain requires both an understanding of the fundamental AI techniques, but also of the features and constraints of energy systems. In this tutorial, we will look at the fundamental opportunities and challenges in applying AI, game theory and multi-agent techniques to this key domain. The tutorial is aimed at both AI researchers (with a particular focus on PhD students and young researchers) wanting to learn more about challenges in applying their work to this area, as well as practitioners from the energy sector, exploring the potential of multi-agent solutions.

Overview

In recent years, energy systems have been experiencing rapid changes. These include the increasing generation coming from intermittent renewable resources, new and increasing sources of demand (such as electric vehicle charging, heat pumps, data centers, and AI software), and reinforcing the trend to increasing decentralisation, with many prosumers owning their own generation and storage units. Demand flexibility, with its inherent decentralized nature, is also gaining importance and reinforcing energy systems decentralised nature. For all these challenges, AI techniques play an increasingly crucial part in the solutions being developed. Examples include the use of machine learning for forecasting (e.g. for renewable generation, demand response and load), smart and distributed control (e.g. for charging electric vehicles and many other energy-consuming appliances), and the use of algorithmic game theory, agent and multi-agent techniques to deal with large systems, energy communities, and electricity markets with strategic agents. Yet, energy systems do not yield to a straightforward application of AI or multi-agent techniques, since they often involve understanding the underlying physical and economic constraints of energy networks, and their real- or near-real time operation. In this tutorial, we aim to cover both the fundamental challenges and techniques that enable the use of multi-agent systems in the energy field, as well as a range of practical research examples.

From a network management viewpoint, recent technological advancements, including digital communication technologies, advanced metering devices and sensors, have dramatically changed the architecture and functionality of electrical networks. While these advancements have the potential to improve efficiency, reliability, and sustainability of electrical networks, they have also given rise to various technical challenges traditional methods were not designed to address. This promotes the need to develop novel decentralized and adaptable methods for energy network management at two related levels of abstraction. The first focuses on maintaining the power grid's stability while considering a large number of network entities and components, while the second level deals with the management of the electricity markets, in which the different agents trade energy and energy-based products, and with the regulation of prices within the market.

This half-day tutorial will start with an overview of the key technical and computational challenges that are involved in managing energy systems. We will then provide background on multi-agent AI, with a focus on agent-mediated electronic markets, and show how many of the challenges of current energy systems can be addressed by representing components of the system as autonomous or semi-autonomous agents. The second part of the tutorial will focus on the use of multi-agent modeling and game-theoretic methods (both cooperative and competitive) to provide efficient management solutions and decision-support in energy systems composed of many agents (generators, prosumers, EVs etc.) with their own goals and private information.

Outline and Topics Covered

The tutorial will cover a number of topics, divided among the two presenters depending on their experience. A list of potential topics is provided below, with the final presentation schedule to be agreed before the tutorial:

Length and Target Audience

The tutorial will take half a day, with roughly two equal slots of around 1.5 hours + breaks. The target audience of the proposed tutorial is two-fold: This tutorial seeks to provide the necessary background on energy systems and multi-agent AI for both junior and senior AI researchers who are considering exploring this rapidly evolving field. While a basic understanding of AI and multi-agent techniques helps, no specific prior knowledge is required from the participants.

Presenters

Zita Vale

Homepage: https://www.cienciavitae.pt/portal/pt/721B-B0EB-7141

GECAD-LASI
Polytechnic University of Porto
Porto, Portugal
Email: zav at isep.ipp.pt

Valentin Robu (main contact)

Homepage: https://homepages.cwi.nl/~robu/

Intelligent and Autonomous Systems (IAS) Group
CWI, Dutch National Research Institute
for Mathematics and Computer Science
Work Address Science Park 123, 1098XG
Amsterdam, NL
Email: v.robu at cwi.nl