Machine Learning for Sustainable Power Systems

Introduction

The Machine Learning for Sustainable Power Systems (ML4SPS) workshop aims to bring together researchers in the fields of machine learning and energy systems

Energy systems around the world are facing the challenge of integrating massive amounts of new renewable energy production. Unlike traditional energy production methods, new renewables are highly variable, decentralized and only partially predictable. At the same time, new consumption patterns and additional uses of electricity are emerging, such as self-consumption, heat pumps or electric vehicles. These new consumers increase the complexity of the grids in terms of monitoring and control, but also allow for new flexibility in the future. The use of machine learning methods can help to exploit these new flexibilities to overcome the increasing complexity of modern energy systems. To this end, machine learning can take advantage of technological advances such as smart meters and novel communication devices. 

Consequently, ML4SPS aims to bring together researchers and practitioners to share their knowledge and experience in the field of renewable energy systems, grid management and machine learning. The workshop itself will be organised by experts in the field of renewable energy systems and machine learning who will share their knowledge and experience with the participants. The workshop will be interactive and will include the presentation of the submitted papers, a poster session and two keynote speakers from the field of machine learning in energy systems.  

Research Topics

The workshop ML4SPS focuses on, but is not limited to, several key topics within the energy system, as listed below. However, contributions to other related research areas are also welcome. In terms of methods and algorithmic approaches, the workshop does not limit any contributions as long as they are from the field of machine learning.  

Forecasting in energy systems 

• Advanced predictive models for forecasting of electricity demand, wind energy, and solar/PV power generation using deep learning and time series analysis. 

• Ensemble methods and uncertainty quantification in energy forecasting to enhance decision-making processes. 

Grid Control and Optimization: 

• Machine learning algorithms for real-time grid management and operation, including load balancing, outage management, frequency regulation, grid calculations and voltage control. 

• Integration of distributed energy resources (DERs) and energy storage solutions through intelligent control systems. 

• Approaches on generalizability und adaptability of power system applications 

Robustness and Trust in Industrial AI Applications: 

• Methods to enhance the robustness of machine learning models against adversarial attacks, data corruption, and model drift in energy systems. 

• Strategies for building trust and ensuring transparency in AI-based decision-making, including explainable AI (XAI) techniques for energy applications.

Asset Management and Predictive Maintenance: 

• Predictive analytics for asset health monitoring, leveraging IoT data, forecasting equipment failures. 

• Machine learning frameworks for life-cycle management of energy infrastructure, enhancing reliability and optimizing operational costs. 

• Techniques for real-time anomaly detection and diagnosis in power systems.

Energy Markets and Trading Strategies: 

• Application of machine learning in analysing energy market dynamics, price forecasting, and risk assessment  

• AI-driven tools for optimizing energy procurement, portfolio management, and demand-side response. 

• Impact analysis of renewable energy penetration on energy markets.

Energy Integration and Planning: 

• Optimization models for the effective integration of renewable energy source 

• Simulation and scenario analysis AI-tools with respect to grid planning, renewable energy deployment and infrastructure resilience.

Integration of Electric Vehicles into the Power Grid: 

• Machine learning for managing the impact of electric vehicle charging on the power grid. 

• Smart charging algorithms and strategies for electric vehicles.

Ethical AI and Socioeconomic Impacts of Machine Learning in Power Systems: 

• Ethical considerations and governance frameworks for the deployment of AI in energy systems 

• Machine learning's role in addressing energy poverty and ensuring equitable access to clean energy.