A researcher from the University of the Free State (UFS) is part of a prestigious team awarded a £499,258 (approximately R 11.5 million) research grant by the Science and Technology Facilities Council of the United Kingdom Research and Innovation (UKRI) organisation. This grant will support a statistical physics-based analysis of South Africa’s power grid. Dr Jacques Maritz, Senior Lecturer in the UFS Group of Engineering Sciences, joins leading experts Christian Beck, Professor of Applied Mathematics at Queen Mary University of London (QMUL), and Chantelle van Staden and Cristina Trois from Stellenbosch University to explore innovative solutions for the country’s power grid challenges.
Addressing South Africa’s power grid challenges
Dr Maritz highlights the major challenges facing South Africa’s power grid, particularly the increasing demand for electricity and the financial constraints limiting grid expansion and maintenance. These issues have resulted in frequent scheduled and unscheduled power outages, leaving many without electricity or dependent on costly diesel generators. This solution exacerbates both environmental and economic difficulties, especially in the country’s poorest communities.
A physics-driven approach to power fluctuations
To tackle these power fluctuations, Dr Maritz and his team employ advanced methods from statistical physics and machine learning. Power grids, with their vast networks of components and data, are notoriously complex to analyse. “Statistical physics enables us to treat the power grid as a collection of particles, helping us identify emergent phenomena and predict system behaviour,” explains Dr Maritz. Machine learning further refines this analysis, allowing the team to interpret the intricate dynamics of the system.
Dr Maritz leads the UFS Grid Related Research Group (GRRG), which specialises in solving problems related to large, complex systems. “The GRRG focuses on one specific complex system - the UFS microgrid - allowing for a data-driven approach to this project,” he adds.
Sustainable alternatives: Moving beyond diesel generators
A key objective of the research is to advance the integration of renewable energy into the national grid. By analysing long-term trends in system behaviour under varying weather conditions and renewable energy availability, the team seeks to understand how renewable components affect grid stability. These insights will aid in optimising energy distribution and mitigating risks associated with power fluctuations.
One of the primary challenges in integrating renewable energy into South Africa’s grid is its dependency on weather patterns. Unlike conventional power systems, which rely on stable, predictable energy sources, wind and solar power generation fluctuates with climate conditions. “Wind and solar power production depend heavily on weather conditions, and these fluctuations can create instability within the power grid,” notes Dr Maritz. The research aims to improve weather forecasting, assisting grid operators in better managing energy demand and renewable energy production, ultimately enhancing grid stability.
The team is also exploring sustainable alternatives to diesel generators, with a focus on technologies such as flywheel energy storage and green hydrogen production. These alternatives not only offer cleaner, more reliable energy solutions but also reduce the environmental impact of diesel-powered backup systems. “We are particularly interested in sustainable alternatives that emerge within large, complex systems,” says Dr Maritz.
The future of power grid research
The findings from this research will have significant implications not only for South Africa but also for other countries facing similar power grid challenges. “Power systems are becoming increasingly complex, exhibiting dynamics and emergent behaviours that have never been theorised before,” explains Dr Maritz.
Looking ahead, the next phase of the project will involve applying stochastic modelling to gain deeper insights into the complexities of South Africa’s power system. This approach will allow researchers to simulate and predict grid behaviour under various conditions, improving control strategies and enhancing power system stability.