Smart Technology for Climate Action — How Artificial Intelligence Can Optimize Renewable Energy Efficiency in Africa
Curated by the Research Partnership Group (RPG) of Clean Technology Hub
Written by Sayuri Moodliar, PhD (August, 2024)
- Introduction
A critical component of climate action under the Paris Agreement is that countries are required to publicly pledge their commitments to reducing greenhouse gas emissions in the form of Nationally Determined Contributions (NDCs).
African countries face a substantial financial burden in seeking to achieve their NDC commitments while also pursuing sustainable development goals, like eradicating poverty and providing electricity, clean water and sanitation to their citizens. Foreign investments for climate action projects can help to reduce this burden.
Renewable energy plays a significant role in helping countries to achieve their climate action goals. The International Energy Agency reports that about US$40 billion is set to be spent on clean energy technologies in Africa in 2024. Although most of the recent clean energy investments in Africa have been in renewable power generation projects, the prospects for further growth are limited due to inefficient grids and insufficient interconnections.
This article explores how Artificial Intelligence (AI) can be used to create integrated, stable and sustainable grids that provide opportunities to optimise renewable energy efficiency.
2. Using AI to address challenges in renewable energy efficiencyAI algorithms enable computer systems to process and analyse large amounts of data, recognise patterns and trends, and make recommendations for decision-making based on predictive analysis. This forecasting ability provides the key to greater efficiency for renewable power producers.
The main efficiency challenges faced by the renewable energy sector include the following:
- The maintenance of renewable energy infrastructure, such as wind turbines and solar panels, is costly and time-consuming, with unexpected downtime reducing overall efficiency.
- Solar and wind energy are inherently intermittent, depending on weather conditions and time of day or year, leading to variable energy supply at different times and seasons.
- Effective storage and management of energy are crucial to address intermittency and ensure a consistent energy supply, but current storage solutions can be inefficient and costly.
- Integrating renewable energy into the existing grid infrastructure is often challenging, leading to instability and inefficiencies.
- Predictive maintenance powered by AI can analyse sensor data from renewable energy installations to predict equipment failures before they happen, thereby reducing maintenance costs and minimising downtime. AI’s predictive analysis capability can also enhance energy forecasting by analysing weather patterns and historical data to predict solar and wind energy production relatively accurately, allowing for better grid management and scheduling of backup power sources.
AI can optimise energy storage by managing when and how energy is stored and released, improving the efficiency of battery systems. AI algorithms can also forecast energy demand and adjust storage operations accordingly, thereby maximising the use of stored renewable energy.
The problem of grid integration and stability requires a holistic and innovative approach that addresses the entire electricity network. Smart grid management systems can provide an integrated and sustainable solution that leverages technology and enhances renewable energy efficiency.
3. Smart grid management — using technology for sustainable energy solutions
A smart grid is an advanced electricity network that uses digital and other advanced technologies to enhance the reliability, efficiency, and sustainability of energy delivery. This is achieved through integrating system components, such as sensors that collect data, communication networks that connect the various components and enable seamless data exchange, control systems that monitor and control grid operations, and advanced metering infrastructure that provides detailed usage information.
The African Development Bank has highlighted that smart grids offer Africa the opportunity to revolutionise its energy infrastructure by enhancing connectivity between supply and demand, reducing transmission and distribution costs, and integrating renewable energy sources like solar power and electric vehicles. Overall, smart grids have the potential to future-proof Africa’s energy infrastructure, making it more sustainable, affordable, and reliable.
Technological advancements have enabled smart grid systems to evolve, becoming progressively more efficient and resilient. AI tools have become standard in smart grid management systems due to their ability to analyse large datasets and conduct predictive analysis, and improve outcomes over time through machine learning.
AI can be combined with other technology like the Internet of Things (IoT). IoT devices allow smart meters, substations, and other grid elements to exchange information for seamless interaction between grid components. IoT facilitates real-time monitoring of the entire grid infrastructure and empowers utilities to implement demand response programmes efficiently.
In addition to AI and IoT, distributed ledger technology like blockchain can be used to enhance grid security, facilitate peer-to-peer energy trading, and enable transparent transactions. Blockchain’s distinctive characteristic of immutability ensures data integrity, creating a system that grid participants can trust.
4. How AI-enabled smart grids contribute to renewable energy efficiency
The benefits of AI are enhanced exponentially when its capabilities are applied to smart grids that run national, regional or even local electricity production and distribution. Some examples of this synergy are the following:
- Energy production from various distributed energy resources, including solar panels and wind turbines, can be predicted based on weather forecasts and historical data. This allows for optimal scheduling and dispatching of energy resources, ensuring that the energy produced is efficiently integrated into the grid.
- Demand response systems can analyse real-time data to predict and respond to changes in electricity demand. Machine learning models can forecast peak load times and adjust the grid’s operations accordingly, enabling efficient load sharing and maintaining electricity quality.
- The smart grid’s integrated communication system is enhanced by enabling real-time data analysis and decision-making. Processing vast amounts of data from various sources results in improved coordination and management of renewable energy resources and ultimately more efficient energy distribution and reduced losses.
- Data from the smart grid’s advanced metering infrastructure can be analysed to detect patterns in energy consumption and generation. This analysis can be used to optimise energy distribution, identify inefficiencies, and provide consumers with insights to manage their energy use better.
- Demand profile shaping (load shifting) and peak shaving (load shedding) can be optimised by analysing consumption patterns and predicting future demand. Real-time pricing models powered by AI can incentivise consumers to shift their energy usage to off-peak times, reducing strain on the grid and enhancing the efficiency of renewable energy sources.
AI-driven tools and platforms make smart grids more efficient, reliable, and capable of effectively integrating renewable energy sources, thereby promoting the overall optimization of renewable energy generation and consumption within the grid system.
5. AI in Africa’s energy landscape — smart grids, microgrids, and electricity access
Various research programmes — like IBM Research Africa and AI for Climate Action Africa — are funding projects to explore the potential for using AI and other smart technologies to reduce energy consumption and achieve climate action goals in Africa. One of these projects (modelling grid electricity demand using AI) is using supervised machine learning processes to identify the optimal grid demand and supply required for Africa’s energy transition.
Recent research showcases examples of how AI projects in Africa are contributing to more efficient grids, and even achieving sustainable development impact by providing energy access in off-grid areas.
In South Africa, smart grids are being used at both national and city level. The state-owned electricity provider (Eskom) has implemented AI in predictive maintenance for its power grid infrastructure which has improved grid reliability and minimised service disruptions, contributing to a more stable and resilient electricity supply. The City of Johannesburg, through its utility company City Power, uses AI systems to analyse consumption patterns and incorporate weather data for demand forecasting and real-time grid management, helping to reduce outages and improve efficiency.
In several African countries, projects have achieved the goal of improving electricity access as part of efforts to optimise energy distribution. In Rwanda, an organisation called Off Grid Box worked with the government to provide solar-powered electricity in rural areas. AI-powered mini-grids were installed that used machine learning algorithms to adapt to local energy needs. This project positively impacted remote communities’ access to education, healthcare and even water (the solution included a water purification system). It also demonstrated the scalability of AI-powered mini-grids for off-grid electrification.
There are also examples from Kenya, Tanzania, South Africa and other African countries of successful projects to implement AI-powered micro or mini-grids. Solar-powered micro grids serve to extend electrification in African countries through solutions that align with climate action commitments, as well as sustainable development goals.
6. Conclusion
AI capabilities are significantly enhancing the efficiency and reliability of energy networks. From predictive maintenance for renewable energy infrastructure and AI-driven energy storage solutions to integrated smart grid management, AI has the capacity to enable a more sustainable and resilient energy future. Combining AI with other smart technologies like IoT and blockchain can further improve renewable energy efficiency.
In Africa, AI-enabled solutions are being implemented in smart grid systems and in micro or mini-grids that enable countries to expand electrification to rural areas, thereby contributing to sustainable development impact in addition to climate action commitments.
As technology like AI continues to evolve, positive impact on the renewable energy sector is expected to grow, paving the way for a cleaner and more efficient energy landscape for Africa.
Dr. Sayuri Moodliar is the ESG Director at Open Access Data Centres and a member of Clean Technology Hub’s Research Partnership Group. Her areas of interest are international law and governance, sustainable finance, biodiversity conservation, and using technology for the achievement of sustainability goals.
