A huge revolution is taking place in the global energy system right now, and in the decades to come, energy systems around the world will become increasingly decentralized, digitalized, and decarbonized. Digital technologies are permeating every aspect of our lives, including how we work, live, travel, and enjoy our leisure time. Energy systems all across the world are becoming more efficient, reliable, accessible, and safe as a result of digitalization.
The world’s energy systems are undergoing transformation. One of the reasons for this process is the rising demand for clean energy and the growing impact of climate-related extreme weather. Furthermore, organizations all around the world are setting ambitious goals to reduce emissions from the use of fossil fuels, which has powered economic growth for more than a century. It is a known fact that renewable energy plants generate more electricity than is required at certain times of the day, and in the absence of wind or sunlight these plants can meet only a small percentage of demand. To counteract these fluctuations, we need intelligent, innovative solutions and networks in the new energy world, which are more adaptable and intelligent in their approach to the challenges of the energy transition.
The term ‘artificial intelligence’ (AI) refers to a collection of technologies which allow us to find proper answers to challenges for which we previously had no solutions through the usage of data. Various examples of AI technologies and applications include Siri, Alexa and other smart assistants, self-driving cars, robo-advisors, conversational bots, email spam filters and Netflix recommendations. These AI tools are used on a daily basis by the average citizen , most likely, because they increase productivity and simplify certain activities and tasks.
Among the many aspects of the energy transition, will be a rapid increase of the production, circulation and reliance on renewable energy sources as well as widespread clean electrification of heat, industries and transportation. According to the International Energy Agency (IEA), the share of the global energy demand met by electricity is expected to grow by 60% from 2019 to 2050 as a result of the increased adoption of electric vehicles (EVs), the declining costs of battery storage, and the transition to net-zero electricity in buildings and heavy industry. Electricity will increasingly be used to supply heating and cooling (e.g. heat pumps), transportation (e.g. electric vehicles), and even raw materials such as hydrogen(electrolyzers) in the future. Electricity will become the primary source of energy for ever more industries and applications. In fact, it will become the primary source of energy in the entire world.
How can AI help to improve Energy Systems?
AI operates with a variety of different types of data as input, which include audio, speech, photos, videos, data acquired from sensors, data collected manually or robotically, and so on. AI can be used to recognize patterns or to provide probabilistic predictions of future outcomes based on the patterns, which were identified within data collected from various sources such as electricity consumption data, electricity price data and weather data. This data, which is a collection of data points that have been gathered over a certain period of time, can be organized chronologically in a graph in order to give information to the grid when required.
Notably, picture and video data can be used to distinguish objects or circumstances in photos (e.g. satellite images can be used to determine cloud cover patterns so that predictions about the output of a solar power plant can be made). Meanwhile, equipment and “smart” devices, which integrate sensors with communications and networking capabilities, are used to provide real-time digital connectivity and the coordination of physical assets. These systems of sensing and device-level control are necessary for the intelligent coordination and automation of the energy system, which will be enabled by AI.
As industrial operating environments become more complicated and reliant on digital applications, advances in digital technology are helping to increase efficiency and security through the detection of machine-speed threats in these more complex operating environments. Grid operators can make the most of their electricity grids by adjusting their operations based on the weather conditions at any moment using AI algorithms and large volumes of weather data. Short-term forecasting is more accurate and can lead to greater unit commitment as well as increased dispatch efficiency, which improves the reliability of algorithms/certain predictions, while reducing the amount of operational reserve required.
Improving Africa’s Energy Transition through AI
With a population, which is predicted to increase by more than double of its current size by 2050, Africa is the world’s fastest expanding population. The increase in demand for energy is directly related to this growth. In fact, the African Energy Chamber (AEC) predicts that the continent’s need for electricity will continue to rise at a rate of 4-5% per year, perhaps doubling by 2050. The African continent is home to 580 million people out of the world’s 770 million people who do not have access to electricity at the current moment. However, at least 110 million of this population resides in close proximity to existing grid infrastructure.
Introducing renewables into the energy mix of African countries would mean injecting more megawatts from various renewable energy sources into the grid. The ability to estimate capacity levels will become increasingly important in order to maintain a stable and efficient grid. The reason for this is linked with the following: As renewables take a bigger part of the grid, baseload generation from sources such as coal, which provide grid inertia through the presence of heavy rotating equipment such as steam and gas turbines, is being phased out of the grid altogether. Power networks will become unstable and subject to blackouts if grid inertia is not maintained. With the use of AI software, decentralized energy sources can transmit any excess electricity which they produce to the grid and also route electricity generated to where it is required. In a similar vein, energy storage in industrial facilities, office buildings, residences, and automobiles can store excess energy while demand is low and the use of AI technology can help to deploy this excess energy when electricity generation is insufficient.
In South Africa, load shedding has always been an issue and there are predictions that due to continuous energy demand, the latter will not end anytime soon. To prevent the occurrence of load shedding, smart grids need to be utilized, which can help in predictions. AI can play a major role here, namely by enabling computers and systems to learn from their mistakes, adapting to changing inputs, and carrying out activities.
When dealing with intermittent and volatile energy sources such as solar and wind, it is more critical than ever to achieve a successful balance between consumption and generation. One of the hopes in relation to AI and its application to the energy sector is that AI will assist us in dealing with climate change, the emission-reduction consequences of technical advancements , energy imbalances and environmental impact, and other issues related to the energy industry. One of the most fundamental applications which is machine learning (also known as automatic learning, which is a key component of AI), can be used to improve the efficiency of generation systems as well as the efficiency of design technologies and the creation of energy-efficient objects.
Machine learning and AI are required in every industrial context. The energy industry is a sector which, with the application of AI, has tremendous potential for growth. To put it simply, AI gives a computer the ability to learn and make judgments in order to solve problems or optimize results in order to achieve a specific goal. There are several decisions to be made in the energy sector, which necessitate a quick response as well as the ability to process large amounts of data. AI can optimally conduct these critical judgments, which necessitate the collection and analysis of massive volumes of data in real time, while also being able to process the data as quickly and efficiently as possible.
Feel free to contact the Energy Transition Centre today for questions.
· Julius Moerder, Head of Energy Transition Centre firstname.lastname@example.org
· Oneyka Ojogbo, Head of Energy Transition Centre, Nigeria & West Africa email@example.com
· Leon van Der Merwe, Head of Energy Transition Centre, South Africa firstname.lastname@example.org