The transition to net zero is driving significant change in the energy sector. From the rise of renewables to the increasing electrification of our homes and transport, to the ever-increasing need for grid balancing services to keep an ageing electrical infrastructure working and fit for the future. The result is a significant requirement for scalable technologies that can provide ever more intricate and real-time solutions to manage the energy system of tomorrow.
The transition has already begun to take shape with the accelerated deployment of energy storage, renewable energy technologies, electric vehicles and infrastructure and energy efficiency. But world leaders are under increasing focus to decarbonize economies ever faster as we move closer to key climate commitments. The challenge is not technology readiness, but how to effectively manage these technologies within markets.
The last 24 months have provided an opportunity to see what the future energy market could look like and demonstrated the importance of technologies that can support utilities in managing a volatile supply system, fueled by ever-increasing levels of renewables. Navigating through this transformation requires integrated AI and automation technology systems designed to handle or avoid altogether the more frequent and more severe power interruptions that lie ahead, and the increasing number of grid events aimed at preventing these.
Artificial Intelligence (AI) helps improve forecasts, making it simpler to systematically evaluate large amounts of data, such as weather data, historical data and real time demand. Machine learning and neural networks also play an important role in improving forecasts and are essential for analyzing the vast quantity of data collected and can help evaluate, analyze, and control the data of the various participants (consumers, producers, storage facilities) connected to each other via the grid. This leads to market forecasting, revenue and saving enhancements, dispatch optimization, and auction bidding strategies that ensure that energy assets achieve their full value potential within the market. The result is that AI helps facilitate and speed up the integration of renewables, by ensuring that asset owners achieve maximum value for their asset, supporting the business case for further investment.
AI can also help to stabilize the power grid by detecting anomalies in generation, consumption, or transmission in near real time and to help coordinate maintenance work and determine optimal times for the maintenance of networks or individual systems. This helps minimize costs and disruption for all energy users. From the last bastion of 20th Century technology, the Energy sector now needs software and AI to function effectively, as it manages all aspects from construction and operations to shutdowns.
To make the right decisions, asset owners, operators and managers need well-processed data too. All this new data requires new technologies and faster delivery mechanisms to ensure the market can adequately respond. AI and associated technologies, enable traders to not only see the value of their asset within markets, but to trade them with the best information in real time. It allows data to be accessed, integrated, and queried in the way that helps the asset owner to unlock new value and improve the process and speed of trading. They also enable companies to model the complex global integrated energy system, navigate the flood of data, to increase transparency and facilitate emissions reductions in supply chains through more efficient use of resources.