Artificial Intelligence (AI) has revolutionized the way we make sense of big data; its applications are crucial for every industry that generates or has access to large amounts of data. In an energy ecosystem that is increasingly becoming digitalized, and generating big data, the applications of AI are endless throughout the ecosystem. AI is helping the power grid become more diverse, unlocking the potential of wind and solar generation and large-scale energy storage systems. Its applications can help the power and utilities better manage and maintain their assets, understand their customers better and offer personalized service and engagement while increasing energy efficiency by optimizing generation, transmission, storage and consumption. Here we look at some ways in which Artificial Intelligence (AI) and Machine Learning are disrupting the power & utility industry.
AI in the Power Grid- Smart Grids
We are witnessing an increase in digitalization and decentralization of the power grid- an unprecedented number of volatile power generation plants such as wind and solar farms of varying sizes, and an increased capacity of energy storage infrastructure, entering the power grid. AI will play a crucial role in keeping such a complicated power grid with a large number of variables stable by keeping the demand and supply in balance- towards a clean, renewable energy future. Traditionally, power companies use ‘peaker plants’ that run on fossil fuels to supply power into the grid instantaneously whenever demand outpaces the supply. This act of balancing demand and supply is expensive and contributes to an increase in pollution. AI-based technologies, when applied to a decentralized smart-grid with diversified sources of energy and sufficient storage capacity, can eventually reduce and eliminate our reliance on ‘peaker plants’ by optimizing the generation, storage, transmission and consumption in the grid.
AI in Energy Trading
Algorithmic trading has been a part of financial markets for quite some time. However, in recent years it has become more commonly used in energy and commodity exchanges. Huge amounts of historical data are available on power generation, energy prices, weather patterns and forecasts. Using machine learning and algorithms, this data is processed to identify patterns and automate trades. It is impossible to fully remove mixing up numbers and decision-making based on emotions when it comes to real-life traders, no matter their level of experience and expertise. Algorithmic trading has already made a big impact in the world of power trading, removing human factors and errors and making trading more efficient, easier and secure and giving experts more time to improve and optimize the algorithms. The improved accuracy of forecasts is directly enabling better utilization and integration of renewables in the grid.
AI for Asset Management and Predictive Maintenance
Renewable energy assets such as wind farms and massive solar fields are often located in remote and harsh environments, posing a safety risk to workers during installation, inspections, maintenance and repairs. Deployment of sensors in the equipment allows energy companies to have visibility of their assets remotely and see how well they are performing. Analysis of the big-data from this multitude of sensors can help energy companies better plan their maintenance operations. The system can alert operators when there is an anomaly in the equipment performance that might be an indicator of equipment or part malfunction, thus helping prevent possible downtime, lost revenue as well as the time and money spent on avoidable physical inspections.
AI for Energy Efficiency
Smart home and smart grid solutions can reach their full potential with the help of Artificial Intelligence systems. A smart home/building solution can optimize consumption and save costs by changing the energy consumption patterns in the building in response to the prices in the electricity market. Heating and cooling systems that use more energy can thus increase their output when electricity is cheap. AI is making intermittent wind and solar generation more predictable using weather forecast data and turbine and solar output data. This allows power companies to advance schedule the delivery to the grid and thereby change the load profile planning to better utilize clean and renewable energy sources.
AI for Better Customer Experience
Understanding your customer is the first step in serving them effectively. Artificial Intelligence can help power and utility companies personalize their service and engagement with the customers. Historically, utilities achieved some level of personalization by segmenting their customers based on available information such as income, age, home type. The data from smart home and smart meter solutions can be analysed to create meaningful segmentation of the customer base such as energy profiles for each customer, based on their consumption habits, the appliances that are used, or lifestyle profiles such as households with stay-at-home or those with working parents. This segmentation can be used to personalize engagement with the customer such offering the ideal energy plan, help them optimize energy usage, or helping EV owners save money by choosing the ideal times for charging their vehicles.
The applications of AI and machine learning could change the way power and utility companies generate, store, transmit and supply energy and how they interact with their customers. The industry would, however, need to change their risk-averse nature to embrace new technologies while strategically preparing their infrastructure, processes and people for the big changes AI can bring in.AI & ML: Data-driven strategies for the Power & Utilities Industry brings together AI, machine learning and data science experts and decision-makers from all verticals of leading power & utility companies in Europe to discuss strategic challenges and the way forward for making use of AI technologies to help their business become faster, smarter and more accurate and efficient. Connect with us to join the discussion.
- January 26, 2021