Q&A: Finning’s General Manager, Electrical Power Kelly Cole

Kelly Cole, General Manager for Electric Power at Finning UK & Ireland
In this chat with Energy Digital, Finning’s Kelly Cole dives into the transformative potential of AI in accelerating the clean energy transition

AI and other types of intelligent technology undoubtedly play a significant role in enhancing operations across energy and utilities, but how vital is it to the energy transition?

Kelly Cole, General Manager for Electrical Power at Finning UK & Ireland — one of the world's largest distributors of Caterpillar equipment — says that although there are barriers AI needs to overcome in relation to power generation, AI plays a vital role in optimising energy consumption, facilitating the integration of renewable energy sources, and enhancing grid management.

Kelly has a front-row seat in observing and enabling how technologically-driven solutions drive sustainability and efficiency. Finning supports the power sector by providing reliable, efficient and sustainable power solutions tailored to the diverse needs of its customers across various industries and applications.

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In building integrated power solutions for prime power and standby power applications, the company reliable and uninterrupted electricity supply for critical operations in various industries, including data centres, healthcare and banking.

In this exclusive Q&A with Energy Digital, Kelly discusses how transformative technology innovations, like AI, optimise energy consumption, the barriers needed to enhance its adoption and how it optimises existing infrastructure.

Q. Could you elaborate on how AI optimises energy consumption and facilitates the integration of renewable energy sources within the power generation sector?

We need to leverage AI in order to optimise energy consumption and facilitate the integration of renewable energy sources within the power generation sector in several ways.

AI firstly can help us forecast the energy demand based on historical energy consumption data, weather patterns and real-time grid conditions to forecast electricity demand. By predicting peak demand periods and balancing supply and demand, we can reduce peak loads and optimise overall energy consumption.

The prediction of peak demands can help optimise the charging and discharging schedules of energy storage systems, such as batteries and pumped hydro storage, based on electricity prices, renewable energy generation forecasts and grid conditions. By intelligently managing energy storage assets, AI could help us to store excess renewable energy when it is abundant and inexpensive and discharge it when demand is high or renewable generation is low, thereby increasing grid stability and reliability.

By analysing sensor data and identifying patterns indicative of equipment degradation or failure, AI-driven predictive maintenance can help identify needs before they occur, minimising downtime, optimising asset performance and reducing maintenance costs.

AI can also help us optimise energy consumption and facilitate the integration of renewable energy sources within the power generation sector by enabling demand response, predictive maintenance, energy storage optimisation, renewable energy forecasting and energy trading.

Q. What specific barriers does AI encounter in the realm of power generation and how do these barriers impede the clean energy transition? How can these be overcome?

For widespread adoption of AI in power generation we will need to overcome several barriers.

Firstly, access to high-quality data is crucial for training AI algorithms effectively. Today, we see fragmented or incomplete data, making it challenging to develop accurate predictive models and optimisation algorithms. Additionally, today we face data privacy or security concerns which are often limiting data sharing among stakeholders.

Whilst achieving interoperability and compatibility between different components and systems can be complex, I believe this can be overcome, allowing the seamless integration of AI solutions into existing infrastructure. However, implementing AI technologies into existing power generation may entail significant up-front costs for data acquisition, infrastructure upgrades and software development.

Moreover, the return on investment of AI projects may not always be immediately apparent, particularly for smaller utilities or renewable energy developers with limited resources.

Selecting the right projects will be key to demonstrate the impact AI can have on power generation projects.

Q. In what ways does AI enhance grid management, particularly in the context of accommodating the variability of renewable energy sources and ensuring grid stability?

AI enhances grid management by providing real-time insights, predictive analytics and optimisation capabilities that enable grid operators to efficiently integrate renewable energy sources, balance supply and demand, and ensure grid stability and reliability in the face of increasing variability and complexity.

We know there will be variation in the supply from renewables, so AI can help improve our forecasting ability. Combining meteorological data, historical renewable energy generation patterns and real-time sensor data, AI can accurately forecast the output of renewable energy sources, such as solar and wind power. These forecasts enable grid operators to anticipate fluctuations in renewable energy generation and adjust grid operations accordingly to maintain grid stability and reliability.

Similarly, we can use AI to help us improve our demand forecast. Looking at historical consumption patterns and grid conditions to predict peak demand periods and dynamically adjust electricity consumption in response to price signals or grid constraints.

This forecast can be updated in real time and used to manage the charging and discharging schedules of energy storage systems, such as batteries and pumped hydro storage, based on renewable energy generation forecasts, electricity prices and grid conditions.

By intelligently managing energy storage assets, AI can help store excess renewable energy when it is abundant and inexpensive and discharge it when demand is high or renewable generation is low, thereby stabilising the grid and improving reliability.

When the energy storage systems are low or are not sufficient to meet demand, other technology such as gas generators can be used to generate electricity. This holistic and real-time view will help us balance the demand with the supply of energy from renewables, storage and demand response power generation.

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