When you hear about artificial intelligence (AI), you probably think of autonomous robots and cutting edge software, but AI is improving all sorts of industries, including the energy sector. In this article, we’ll go over what the energy transition is, the current role of AI in that transition, and what a future AI-powered energy transition looks like.
An energy transition refers to any widespread structural change in an energy system. For example, Germany’s Energiewende is an ongoing effort to transition to low carbon energy by 2050. The initiative has been in effect since 2010, and so far, the country is on track to shut down its last nuclear power plant in 2022 and retire all coal-fired generation by 2038.
Like Germany, the whole world is shifting away from fossil-based to renewable energies. We’re seeing governments and corporations across the globe invest in environmental, social and corporate governance (ESG). The 2016 Paris Agreement is a prime example. It was signed by nearly every country in the world in a combined effort to reduce carbon emissions and work towards a cleaner future. This means traditional sources of energy like oil, coal, and natural gas are being set aside for solar and wind power in a large-scale shift called decarbonization. The key drivers of decarbonization include improvements in electrification and energy storage like lithium-ion batteries. From electric lawn mowers to electric cars, it’s becoming easier to power machines through electricity.
But right now fossil fuels still supply more than 80% of global energy. All the while, we’re releasing large amounts of carbon dioxide into the air that get trapped in our atmosphere through the greenhouse effect. Over time, this leads to global warming. To prevent catastrophic climate change and create a more sustainable future, we need to leverage renewable energies as much as possible.
But to power the world through electricity is a massive undertaking. Widespread adoption of renewable energies will require increased coordination and networking across electrical power grids and distributed electricity generation and storage units. Power and generator systems will need to be connected to solar plants and wind turbines. New lightning systems, boilers, and heat pumps will need to be networked as will electrical vehicles and devices. Plus, a renewable energy landscape will create new demand for forecasting electrical power output and consumption levels. All this requires considerable restructuring of the current energy landscape.
Luckily, artificial intelligence (AI) can help. AI simulates human decision-making by automatically adapting to new data. Through advanced algorithms and machine learning, this digital transformation in energy can arrive at solutions that would otherwise require human intelligence. This can save countless hours in building, operating, and maintaining the renewable energy landscape.
Advanced AI technology can accelerate the energy transition on many fronts. It can help identify data patterns and insights to improve energy performance, coordinate distributed energy assets, manage energy demand, forecast energy output to optimize power grids, and even discover and innovate new energy materials.
Together, these applications will result in intelligent wind power and smart solar plants. By helping wind turbines integrate with the power grid and communicate with each other, active wind turbines can compensate for offline turbines to ensure overall output is maintained. By monitoring solar operations and performance data, AI can predict weather patterns and adjust power generation accordingly. It can also vary power generation based on peaks and troughs in energy demand. This way, renewable energy remains maximally efficient and easier to adopt.
However, AI has yet to be widely used in the energy transition. Despite all its benefits, AI needs further development and innovation before it can perform all the energy operation and maintenance tasks listed above. To this end, the World Economic Forum recently published a white paper that identifies 9 useful principles for using AI in the energy transition. Here are the nine principles across three broad stages of AI implementation:
Automation—design generation equipment and grid operations for automation and increased autonomy of AI
Sustainability—adopt the most energy-efficient infrastructure as well as best practices around sustainable computing to limit the carbon footprint of AI
Design—focus AI development on usability and interpretability
Data—establish data standards, data-sharing mechanisms and platforms to increase the availability and quality of data
Incentives—create market designs and regulatory frameworks that allow AI use cases to capture the value that they create
Education—empower consumers and the energy workforce with a human-centered AI approach and invest in education to match technology and skill development
Risk management—agree upon a common technology and education approach to managing risks presented by AI
Standards—implement compatible software standards and interoperable interfaces
Responsibility—ensure that AI ethics and responsible use are at the core of AI development and deployment
Together, these principles provide a roadmap for using AI in the energy transition. Though implementing AI will be challenging, we should leverage the technology as much as possible so that we can create a collaborative energy ecosystem that will benefit us and future generations.
To get the most out of AI, the energy industry needs to take the initiative in developing smart AI applications. Energy companies should start investing in AI to prepare for the oncoming decentralization of the global power system. Policy makers should encourage innovation by giving entrepreneurs and start-ups equal access to the energy industry. And finally, governments should build regulatory frameworks that keep AI and software innovations for renewable energy safe and ethical. Only then, can a clean energy future become a reality.
If you need AI applications to help operate a renewable energy system, Dev.co can help. Our software developers have created custom software solutions across several industries. Together, we can design, develop, and deploy a custom app to streamline your renewable energy operations. Contact us today to get started.
Ryan is the VP of Operations for DEV.co. He brings over a decade of experience in managing custom website and software development projects for clients small and large, managing internal and external teams on meeting and exceeding client expectations–delivering projects on-time and within budget requirements. Ryan is based in El Paso, Texas.