How Artificial Intelligence Will Revolutionize the Energy Industry

(Harvard) – Last year, Bill Gates wrote an essay online at “The blog of Bill Gates” to all graduating college students around the world: “If I were starting out today… I would consider three fields. One is artificial intelligence [“AI”]. We have only begun to tap into all the ways it will make people’s lives more productive and creative. The second is energy, because making it clean, affordable, and reliable will be essential for fighting poverty and climate change.” The third field he mentioned was biosciences.

What is inspiring for individuals who are dedicated to improving living conditions today and for future generations to come is that AI and energy are not mutually exclusive career paths. In fact, they are becoming increasingly interconnected as computing power, data collection, and storage capabilities scale exponentiallyAccording to Dan Walker, who leads the emerging technology team in British Petroleum’s (BP) Technology Group, “AI is enabling the fourth industrial revolution, and it has the potential to help deliver the next level of performance.”  Next level indeed.  Although still in an early stage of development, AI is poised and ready to revolutionize the way we produce, transmit, and consume energy.

Why does the energy grid need to be modernized?

In 1882, Thomas Edison opened America’s first power plant at Pearl Street Station in lower Manhattan to deliver power to 59 customers. The customer base has since swelled to hundreds of millions of users, but the overall structure has yet to receive a modern overhaul. The US grid consists of a vast network of power plants, transmission lines, and distribution centers (comprising roughly 5,800 power plants and over 2.7 million miles of power lines) with an average age over 30 to 40 years old. This aged transmission system was a root cause of the 2003 Northeast blackout, the largest failure in U.S. history –  50 million people were without power for several days when an overloaded transmission line sagged and struck in a tree. Instances like these have cascading effects on regional grids and pose a difficult task for utility companies to manage, especially as the demands on the grid becomes more substantial.


Figure 1: The above chart demonstrates the rising trend of U.S. renewable energy supply over the past decade. Hydropower includes conventional hydroelectric power only, and excludes pumped storage generation. Liquid biofuels include ethanol and biodiesel. Other renewables include biofuels production losses and co-products. Data retrieved from the U.S. Energy Information Administration.


At the same time that grid infrastructure ages out, utilities also struggle with the rise of distributed energy resources (“DERs”) and the generation of electricity by private consumers (“prosumers”).  DERs create enormous opportunities, but they also complicate the market. For instance, many utility companies are required to buy excess energy from private users who generate more electricity than they use.  Since solar use has more than tripled since 2010, this trend is poised to continue into the future as photovoltaic cells, the devices that generate electricity from sunlight, decrease in cost and increase in efficiency.

The current grid system is too outdated to gracefully accommodate this level of technological or market complexity.  Current solution used by utilities to deal with the complexity is equally outdated, such as when demand outpaces supply: utilities turn on backup fossil fuel-powered plants, known as ‘peaker plants’ as an emergency measure to avoid a cascading catastrophe. This procedure is an expensive and wasteful solution and leads to higher electricity bills and greater greenhouse gas emissions. These problems will be exacerbated if U.S. energy demand increases again in the future.

How can the energy grid be modernized?

To combat these problem, the U.S. Department of Energy (DOE) has made support of the ‘smart grid’ (“a fully automated power delivery network that monitors and controls every consumer and node, ensuring a two-way flow of electricity and information”) a national policy goal. Since 2010, the DOE has invested $4.5 billion in smart grid infrastructure and installed over 15 million smart meters that monitor energy usage per device and alert utilities of local blackouts. It is estimated that while total U.S. energy demand is expected to increase 25 percent by 2050; however, these smart meters will limit the rise in peak electricity load on the grid by only 1 percent.

Figure 2: Past and projected U.S. energy consumption in quadrillion Btu. By 2040, world energy consumption is expected to increase by 15.3%. Data retrieved from the U.S. Energy Information Administration.


The more promising solution is AI – brain of the future smart grid. These more intelligent technologies will continuously collect and synthesize overwhelming amounts of data from millions of smart sensors nationwide to make timely decisions on how to best allocate energy resources. Additionally, the advances made from ‘deep learning’ algorithms, a system where machines learn on their own by spotting patterns and anomalies in large data sets, will revolutionize both the demand and supply side of the energy economy. As a result, large regional grids will be replaced by specialized microgrids that manage local energy needs with finer resolution. These AI technologies can be paired with new battery technologies that allow power to continually flow to and between local communities even when severe weather or other outages afflict the broader power system.

On the demand side, smart meters for homes and businesses as well as sensors along transmission lines will constantly monitor demand and supply. Further, briefcase-sized devices known as ‘synchrophasers’ will measure the flow of electricity through the grid in real time, allowing operators to actively manage and avoid disruptions. These sensors communicate with the grid and modify electricity use during off-peak times, thereby relaxing the workload of the grid and lowering prices for consumers. Google recently applied synchrophaser sensor technologies to reduce its total data center power consumption, which translated to millions of dollars in savings.

On the supply side, AI will allow the U.S. to transition to an energy portfolio with greater renewable resource production and minimal disruptions from the natural intermittency that comes with these sources due to variable sunlight and wind intensity. For example, when renewables are operating above a certain threshold, either due to increases in wind strength or sunny days, the grid would reduce its production from fossil fuels, thus limiting harmful greenhouse gas emissions. The opposite would be true during times of below-peak renewable power generation, thus allowing all sources of energy to be used as efficiently as possible and only relying on fossil fuels when necessary. Additionally, producers will be able to manage the output of energy generated from multiple sources to match social, spatial, and temporal variations in demand in real-time.

Are there concerns with the future smart grid?

One of the major concerns with the smarter grids of the future is the increased use of Information and Communication Technologies (“ICTs), which rely on the Internet as well as computing and processing power to run. Th ICT industry has become a large contributor of greenhouse gas emissions in recent years as companies shift to machine-run Internet-based operations. To process the amount of data necessary to run a smarter grid, additional machines and computing power will be needed, and the impact of greater energy consumption on the environment is sure to increase. Therefore, stakeholders in the AI energy grid industry will need to address this problem.

Fortunately, industry leaders are aware of this challenge and are already taking steps to improve conditions. The three leading greenhouse gas emitters in this industry – computer makers, data centers, and telecoms – are looking to reduce emissions. For example, computer makers are investing in new hard drives, screens, and fuel cells; data centers are monitoring temperatures, pooling resources and researching cloud computing; and telecoms are looking into network optimization packages, solar-powered base stations, and fiber optics.

If the smart grid is able to use fossil fuels as a back-up to more advanced renewable resources, the entire system may be able to reduce its carbon footprint. Despite the uncertainty associated with future technological innovation, we can also expect the future smart grid system to lower electricity bills and prevent catastrophic blackouts by optimizing supply and demand at local and national levels.

For those looking to make a difference in shaping the future of society, the interface between AI and energy is a great place to start. Technological innovation is drastically changing the way we think about these two industries – and their integration is just getting started. Synergies within these two powerful sectors may change the world like we never knew it, and they are primed for innovative thinkers to make their mark.


Franklin Wolfe is a graduate student in the Earth and Planetary Science program at Harvard University.

This article is part of a Special Edition on Artificial Intelligence.

Harvard Future of Energy Initiative at

Images are courtesy of Franklin Wolfe and Kimia Mavon

This article was originally published here.

Pin It on Pinterest