If a corporate venture capital firm's portfolio can be seen as a signal of its parent company's strategic priorities, National Grid has high hopes for automation as the future of the utility industry.
The heavy emphasis on automation and machine learning from one of the largest private utility companies in the country with a customer base of around 20 million people is substantial. And a sign of where the industry could go.
Since its inception, National Grid Partners grid company has invested in 16 machine learning startups. Most recently, the company supported AI Dash, which uses machine learning algorithms to analyze satellite imagery and infer vegetation infiltration into National Grid power lines to prevent outages.
Another recent investment, Aperio, uses data from sensors that monitor critical infrastructure to predict loss of data quality due to degradation or cyberattacks.
Of the $ 175 million the company made, roughly $ 135 million was allocated to companies that use machine learning for their services.
"AI will be critical to the energy industry to achieve aggressive decarbonization and decentralization goals," said Lisa Lambert, chief technology and innovation officer at National Grid and founder and president of National Grid Partners.
National Grid got off to a slow start to the year due to the COVID-19 epidemic, but the pace of its investments has picked up and the company is on track to meet its investment goals for the year, Lambert said.
Modernization is vital for an industry that is still largely based on spreadsheets and collective knowledge limited to an aging workforce with no contingency plans in the event of retirement, Lambert said. This situation is forcing National Grid and other utility companies to automate more of their business.
“Most companies in the utility sector are now trying to automate for efficiency and cost reasons. Most companies these days have everything written down in manuals. As an industry, we still basically run our networks through spreadsheets and the skills and experience of the people who run the networks. So we have serious problems when these people retire. Automation [and] digitization is paramount for all of the utilities we've talked to in the Next Grid Alliance.
So far, a large part of the automation work carried out has been the basic automation of business processes. However, there are new features on the horizon that will drive automation of various activities in the value chain, Lambert said.
“ML is the next level – predictive maintenance of your assets that is provided to the customer. Uniphore, for example: You learn from every interaction with your customer by integrating them into the algorithm. The next time you meet a customer, you'll do better. So this is the next generation, ”said Lambert. "Once everything is digital, you learn from those commitments – whether you are hiring an asset or a person."
Lambert sees another source of demand for new machine learning technologies in the need for utilities to decarbonize quickly. The move away from fossil fuels requires completely new ways of operating and managing a power grid. One that people are less likely to be up to date.
"In the next five years, utilities will have to get automation and analytics right if they want a chance at a net-zero world. They have to operate these assets differently," said Lambert. “Windmills and solar panels are not [part] of traditional distribution networks. Many traditional engineers probably don't think about the need to innovate because they are expanding the engineering technology that was relevant in building assets decades ago – while all of these renewable assets were built in the age of OT / IT. ”