Massachusetts is one of the first states rising to meet the challenge of climate change. The Commonwealth’s recently released Comprehensive Energy Plan sets aggressive targets to clean the electric grid over the coming decade – 1,600 MW of both offshore wind and solar, 500 MWh of storage, and nearly 10 TWh of new hydro power.

Tremendous declines in the cost of renewable energy and storage make this plan possible without burdening consumers. In fact, Massachusetts projects that its clean energy investments will actually decrease customer bills. The cost of clean energy technologies is no longer the central challenge of decarbonizing our electric and transportation sectors.

However, a new challenge is emerging: how do we integrate all of these clean energy resources that do not behave the way traditional assets do? The 20^th century electric grid was built around assets that were owned, operated and controlled by the utility. Today, the grid, and the software that runs it, still largely reflect that model. But a 100% clean grid will be dependent on factors totally out of the utility’s control – when the wind blows, when cloud cover rolls in, and when customers plug in their EVs. This challenge cannot be met solely by building more infrastructure, pushing complex time-of-use rates to customers, or having the utility assume control of customer assets. These solutions alone are neither affordable nor scalable.

If we don’t solve this problem, we will never achieve a 100% clean grid, which is being proposed as part of the Green New Deal, because we will have violated the baseline requirement of the electric industry – safe, reliable and affordable electricity.

Fortunately, the model for solving this problem can be found all over our modern economy. We need to optimize the electric grid the same way Lyft optimizes vehicles or Waze optimizes traffic flow – deploy software that uses real-time data and machine learning to automatically match distributed assets with system needs, responding to and influencing those assets rather than using command and control.

Developing such software may seem like an expensive, long-term project, but it’s not – Massachusetts utilities are already deploying technology built to solve this problem. The first use case is voltage optimization. National Grid is currently using Utilidata’s software platform to capture real-time power flow data and use machine learning to optimize voltage levels, responding to things like the impact of distributed solar, in order to make the grid more efficient and reliable. These same principles can be expanded to the wide range of use cases necessary to integrate the Green New Deal’s proposed 100% clean grid.

The next step in the evolution of Utilidata’s platform is to push our software right to the grid edge and deploy it on the smart meter. This will further enhance grid-edge visibility and optimization outcomes, and will move the utility another step closer to the Waze model of software that is needed to run the clean grid.

As the national debate about the Green New Deal heats up, the federal government will look to pioneering states like Massachusetts to understand how to execute aggressive clean energy goals. Thanks to the state’s visionary leadership, they will see that distributed optimization software is the connective tissue necessary to run the 100% clean grid.