The foundational role of forecasting in the energy transition
By: Titiaan Palazzi
Tags: Energy, Software Engineering, Data Science, Artificial Intelligence, Forecasting

The foundational role of forecasting in the energy transition

The climate crisis is more urgent than ever. Fortunately, the solution is clear. To minimize global warming we must electrify everything and decarbonize electricity. This could lead to an electricity system roughly 4.5 times the size of today’s system. According to Bloomberg New Energy Finance, we will need to grow yearly additions of solar by 3x, wind by 5x, and batteries by 26x by 2030 to be on track to reach net zero energy-related emissions by 2050.

En route, power grids around the world are modernizing and digitizing. Intelligent, Internet-connected technologies such as electric vehicles, in-home batteries, and smart thermostats are scaling rapidly. And renewable energy — from large wind farms to rooftop solar panels — now accounts for 80% of the new electric generating capacity being added around the world.

Forecasting: why power grids need to predict the near future

In the context of this article, when we talk about forecasting, we mean predicting the near future for power grids. It is no small task. The North American power grid, for example, is often called “the world’s largest machine.” Each year it delivers more than $400 billion of electricity across nearly 7 million miles of transmission and distribution power lines.

A quirk of that massive machine is that electricity supply and demand must always be in balance. Too much supply and you get frequency spikes that fry electronics. Too much demand and you get brownouts or, worse, blackouts that cost billions of dollars in lost economic productivity. Plus, because electricity literally powers everyday life, society demands near-perfection. The U.S. power grid, for example, boasts about a 99.9% uptime — and yet still has more power outages than any other developed country.

All of this is akin to orchestrating the world’s biggest symphony orchestra. It’s hard to do that if you don’t have the sheet music to see what’s coming up next. And that’s exactly why forecasting is so central to the equation. Without it, we don’t stand a chance to realize a zero-carbon electricity system. This article takes a closer look at four specific ways in which forecasting plays a foundational role in the energy transition.

(1) Balancing the grid

A key area in which forecasting matters is in balancing the grid. Grid operators typically set themselves three goals: reliable access to electricity, affordable prices to customers, and increasingly, low-carbon sources of generation.

During the second half of the 20th century, large-scale thermal power plants (think coal- and natural gas-fired generation) were the norm. Forecasting near-term demand was not very important, because these thermal generators ramped up or down to match gradual changes in demand. But times have changed.

Increasingly, power generation comes from variable renewables like wind and solar, and electricity demand fluctuates more suddenly. For instance, in California, one of the big challenges is managing the late afternoon ramp. As the sun lowers and then sets, solar generation falls rapidly. This is also when people get home from work, turn on the lights and the air conditioning, and cook dinner, causing electricity demand to surge. Other sources of generation, such as natural gas power plants and/or battery energy storage, need to ramp up to meet this demand while solar is tapering off.

This makes keeping the lights on challenging. California’s grid operator identified improved forecasting as one of the critical components in their review of last summer’s rotating blackouts. Similarly, in response to the February winter storm outages, the grid operator in Texas marked improved forecasts as one of the action items on their roadmap to improve grid reliability.

(2) Optimizing renewables

Ten years ago, utility-scale renewable assets such as big wind and solar farms were typically paid a guaranteed price for every unit of energy production. Increasingly in the U.S. and Europe, these assets earn a floating price set in wholesale electricity markets.

Because market prices can vary wildly — with peak hours up to 500 times more expensive than the average in markets like Texas — selling the renewable production or other services in the most optimal market can greatly influence the value of the renewable asset. Accurate renewable supply and market price forecasts make the difference between an asset that’s losing money and one that’s profitable. To unlock the 3x growth in solar and 5x growth in wind that’s needed by 2030, it’s imperative we make the economics attractive.

(3) Dispatching battery storage

As more variable renewables are added, battery storage can be one way to balance short-term supply and demand to power grids. Analyst firm WoodMac expects 12 GWh will be added in the U.S. in 2021, three times the amount that was added in 2020.

In most places, at least part of a battery’s revenue comes from participation in wholesale markets. Batteries need predictions to decide when to charge and discharge, also called their dispatch schedule. For batteries, that schedule is based on different types of price signals, such as the difference between day-ahead and real-time prices or the value of grid services they provide.

Having accurate price forecasts for the next few hours determines a large part of the value of a battery. As a recent EnergyGPS report showed, on a summer day in ERCOT (Texas), a perfect price forecast would have created four times the value of a very good price forecast. Without good price forecasts, large-scale storage will not deliver much value for its owners, halting further investments.

(4) Operating virtual power plants

Increasingly, the grid is influenced by small things like rooftop solar, electric vehicles, and heat pumps that can be cycled on or off. In aggregate, these are known as distributed energy resources (DERs).

When DERs are organized jointly to provide services to grid operators or utilities, these are sometimes referred to as virtual power plants (VPPs). For example, Sunrun has installed more than half a million rooftop solar systems and more than 20,000 residential battery storage systems. The company has won contracts to provide grid services in Hawaii, Massachusetts, and California.

To optimally operate such groups of DERs — serving both individual customers and the grid as a whole — a variety of forecasts are needed, including for energy demand (load), renewable supply (generation), and sometimes market price forecasts.

Working towards a cleaner future

The four use cases above — balancing the grid, dispatching batteries, optimizing renewables, and operating virtual power plants — are just some of the ways in which accurate near-term forecasting is critical for the transition to a low-carbon power system. Myst aims to make it easier for all kinds of organizations, from existing utilities to newly-founded DER startups, to develop and leverage sophisticated, flexible, and highly accurate forecasting models. With a team of top Silicon Valley talent and backing from top VCs including Gradient Ventures (Google’s AI venture capital fund), we leverage software and machine learning to help companies solve the forecasting problems that get in their way. If you want to accelerate the clean energy transition, join us!