Skip to Content

Plug-in Hybrids Reduce Power-Plant Emissions

A study suggests that increased power demand from plug-ins can actually reduce pollution.
January 30, 2009

A recent study published in the journal Environmental Science and Technology suggests that plug-in hybrids can reduce emissions not only from tailpipes, but also from the smokestacks of power plants.

Plug-in hybrids have larger battery packs than do conventional hybrids and can be recharged by plugging them in, allowing them to rely far more on electric power than do conventional hybrids. While this can substantially reduce gasoline consumption and vehicle emissions, plugging the cars in will increase demand for electricity, which could lead to more harmful emissions from power plants.

The new study, which was conducted by researchers at the National Renewable Energy Laboratory (NREL), concludes just the opposite: in some parts of the country, the added demand for electricity from plug-in hybrids could actually decrease harmful emissions. “We were surprised by the results,” says Paul Denholm, the NREL researcher who led the study.

The researchers looked at two scenarios, both involving smart charging systems. In the first, utilities had the ability to control when plug-in hybrids are charged, but within certain parameters are set up to make sure that the cars are charged up when drivers want them. For example, a car could be plugged in overnight with the expectation that in the morning it will be ready for driving. Since the batteries recharge in less than eight hours, they don’t have to be actively charging the whole time. In the second scenario, the smart chargers also had the ability to take charge from the cars and deliver it back to the grid–a technology called vehicle-to-grid technology, or V2G.

In the first scenario, control over when the cars charged reduced the amount of smoke forming nitrogen oxides. Here’s an explanation of how this works, which Denholm calls a “gross simplification” but says still conveys the basic idea. Electricity comes from two basic types of power plants: base load plants, which are the more efficient, and peaking plants, which supply bursts of power when they’re needed, but are less efficient and produce more nitrogen oxides. Base load plants need to keep running at a steady pace. If grid operators aren’t sure that there will be enough demand for power to keep the plants running at an optimal pace, they’ll shut down some of these plants and instead use dirtier peaking power plants. If the operators know that they have a lot of plug-in hybrids waiting to draw power, which is a big source of demand that they can control, they can leave the base load plants running. If demand dips, they simply tell the cars to start charging to bring it up again. The end result is that there are fewer dirty power plants running, so that even though power demand is higher, there’s less pollution.

In the second scenario, grid operators have even more control. This allows them to rely less on inefficient power plants, causing a dip in carbon-dioxide emissions from power plants as well.

The study comes with one big caveat. It’s specifically for Texas, where base load power comes largely from natural gas, a fuel that emits relatively little carbon dioxide compared with other fossil fuels. In places that use coal for base load power plants, Denholm doesn’t expect to see lower pollution levels.

Keep Reading

Most Popular

Large language models can do jaw-dropping things. But nobody knows exactly why.

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.

OpenAI teases an amazing new generative video model called Sora

The firm is sharing Sora with a small group of safety testers but the rest of us will have to wait to learn more.

Google’s Gemini is now in everything. Here’s how you can try it out.

Gmail, Docs, and more will now come with Gemini baked in. But Europeans will have to wait before they can download the app.

This baby with a head camera helped teach an AI how kids learn language

A neural network trained on the experiences of a single young child managed to learn one of the core components of language: how to match words to the objects they represent.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.