Too Clever by Half ?

Earlier this summer, I posted a note here about the smart grid initiative announced by the White House Office of Science and Technology Policy.  In order to increase the proportion of our energy use supplied by renewable sources, such as wind and solar power, we need a power distribution system (the grid) that is more responsive to changes in the availability and relative cost of power, because these renewable sources are subject to natural fluctuations: some are predictable (the sun will set this evening), some (it may get really windy this afternoon) not so much.

The adoption of smart grid technology is not without its potential pitfalls.  In January of this year, the US Government Accountability Office [GAO] issued a report warning of the security risks involved.  I’ve written about some of the security concerns specific to smart electricity meters.  The MIT News site has posted a report of some new research, pointing out another potential problem with a grid that is “too smart for its own good”.

One of the potentially attractive consequences of having a smart grid is that consumers could be provided with information about the varying cost of energy throughout the day, in different seasons.  The idea is that the customer might choose to run certain energy-intensive appliances (like a clothes dryer) at off-peak times, when electricity would presumably be cheaper.  Time-varying rates (typically, cheaper at night) have been tried in some places, and have resulted in some smoothing of electricity demand.  But a really smart grid could, in principle, deliver varying price information in close to real time.

One envisioned application of these “smart meters” is to give customers real-time information about fluctuations in the price of electricity, which might encourage them to defer some energy-intensive tasks until supply is high or demand is low.

However, the MIT researchers found [paper PDF] that there is a risk of making the system too responsive.

Recent work by researchers in MIT’s Laboratory for Information and Decision Systems, however, shows that this policy could backfire. If too many people set appliances to turn on, or devices to recharge, when the price of electricity crosses the same threshold, it could cause a huge spike in demand; in the worst case, that could bring down the power grid

Although the pricing information can be delivered quickly, the utility cannot necessarily respond to changes in demand quickly.  It takes time to start up or shut down a coal- or gas-fired power plant (these restrictions are called “ramp constraints”).  Moreover, events in other markets that feature nearly real-time information show that instability is not just a theoretical concern.  The “flash crash” in the equity market in May, 2010 is one example.

The authors do find that there are some relatively simple changes to reporting mechanisms that could reduce this risk.  Their paper is highly technical, but a first step might be to present a “smoothed” price value to consumers, so that short-term fluctuations would not lead to instability.  The authors suggest that, down the road, a market with more complete information, including information on customers’ preferences, could lead to even better results.

There is still a good deal of work to be done on resolving these issues; I hope it is done before, rather than after, the smart grid is fully implemented.

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