Plug-in hybrid vehicles (PHEVs) open a new intersection between wind power and transportation.
Forecasting brings wind energy under control.
load, which operators forecast with relative error rates from about 1 to 4 percent.
“Bulk power system operators always make assumptions about load, based on experience with weather and load forecasts,” says Mark Lauby, director of reliability assessment and performance analysis for the North American Electric Reliability Corp. (NERC). “Wind forecasting is just another tool operators will use to ensure they have the flexibility they need to respond to changing conditions.”
Further, because weather phenomena influence both load and variable power sources, it makes sense for system operators to integrate the way they use forecast data. Indeed, NERC is considering recommendations 2 that anticipate eventual convergence between load forecasts and generation forecasts for wind and other variable power sources, including solar and run-of-river hydro. In the short term, NERC’s new recommendations would make wind forecasting a standard part of control-room procedures and energy management systems (EMS). And earlier this year, the Federal Energy Regulatory Commission (FERC) issued a notice of inquiry seeking input on wind-integration issues, including several possible forecasting requirements that would affect wind generators, utilities, transmission owners and system operators. 3 A large share of market participants and ISOs with substantial amounts of wind capacity already are using forecasts, 4 but before now neither FERC nor NERC have defined it as standard procedure.
“Over time, as we gain experience with [wind forecasting] technology and learn its strengths and weaknesses, we’ll get more certainty around it,” Lauby says. “It’s the same with every new technology that comes onto the system, whether it’s gas turbines or nuclear plants. At first there’s some skepticism, but with experience we learn how to optimize it.”
Wind vs. Load
Smart-grid and dynamic pricing programs are turning customer load into a more easily controllable factor, potentially making it a more important tool for system balancing. Utilities and system operators historically have tapped into demand-response mechanisms mostly to manage the critical peak, curtailing power for a few large electricity customers, such as factories and aluminum smelters, to prevent brownouts and blackouts from rolling through the network. But retail-level DR programs—which trim demand across entire communities—might give grid operators another tool for managing variables.
“On the left and right, these two variables can be used to balance each other,” says Don Leick, product management director with Telvent, which provides smart-grid systems and weather- and load-forecasting services. “Load-modeling software ties into an EMS. I don’t see anyone with a completely integrated solution yet, but these are pieces that can fit together.”
Eventually, if the system gets smart enough, the operators’ EMS might present variable generation and demand factors together for decision-support purposes—perhaps someday as virtual levers the operator might use to balance the system in a way that makes optimal use of current and forecasted resources.
“The smart grid and distributed demand response allows you to integrate large amounts of renewable energy,” says Pascal Storck, a vice president with renewable energy forecasting and resource assessment company 3Tier. “Forecasting allows distributed DR to be deployed and used more intelligently than if you didn’t have forecasting.”
But as with all new ideas,