Saturday, October 18, 2014

Software Aids Grid Operators Struggling with the Vagaries of Renewable Energy

Work on a rooftop solar array. What will it take for utilities to adjust to the on-off nature of more renewable energy?  (Credit: Department of Agriculture) Click to enlarge.
One of the top challenges facing electric utilities and grid operators over the coming decade will be how to integrate distributed energy, such as that produced by rooftop solar panels and wind turbines, into the electricity mix without compromising the steady flow of electrons to the end-users when and where they need them.

The exploding growth of such energy resources, called distributed generation (DG), has already become a central issue in states like California, Hawaii, New Jersey and even North Carolina, where solar and wind power represent ever-larger parts of the electricity mix.

But now, as the costs of renewable energy systems fall to historic lows and the number of ratepayers choosing to become independent power generators rises, utilities across the country must figure out how to manage thousands of new kilowatts of electricity flowing onto their power lines from what has come to be known as "the grid edge."

One emerging solution, according to experts, is software technology known as "distributed energy resource management systems," or DERMS.  These systems help bridge the transition from older, highly centralized electricity generation and distribution networks to more advanced systems where power inputs and outputs are more fluid and the management of electricity is more agile.

In essence, DERMS provide the highest level of "smart grid" capability by providing real-time information about grid performance using computer algorithms that measure how much distributed generation is flowing onto transmission and distribution networks and tailoring that energy supply to meet specific needs or conditions.

For example, a DERM application might allow a utility or electricity aggregator to adjust the amount of electricity flowing to electric-vehicle charging stations during periods of high or low demand.  Another could forecast how much distributed generation -- say, from a solar array or wind farm -- is expected to come onto a utility's network based on shifting weather conditions.

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