September 2007

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Demand Response
Information Is Power

Patrick Mazza

Patrick Mazza
Research Director
Climate Solutions

It was a sweltering day in the eastern U.S. Air conditioners were cranked up, lights were flickering and the straining power grid was on emergency status. Grid operator PJM Interconnection called on customers to cut demand. They responded with a load reduction of 1,945 megawatts, enough to supply a major city. For the PJM grid serving 51 million customers from Illinois to New Jersey, that was a record demand response and a dramatic illustration of power load as a power resource.

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That day, August 8, was a “needle peak.” On the annual power demand chart such days show up as spikes. Out of the year’s 8,760 hours, needle peaks will occupy 200 hours or less. Extreme demand periods like these are why the grid maintains roughly twice as much power generating and transmission capacity as it uses on an average day. Even though power plants and lines sit idle most of the year, this costly overbuilding is needed to cover all contingencies. The grid is built for “just in case.” This pushes power rates up. Generating peak power can cost 10 times more than costs at average, the U.S. Government Accountability Office (GAO) notes.1

But, as PJM demonstrated, another power resource is available that can dramatically reduce that peak demand, one that involves generating and transmitting no power at all, only information between power producers, deliverers and users. Power load adjusted through information and communications networks is a potent energy resource that can keep the lights on while reducing the overall cost of the power system, and thus customer bills. A study done for PJM shows that a three percent load reduction in a year’s peak 100 hours reduces energy prices $66 million-$208 million per year, and annual capacity costs by $73 million.2 Pacific Northwest National Laboratory (PNNL) calculates that moving to smart grid technology over the next 20 years will eliminate $46 billion-$117 billion in conventional utility infrastructure.3 That does not count investments in new smart grid technology. But one PNNL calculation gives an indication of comparative costs – Smart appliances that can adjust their demand to grid conditions could for $600 million provide reserve capacity equal to power plants costing $6 billion, proving that “bytes are cheaper than iron.”

Demand response (DR) is a fundamental aspect of the smart grid. On the old “dumb” grid, almost all the information flow from power users to suppliers is 12 meter readings a year, while from suppliers to users it is 12 power bills. One of the most profound changes introduced by the smart grid, indeed what makes it smart, is a communications backbone that allows massive two-way information flows. An information network is overlaid on top of the power network. DR employs these information/communications capabilities to engage power users directly in managing the grid. Automated buildings rich in digital control systems are ideally positioned to supply DR services to utilities and gain benefits in the form of reduced power bills.

Demand response (DR) is one of the first pieces of the smart grid to emerge. Since the 1980s utilities have worked with customers to place automatic controls on water heaters, air conditioners and other electricity-hungry devices to lower demand during peaks. Florida Power is a good example with nearly half a million customers in its program.4 Utilities work with farmers to control irrigation pumps during peaks. Idaho Power, with one of the nation’s heaviest irrigation loads, is actively engaged in this form of DR. Many grid operators also engage large industrial and commercial customers to reduce loads during extreme demands. Sometimes the utility provides credits on bills as an incentive to cut loads. In other cases utilities set higher costs to large customers during peaks to provide incentives to reduce loads. From the utility standpoint it’s a complex calculus balancing DR costs with the avoided costs of serving peaks with standard generation and transmission. For customers the calculation involves the economic benefits of credits or avoided higher rates with the costs of systems to adjust load. DR can provide an added value stream for Building Energy Management Systems.

Today’s deep decline in costs for computing technologies and communications bandwidth is changing the overall DR calculus, making it easier and more economical. At the same time the emergence of Independent Systems Operators (ISOs) to run regional power transmission systems is creating vibrant and large new markets for DR. They provide a level playing field where DR can compete with traditional power resources. Companies such as EnerNOC, Comverge and Energy Connect are aggregating DR loads from multiple customers and marketing them to ISOs, particularly PJM, a notably innovative ISO. These companies are offering demand resources equal to large power plants, 796 megawatts in the case of EnerNOC, 948 for Comverge.

DR comes in several flavors, and not all are as sweet for utilities. The basic division is between “firm,” meaning that customer-end equipment is under a form of direct load control (DLC) that makes it completely predictable, and “non-firm,” meaning the load is under customer control. Utilities rely on non-firm DR to reduce costs during peaks and carry them through emergencies. But utility engineers will not cancel that beefed up transmission line in favor of DR unless the load is firm, aka, “fully dispatchable.” They want to be able to basically turn it on and off themselves or be fully assured the customer will do that for them. And you can’t blame them – If the lights go out they’re the dogs.

DR should not be confused with its close relative, energy efficiency (EE). EE seeks to reduce overall power use. DR aims to reduce use at specific hours. Sometimes that means an absolute cut in electricity consumption. For instance when an air conditioner is cycled down during the day it will not necessarily return to full operation in the evening. But when, say, a hot water heater is turned down, it typically will shift the load to later.

Reliable Controls That sets up a potential unintended consequence. DR could actually increase overall pollution. For example, if generation is shifted from daytime hydroelectric or cleaner gas generation to evening coal, then overall emissions could rise. So in designing DR markets this pitfall needs to be avoided. The New England Demand Response Initiative, a multi-stakeholder group, made sure to take a look at the impacts there and found a small overall emissions reduction benefit on the New England grid from DR.5

DR provides some clear environmental benefits. DR could serve as a substitute for spinning reserve, power plants that run ready to supply power on short notice, typically around 10-15 percent of overall power generation. The less spinning reserve, the fewer emissions. And DR could sharply reduce the need for peaker power plants and infrastructure with all their embedded energy and land use impacts.

DR has some intriguing further potentials to promote a far greener grid, and they illuminate key aspects of the smart grid. Advanced DR is built on smart systems that not only control power use by individual devices – They also provide detailed information on power use down to the device. They will also let utilities assemble power use data with unprecedented detail. This knowledge can be leveraged for economic value in a number of ways. One is to validate the actual effects of EE programs. Rob Pratt with PNNL’s GridWise Program gives an example. Say a utility pays for efficiency improvements at a number of houses, and seeks to use the resulting emissions reductions to gain carbon market credits. As more regions move to carbon cap-and-trade systems such credits will become more valuable and important. With today’s primitive information flow these reductions could only be approximated, not verified, so gaining credits could be difficult and the utility would have less incentive to make the investments. But validated information can be taken to the carbon marketplace, so the EE is more likely to be done. This is just one of a number of ways in which DR and EE are synergistic.

DR might also provide cost-effective, emissions-cutting means to balance intermittent renewable energy sources such as wind. Wind generation varies with availability and intensity of the resource. So wind farms are partnered with reserve power plants that fill the gap when wind speed diminishes. Often these are natural gas turbines, though in the Pacific Northwest hydroelectric dams are employed. But what if a smart grid could automatically balance wind with adjustments in power demand? Thinking on this is still at early stages, but it is theoretically possible. Since wind varies minute to minute, the system would have to be quite sophisticated and have a broad diversity of demand resources to tap. Fraunhofer Institute modeled such a system for Germany and found “the costs of additional reserve power could be reduced . . . demand response may be a valuable option for integrating wind power into electricity systems.” 6 Of course, this would also reduce greenhouse emissions from balancing fossil resources.

The application of smart technologies to the grid makes a largely one-way flow of power and information into a far richer and complex two-way stream. The greater the penetration these technologies achieve, the more power demand will also become a power resource. The power grid and power users will benefit greatly.

About the Author
Patrick Mazza is Research Director for Climate Solutions, a research and advocacy group focused on accelerating global warming solutions. Mazza has written extensively on energy systems topics, including an extensive overview of the transformation of the power grid by digital technology, Powering Up the Smart Grid, He is currently researching synergies between smart buildings and the smart grid.

[1] U.S. Government Accountability Office, Electricity Markets: Customers Could Benefit from Demand programs, but Challenges Remain, Aug. 2004,
[2] Brattle group, Quantifying Demand Response Benefits in PJM, Jan. 29, 2007
[3]L.M. Kannberg et al, GridWise: The Benefits of a Transformed Energy System, Pacific Northwest National Laboratory, Sept. 2003,
[5]Geoff Keith et al, Modeling Demand Response and Air Emissions in New England, Synapse Energy Economics, Sept. 2003,
[6]Marian Klobasa and Ragwitz, Mario, Demand Response: A New Option for Wind Integration, Fraunhofer Institute


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