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The Smart Grid: An Estimation of the Energy and CO2 Benefits

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The Smart Grid: An Estimation of the Energy and CO2 Benefits ( the-smart-grid-an-estimation-energy-and-co2-benefits )

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response used to provide ancillary services involves time periods of a few minutes. Any associated loss of amenity is occasional and short term, and in some cases negligible. When the objective is to save energy, conservation must affect a large fraction of the hours the device is used, and the loss of amenity is more or less continual. Demand response itself can reduce energy consumption because controlling an end-use to lower peak load demand shifts the load to other times, or in some cases actually eliminates some consumption. A prime example of the latter is lighting—dimming lights on peak load also saves energy. Other mechanisms are more subtle, second-order effects. For example, deferring air-conditioning loads until later in the evening allows the air conditioner to run when it is cooler outside, hence, when it operates more efficiently. Counteracting this effect, control strategies that pre-heat or pre-cool in advance of peak load demand periods can result in slightly higher overall energy use. Controls that cycle water heaters off effectively reduce the water temperature somewhat, and can produce substantial savings if hot water is used during those times.1 Most of the large end uses, aside from lighting and electronics, are fundamentally controlled by a thermostat (heating, air conditioning, water heating, refrigeration, and drying). So, to a first order, deferring energy input into a device simply results in an equal amount of energy input later to heat the device back up or cool it back down to its original temperature. It is this eventual restoration of service that distinguishes demand response from conservation, and the reason the latter can produce large energy savings when the former typically does not. Although there may be some physical explanation for the energy savings reported by demand response programs, we believe the primary contribution comes from heightened awareness of energy use on the part of the participants. This awareness can come simply from the decision to participate, but demand response programs usually offer formal feedback mechanisms to the consumer, based on the AMI interval consumption data that shows patterns of usage over the day and week. In some cases, these feedback mechanisms are supplemented by web-based portals or in-home displays that deliver the information and may include a breakdown of consumption by end use. The focus of our analysis of this mechanism is to determine the potential benefits of leveraging these smart grid assets to provide detailed and timely energy feedback and a variety of usage information. Fundamentally, the objective of feedback is to overcome the issue of energy invisibility, which refers to the gradual de-coupling of overt human behavior from energy use, reflected by the historical transition from chopping wood for fuel, to shoveling coal for a furnace, to gas and electric power delivered seamlessly and automatically on demand. To do this, we examined the results from a wide range of studies of feedback mechanisms on consumers (primarily residential). The studies reviewed provide convincing evidence that consumers will 1 Peak load demand reductions can be obtained from either energy efficiency or conservation measures. The peak load reductions are larger for measures that reduce consumption for an end use that tends to be higher during peak load periods (hot summer days in most of the United States), like air conditioning and commercial lighting. Thus, energy efficiency and conservation can make a valuable contribution to the same objective (managing peak load demand) and, hence, compete with smart grid assets like demand response. Unlike peak load demand, however, they have a significant negative impact on utility revenues and may require regulatory action to motivate utilities to make increased use of them. They do not require the communications or the coordinated control that characterize smart grid assets, so the peak load effect of energy efficiency and conservation is not a subject of this analysis. 3.8

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