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The unit commitment problem (UCP) for an electric power system is used to determine the schedules of power units that minimize the total production cost over a planning horizon while satisfying the load demand, spinning reserve, and operating constraints of individual units. When the number of units is large and the planning horizon is long, the UCP is a large-scale problem, for which an exact optimal solution is difficult to obtain within a reasonable computation time. The Lagrangian relaxation (LR) method is known to be useful for large-scale UCPs. The LR method first solves the dual problem of the UCP, and then constructs a feasible solution from the dual solution by using some heuristics. In this paper, we propose new local search (LS) methods for improving the feasible solution obtained by the LR method. We define the neighborhood of the local search as the feasible set in which the on-off states of all but one or two units are fixed. The neighborhood search can then be executed by solving the one unit or two UCPs, which are efficiently solved by dynamic programming if no ramp-rate limit constraint exists. Numerical results show that the proposed LS methods can find feasible schedules for which the costs are lower than those obtained by the existing methods. The applicability of the proposed methods to long-term UCPs is also demonstrated.

While many studies have estimated the energy saving potential of office equipment power management, there is no recent study about the energy saving potential of shortening the power management delay time. In this paper, we estimated the energy saving potential of the complete saturation of power management, and also the additional energy saving potential of shortening the power management delay time for PCs, displays, copiers, and laser printers in Japanese offices. To obtain key data, we conducted the surveys and field measurements of usage in business hours and the turn-off rate at night. We found that office equipment power management can save as much as 3.5 TWh per year, which is nearly equal to 2% of commercial electricity consumption in Japan. We also found that the energy use of office equipment in Japanese offices with no use of power management is even lower than that in US offices with maximum use of power management. This is primarily because the portable computer is much more popular in Japan, and also because the manual-off rate at night is more than twice that in the US.

In spite of the recent explosive growth in the use of office and network equipment, there has been no recent study (until this one) that estimates in detail how much electricity is consumed by that equipment in the United States. In this study, we examined energy use by office equipment and network equipment at the end of 1999. We classified office equipment into 11 types; for each type we estimated annual energy consumption for residential, commercial, and industrial use by combining estimates of stock, power requirements, usage, and saturation of power management. We also classified network equipment into six types and estimated the annual energy consumption for each type. We found that total direct power use by office and network equipment is about 74 TWh per year, which is about 2% of total electricity use in the US. When electricity used by telecommunications equipment and electronics manufacturing is included, that figure rises to 3% of all electricity use. More than 70% of the 74 TWh/year is dedicated to office equipment for commercial use. We also found that power management currently saves 23 TWh/year, and complete saturation and proper functioning of power management would achieve additional savings of 17 TWh/year. Furthermore, complete saturation of night shutdown for equipment not required to operate at night would reduce power use by an additional 7 TWh/year. Finally, we compared our current estimate with our 1995 forecast for 1999. We found that the total difference between our current estimate and the previous forecast is less than 15% and identified the factors that led to inaccuracies in the previous forecast. We also conducted a sensitivity analysis of the uncertainties in our current forecast and identified the data sets that have the largest impact on our current estimate of energy use.

In spite of the recent explosive growth in the use of office and network equipment, there has been no recent study that estimates in detail how much electricity is consumed by that equipment in the United States. In this study, we examined energy use by office equipment and network equipment at the end of 1999. We classified office equipment into 11 types; for each type we estimated annual energy consumption for residential, commercial, and industrial use by combining estimates of stock, power requirements, usage, and saturation of power management. We also classified network equipment into six types and estimated the annual energy consumption for each type. We found that total direct power use by office and network equipment is about 74 TWh per year, which is about 2% of total electricity use in the U.S. When electricity used by telecommunications equipment and electronics manufacturing is included, that figure rises to 3% of all electricity use (Koomey 2000). More than 70% of the 74 TWh/year is dedicated to office equipment for commercial use. We also found that power management currently saves 23 TWh/year, and complete saturation and proper functioning of power management would achieve additional savings of 17 TWh/year. Furthermore, complete saturation of night shutdown for equipment not required to operate at night would reduce power use by an additional 7 TWh/year. Finally, we compared our current estimater with our 1995 forecast for 1999. We found that the total difference between our current estimate and the previous forecast is less than 15% and identified the factors that led to inaccuracies in the previous forecast. We also conducted a sensitivity analysis of the uncertainties in our current forecast and identified the data sets that have the largest impact on our current estimate of energy use.

Many innovations in refrigerator design rely on microprocessors, sensors, and algorithms to control automatic defrost, variable speed,and other features. Even though these features strongly influence energy consumption, the major energy test procedures presently test only a refrigerator's mechanical efficiency and ignore the ''software'' aspects. We describe a new test procedure where both ''hardware'' and ''software'' tests are fed into a dynamic simulation model. A wide range of conditions can be tested and simulated. This approach promotes international harmonization because the simulation model can also be programmed to estimate energy use for the ISO, DOE, or JIS test. The approach outlined for refrigerators can also be applied to other appliances.