Does demand response increase or decrease overall electricity usage?
Gas-fired Generation: Can Renewable Energy Reduce Fuel Risk?
a random walk and one-year load shocks due to weather and other factors. Based on TU's projections (and taking into account planned demand-side management efforts), we assume a mean rate of increase of 1.93 percent, with a standard deviation of 3.8 percent to match the range of TU's 10-year forecast. One-year load shocks are allowed a standard deviation of 3.25 percent, for total standard deviation in load changes of 5 percent. Environmental Costs. Environmental regulatory risks are more difficult to simulate because limited historical data exists. The potential liability for electric utilities and their customers appears large. According to Energy Information Administration data, investor-owned utilities have invested about $60 billion in environmental compliance costs over the past several decades; TU electric's cumulative investment is $2.4 billion. The greatest future cost may come from greenhouse-gas regulation. For simplicity, all potential environmental regulatory costs are represented as a CO2 tax or fee. In the high-risk case, the probability is 70 percent over the 20 years after the first year of operation of the wind or gas plant that such regulation will occur. In the low risk case, the probability over 20 years is 30 percent. We believe a fair range of estimates for the probable cost of control would be $5 to $35 per ton, with a mean value of $25 per ton. Values are drawn from the right half of a normal distribution with zero mean and $31.3/ton standard deviation. Wind Plant Availability. Uncertainty in plant availability is often ignored in utility resource planning, although it can have a powerful impact on reliability and cost of service. It is especially important in this study because the availability of wind plants is likely to vary more than fossil-fuel plants. The variability in the annual output of wind power plants is well understood and easily modeled. To estimate its magnitude we simulated the performance of a wind plant using the Enercon E-40 wind turbine and four years of wind data collected in the DOE Candidate Wind Site program near Amarillo, Texas. The resulting annual average capacity factor of the wind plant is approximately 36 percent (assuming a 5 percent wind speed reduction due to wake losses and a 2 percent average power reduction caused by individual turbine outages), with a standard deviation of 6.5 percent. The uncertainty in wind plant output is incorporated into the model by randomly selecting a capacity factor in each year from a normal distribution with the given mean and standard deviation. When the capacity factor is lower than expected, the model draws more generation than usual from fossil resources. When the capacity factor is higher than expected, the opposite occurs. Estimates of fluctuations in the availability of fossil-fuel and nuclear plants were derived from five-year historical data for numerous plants published in the National Electric Reliability Council's Generating Availability Report. Since the figures in this report no doubt include some plants that are especially prone to failure, we scaled down the resulting estimates for this study. For existing plants and new coal plants, we assume a standard deviation in FOR