Most forecasting reports concentrate on political or regulatory events to predict future industry trends. Frequently overlooked are the more empirical performance trends of the principal power generation technologies. Solomon & Associates queried its many power plant performance databases and crunched some numbers for us to identify those trends.
Market forces in the worldwide power generation industry have been dramatically changing over the past several years. Fuel prices, deregulation, environmental regulation, and renewable energy additions all interact and affect generating unit operations. To investigate the utilization, reliability, and cost manifestations of these forces, HSB Solomon Associates LLC (Solomon) evaluated operational performance trends over the past six years, concentrating on North American and European thermal generating units across conventional steam (that is, Rankine cycle) generation and combined-cycle technologies.
Though some basic trends are well known from reading news articles and industry sources, other trends might not be so obvious. As an example, utilization is well known to have been changing over the past few years due to fluctuations in fuel prices. But whereas the trends in reliability may not have changed significantly, there are very poignant lessons to be learned by investigating the components of reliability to understand whether there is a trend toward proactive or reactive maintenance. To that end, this investigation looked at the more difficult-to-recognize trends in generating unit operations.
Study Methodology
The areas of investigation included:
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Reliability. Equivalent unavailability factor (EUF) and equivalent forced outage rate (EFOR) as defined by the North American Reliability Corp. (NERC) Generation Availability Data System (GADS).
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Utilization. Measured net output factor (NOF) and net capacity factor (NCF) as defined by NERC GADS.
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Cost. Operating costs were segregated into total cost less fuel (TCLF) and fuel cost.
Data for each region, technology, and timeframe were compiled from Solomon’s proprietary database of power generation operating and financial performance. For EUF, EFOR, NOF, and NCF, a two-year average was used in conjunction with a yearly average to capture the effect of annual variations in overhauls.
Inferring industry trends solely by using classical statistics (for example, averages with confidence intervals) can overlook information contained within the data. Skewness, kurtosis, distribution forms (for example, bimodality), and other shape/value changes in the data are important in identifying and understanding trend dynamics. For that reason, a Monte Carlo – based resampling with replacement procedure (statistical bootstrapping) was used to compute standard statistics estimates (mean, standard deviation, percentiles, and the like), as well as more detailed results that provide insights into or information relating to the actual shape of the industry distributions and their effect on the trends.
Predicted Results
The statistical results for the North American and European conventional steam analysis are presented in Table 1. Table 2 presents the North American and European combined-cycle results.
An in-depth analysis for North America and Europe is presented in the following sections by region and generation technology.
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