Power Plant Equivalent Availability Efficiencies

Table of Contents

Section 1. Introduction and Project Description page 2

Section 2. Literature Review page 3

Section 3. Data Description page 5

Section 4. Data Envelopment Analysis (DEA) page 7

Section 5. Analysis of results page 9

Section 6. Conclusions page 10

Section 7. References page 13

Section 7. Appendix page 14

Section 1. Introduction and Project Description

In today's world, business is driven by technology. This in turn, creates a significant demand for electricity. This demand is ramping at a rate that is causing a shift in the way the electric industry conducts business. Electric generation companies are requiring that their facilities make the operation of their fleets more robust, improving the production capacities and will also is expected to deal with challenges to minimize costs and environmental impacts. Certain key performance indicators can be the basis for identifying and analyzing critical metrics which will allow for improving processes involved in electricity generation. Equivalent availability is an indicator which can address various categories if a "scorecard." The areas can include, but are not limited to financial, operational, customer and environmental concerns, and employee / training.

The task for predicting unit availability has been re-invigorated by the dynamics in the industry and consequently, the new take-charge approach at the power generating facilities. The task can be very simple, or can be very complicated; the scope of this study is to begin at the most fundamental stage with the task of finding and quantifying the connection between availability and benefits of up-front spending with the associated generating units in order to help benchmark and establish cost-effective, performance improvement /goals. This initial effort has begun by utilizing the LINDO Methodology to identify efficiencies and consequent improvement programs will eventually be implemented in a "top-down" approach.

Section 2. Literature Review

There is a certain amount of literature on the approaches in the industry to determine the necessary steps in addressing future unit availability(i). Studies which have been proposed and implemented include:

* The Influence of Maintenance Spending and Upgrading on Generating Unit Availability Performance by Houston Lighting & Power Company and General Electric Company

* Predicting Unit Availability: Dominant Factors Method, Southern Company Services, Inc.

* Statistical Models for Predicting Coal-Fired Generating Unit Costs and Performance, Illinois Power Company

* Equivalent Availability Studies, Public Service Electric and Gas Company and Applied Economic Research Company

Houston Lighting & Power Company used historical industry data and a linear regression model to determine the effects on availability of five key factors: maintenance and upgrade spending, unit aging, plant duty or cycling, availability incentive, and

Individual unit performance. These factors were selected by consensus of the engineering and business teams' judgment. Statistical correlation techniques were then used to quantify the effects of O&M spending on the equivalent availability figures and determine availability, adjusting for the effects of plant cycles and related age of the fleet.

Another methodology for predicting unit availability is based on a paper(2) presented by General Electric Company (GE) addressing trends in generating plants performance. Southern Company found that GE's methodology for predicting unit availability was acceptable for a system level, but was less reliable for a per-unit tool performance. Instead of focusing on equivalent availability as Houston Lighting & Power Company, the Southern Company researched alternative methods to develop a better predictive tool. The Southern Company decided to utilize equivalent forced outage figures. The Southern Company used the analysis to determine which variables affect forced outage figure and not to quantify the effects of predetermined variables. Southern Company's data base contained historical performance and outage data, design data, and historical spending data for each unit-year.

The power generation division will set out to address the similar results. We should keep in our focus that the primary function of this study is to initiate the existing corporate culture, and to raise awareness by identifying and outlining the current performance and the benefits of transitioning to a new operational culture. The ultimate reward in engaging in this project will be the company's bottom line: the average realized margin on all electric sales, including sales to affiliates and excluding trading activity, is trending upward, compared with the fourth quarter of 2008. The nuclear plants have achieved a relatively high capacity factor; however, with the uncertain future of coal-fired generation fleet in New Mexico, the output from the local gas and steam units will be more critical.

Section 3. Data Description

Among the traditional measures of plant reliability have been the Equivalent Availability Factor (EAF) and the Equivalent Forced Outage Rate (EFOR) in North America, China and some other countries. The measures or, "factors" use as a base the entire time period for which they are considered (typically one year) without regard to whether or not the unit required to generate. Therefore the more cyclic the demand is, the greater the effect. So if a Gas Turbine unit is used exclusively for meeting peak demand periods it may only be required to generate just a few hundred hours per year. If it were unavailable during 25% of those hours it would still have high an availability figure. For example peaking unit was required to generate 100 hours per year but experienced forced outages during 25 of those demand hours (and no outages over the 8760 hours in the year) it would still have a EAF of (8760-25)/8760 x 100 = 99.71% . Those numbers might look good on paper but the reality is that the unit could only produce 75% of the power required of it. So these factors don't come close to describing the unit's ability to produce its rated capacity when demanded. The fleet at that is at