Prepared Direct Testimony of Robert McCullough on Behalf of the ...

Page 21 <strong>of</strong> 76SEATAC-400each <strong>of</strong> the plants ten thousand times. In each iteration the plant is modeled as being available ifthe random number chosen by the computer is less than the availability rate takm from GADS.Contra Costa 6, for example, is available 82.7% <strong>of</strong> the time on average, but it will be placed out<strong>of</strong> service depending on the random number chosen in each game.The blue line is the distribution <strong>of</strong> generation during Stage 3 Emergencies during the Californiacrisis. Ten thousand iterations provide an average availability in the 85% range. Averageavailability <strong>of</strong> the generator's units during Stage 3 Emergencies was a surprisingly low 52.9% <strong>of</strong>nameplate capacity.How did you approach the problem <strong>of</strong> dispatching the units?The first step is to calculate the operating cost for the units for each hour. As opposed to theassumed NOx prices and NOxikWh ratios, we used actual experienced prices and rates.Natural gas prices were taken from actual market data.Each one <strong>of</strong> the units purchased by the five generators from SDG&E (San Diego Gas &Electric), SCE (Southern California Edison), and PG&E (Pacific Gas &Electric) are modeledseparately. We obtained our NOx prices directly from the RECLAIM bulletin board. Whenmonths are missing, we used the average <strong>of</strong> prices for the remainder <strong>of</strong> the cycle since theopportunity cost <strong>of</strong> current use is the loss <strong>of</strong> the RECLAIM credits in later months.