Interstate Power & Light (IPL) 2013/2014

Transcription

1 Page 1 of 8 I. Executive Summary MISO requires each load serving entity (LSE) to provide a forecast of peak at the time of the MISO peak. LSE ALTW is shared by Alliant Energy Interstate Power & Light (IPL) and Central Iowa Power Co (CIPCO). Each company produces an independent forecast for their respective loads at the time of the MISO peak, which are added together to forecast ALTW at the MISO coincident peak (CP). This filing supports the IPL portion of the ALTW coincident peak forecast. Calculations are shown in Alliant Energy Coincident Peak & Weather.xlsx. The CP forecasting process includes the following steps: - Calculate diversity factors on a monthly and an annual basis - Compute diversity factor per MISO example - Consider alternate models - Compute normal input values - Remove Losses - Allocate to the Legacy Transmission System (LTS) and State IPL s CP forecast for 2013 by LTS is shown in Table A below: Table A: IPL Coincident Peak Forecast by LTS IPL Less Split by Legacy Times T ransmission Transmission System Peak TID Coincident Factor Loss Factor MEC. ALTW NSP. ALTW DPC. ALTW ALTW. ALTW Yr Forecast 96.35% 3.09% 0.14% 0.37% 0.44% 99.05% , , , ,852.8 II. Noncoincident Peak and Energy Forecast A. Methodology IPL energy forecasts use econometric models by class for short term and long term forecasts. The IPL peak forecast uses an econometric model of annual peaks. To arrive at the ALTW Noncoincident Peak (NCP), the IPL peak forecast is reduced by diversity with CIPCO, as described in ALTW forecast documentation. The statistical detail of the current IPL forecast is included in section C of IPL Forecasting Process. IPL s forecast is based on the methodology accepted in August 2008 by the Iowa Utility Board (IUB) 1. In their order the IUB states: The Board finds that IPL s load forecast is reasonable. The evidence demonstrates that IPL s load forecast methodology has been the same for many years, so there can 1 IUB Final Order, Docket No. GCU-07-1, Alliant Energy Page 1

2 be no allegation that IPL altered its forecast methods for this case to produce a desired result. Also, the factors relied upon by IPL are reasonable for determining a load forecast. IPL considers population, economic, industrial and technological growth rates in its forecasting 2. B. Forecast Treatment of Load Modifying Resources Demand Resources (LMR-DRs) For the calculation of the forecasted peak, IPL subtracts Load Modifying Resources Demand Resources (LMR-DRs) from historical data and computes the forecast based on firm peaks. IPL does not reduce load forecasts for specific future LMR-DR or Energy Efficiency Resources (EER) savings. The historic data, upon which the forecast is based, reflects reductions both in historical loads and in historical growth rates. Therefore the forecast is assumed to incorporate not only existing programs but also additional program savings as well. IPL does not recognize in the forecast calculations LMR-DR or EERs, which are not registered with MISO. Page 2 of 8 III. Coincident Peak Forecast A. Background The CP forecast is necessary to accommodate MISO s new process of assigning diversity savings, which result from the pooling of resources, disproportionately to individual companies. Under the new Resource Adequacy construct, MISO is no longer allocating the savings as an equal percentage to all companies; rather the allocation is based on the relative difference between the CP and the company s NCP. This new allocation requires the calculation of CP forecasts for each individual LSE. Diversity represents the portion of the load that IPL expects to have on its system at the hour of greatest demand, but for which MISO does not require IPL to maintain capacity to cover because other MISO members should have capacity available. As a result the annual forecasted non-coincident peak (NCP) of IPL, as an individual company, is larger than the required capacity, by the amount of the diversity. B. Methodology 1. Calculate Diversity factors on a monthly and an annual basis IPL computed diversity factors based on load after LMR-DRs are added back (theoretical loads). Due to the influence of the interruptible load and the recommendation of MISO, the remaining diversity calculations are based on the theoretical load data. The IPL theoretical peak, IPL s 2 Id, Page 33. Alliant Energy Page 2

3 theoretical demand value at the time of the MISO peak, and the corresponding weather at the Cedar Rapids Airport are identified and shown in Table B below. Load values in orange represent numbers influenced by LMR-DR, while temperatures in orange indicate differences in temperature between CP and NCP. Next the diversity factors were computed both on a monthly basis and on an annual basis. As an example of the difference, in 2008 IPL peaked in August while the MISO coincident peak was in July. Page 3 of 8 Table B: IPL diversity values and weather (MW) IPL NCP Peaks Based on Theoretical Loads (MW) Weather Temperature at NCP hour IPL Jun Jul Aug Sep Annual IPL CP Peaks Based on Theoretical Loads (MW) Temperature at CP hour MISO-IPL Jun Jul Aug Sep Annual Diversity Factor Absolute Value of Temperature Difference Temp-diff % 13.6% 1.6% 14.2% 1.7% 0.0% 1.5% Jun % 0.0% 3.6% 1.7% 4.9% 12.1% 1.3% Jul % 0.1% 11.6% 5.0% 9.8% 2.0% 2.4% Aug % 0.2% 0.7% 0.7% 4.0% 3.2% 0.0% Annual average Sep % 0.0% 11.6% 3.0% 1.7% 2.0% 1.3% 3.27% Annual % 3.5% 4.4% 5.4% 5.1% 4.3% 1.3% Average Monthly Average 3.8% 2. Compute diversity and temperature relationship per MISO Example As in the MISO example provided in the July MISO forecasting workshop, the next step is the calculation of the absolute value of the difference in temperature between the NCP and CP periods illustrated in the lower right portion of Table B, above. Regression analysis was done on the monthly diversity values with the absolute difference in temperature as the independent variable. These equations would be equivalent to Equation #1 in the MISO example. Results are found in Table C. Alliant Energy Page 3

4 Page 4 of 8 Table C: Regression of Monthly IPL DF and Temp-Diff Multiple R R Square Adjusted R Square Standard Error Observations 28 Regression Residual Total Coefficients Standard Error t Stat P-value Intercept Temp-diff E Consider Alternate Models In addition to the MISO example, IPL considered some alternate drivers of the diversity. These include regressions that focus on annual peaks, peak timing, and the actual temperature difference. Given the significant difference between the average monthly diversity and the average annual diversity at IPL s sister company WPL, a regression, shown in Table D, was run using only the annual data. Table D: Regression of Annual IPL DF and Temp-Diff Multiple R R Square Adjusted R Square Standard Error Observations 7 Regression Residual Total Coefficients Standard Error t Stat P-value Intercept Temp-diff Alliant Energy Page 4

5 The annual results were found to be in line with the monthly values. Furthermore if the average temperature differential is assumed, the resulting diversity is the same as the annual average. Further investigating the disparity in the WPL monthly vs. annual diversity led to the insight that when the NCP and CP are on the same day, the diversity seems to be small, while when the peaks occur on different days, the diversity seems to be larger. This makes sense because on different days, load characteristics, such as industrial load are more likely to differ. To model the possibility that the NCP and CP would occur on different days, IPL developed a different-day indicator variable, with one (1) indicating that the NCP and CP occur on different days. An illustration of the monthly peaks is shown below. Chart E: Illustration of Difference in Days Page 5 of 8 Results of a regression using the different-day indicator and the absolute value of the temperature difference are shown below in Table F. While for IPL the different-day variable wasn t statistically significant, the differentday variable was significant at WPL and was used in both for consistency. Table F: Diversity Regression with absolute temperature difference and different-day indicator Alliant Energy Page 5

6 Page 6 of 8 Multiple R R Square Adjusted R Square Standard Error Observations 28 Regression Residual Total Coefficientsandard Err t Stat P-value Intercept Temp-diff E-08 different day IPL also examined using the actual difference in temperatures, rather than the absolute value. This model is similar to the example and avoids the confusion caused by saying that higher CP temperatures lead to lower NCP. Results of the regression are shown below in Table G, below. Table G: Diversity regression with actual temperature difference and different-day indicator Multiple R R Square Adjusted R Square Standard Error Observations 28 Regression Residual Total Coefficientsandard Erro t Stat P-value Intercept Temp-diff E-07 different day Compute Normal input values Next, the expected value of the temperature difference at the time of the MISO peak needs to be determined. The estimating process involves a second regression equation where the CP temperature is a function of the NCP temperature. Values are shown in Chart H1 and regression results are found in Table H2. Alliant Energy Page 6

7 Page 7 of 8 Chart H1: CP and NCP Temperatures Table H2: Regression Results for CP and NCP Multiple R R Square Adjusted R Square Standard Error Observations 28 Regression Residual Total Coefficients Standard Error t Stat P-value Intercept NCP temp E-06 Substituting the average annual NCP temperature of 90.6, the estimate temperature at the CP is found in equation 1. Equation 1: (1.084*90.6)-10.31= 87.9 The normal value for the temperature differential is then Equation 2: Equation 2: = 2.7 For the daily indicator, the average of the historical annual peaks is Substituting the 2.7 temperature differential into the equation in Table G, results in Equation 3. Equation 3: (0.0086*2.7)+(0.0083*0.86)= a DF of 3.65% Coincidence factor = % = 96.35%. Alliant Energy Page 7

8 5. Remove Losses Since IPL s forecasted load includes losses, transmission losses need to be backed off to arrive at the requested load less transmission losses. Transmission losses are estimated at 3.09% of total internal demand IPL. 6. Allocations within LTS The IPL load forecast is allocated to the various LTS based on historical percentages, as shown in Table H. LTS is used instead of LBA, since an LBA is an electronic representation (including telemetered data) of a system and is not based on a physical location. Data from 2010 is used because the MEC.ALTW load began in the fall of Table H: IPL Allocation to LTS Page 8 of 8 IPL Portion of IPL Portion of IPL Portion of IPL Portion of (in MW) Hour Ending MEC.ALTW NSP.ALTW ALTW.DPC ALTW.ALTW IPL total MEC ratio NSP ratio DPC ratio ALTW ratio 8/10/ , , % 0.4% 0.5% 99.0% 7/20/ , , % 0.4% 0.3% 99.1% Average 0.1% 0.4% 0.4% 99.1% IPL forecast by LTS is added to the CIPCO forecast by LTS to arrive at the ALTW forecast by LTS as reported in the MECT tool and shown in Table C in the ALTW forecast documentation. 7. Allocations by State In addition, MISO requested a state level split. IPL has load in IA, MN, and IL. To allocate peak load between the states, IPL used the average of the 12 monthly coincident peaks. The total IPL portion is shown Table I: State split of the IPL peak below. The IPL demand split by state and LTS is added to similar data for CIPCo to determine the ALTW demand split by state and LTS. Table I: State split of IPL peak 12 Month IPL Iowa Minnesota Illinois average , , Percent 100.0% 92.2% 5.5% 2.3% Alliant Energy Page 8