Wind Integration Study for Public Service of Colorado Addendum Detailed Analysis of 20% Wind Penetration

Transcription

1 Final Report: Wind Integration Study for Public Service of Colorado Addendum Detailed Analysis of 2% Wind Penetration Prepared for Xcel Energy 55 15th Street Denver, Colorado 822 c/o Mr. Tom Ferguson Prepared by EnerNex Corporation R.M. Zavadil Vice President & Principal Consultant Jack King Consulting Engineer 17C Market Place Boulevard Knoxville, Tennessee Tel: (865) ext. 149 FAX: (865) December 1st, 28

2 CONTENTS Contents 1 Figures 2 Tables 3 Technical Review Committee 4 Project Team 5 Executive Summary 6 Background 6 Summary and Conclusions 6 Introduction 1 Overview of Project Objectives 1 Integration Costs quantified in this study 11 Models and Data 11 Assumptions 12 Description of Automated Unserved Energy Dispatch Algorithm 13 Importance of Wind Generation Forecasting 15 Geographic Diversity 17 Gas Price Sensitivity 2 Flexible Resource Impact on Integration Cost 22 Application of the results 23 Summary and Discussion 24 Improved Forecasting Reduces Integration Costs 24 Forecast Averaging and Integration Costs 24 Geographic Diversity and Integration Costs 24 Effect of Gas Cost on Wind Integration Cost 24 Effects of Additional Flexible Resources (5 MW CC plant) 24 Effect of Transmission Constraints on Wind Integration Costs 25 Impact of Wind on Coal Plant Operation 25 Next Steps 25 Appendix A - Wind Forecasting Details 26 Developing the Wind Generation Forecasts 26 Results of Wind Generation Forecast Sensitivities 3 Analysis of Gas Turbine Start Cost Contributions to Integration Cost 32 Analysis of Three Forecast Case Results 32 Effects of Wind Generation Forecast Averaging on Integration Cost 39 Additional Statistical Analysis of Sensitivity to Wind Forecast 41 Pumped Storage Sensitivity 45 Appendix B - Discussion of Coal Unit impacts 47 How much electricity from coal is displaced? 47 Are coal units ever required to shut down? 47 Page 1

3 FIGURES Figure 1: Proxy tower locations for high geographic diversity 17 Figure 2: Proxy tower locations for realistic geographic diversity (using WWSIS data) 18 Figure 3: Approximate actual location of PSCO wind resources in 28 (numbers are MW capacity) 19 Figure 4: Graph of integration cost vs. annual average gas price 21 Figure 5: Appendix A - 12x24 Historical Average Forecast Pattern 28 Figure 6: Appendix A - Summary Results for 31 Wind Generation Forecasts ($5. avg. gas price) 3 Figure 7: Appendix A - Net Load Forecast MAE for 3 Forecasts (Forecasts 2, 13 and 31) by Month 33 Figure 8: Appendix A - Net Load Forecast MAE for 3 Forecasts (Forecasts 2, 13 and 31) by Month for Daytime Hours 34 Figure 9: Appendix A - Net Load Forecast MAE for 3 Forecasts (Forecasts 2, 13 and 31) for Nighttime Hours 34 Figure 1: Appendix A - Net Load Forecast MAE for 4 Forecasts by Week for July 35 Figure 11: Appendix A - Net Load Forecast MAE for 4 Forecasts By Week for July Day and Night Hours 35 Figure 12: Appendix A - Net Load Forecast MAE for 3 Forecasts by Week for August 35 Figure 13: Appendix A - Histogram of Stack Depth vs. Hours/Year 36 Figure 14: Appendix A - Histogram of Stack Depth vs. Hours/Year Upper Tail 37 Figure 15: Appendix A - Integration cost based on Wind Generation Forecast averaging versus no averaging 4 Figure 16: Appendix A - Probability Density for Integration Cost 43 Figure 17: Appendix A - Probability Density for Integration Cost of Averaged Forecasts 44 Figure 18: Appendix A - Pumped Storage Sensitivity Cases ($5/MMBtu gas price) 46 Figure 19: Appendix B - 1/13/4 with Plant Outages Models and 2% Wind Penetration 51 Figure 2: Appendix B - 1/13/4 with Plant Outages Models but No Wind 51 Figure 21: Appendix B - 1/13/4 with No Plant Outages Models and 2% Wind Penetration 52 Figure 22: Appendix B - 1/13/4 with No Plant Outages Models and No Wind 52 Figure 23: Appendix B - 1/1/4 with Plant Outages Models and 2% Wind Penetration 53 Figure 24: Appendix B - 1/1/4 with Plant Outages Models but No Wind 53 Figure 25: Appendix B - 1/1/4 with No Plant Outages Models and 2% Wind Penetration 54 Figure 26: Appendix B - 1/1/4 with No Plant Outages Models and No Wind 54 Page 2

4 TABLES Table 1: Integration Costs in $/MWh (earlier results included for comparison; $5/mmBtu gas) 7 Table 2: Integration Costs from Un-Smoothed and Smoothed Wind Forecasts ($5/MMBtu gas) 7 Table 3: Integration Costs from Different Geographic Dispersion of Wind Farms ($5/MMBtu gas) 8 Table 4: Table of Integration Cost vs. Annual Average Gas Price 8 Table 5: Integration Cost with Addition of Flexible Resource ($1/MMBtu gas) 9 Table 6: Parameters for costing Unserved Energy 14 Table 7: Total Integration Costs ($5/MMBtu gas) 16 Table 8: Integration Costs from different geographic dispersion of wind farms ($5/MMBtu gas) 19 Table 9: Gas Price Assumptions for Gas Price Sensitivity analysis ($/MMBtu) 2 Table 1: Table of integration cost vs. annual average gas price 2 Table 11: Effect of 5MW combined cycle addition ($1/MMBtu Gas) 22 Table 12: Appendix A - Regional Forecast Error Series Components 27 Table 13: Appendix A - Descriptions of Wind Generation Forecasts 29 Table 14: Appendix A - Integration costs by Wind Generation forecast ($5./MMBtu avg. gas price) 31 Table 15: Appendix A - Turbine Start Costs 32 Table 16: Appendix A - Stack Depth Histogram Data 38 Table 18: Appendix A - Comparison of integration cost results from normal and averaged forecasts 39 Table 19: Appendix A - Statistics of integration costs for Forecasts Table 2: Appendix A - Summary Statistics for Integration Cost 41 Table 21: Appendix A - Summary Statistics for Integration Cost (with smoothed forecast results) 45 Table 22: Appendix A - Comparison of Pumped Storage Size Sensitivity Cases 46 Table 23: Appendix B - Summary of Energy Displacement by Fuel Type by Wind (GWh) 47 Table 24: Appendix B - Ten Lowest Net Load Hours of the year (MWh) with 2% wind penetration 48 Table 25: Appendix B - Ten Lowest Net Load Hours of the year (MWh) with no wind 48 Table 26: Appendix B - Minimum loading for fossil units 49 Page 3

5 TECHNICAL REVIEW COMMITTEE The following individuals comprised a technical review committee (TRC) for this project. The TRC was kept apprised of the approach, methodology, and assumptions for the analysis described in this report, and provided valuable comments, suggestions, and guidance at several critical junctures from project commencement to conclusion. PSCO Staff Tom Ferguson Jim Hill Curt Dallinger PUC Staff Rich Mignogna External Brian Parsons Michael Milligan Ed DeMeo National Renewable Energy Laboratory National Renewable Energy Laboratory Wind Consultant Brendan Kirby Charlie Smith Wind Consultant Utility Wind Integration Group Mike Mendelsohn Bob Zavadil Mark Ahlstrom Western Resource Advocates EnerNex Corporation WindLogics Page 4

6 PROJECT TEAM Xcel Energy (the Company) retained EnerNex Corporation of Knoxville, Tennessee for this project to assist the Company in analyzing various aspects of wind integration issues for the PSCO system. EnerNex Corporation is an electric power engineering and consulting firm specializing in the development and application of new electric power technologies. EnerNex provides engineering services, consulting, and software development and customization for energy producers, distributors, users, and research organizations. The company has substantial expertise with a broad range of technical issues related to wind generation, from turbine electrical design to control area operations and generation scheduling. As a subcontractor to EnerNex, WindLogics of St. Paul, Minnesota provided meteorological expertise for the project by way of developing and characterizing the wind energy resource in and around the PSCO service territory. Based in St. Paul Minnesota, WindLogics is a leader in advanced wind resource analysis, long-term wind variability and weather forecasting services. Using decades of weather data and advanced computer modeling techniques, WindLogics services help wind project developers, owner/operators, financiers and utilities reduce their financial risk and maximize their return through a better understanding of the wind. As part of the EnerNex team for assessment of wind integration costs and impacts, WindLogics provides expertise on wind modeling and forecasting issues which are both critical to the project results. EnerNex and WindLogics are also engaged in wind integration studies for a large number of clients across the U.S. In 23, they were selected to conduct an assessment of 15 MW in the Xcel-NSP control area in Minnesota. The approach and methods devised for that groundbreaking effort have been continually extended and augmented for application in this PSCo project as well as a number of other wind integration studies. Page 5

7 EXECUTIVE SUMMARY Background In order to address several of the specific questions and directives of the Order and Stipulation for Docket No. 4A-325E, Xcel Energy initiated a study in 24 on the technical and economic impacts of adding significant wind generation to its Colorado electric supply portfolio. That effort resulted in the publication of the Wind Integration Study for Public Service of Colorado, released in May of 26. That report addressed wind penetrations of 1% and 15% (15% penetration means that the nameplate capacity of wind equals 15% of the annual peak load) and will be referred to in this addendum as the 15% study. The 15% study included two phases of analysis. Phase I examined the integration costs of 1% and 15% penetrations; Phase II looked at the impacts of wind-farm geographic diversity on integration costs, as well as issues associated with forecasting wind generation in Colorado. In the course of the 15% study, the Company also initiated analyses for a 2% level of wind penetration. The preliminary 2% results showed that integration cost varied significantly depending on the methodology used to develop the day-ahead wind forecast. As a result, the Company, its consultant, and members of the study Technical Review Committee (TRC) decided not to publish the 2% study results until additional analyses into these results could be completed. In summer 28, forecasts of wind generation data developed by NREL for the Western Wind and Solar Integration Study (WWSIS) for areas in the western US became publicly available. At a meeting of the study s TRC in August 28, all parties decided that given the sensitivity of integration cost to wind forecasting, it would be preferable to redo the study using this NREL wind forecast data. This presentation of the 2% study results with the new Colorado forecast data represents Phase III of the overall project. The focus of this study is to provide an estimate for the hidden costs of integrating wind energy onto the PSCo system. The hidden costs are associated with the uncertain and variable nature of wind generation and are used in the resource planning and selection process to make sure that wind generation is compared on a level playing field with the other resource technologies. The Company does not intend that this study be used as a reliability study. While loss of load risks can be monetized, that is beyond the scope of this study. Summary and Conclusions Table 1 includes results of the 1% and 15% study as well as the 2% study. Two general categories of integration costs were examined in these studies; 1. Electric Production Costs the costs of operating PSCo s electric generation system (fuel costs, variable and fixed O&M, etc.) 2. Gas Supply System Costs - the costs of operating PSCo s gas supply system plus expansion costs due to wind Note that the 1% and 15% results were derived using a less refined method for estimating the day-ahead forecast of wind as that used in the 2% study. Page 6

8 Table 1: Integration Costs in $/MWh (earlier results included for comparison; $5/mmBtu gas) Wind Penetration Electric Production Cost Impact Gas Supply System Impact Total Integration Cost (~$5/MMBtu gas) 1% (Phase I) $2.25 $1.26 $3.51/ MWh 15% (Phase I) $3.32 $1.45 $4.77/ MWh 2% (Phase III) (CO wind forecasts using WWSIS data) $3.95 $1.18 $5.13 / MWh The integration costs prior to using the WWSIS data were estimated at $7.42 with all other inputs and assumptions being the same. This decrease (from $7.42 to $5.13 per MWh) is not surprising since the WWSIS data assumes better forecasts and better wind forecasts tend to reduce the integration costs. It should be noted that the forecasting method assumed in the WWSIS data is state-of-the-art for 28 and far superior to that which PSCO currently uses. Wind Forecast Averaging The Couger computer model was used to simulate commitment and dispatch of the PSCo electric supply system throughout all phases of the Company s wind integration study efforts. As part of the 1% and 15% study efforts the Company observed that Couger was creating a commitment plan that appeared overly specific in responding to the swings in load that result from wind on the system. This potential for an overly specific commitment stems from the fact that the model functions as if it has 1% accurate day-ahead knowledge of the hourly loads it must serve. To explore this issue, adjustments to the day-ahead forecasts of wind were made in an attempt to get the model to develop a commitment plan closer to that of a system operator. These wind forecast adjustments involved taking a five hour moving average of each forecast to smooth the rapid fluctuations in wind speed from hour to hour. Integration costs using smoothed forecasts were generally lower. Table 2: Integration Costs from Un-Smoothed and Smoothed Wind Forecasts ($5/MMBtu gas) Integration Cost ($/MWh) ($5 / MMBtu gas) (high geographic diversity) 5 Hr forecast Averaging (smoothed) No Forecast Averaging (un-smoothed) $4.98 $5.13 The rest of the Couger modeling for the 2% penetration study was done using both the original wind forecasts from the WWSIS data, and the smoothed and un-smoothed forecasts. The results from both cases are reported. Effect of Geographic Diversity on Integration Costs In addition to examining the effects of smoothing the day-ahead wind forecast, the impacts of geographic diversity of wind facilities on integration costs was examined. In particular, the advent of publicly available WWSIS wind data allowed the Company to more accurately reflect the actual locations of wind facilities on the PSCo system (including the associated geographic diversity of those facilities). Page 7

9 The fact that the WWSIS wind and wind forecast data is available for many more locations in Colorado than prior forecast data allowed for this refinement. Table 3: Integration Costs from Different Geographic Dispersion of Wind Farms ($5/MMBtu gas) Geographic Dispersion of wind farms High geographic diversity (similar to past inputs) Low geographic diversity (closer to reality) Integration Cost ($/MWh) ($5 / MMBtu gas) 5 Hr forecast Averaging No Forecast Averaging (smoothed) (un-smoothed) $4.98 $5.13 $5.25 $6.3 Effect of Natural Gas Price on Integration Costs Wind integration costs were estimated for four different price levels for natural gas ($5.6, $7.83, $9.83, and $11.83/MMBtu). The $5.6 price is consistent with prices used in the 1% and 15% studies and allows for comparison to the results from analyses of those lower penetration levels. The remaining three price levels represent forecasts of gas prices in the timeframe (about when the 14 MW of wind is expected). The analysis showed a strong correlation between gas price and wind integration cost when using the unsmoothed wind forecast of about $.5/MWh increased integration cost for each $1./MMBtu increase in the price of natural gas. With smoothed forecast data however, the relationship between gas prices and integration costs seemed less correlated integration costs rose as gas prices rose from $5.25 to $1/MMBtu, but then decreased when gas prices approached $12/MMBtu. Table 4: Table of Integration Cost vs. Annual Average Gas Price Gas price sensitivity case Annual Average Gas Price ($/MMBtu) Integration Costs ($/MWh) 5 hr Forecast Averaging No Forecast Averaging (smoothed) (un-smoothed) Previous Inputs $5.6 $5.25 $6.3 Low $7.83 $6.81 $6.65 Mid $9.83 $8.56 $8.8 High $11.83 $7.55 $9.55 It is important to note that while higher gas prices increase the integration cost of wind generation, they increase the value of the wind far more by displacing higher-cost gas. Effect of Flexible Resources on Integration Costs The effect of an additional flexible generation resource on reducing wind integration cost was examined by adding a 5 MW combined cycle plant to the system. This reduced integration costs approximately $.1/MWh (un-smoothed) or $.3/MWh (smoothed). Page 8

10 Table 5: Integration Cost with Addition of Flexible Resource ($1/MMBtu gas) Addition of flexible resource Base case (like current system) With flexible resource (addition of 5 MW CC) Integration Cost ($/MWh) ($1 / MMBtu gas) 5 Hr forecast Averaging No Forecast Averaging (smoothed) (un-smoothed) $8.56 $8.8 $8.23 $8.7 The impact of this additional CC unit on reducing integration costs was between $.1 and $.33/MWh of wind generation. This relatively small impact is in large part due to the fact that the PSCo system was flush with flexible generation resources in 27 (the year being modeled in the study). To the extent that the PSCo system has fewer flexible generating resources in the future than what was available in 27, the impact of an additional 5 MW CC unit in helping reduce wind integration costs is expected to be higher. Page 9

11 INTRODUCTION In order to address several of the specific questions and directives of the Order and Stipulation for Docket No. 4A-325E, Xcel Energy initiated a study in 24 on the technical and economic impacts of adding significant wind generation to its Colorado electric supply portfolio. That effort resulted in the publication of the Wind Integration Study for Public Service of Colorado which, released in May of 26. That report addressed wind penetrations of 1% and 15% (15% penetration means that the nameplate capacity of wind equals 15% of the annual peak load) and will be referred to in this addendum as the 15% study. The 15% study included two phases of analysis. Phase I examined the integration costs of 1% and 15% penetrations; Phase II looked at the impacts of how geographic diversity of wind farms impacts integration costs as well as issues associated with forecasting wind generation. In the course of the 15% study, the Company also initiated analyses for a 2% level of wind penetration. The preliminary 2% results showed that integration cost varied significantly depending on the methodology used to develop the day-ahead wind forecast. As a result, the Company, its consultant, and members of the study Technical Review Committee (TRC) decided to hold off on publishing the 2% study results until additional analyses into these results could be completed. In summer 28, forecasts of wind generation data developed by NREL for the Western Wind and Solar Integration Study (WWSIS) for areas in the western US became publicly available. At a meeting of the study s TRC in August 28, all parties decided that given the sensitivity of integration cost to wind forecasting, it would be preferable to redo the study using this NREL wind forecast data. This presentation of the 2% study results with the new Colorado forecast data represents Phase III of the overall project. Overview of Project Objectives The focus of this study (as well as the 1% and 15% studies) is to provide an estimate for the hidden costs of integrating wind energy onto the PSCo system. The hidden costs are associated with the uncertain and variable nature of wind generation and are used in the resource planning and selection process to make sure that wind generation is compared on a level playing field with the other resource technologies. The Company does not intend that this study be used as a reliability study. While loss of load risks can be monetized, that is beyond the scope of this study. The initial wind integration study project (Phase I) was designed to address issues and questions stemming from the Colorado Public Utilities Commission Decision Number C5-49 that approved the Comprehensive Settlement of PSCo s 23 Least-Cost Resource Plan. Phase I of the project included the following tasks: Estimate the ancillary service costs of 72 MW (22 MW existing + 5 MW from a renewable Request for Proposal) of nameplate wind based on PSCo s estimated 27 peak demand (projected to be 7,148 MW), or the best available peak demand forecast at the commencement of the study (~1% penetration). The ancillary cost estimate should provide an estimate of all PSCo s generating units (owned and/or controlled under tolling agreements) cost factors including regulation, load following, and unit commitment (including start up costs and ramp rates). Estimate the ancillary service costs of a quantity of nameplate wind at 15% penetration based on its 27 peak demand (or the best available peak demand forecast at the commencement of the study). Include one full year s worth of data from the Lamar wind project. Page 1

12 Keep Commission staff inform of study progress and invite Staff to participate in technical review meetings. In the follow-on Phase II effort, the study scope was expanded to include an analysis of how the results from the Phase I effort were impacted by how wind generation might be developed within the state including: Determine whether ancillary costs remain nearly the same for different sized facilities within certain ranges. The Commission will not specify the range, but instead instructs PSCo to examine the data to determine if it is appropriate to assume different ancillary costs depending upon size and geographic region, instead of a system-wide figure. The study should analyze the effect of contracted wind bidder projects on PSCo s system, because ancillary costs may vary significantly based on wind penetration level, geographic location, and diversity of wind resources, and these factors can not be fully considered until 25 renewable Request for Proposal (RFP) projects are under contract. In this Phase III report, three additional aspects of wind integration were investigated: How different methods for developing the day-ahead forecast for wind affect the estimated integration costs of wind. In this process, different approaches for developing the day-ahead wind forecast (based upon real forecast error series) were evaluated. While this was an important part of the initial Phase III scope, it was mostly eliminated when Colorado wind and wind forecast data became available in the summer of 28. This investigation of the impacts of different forecasting methods represented an important part of the initial Phase III study scope, but largely abandoned once the WWSIS wind data became available in the summer of 28. The effect of natural gas prices on the wind integration cost estimates. Since much of the variability of the wind supply is made up by gas burning units, a significant correlation is expected. How different types of generation additions to the PSCo system might help reduce integration costs. Addition of combined-cycle gas plant is examined. Integration Costs quantified in this study This study attempted to quantify three categories of integration costs: 1. regulation, 2. system operations (opportunity costs, higher production costs due to less-than optimal operations, etc), 3. gas supply. The study made no attempt to quantify any additional integration costs associated with curtailment of wind generation, electricity trading inefficiencies introduced by wind uncertainty, or increased O&M costs at existing thermal units that may be called upon more often to ramp output over a broader range with shorter notice. Additionally, this study does not attempt to calculate the value of wind. For example, rising gas costs increase the integration costs of wind; however, the wind also becomes more valuable because it displaces higher-cost gas. While a calculation is provided in the report body to help explain an example, it is not meant to be a complete calculation of the value of wind. Models and Data Per the Commission Order and Settlement Agreement, calendar year 27 was the focus of the study. Year 27 was represented with: A (modified) projected peak load of 6,922 MW Page 11

13 Projected energy requirements of 34,224 GWH Approximately 15 MW of customer-sited solar electric power Updates to various existing power purchase and sale contracts The chronological simulation methodology that was used in this study requires extended sets of hourly data. The preference is for this data to be based on recent historical years so that the daily patterns are most representative of the recent behavior of actual system loads. In this vein, it is also important that the chronological wind generation data be drawn from same historical year so that correlations between wind and load due to meteorology are properly represented in the input data. Historical load data from three years 22, 23, and 24 were used to develop the hourly load patterns. The data sets were scaled so that the peak hour matched that projected for the year 27. Other types of data were also collected to define the study year, including: Day-ahead forecasts of hour-by-hour load, which were used for forward scheduling and power marketing activities in addition to nomination of natural gas for both direct use and gas-fired generation Planned, maintenance, and forced outage history for generating units Insolation data, used to construct an hour-by-hour production pattern for the customersited solar electric resources on the PSCo system Assumptions This study used the Global Energy Solutions Couger model to simulate the commitment and dispatch of the PSCo electric supply system with a 2% penetration level of wind (i.e., wind nameplate as a percent of annual peak load). Couger is a Unit Commitment and Dispatch model that has been used by Xcel s Power Operations group for establishing day-ahead dispatch plans for the PSCo and NSP systems. Couger was used to mimic the day-ahead activities that system operators use to develop the best plan for meeting the load requirements of the system for the next day (i.e., the day-ahead plan). The model is first run in optimization mode to create a day-ahead plan to meet forecasted load. This day-ahead plan is then used in simulation mode with the actual system load (net of wind generation) that materializes. Other assumptions include: 1) Wind penetration of 2% capacity (~1,4 MW with 6,922 MW peak) Note that the price or cost of the wind generation is not a factor in this study. It is assumed that wind generation is a must take resource, and the PSCo will manage its other generation resources in a manner as to accommodate wind. The added costs associated with using these other resources to accommodate wind opportunity costs, higher production costs due to lessthan optimal operations, etc. is defined in this study as electric system integration cost. Page 12

14 2) New Thermal Resources Added (substitute for wind when needed) a. Type = Three (3) Generic Combustion Turbines in 27 b. Ratings - Summer = 12 MW (each); Winter = 139 MW (each) c. Heat Rate = 1,45 MMBTU/MWh d. Variable O&M = $4.3/MWh e. Fixed O&M = $ 1.74 kw-yr (based on 16 MW design) f. Min/Max Loading i. 25% = 17, 568 MMBTU/MWH ii. 5% = 12,759 MMBTU/MWH iii. 75% = 11,24 MMBTU/MWH iv. 1% = 1,45 MMBTU/MWH g. Min Run Time = 4 hours h. Max # of starts/day = 2 times i. Start-Up Costs = $6, 3) Solar Resources a. Approximately 1-15 MW with half at customer on-site b. Modeled based on historical solar insolation data 4) Gas Prices a. Per PSCO internal forecasts b. Sensitivities provided by PSCo. 5) Wind Generation and Forecasts (WWSIS data for Colorado) a. Wind speed data and forecasts for 49 different locations in Colorado b. Smoothed versions of the same forecasts were also examined. In the course of this study, other forecasts were developed and results with those forecasts were presented to the TRC. The Company, consultant, and the TRC members decided to re-do the study with the Western Wind and Solar Integration Study (WWSIS) data when it became available. To simplify this report, previous detailed results were moved to Appendix A. 6) Combined Cycle Plant Addition a. 5 MW facility capable of operation in 1x1 or 2x1 mode b. No outages or other constraints on this plant s operation Description of Automated Unserved Energy Dispatch Algorithm In Phases I and II of the wind integration study, the Couger modeling of the PSCo system showed periods when generation was insufficient to serve customer loads (a.k.a., unserved energy) In order to eliminate these periods of unserved energy it was necessary to post-process each computer model run and manually add the costs associated with starting quick-start gas turbines. This was a very time consuming process, and more importantly, a human was performing the manual dispatch making more prone to error and inconsistency. For the Phase III study effort, an automated process was developed to address unserved energy using Visual Basic for Applications (VBA), a programming language imbedded in Microsoft Access. Procedures were written to automatically run 52 weekly Couger simulation cases with a post processing function to resolve any unserved energy and add those costs into the previously calculated production costs, gas consumption and unit hourly loading for gas units. Page 13

15 The parameters for calculating the added costs to eliminate unserved energy are as follows: Number of Gas unit starts Hours of operation MWH generated Other parameters used in these calculations are: Table 6: Parameters for costing Unserved Energy Parameter Value Average Heat Rate 1.45 MMBTU/MWH Cost of startup $6 O&M for hours of operation $126/hr O&M for MWH of generation $4.3/MWH Gas cost As specified in the inputs Unit starts were determined by analyzing how many 13MW turbines are required to meet or eliminate the unserved energy. The algorithm requires units are left on for at least 2 hours and does not incorporate minimum down times. Page 14

16 IMPORTANCE OF WIND GENERATION FORECASTING At the completion of Phase II of this study, it became apparent that the quality of the hourly day-ahead wind generation forecast significantly influenced the results. This sensitivity became evident when different sets of wind forecasts with similar energy and Mean Absolute Error statistics yielded significantly different wind integration costs. One key aspect of Phase III was to better understand the effect of wind forecasting on the estimates of integration cost. When this Phase III effort began, data representing wind facility generation along with a corresponding day-ahead forecast of that generation was not available for Colorado. As a result, prior to gaining access to the WWSIS wind data, the Phase III study effort utilized wind generation data for Colorado locations developed by WindLogics but was left to develop dayahead wind forecasts using data derived from Xcel s Minnesota system wind facilities. This Minnesota data was represented by an error series developed where both day-ahead forecasts and measured wind data were available. The day-ahead forecasts of Colorado wind generation were then developed by adding (or subtracting) the error series to (from) the wind generation data developed for Colorado by WindLogics (see Appendix A for details). In the course of studying these forecasts, the Company observed that Couger was creating a commitment plan that appeared overly specific in responding to the swings in load that result from wind on the system. This potential for an overly specific commitment stems from the fact that models like Couger function as if they have 1% accurate day-ahead knowledge of the hourly net loads (load minus wind) they must serve. To explore this issue, adjustments to the day-ahead forecasts of wind were made in an attempt to get the model to develop a commitment plan closer to that of a system operator. These wind forecast adjustments involved taking a five hour moving average (2 hours back, and 2 hours forward) of each forecast to smooth the rapid fluctuations in wind speed from hour to hour. The results generally showed that the smoothed or averaged forecasts provide a lower integration costs. The forecast smoothing technique merely attempts to over come some of the limitations with modeling electric system operation in a model like Couger. The Couger model used in this 2% study could only commit units on a day-ahead schedule using simple rules. In reality, system operators use much more sophisticated methods to commit and dispatch units, and they do so on a much more frequent basis. The Company is not advocating that this or any other smoothing of the day-ahead forecast of wind be done in the actual day to day operation of the PSCo system. The Company believes that every effort should be made to forecast the day-ahead wind as accurately as possible and that such forecasts will ultimately lead to lower integration costs. However, since the smoothed wind forecasts seemed to help the Couger model better emulate the system-operator behavior, the model was always run with both sets of input data (smoothed and un-smoothed wind forecasts) for the remainder of the study so results could be compared. The table below summarizes integration cost estimates using the old forecast methodology (detailed in Appendix A) and the new WWSIS forecasts, both smoothed and un-smoothed: Page 15

17 Table 7: Total Integration Costs ($5/MMBtu gas) Source of wind and wind forecast data Forecasts built using error series (17.5% MAE) WWSIS wind data (15% MAE) Integration Cost ($5 / MMBtu gas) (high geographic diversity) 5 Hr forecast Averaging No Forecast Averaging (smoothed) (un-smoothed) $6.18 (avg.) $7.42 (avg.) $4.98 $5.13 Compared to the old forecasts, the WWSIS forecasts yield a lower integration cost for both the original (un-smoothed) forecasts (~$2.3 lower), and the smoothed forecasts (~$1.2 lower). This makes sense because the WWSIS forecast data assumes a more sophisticated forecast method with a lower Mean Absolute Error (MAE). Also, the integration costs for the smoothed forecast ($4.98) is still lower than that for the original forecast ($5.13) by 15 cents, although the difference is not as dramatic as the $1.24 difference ($ $6.18) using the old forecasts. It should be noted that the forecasting method assumed in the WWSIS data is state-of-the-art for 28 and far superior to that which PSCO currently uses. The current MAE for PSCO wind forecasts is about 18-2%, while the WWSIS forecast error is closer to 15% (for the whole state). PSCO is investigating better forecasting tools and expects to have a forecast error closer to 15% by the time the wind penetration on the system reaches 2% (based on annual peak demand). This result strongly supports the Company s initiative to develop these tools. As a rough approximation, this result suggests that the Company could save between $5.2M (43 GWh X $12/GWh) and $9.9M (43 GWh X $23/GWh) per year in integration costs by using state-of-the-art wind forecasting (with 14 MW of wind on the system). Page 16

18 GEOGRAPHIC DIVERSITY Prior to WWSIS wind data becoming available, the Company was limited to modeling the 1,4 MW of wind that reflect a 2% penetration from wind data locations developed in the 1% and 15% studies. Given that this data was available for a limited number of locations, and each location can only represent a limited amount of wind generation, application of this data to the 2% penetration level resulted in more geographic diversity of wind than could exist on PSCo s system. The red, blue, and green pins on map below depict the modeled wind resource locations ( proxy towers ) for previous studies. The 1% and 15% studies used a subset of these locations, while the 2% study (before WWSIS data was available) used all of the locations (except the gray pins). The dispersion of these proxy towers is much more geographically diverse than could actually be achieved on the PSCO system with 14 MW of capacity. Figure 1: Proxy tower locations for high geographic diversity Page 17

19 In contrast, the WWSIS wind and day-ahead wind forecast data is available in far more locations. This allowed for a much more accurate representation of PSCO wind facility locations and as a result, a more realistic representation of the geographic diversity of these facilities. The map below depicts the locations of WWSIS wind data that was used to reflect a 2% level of penetration. Figure 2: Proxy tower locations for realistic geographic diversity (using WWSIS data) Page 18

20 The actual location of the wind generation in Colorado is depicted below: Figure 3: Approximate actual location of PSCO wind resources in 28 (numbers are MW capacity) The impacts of decreasing the geographic diversity to reflect reality are shown in the table below: Table 8: Integration Costs from different geographic dispersion of wind farms ($5/MMBtu gas) Geographic Dispersion of wind farms More geographic diversity (similar to past inputs) Less geographic diversity (closer to reality) Integration Cost ($5 / MMBtu gas) 5 Hr forecast Averaging No Forecast Averaging (smoothed) (un-smoothed) $4.98 $5.13 $5.25 $6.3 With un-smoothed forecast data, the lack of geographic diversity increased integration costs over $1./MWh. Smoothing the wind forecast reduced this increase to ~$.25. Also note that the integration cost difference between using smoothed and un-smoothed wind forecast data increased to $1.5 (from $.15) when the geographic diversity of the wind farms is closer to reality. Page 19

21 GAS PRICE SENSITIVITY The actual price of natural gas is a key factor in the wind integration cost estimates since much of the wind variability is accommodated by starting, operating, and stopping gas units. The table below shows the gas cost assumptions used in this analysis. The values in the row labeled Previous are the monthly gas prices for all of the integration cost estimates discussed to this point of the report, including the integration cost estimates calculated in Phase I (1% and 15% penetrations) of the study. To investigate how gas prices influence integration cost estimates, three additional sets of gas prices (Low, Mid, and High) were created. Table 9: Gas Price Assumptions for Gas Price Sensitivity analysis ($/MMBtu) AVG Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Previous Low Mid High The integration costs for each of the gas price scenarios are presented in the table and graph below. Integration costs are given in $/MWh and gas costs are given in $/MMBtu. Table 1: Table of integration cost vs. annual average gas price Gas price sensitivity case Annual Average Gas Price ($/MMBtu) Integration Costs ($/MWh) 5 hr Forecast Averaging No Forecast Averaging (smoothed) (un-smoothed) Previous $5.6 $5.25 $6.3 Low $7.83 $6.81 $6.65 Mid $9.83 $8.56 $8.8 High $11.83 $7.55 $9.55 Page 2

22 Integration Cost ($/MWh) $12 $1 $8 $6 $4 $2 Integration Cost 5 Hr forecast Averaging Integration Cost No Forecast Averaging Linear (Integration Cost No Forecast Averaging) Linear (Integration Cost 5 Hr forecast Averaging) y =.52x y =.4x $ $ $2 $4 $6 $8 $1 $12 $14 Gas Price ($/mmbtu) Figure 4: Graph of integration cost vs. annual average gas price The analysis showed a strong correlation between gas price and wind integration cost when using the unsmoothed wind forecast. Looking at the trend line for the runs using the unsmoothed forecasts, the integration costs increased about $.5/MWh for each $1. /MMBtu increase in the average annual price of natural. With smoothed forecast data, the relationship between gas prices and integration costs seemed less clear integration costs rose as gas prices rose from $5.25 to $1/MMBtu, but then decreased when gas prices approached $12/MMBtu. Overall the results demonstrate that the relationship between gas price and integration cost is fairly predictable within the range investigated. Smoothed wind forecasts seem to have a less predictable relationship with gas cost. The non-linear nature of the unit commitment logic in Couger may contribute to this reduced correlation. It is important to realize that while increasing gas prices increase the cost of wind integration, the overall costs to the system are generally reduced with the addition of wind. For example, if one assumes that wind has a 3% capacity factor in Colorado, and that the wind energy displaces electricity generated by an average gas-fired plant with a 1 MMBtu/MWh heat-rate, then: 3% X 876hrs X 14MW = 3,679,2 MWhs of electricity displaced by wind In order to meet this with the average gas plant: 3,679,2 MWhs X 1 MMBtu/MWh = 36,792, MMBtu of gas needed to replace wind generation. If gas prices rise from $5.6 to $9.83 per MMBtu, this means that wind integration costs rise by $2.5/MWh ($8.8 - $6.3 = $2.5). The total added cost to the system due to wind integration costs will be $2.5 X 3,679,2 = $ 9,198,. However, if there were no wind on the system and the energy had to be served by gas fired generation, then the fuel costs would rise by 36,792, X ($9.83-$5.6) = $ 175,497,84. Overall, as gas prices rise, increasing fuel costs (with no wind generation) would far outweigh increasing wind integration costs. In this example, the value rises by over 17 times the integration cost. While this example is provided to help explain how the value of wind increases with higher priced gas, it is not meant to be a complete calculation of the value of wind. Page 21

23 FLEXIBLE RESOURCE IMPACT ON INTEGRATION COST The effect of a flexible generation resource was evaluated in the study by adding a 5 MW combined cycle plant to the PSCo electric system representation within the Couger model. Combined cycle plants have a much higher efficiency than simple cycle combustion turbines. However, they also have more flexibility in terms of mode cycling and much higher ramp rates than coal units. The combined cycle plant added to the system was a 5MW, two turbine plant with two operating modes: 1x1 and 2x1. Operation and performance in these modes was modeled similar to that of existing combined cycle plants on PSCo s system. The effects of an additional 5 MW of combined cycle plant on the PSCo system on reducing wind integration costs were relatively small. Table 11: Effect of 5MW combined cycle addition ($1/MMBtu Gas) Addition of flexible resource Base case (like current system) With flexible resource (addition of 5 MW CC) Integration Cost ($/MWh) ($1 / MMBtu gas) 5 Hr forecast Averaging No Forecast Averaging (smoothed) (un-smoothed) $8.56 $8.8 $8.23 $8.7 The impact of this additional CC unit on reducing integration costs was between $.1/MWh ($8.8 - $8.7; un-smoothed wind forecast) and $.33/MWh ($ $8.23; smoothed wind forecast) of wind generation. This relatively small impact is in large part due to the fact that the PSCo system was flush with flexible generation resources in 27 (the year being modeled in the study). So, additional flexible resources were not particularly helpful when modeling 14 MW of wind on the system. To the extent that the PSCo system has fewer flexible generating resources in the future than what was available in 27, the impact of an additional 5 MW CC unit in helping reduce wind integration costs is expected to be higher. Page 22

24 APPLICATION OF THE RESULTS The $8.56/MWh integration cost estimated by this study for the $1 gas price scenario (a.k.a., mid price sensitivity) represents the integration cost of having 14 MW (~ 4,3 GWh) of wind on PSCo s system per MWh of wind generation. For example, in this case the total integration cost estimate is ~$36.8M (4,3GWh X $8,56/GWh). This integration cost represents the Company s best estimate of the hidden costs associated with the uncertain and variable nature of wind generation. As such, this integration cost is added to the explicit costs (capital + O&M, or contract price for PPAs) of wind facilities in the resource planning and selection process. Adding integration costs to wind generation in the planning process ensures that wind generation is compared on a level playing field with the other resource technologies. Page 23

25 SUMMARY AND DISCUSSION Improved Forecasting Reduces Integration Costs The improved forecasting of day-ahead wind generation that was included in the WWSIS data resulted in a reduction in wind integration costs between $1.2 (smoothed) and $2.3 (unsmoothed) per MWh of wind generation (i.e., compared to the forecasting approach that used error series). This result supports the Company s initiative to improve its wind forecasting capabilities. As a rough approximation, this result suggests that the Company could save between $5.2 M (43 GWh X $12/GWh) and $9.9M (43 GWh X $23/GWh) per year in integration costs by using state-of-the-art wind forecasting at the 2% penetration level. Forecast Averaging and Integration Costs The smoothed forecasts produced lower integration costs estimates than the exact forecasts. A conclusion from this analysis is that smoothed forecasts cause the unit commitment application to create a better solution in the presence of both the volatility and variability of wind and load. It should be noted that this smoothing of the day-ahead forecast of wind generation was merely an analytical tactic to get the Couger model to more accurately simulate what the Company believes to be the commitment and dispatch decisions of real live system operator. The Company is not advocating that this or any other smoothing of the day-ahead forecast of wind be done in the actual day to day operation of the PSCo system. The Company believes that every effort should be made to forecast the day-ahead wind as accurately as possible and that such forecasts will ultimately lead to lower integration costs. Geographic Diversity and Integration Costs Assuming a gas price of $5/MMBtu gas, increasing the geographic diversity of the wind facilities resulted in a decrease in integration costs of 5-2%, depending on whether smoothed or un-smoothed forecasts were being used. Accurately quantifying the effect of geographic diversity on wind integration costs is difficult and was not a major area of focus in this study. The Company does believe however that the reduction in integration cost with increased diversity makes intuitive sense and thus the general findings of this study in this regard are valid. Future wind studies will analyze more realistic changes in geographic diversity using gas prices more in line with current expectations. Effect of Gas Cost on Wind Integration Cost The cost of natural gas had a direct impact on integration cost since flexible gas generation units are often used to maneuver and react to variations in output from the wind. Analysis of four different gas prices showed that the integration costs did increase with gas cost. Trend lines calculated for the results show that integration cost increases between $.4 and $.5 for each $1. increase in the average annual gas price. It is important to note that, while increasing gas prices increase the wind integration cost (and visa versa), the value of wind increases far more than the integration cost. Effects of Additional Flexible Resources (5 MW CC plant) Flexible resources such as a large combined cycle plant seem to reduce integration costs, although the decrease is only a few percent in this analysis. This relatively small impact is in large part due to the fact that the PSCo system was flush with flexible generation resources in 27 (the year being modeled in the study). To the extent that the PSCo system has fewer flexible generating resources in the future, the impact of an additional 5 MW CC unit in helping reduce wind integration costs is expected to be higher. Page 24

26 Effect of Transmission Constraints on Wind Integration Costs This study did not attempt to quantify the impact of transmission limitations from the northeast parts of the PSCo territory (where much of the wind resource is located) to the Denver load center. While such limitations may impact wind integration costs in the short run, they are not expected to persist long term as a result of the passing of Colorado Senate Bill 1 which encourages transmission development to locations in Colorado where electric generation is likely to be developed). Work has already begun to expand transmission from northeast Colorado to the Denver load center. Impact of Wind on Coal Plant Operation The Couger modeling of a 2% level of wind penetration shows that the majority of the energy displaced by wind generation is from outside purchases or conventional gas-fired facilities rather than energy from base load units. While the modeling did not predict any significant impact on the operations of base load coal facilities, PSCO is currently seeing increased cycling of base load units at the current 15% level of wind penetration (please see Appendix B for details). Some of this increased cycling of coal facilities does appear to be correlated to generation output from wind facilities. During the shoulder seasons, at night, when load is at its lowest and output from the wind tends to be high, it seems that coal units are being reduced because their output isn t needed. At this point, we cannot provide an estimate of the costs associated with this increased coal plant cycling for two reasons: 1) while Xcel has started a study to determine the costs of cycling coal units, the study is not finished, and 2) upon completion of the coal unit cycling study it will be necessary to establish what part of the costs can be attributed to wind generation. Next Steps The Company has already retained EnerNex to analyze wind integration cost for wind penetrations of ~ 3% to 35% by capacity. The results of these analyses will inform the Company efforts to add more wind to the PSCo system at levels in excess of 2% penetration. As mentioned above, PSCO is already seeing increased cycling on our coal plants at levels higher than that predicted by the Couger modeling. The Company will continue to work to better understand the correlation between this cycling and wind generation. The Company is working to improve our day-ahead forecast of wind generation. It is believed that this will act to reduce the cost of integrating wind onto the system. Efforts will also be pursued to improve same-day forecasting and emulation of operator action as a result of such forecasting. These efforts are important since computer models such as Couger do not account for the ability of operators to use same-day wind forecast information to continually modify commitment and de-commitment schedules. Addressing issues such as these in future studies will tend to reduce estimates for integration costs compared to what they would be otherwise. Page 25

27 APPENDIX A - WIND FORECASTING DETAILS This appendix is included as an illustration of some of the analysis that was done in the earlier stages of this 2% study effort, prior to the time when WWSIS wind data became available. The actual wind and day-ahead wind forecast data referred to in this appendix were not used in calculating the final wind integration cost estimates that are contained in the various tables and figures of the main report. However, various analyses using these earlier forecasts did help the Company and the TRC better understand the inner workings of the Couger model which ultimately helped shape the analytical methodologies used in developing the final integration cost estimates of the main report. Developing the Wind Generation Forecasts One aspect of Phase III was to understand the effect of wind forecasting on the estimates of integration cost. This section discusses how different wind generation forecasts of seemingly equal statistical merit resulted in a range of integration cost estimates. Forecasting Calculations The methodology used in this study requires day-ahead forecasts for both load and wind generation. Historic day-ahead load forecasts are archived, so these do not have to be recreated for the study. However, day-ahead wind generation forecasts were not archived for the time period studied (the Company is now archiving this data), so they were created. This section describes how day-ahead wind generation forecasts were created from actual wind data and error series for the same time period before the WWSIS data became available. The hourly error series used in this study were derived using information obtained from previous Minnesota wind studies where detailed tower level wind modeling was done and day ahead wind forecasting was performed using state-of-the-art mesoscale forecasting tools. These particular studies were performed by WindLogics in 24 and 26. In the 24 Minnesota study, 3 towers were selected from the extraction points defined for the study. At each of these points, for each day of the year, an hourly day-ahead wind forecast was developed using the MM5 model. The day-ahead forecasts for these 3 towers are said to be coupled by geography (they are from the same state), meteorology (meteorological events would impact all the towers) and time (these are from 24). These forecasts were the only ones used in the 15% study. In the 26 Minnesota study, 4 towers were selected and forecasts were generated in the same way. The forecasts from these 4 towers are also coupled. Taken together, forecasts from these 7 towers are the 7 sources of data used to generate most of the wind generation forecasts used in the Phase III study (how the seven are combined to create forecasts is described below). The error series for this study were created by taking the difference between wind generation based on forecasted wind speed and that based on actual wind speed. This difference is then normalized by the rated nameplate capacity of the hypothetical wind turbine used to create generation from wind speed. When this is done for each hour of the year, an error series is created that is independent of turbine size. The forecasts and actual data from the 7 towers mentioned above were used in this way, creating 7 error series (referred to as Alt1 Alt7 in following sections). A forecast for an arbitrary actual wind generation output is made by applying an error series in the following way. The error series is scaled by multiplying it by the nameplate capacity of the wind generation that is being forecasted. The series is also multiplied by an adjustment factor that we will explain in a moment. The scaled error series is added to (or subtracted from) the actual wind generation to form the day-ahead forecast of that actual wind generation. The Page 26

28 day-ahead wind forecast values are clipped at and the MW nameplate value for the wind level being forecasted. The MAE (mean absolute error) of the forecast verses the actual output is calculated. The adjustment factor is manipulated iteratively until the MAE approaches a target value. For all cases mentioned in this Appendix, that target MAE for the day-ahead wind forecast was 17.5% of the wind plant rating. Description of Wind Generation Forecasts 1 through 28 The 7 individual error series introduced above were applied in distinctly different ways to develop 14 different wind generation forecasts. Seven of these wind generation forecasts were developed by adding the error term to the actual wind generation and the other 7 wind generation forecasts were developed by subtracting the error series. These 14 different forecasts were named Alternate Forecast 1 through 7 (error series added) and Alternate Forecast 1Neg through 7Neg (error series subtracted). These are wind forecasts 1 through 14 of the 31 total forecasts (see Table 15 below). A total of 14 Regionalized forecasts were also developed based on different combinations of the 7 original error series. The modeled wind plants were divided into three regions, North, Central and South (see Figure 1, above) and one of the 7 error series was applied to the generation in each region. The table below describes how the 7 regionalized forecasts (A through G) were developed based on the 7 forecast error series available. Notice that the coupled error series (1-3 and 4-7) are always used together in a regionalized forecast. Table 12: Appendix A - Regional Forecast Error Series Components Regional Forecast Regionalized Forecasts Error Series Components Error series used in North Error series used in Central Error series used in South A Alt 1 Alt 2 Alt 3 B Alt 4 Alt 5 Alt 6 C Alt 5 Alt 6 Alt 7 D Alt 3 Alt 1 Alt 2 E Alt 2 Alt 3 Alt 1 F Alt 7 Alt 4 Alt 5 G Alt 6 Alt 7 Alt 4 Error series 1-3 are from 24 MN Study Error series 4-7 are from 26 MN Study Each of these forecasts sets has been applied in both the positive (wind + forecast error) and negative sense (wind forecast error) to make a total of 14 regional forecasts (Forecasts 15 to 28 in Table 18). With the 14 forecasts generated from using one series at a time, a total of 28 forecast series have been synthesized. Table 18 shows those forecasts and their constituent forecast error series. In the case of the 14 regional forecasts, the MAE of the three constituent forecasts was controlled on a regional basis to the 17.5% level that was universal in the Phase III study. Aggregating data will tend to reduce the MAE, as a result, the overall MAE of the combined forecast data was between 12% and 17% for these 14 regional forecasts. Wind Generation Forecast 29 - Colorado Based Forecast There was discussion of the validity of using forecast error data from Minnesota. To examine whether a Minnesota error series was having undue influence on the study results, a Colorado meteorological wind forecast model was developed near the end of the study along with an Page 27

29 associated wind forecast error series. This Colorado based forecast is referred to as Forecast 29. Wind Generation Forecast 3 Meteorological Pattern Based Forecast The effects of using historical wind patterns to represent the day-ahead forecast of wind (as opposed to applying an error series) were also investigated. This sensitivity aggregates the wind generation data into 12 individual monthly forecasts, by averaging each hour across the entire month. For instance, the wind forecast for all 31days of January at noon would be the average of all 31 noon values of the wind. This forecasting approach would smooth the wind forecast to historical averages if enough years are applied. The following diagram shows the forecast as developed using the 22 through 24 data. This forecast is identified as Forecast 3. Month-Hour Ave (MW) Monthly Average Hourly Forecast by Month 22 through 24 Monthly Averages Hour of the day Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 5: Appendix A - 12x24 Historical Average Forecast Pattern Wind Generation Forecast 31 the Perfect Forecast In addition to the forecasts outlined above, integration costs were also estimated assuming a perfect day-ahead forecast for wind generation. This is Wind Generation Forecast 31. Page 28