AMERICAN STATISTICAL ASSOCIATION (ASA) ASA ENERGY STATISTICS COMMITTEE- ENERGY INFORMATION ADMINISTRATION (EIA) MEETING Alexandria, Virginia Thursday, April 22, 2004 ASA COMMITTEE ON ENERGY STATISTICS: JAY F. BREIDT, Chair Colorado State University NICOLAS HENGARTNER, Vice Chair Los Alamos National Laboratory MARK BERNSTEIN RAND Corporation MARK BURTON Marshall University MOSHE FEDER Research Triangle Institute BARBARA FORSYTH Westat NEHA KHANNA Binghampton University NAGARAJ K. NEERCHAL University of Maryland SUSAN M. SEREIKA University of Pittsburgh RANDY R. SITTER Simon Fraser University ALSO PRESENT: COLLEEN BLESSING EIA ERIN BOEDECKER EIA HOWARD BRADSER-FREDERICK EIA ALSO PRESENT (CONT'D): TOM BROENE EIA PHILLIP BUDZIK EIA GUY CARUSO EIA STACEY COLE Bureau of the Census DAVE COSTELLO EIA STAN FREEDMAN EIA FRED FREEM EIA CAROL FRENCH EIA JANET GORDON EIA DOUG HALE EIA TAMMY HEPPNER EIA RICK HOGUE Bureau of the Census PAUL HOLTBERG EIA SUSAN HOLTE EIA ALSO PRESENT (CONT'D): CRAWFORD HONEYCUTT EIA ALTHEA JENNINGS EIA DIANE KEARNEY EIA NANCY KIRKENDALL EIA TANCRED LIDDERDALE EIA EDWIN LU EIA RUEY-PYNG LU EIA KEN MARTIN PricewaterhouseCoopers PRESTON McDOWNEY EIA HERB MILLER EIA RENEE MILLER EIA KARA NORMAN EIA JOE SEDRANSK Case Western Reserve University/EIA TOM SPERL EIA ALSO PRESENT (CONT'D): YVONNE TAYLOR EIA PHILLIP TSENG EIA KEN VAGTS EIA SHAUNA WAUGH EIA BILL WIENIG EIA LORIE WIJNTJIS PricewaterhouseCoopers NATHAN WILSON EIA * * * * * C O N T E N T S AGENDA SESSION PAGE Greetings and Remarks 15 Updates Since Fall 2003 26 Meeting Introduction to Short-Term 39 Forecasting Background on Short-Term 46 Forecasting Issues in Short-Term Energy 60 Modeling Summary of Recommendations 115 Natural Gas Prices and 124 Industrial Sector Responses Summary of Recommendations 181 Electricity Transmission 190 Electricity 2005 206 Transmission Data for 226 Public Policy Estimating Weekly Other 246 Oils Stock Summary of Recommendations 285 Natural Gas Production 292 Monthly Survey * * * * * P R O C E E D I N G S (8:32 a.m.) MR. BREIDT: Welcome. This meeting is being held under the provision of the Federal Advisory Committee Act. This is an American Statistical Association (ASA) committee, not an EIA committee, which periodically provides advice to EIA. The meeting is open to the public and public comments are welcome. Time will be set aside for comments at the end of each morning and the afternoon session. Written comments are welcome and may be sent to either ASA or EIA. All attendees, including guests and EIA employees, should sign the register in the hall and include their e-mail addresses. Rest rooms are at the end of the hall toward the back of this room. A fountain is in the same corridor on the way. Telephones in this room share a single number. That number is (202) 586-3071. Kathleen Wert and Tara Stull with the ASA Meetings Department are here and available for committee members for questions on expense reimbursements. In commenting each participant is asked to speak toward a microphone. The transcriber will appreciate that and we have microphones both along the desktop and we have the standing microphones here. You have to speak clearly and into a microphone. These microphones are reasonable sensitive, so you may not need to lean into them. Speakers can use this podium microphone. There's also a lapel microphone. A pointer is available around here somewhere. I'd like to start out now by having us introduce ourselves and I'll start out. My name is Jay Breidt. I'm a professor in the Department of Statistics at Colorado State University and chair of this committee. DR. NEERCHAL: I'm Nagaraj Neerchal, Professor of Statistics at UMBC, University of Maryland, Baltimore County. MS. FORSYTH: I'm Barbara Forsyth. I'm a survey methodologist at Westat. DR. FEDER: I'm Moshe Feder, a statistician from the Research Triangle Institute. DR. HENGARTNER: My name is Nick Hengartner. I'm with LANL and I'm the Vice Chair of the committee. MS. KHANNA: I'm Neha Khanna. I'm an assistant professor of Economics and Environmental Studies at Binghamton University. DR. BURTON: I'm Mark Burton. I'm an associate professor of Economics from Marshall University. MR. BERNSTEIN: I'm Mark Bernstein. I'm from RAND Corporation. MR. CARUSO: Guy Caruso, Energy Information Administration. DR. KIRKENDALL: Nancy Kirkendall, Energy Information Administration. MR. BREIDT: If we could go back into the audience, and please use the microphones. MR. TSENG: My name is Phillip Tseng. I'm with the EIA. MR. HALE: I'm Doug Hale. I'm with EIA. MS. BLESSING: Colleen Blessing, EIA. MS. HOLTE: Susan Holte, EIA. MS. WIJNTJIS: Lorie Wijntjis, Price Waterhouse Coopers. MR. MARTIN: Ken Martin, Price Waterhouse coopers. MR. VAGTS: Ken Vagts, EIA. MS. FRENCH: Carol French, EIA. MS. MILLER: Renee Miller, EIA. MS. HEPPNER: Tammy Heppner, EIA. MR. WILSON: Nathan Wilson, EIA. MS. KEARNEY: Diane Kearney, EIA. MR. BROEM: Tom Broem, EIA. MS. GORDON: Janet Gordon, EIA. MR. FREEDMAN: Stan Freedman, EIA. MR. SEDRANSK: Joe Sedransk, SWRU and EIA. MR. HONEYCUTT: Crawford Honeycutt, EIA. MS. TAYLOR: Yvonne Taylor, EIA. MS. BOEDECKER: Erin Boedecker, EIA. MS. JENNINGS: Alethea Jennings, EIA. MR. PYNG LU: Ruey-Pyng Lu, EIA. MR. LU: Edwain Lu, EIA. MR. MILLER: Herb Miller, EIA. MR. COSTELLO: Dave Costello, EIA. MR. WIENIG: Bill Wienig, EIA. MR. BREIDT: Thank you. For those of you who haven't been here before that mobile microphone is a huge technological advance over the old system so thanks to EIA for that. For your information Nancy J. Kirkendall is the designated federal officer for the advisory committee. In this capacity Dr. Kirkendall may chair but must attend each meeting and she is authorized to adjourn the meeting if she determines this to be in the public interest. She must approve all meetings of the advisory committee and every agenda. Also, she may designate a substitute in her absence. We have an interesting agenda today and tomorrow. We have some plenary sessions and we have a number of breakout sessions, eight of them. This is probably a record. We'll be fairly busy. We'll try to keep on time and, of course, we're already not on time but we'll try to catch up. So the first section of the meeting will begin with a briefing by Guy Caruso, EIA's administrator. Then Nancy Kirkendall, the Director of the Statistics and Methods Group, will comment on EIA work since the Fall 2003 meeting. After Nancy's comments we'll begin the first morning's breakout sessions. Those will be on EIA's work in short-term forecasting as well as measuring the quality of EIA analysis, part of EIA's strategic plan goals for 2004 to 2008. Lunch for the committee and invited guests will be on the first floor at 12:30. It will be in Room 1E226 in the same corridor as we are in now. That's the usual place. This evening we have a reservation for dinner at the Little Viet Garden in Clarendon in Arlington, Virginia. So we need a show of hands but there's a note here that it's a short Metro line on the Orange Line and we'll probably need to stick together. The reservations are at 6:15 if anyone's interested. So could I see a show of hands for committee members for dinner tonight? Bill, did you get that? Tomorrow morning, breakfast for the committee and the EIA participants and management will be here again beginning at about 8:00 so we can meet in the hotel lobby around 7:45 if you care to do that. We will resume tomorrow at 8:30 here in this room. A couple of other announcements. We have two new members on the committee. One is here already. This is Barb Forsyth. And one had a late class last night in Pittsburgh. That's Susan Sereika, who will be joining us later. Bill Moss and Jae Edmonds will not be attending today. Finally, the usual meeting mechanic is that if the panelists have a comment you turn your name tag vertically to be recognized. Now its my pleasure to recognize Guy Caruso, administrator of EIA. MR. CARUSO: Thanks, Jay, and welcome, everyone, especially Barbara to your first meeting. We appreciate your joining the committee. As usual it's been a busy six months since we last met in October. Since then we've done the usual core products that you are all familiar with. The annual energy outlook came out right after our meeting last time. Since then we have had, of course, the international energy outlook just last week. But I think the thing that's been shall I say driving a lot of the activities of the EIA since October have been energy markets particularly for natural gas and gasoline. Some of the topics on todays agenda, in fact several of the topics, will address what we are doing in those areas that we think this committee can be helpful in. I'll talk a little bit more in detail on gasoline in the short term, which, of course, is instrumental in our short-term outlook, and just two weeks ago we did our summer driving season outlook conference using the short-term energy outlook model which Dave Costello is going to talk about a little later. Natural gas prices continue to be very firm with the prospects of them staying there which has important implications for our work on both the supply and the demand side. Both of those areas will be discussed today. Ill talk a little bit about our budget which we submitted in February. We have had all of our hearings and we certainly hope that the Congress will vote on that before they adjourn. I'll mention that as we talk about the budget in particular, where we think that's headed. And the trend that I mentioned probably in all of the meetings that we have been in together that I have had the privilege of addressing the committee on is that the continued trend of being asked to do more policy analysis, what-if analysis, using both the NEMS and the short-term model, and that's continued and accelerated probably in the last six to eight months, mainly because of the energy bill that's still being debated in Congress. The high gasoline prices, of course, are an analytical issue but in particular at this time a political issue. With the Presidential election coming up its taken on even a greater significance and so EIA, the numbers we produce each week, whether they be prices or inventory numbers, and our analysis each month, I think more attention is being paid to that than ever. In particular the media interest in what EIA does has also intensified and so all the more reason why, I think, we want to refine the work we do in the short-term energy outlook and in the collection of the gasoline data which, as some of you know, we have increased the detail in which we collect the weekly gasoline data to include more blending components, which have become very important given the introduction of ethanol in substituting for MTBE. We have redesigned our sample in collecting that gasoline data and the new forms were just reported on April 14. So that was the first weekend. We certainly had the usual running in problems and I'm not sure we'll have a chance to talk about that in more detail later. This next slide just shows that kind of run-up in not only gasoline but crude oil prices that has occurred over the last several years and began to intensify the interest in what we do. The next one shows something that there has been a lot of interest in, not only among our customers but among the regulatory agencies such as FERC and CFTC and that is how EIA data moves markets and there have even been several investigations called for by Congress as to whether there's any possibility of manipulation of the market that certainly, I think, once again shows how important the data we collect and the way we produce and analyze and disseminate it are important not only to the customers but to efficient markets. Natural gas prices have been rising steadily and now are moving hand and glove with crude oil prices. We have been trying to improve and I know Beth Campbell in one of the first meetings I was at in this committee, talked about the way we collect production data. We are now almost ready to release a Federal Register notice on the possibility of a new production survey for natural gas and we are going to discuss that, I know, in this agenda as well so we really seek your advice in that area. We hope to initiate a new natural gas production survey in the issues regarding frames and other statistical measures with respect to that that would be valuable to get your input on that. The other analytical issue which has emerged from the natural gas price run- up is what it has been and what will be the impact on in particular industrial demand for natural gas. This is another area where we believe more analytical work needs to be done improving our models, whether they be the short-term or the long-term, and certainly that's an area where again we think the committee could be of assistance to us. As I mentioned, we submitted the budget and have had budget hearings for FY 05. It's a request for $85 million, which is almost $4 million more than FY 04. It's a bit misleading in that last year in the current FY 04 budget although we have an $81 million budget this year we are actually operating on $84 million of expenditures because we are taking $3 million from de-obligated and uncosted funds of a previous year. So although this looks great, a 5- percent increase, its really a lot less than that and Howard and I are working very hard to try to get more resources, which we all know EIA spends very judiciously. So we hope that that would happen. The latest work from people who follow the Hill very closely is that there's a high likelihood that perhaps Defense and maybe one other major appropriation bill will actually pass this session and that all of the other budgetary items will go into an omnibus bill which is likely to go into a continuing resolution because of the elections coming up and the difficulty of trying to pass all of those budgets before Congress adjourns. So well keep you posted on that. Probably by the next meeting we'll know a lot more. With the increase in the money, as I mentioned, the production survey is one of the major new things we'll be doing in FY 05 if all goes well and we can get the approval from OMB and are ready to move on that as early as 1 January 05 if possible. That's our goal to at least start. We clearly need through Congressional mandate to update the voluntary reporting on greenhouse gas survey and database, the so-called 1605(b), which I know Jae Edmonds has been so close to. We are working on a number of things within the NEMS modeling, the long-term modeling system, one of which is the transportation sector. One that's not listed here but I know we'll be working on is this issue of natural gas and its use in the industrial and manufacturing sectors. What does $5 gas mean for that kind of demand as well as on the oil side the imposition of new fuel standards? Low sulfur gasoline and deisel fuel are important, and the regionalization of our short-term energy model, which will be an important topic on your agenda today. This next slides shows just a sample of the reports we have been asked to do by Congress. They're almost all related to the Energy Bill or the Omnibus or the comprehensive energy bill that was introduced two years ago and reintroduced last year. There have been a number of negotiations going on. Its uncertain whether there will be an energy bill but nonetheless EIA has been asked to analyze not only the whole comprehensive bill by Senators Sununu and Senator Dorgan in two separate versions of the bill but we have been asked to do specific analysis with respect to things like the Alaska Natural Wildlife Refuge, issues with respect to LNG, and global oil markets. So EIA I think in the last six months has been quoted and relied on extensively in Congress and within the administration and some of the work that this committee has assisted with has been extremely valuable in our analytical responses to Congress and the administration as well as the regular products that we produce. So finally this just sums up some of the things that we will be asking your advice and counsel on with respect to these new products that we are working on, improvement of existing systems in terms of surveys and frames and also the cognitive methods in establishment surveys. I'm sure Nancy will go into more detail on some of these things. And the other topic that no matter where I go, and I'm around speaking to a number of different meetings that the EIA website continues to be extremely heavily used and very popular and nowhere around the world or certainly within this country can you go without someone coming up and saying oh, I love your website. Occasionally they will have a little suggestion but basically it's our main vehicle for getting the information and analysis we do across to the public as well as to our other customers. So, once again, what you are doing in helping us to achieve our goals of better quality and more timely and accurate data and analysis is invaluable and I thank you all once again for your time. I appreciate your all being here and look forward to working with you over the next couple of days. Thank you, Jay. MR. BREIDT: Next Nancy Kirkendall will update the committee on progress since the Fall 2003 Meeting. DR. KIRKENDALL: Well, I'd like to welcome the committee before we get the slides started. The purpose of my talk is what weve done regarding some of the advice you gave us last time and how it ties in to some of the things were going to ask you about this time so it's a transition kind of talk. While Preston is looking for it one of the major things that EIA did, I think it was March we agreed to the strategic plan. We talked about the strategic plan with you last time, particularly the action items under goal one. Now the strategic plan has been finalized and agreed to by all EIA senior staff so we are now serious about doing all the things we said we were going to do. We will talk about the status of the strategic plan because there are lots of action items in the strategic plan that have a lot to do with statistics, how do we measure things and how do we report them, so that will be a topic of a number of break-out sessions today as it was at the last meeting. Guy mentioned that we have a number of initiatives. We've talked to you before about natural gas and we have a few of them on the agenda again this time. I'll give you an update on the Confidential Information Protection and Statistical Efficiency Act, CIPSEA, we call it, how EIA is implementing that, so this is just a brief progress report on that activity. We have some sessions on electricity, particularly on electricity transmission, and I'm not really going to say a whole lot about the STEO. That's aa new initiative that well be talking to you about. So I'm really just going to use that as an introduction to Howard Gruenspecht, who will tell you more about that. So these are the three goals from our strategic plan. Not all of you, Barbara is new, but many of you have seen them before. The first one has most statistical content because we are interested in measuring relevance, reliability, consistency, timeliness, accuracy, all the wonderful things you'd like to have be attributes of your data, so that's the area that we are going to concentrate on but you can see what the others are, too. Guy touched on this. We would like our resource base to be sufficient. The third one is we'd like to operate in an effective and an efficient way providing good leadership. So under relevance and reliability a number of these are measures that we have been collecting for a long time so these will just continue. The number of information products requested, these are briefings and things that are requested by high level officials, either senior people in the department or Congress. It includes the service reports that Guy talked about. Tom Broem manages that and is very careful to make it a nice, consistent series. We also have one that talks about the percent of frames that are deemed sufficient. We talked to you about this last time. We've done more work. There's a break-out session, I think, later today on our work on frames. We have decided that we would like to have percent of all EI frames that we think are sufficient so we are taking a broad look at all EIA frames. Weve got about 70 surveys and we are looking at a variety of methods for deciding whether they are sufficient. They are going to tell you what we have done and what we have looked at and ask your advice on how do we come up with measures of whether a frame is sufficient. There are a lot of different ways of going about it and we would like the least cost ways of getting the most appropriate measure. Outside expert evaluations, we talked to you about this last time and you totally rejected what our proposal was. So we went back to square one and redesigned our survey frame. This time we have interviewed people who have been attending our NEMS conference. These are pre-registrants. We have data to show you and we'll ask your advice on interpretation of results and how to move forward in the future. One of the challenges is trying to find a list of experts who can give you good evaluations of your analytical products. On quality the measure is the number of surveys meeting quality targets and we've talked to you about this a number of times. Tom Broem is going to talk about this. I think that's on Friday. Customer satisfied and very satisfied, this year we are going to be using the American Customer Satisfaction Index again. We did that three years ago. Basically our approach is we do something every year but there's no reason to do the American Customer Satisfaction every year. We have to pay to do that, for one thing. Colleen Blessing is our customer satisfaction guru and they frequently do web-based surveys in the intermediate times. Timeliness, senior staff has pretty much agreed to release targets for all of our products. I'm not sure we have actually implemented it yet but there's been an agreement that this is what we will do. I'm not sure that we've actually let people know what our targets are. Then the customer and stakeholder involvement is just a list of events. It includes things like this meeting, talking to the American Statistical Associations Energy Committee, our NEMS conference, some of our other outreach conferences, and other things. We thought that a list with discussion among senior staff was probably more useful than just counting things. So the challenges you are going to be hearing about I've actually already touched on most of these. We have a breakout session to talk about frames. We have another breakout session to talk about our interviews with outside experts and users of our annual energy outlook and our international energy outlook. We also have another session talking about surveys meeting performance targets. So just an update on some of the other things we talked about last time, you have heard a lot about how we estimate natural gas production in Texas. This one is a nice one because there's actually data available from the state. One of the things we talked about was an evaluation of methods and you gave us some advice. I think that what we'll do is to update that and bring it back to you because I think we'd like to use the approach more consistently in the work that we do in SMG to evaluate various methods of doing things. So it's nice to have a statement of how we think we should go about an evaluation. In Texas we talked last time and observed that there was a bias using the method that Randy Sitter's student, Crystal, came up with and that's because of changes in reporting in the State of Texas. So we haven't done any work on that yet although the Dallas field office has done some work and they would like to talk to you about that in the fall. They talked about your recommendations in the Gulf. Of course, Dallas is still using the methods they were talking about. They are trying to do more company-level time series analysis. I'm not sure exactly where they are on that. That's a difficult problem because the data aren't really rich. There's only so much you can do with flaky data. Guy talked about the new natural gas production survey. Inder Kundra is going to talk about that in a session tomorrow. We are going to be doing a sample from the frame for one of our other surveys, the EIA 23, which is an annual survey that goes to producers of oil and natural gas. The intention of the survey is a little different but it ought to be a pretty good frame so he's going to talk about sample selection and some of the difficulties and changes over time in the frame and whatever else comes up. We talked to you about we had a survey that collects commercial and residential prices of natural gas but only in five states. We talked about that with you last time. We're going to implement it in seven additional states at some time. We hope to start in January 2005. It depends on resources and other things but that's where the plans are. It will be seven additional states and the District of Columbia. Industrial prices, haven't done a whole lot of work on that yet. We're still considering going to the Census Bureau. Now we are talking to them about the possibility of an annual survey to get information about natural gas prices in the industrial sector. It would be volumes and prices in the industrial sector. That sounds like it might be promising. So for CIPSEA, the Confidential Information Protection and Statistical Efficiency Act, this act gives EIA for the first time the ability to actually protect data reported to us by filing respondents. In the past we were not able to protect it completely because we had to share it on request with other government agencies if they asked for it for official use, not statistical use but official use. So now we have 11 information collections that are under CIPSEA. We have modified all of our wording so that it clearly defines what kind of information and confidentiality protection we can give. We are developing a training program for staff because CIPSEA also carries with it penalties for release. People can go to jail for releasing information that's collected under CIPSEA so people need to take it seriously and we need to train everybody. And Jay Castlebury is participating in an inter-agency committee led by OMB that is going to put out guidance for all agencies on this. The good thing about that is that way we know it's going to be coming out and our views are reflected in the guidelines, too. Under electricity actually the committee was interesting. You suggested that the data needs depend on industry structure and, of course, its still changing. So you are going to have an update today. Since the last meeting we have done focus groups. We are going to give you a report on the focus groups. Since the last meeting they have redesigned our electricity forums. They have gone out with their Federal Register, so plans on implementing electricity 2005 are well under way. Bob Schnapp will talk to you about that. Doug talked to you about his transmission paper. That has been finalized. He's not going to talk to you about it but we do have time for you to ask him questions about it and he is here in the audience so I encourage you to talk to him if you have any comments about his paper. We talked to you about data edits for the 9:20 and we have not done very much with that at this point. We ran out of time so that's an item that you might actually hear about in another session. So the last item, the short-term energy outlook, this is a new effort that we are starting up. I think that the committee will find it interesting. This is the first time you will have heard about our STEO. Maybe it was discussed years and years ago but not in recent history anyway. So with that I'll introduce Howard Gruenspecht since he is actually going to introduce the STEO modeling effort. MR. GRUENSPECHT: Good morning, welcome. Thanks for coming to help us out. Obviously, as you can see from Nancy's presentation, it's quite helpful, the information exchange we have with you. It does affect what we do. I wanted to talk to you very briefly about the work on doing some regionalization on the short-term forecasting model. The short-term model has been something of great interest to me, really, from the moment I arrived at EIA about a year ago. From the start it was apparent to me that together with our weekly reports on gasoline prices the short-term outlook gets by far the most attention in the mainstream press, not the expert trade press that deals with energy matters but the mainstream press relative to other EIA products. It's pretty understandable. People want to read about the outlook for gasoline prices this coming summer or what their heating costs are going to be next winter and that's what the short-term forecasting model and the short-term energy outlook put out each month look at. So it does get a lot of attention and its important to do the best possible job in these areas of great importance to EIA. The STIFS model and the STEO publication derived from it each month really benefits, I think, from being run by a very strong team within EIA. You will be hearing from Dave Costello, who, I think, leads that work under the direction of Mark Roedecker. They really are very dedicated, very strong intellectually, very open to suggestions and new thinking, and, again, given the importance that the public places and the prominence that the STEO results receive, its really important. Some changes have been made already. I don't know if you look at the STEO but we have gone to a shorter STEO text. We have gone to a more careful approach explaining confidence intervals. I think there is some use of more sophisticated approaches in some aspects of estimating the model and the confidence intervals which are important use of some stochastic estimation. But the focus of what you are going to hear about today is on regionalization. I call it limited regionalization because really we are not going to do a full regionalization of the model. I think that the project to put some more regional content into the model is really a critical element to making the short-term energy outlook more useful and more relevant. The project aims to provide greater depth, consistency, relevance and credibility to the monthly short-term forecast by allowing for unique regional factors that affect energy demand supplies and prices in important ways that tend to get obscured if you are trying to estimate a national model. A good example would be, lets say, gasoline in California. That's a regionally very isolated market for gasoline. You can have very different things going on in that market and going on in the rest of the country. Heating oil effectively used significantly in New England and the northeast, not used much any place else. People don't care about it much any place else. You really want to know what's going on in heating oil markets in those areas, not on a national average basis. So there's clearly a value in developing the information. So the depth we'll get we'll have a better deconstruction of broad US energy developments. The relevance will be tailored better to regional audiences and their areas of interest. The consistency will be better because we'll eliminate some of the biases that creep in when you use highly aggregated data to develop these national average forecasts when you really have different things going on in different areas. And the credibility is that important regional energy market developments are not obscured or ignored. So there's a lot to be gained from this. There's also a need to balance what we are doing here, I think, because, remember, this model unlike some of the other things we do here on an annual basis is the crank we've got to turn every month. Already it can get very demanding to implement this model and estimate it on a monthly cycle. So we want to have this additional limited amount of regional detail but we don't want to develop a tool that in the end if you had a monthly cycle and it took you more than a month to run your model and be comfortable with it you'd have a big problem. So we have to worry about yes, we want regionalization to some degree but we also have to made sure that we're coming up with a workable tool. So you will hear about this from David and also from Phil Tseng. It's a pretty high priority for us. It's something that the Secretary, I think, asked us to do last June along with the natural gas production survey that Guy already talked about and some other things. We think there's a lot of potential there. It's still at a stage where input would be helpful. It's not cast in stone. We're working on it. The goal, I think, is to have the model ready to go at the beginning of the next calendar year. And that's really where we are. You will hear more about it from Dave and Phil, so I'll just get out of the way and let you hear from them. Thank you. MR. BERNSTEIN: Questions? MR. BREIDT: Thank you. Before David starts I wonder for instance we could have the people who just arrived introduce themselves. MR. COLE: My name is Stacey Cole. I'm from the Bureau of the Census. I work on the NEC survey. MR. HOGUE: Rick Hogue, also from the Census Bureau, NEC survey. MS. WAUGH: Shauna Waugh, Statistics and Methods Group, EIA. MR. BRADSHER-FREDERICK: Im Howard Bradsher-Frederick. I'm with the statistics and methods group of EIA. MR. HOLTBERG: Im Paul Holtberg. I'm the Director of the Demand and Integration Division in EIA. MR. SPERL: Tom Sperl, International Policy. MR. FREEM: Im Fred Freem, Coal, EIA. MR. BREIDT: Thank you. Now I'd like to turn it over the David Costello to talk about background on EIA short term forecasting. MR. COSTELLO: That's me, Dave Costello. Basically my life's work here has to do with turning this outlook out every month and all the things that go along with that. Today I just want to basically give you a little background on what it is in the first place, what we do. I'm going to give a little example of some analysis that we do with the model and I'm going to talk about essentially the plan for regionalizing it a little bit and some reasons why we're going to do it, what we hope to get out of it, and when we hope to do it. The first slide here just gives an indication of what the coverage is. It's a national model although we do look at world oil markets because obviously we have to have some background for determining our oil prices. It's a monthly model. The frequency is monthly. We get a lot of our own data from EIA but also from the other agencies and other sources outside of EIA. Generally speaking, the forecast goes from 12 to 24 months ahead. Every January we move the forecast up one more year just as a convention and, as Howard said, we update this every month and we post the information on the web, including a simulating version of the model which is in Windows that you can download. But a lot of offices work on this model. It's not just this forecasting project. It's not just my team but also teams in the CNEAF and OIAF, just to throw out a couple of names that you may not know what the heck that means, but it's a multiple office job and there's a lot of people involved. Just a little more on the general structure of the thing, there are about 900 endogenous variables in there currently although there are a little less than 200 actual stochastic estimating equations so, of course, we have a lot of identities, some of which are very interesting, some of which aren't. The model is currently estimated and simulated in EVIEWS 4.1, which is just a PC-based econometric package, but we find it very handy, not to do a plug but its an improvement on what we had before. So far as who looks at this, not to go into too much detail but so far in April I think we have been averaging about 1300 hits a day on the web at the main STEO page and we have a list serve of about 4,000 customers that get the notification of the report every month. Just to give you an idea of how we put this together, we read a lot of the maintained monthly data from EIA along with some weather data. We get weather forecasts from NOAA, by the way, and, of course, the history is also from NOAA. EIA internally runs a macro model, which is a quarterly modeled US economy from Global Insight, and they provide us with monthly inputs for our model. We pull that all together and create a historical database which is always available. That database goes into EVIEWS and the model is estimated and saved. When we are ready to do a forecast we just pull in the exogenous forecasts, weather. Some of the energy variables are exogenous to this model such as nuclear power, for example. We get that from CNEAF and hydroelectric also 컴컴 the model and put it out to files and make reports. Of course, this happens numerous times in the course of a month because you get data updates and also you have to look at the results and made sure they make some sense and hopefully they do. Usually at the last minute we determine that they finally do make sense. Anyway, I thought I would just do a little example of some analysis that is easy to do and I thought it was a little interesting. It starts with natural gas but I'm really going to talk about electricity because when Phil Tseng gets up he's going to get into a little bit more detail about what we are going to do in that sector for the regionalization. This is just a schematic. I'm not going to give too much detail on how we put together natural gas demands and the supply. Our main focus here is to get an equilibrium spot price for natural gas. Its a fairly well integrated market in the US and we are looking at producing area spot price average as the prime thing. This helps determine end use prices for the rest of the model but basically having the model search for the price that equilibrates demand and supplies, how this particular model works. Electricity demands, we put that together again. We estimate the model by sector nationally. This is the electricity demand side. And we come up with a total demand for electricity which, if you go to the next slide, basically helps us determine how much generation we are going to need. We have a little bit of imports but basically it determines the amount of electric load that we have. As I said before, we are taking nuclear and hydroelectric power as exogenous from other EIA models. Our main problem is, of course, there are some renewables and we make some estimates of those but our main problem is basically to figure out what the possible fuel components are of the electric power generation. We basically do that by a set of equations that use some cross-price elasticities and some other factors. But anyway, not to get to much into the schematic, if we go to the next page, here's the scenario. The reason I did this one is that it's timely. Today EIA projects that natural gas supply will grow domestically about a percent a year through the next two years. We have a very high drilling rate and this is the result that we get. However, there are a lot of analysts that feel that that's too optimistic. So we did a scenario where in fact the production decline is greater than what we are getting out of this model itself. In that case we wind up with about two BCF a day less by the end of the horizon than we have in our current base case. The right panel is the impact that that has on the price. Instead of 5.50 gas we are talking about 6.50 gas on average over the next year or two. So what does that so? Well, it feeds, of course, importantly, into the electricity model. Here's the impact on natural gas demand by sector. Its fairly significant. Eventually we lose about 1.6 BCF a day in total demand by sector in terms of the total. We also have a little bit less electricity demand. Electricity costs do go up and it does affect output a little bit but not very much. Its relatively small. The mix of generation does change. The higher natural gas prices naturally reduce natural gas demand. It pulls down a little but mainly that's because of the output effect on electricity demand. The really only other options are for basically oil-based generation to increase and that's the result. Now, it seems to make sense but if you were to simulate this model over a period of time how would it do? So I figured we'd better do that. This is based on a simulation from January '99 through the latest historical period. This is the scatter on the predicted versus actual coal-based generation number from STIFS. The mean absolute percent error is a little under 2 percent, which sounds pretty good but actually it's a lot of electricity because coal is very big. By the way, this simulation assumes I know what the fuel costs are which is okay for this piece of analysis but obviously in the forecast we don't really know. The mean absolute percent error for natural gas is almost 8 percent and for oil its about 14 percent. Well, all it's not too bad and bias that seems to show up from this sort of exercise is not tremendous, pretty much negligible. But the thing of it is that there are lots of things that would have had to go into determining this. A lot of assumptions were basically made about how we figured out how much oil-based generation was going to replace natural gas. On a national level it works out okay except that it leaves open a lot of questions. Where did it exactly happen? Well, we know it's not going to happen everywhere because there's not a lot of oil generation everywhere. And does this approach tend to overstate or understate? It doesn't seem to do too much of either one way or the other but on the other hand we probably could do better on the percent error for any given month. In order to do that we really do have to get down to a little bit more detail, a lot more detail, really, on the regional composition of generation and in order to do that we have to know more about how demand patterns vary by region. So that's what we are going to do. Well, I think Howard talked about these factors. It may make our national level forecast for some variables to add this regional detail, which, by the way, I think Phil will talk about this a little more. Basically we are talking about looking at the demand for electricity and for natural gas by census reading or census division. We are looking at determining electricity supply by NERC region or pretty much the same supply regions that NEMS currently has. Its a little bit of a variant on some of the standard NERC regions. So it may actually make the national level forecast more accurate but I don't think we can tell that for sure until we get into it but that certainly is the hope. Why now? Actually, regionalizing the short-term model has been an idea that's been around a long time. Its been in our stretch plan for the long term, if you will, for some years. But now we have additional resources that are being allocated to it and a lot of interest in EIA management to do it so we are fortunate to have that. As I said, a lot of the demand detail will be by census region or census division. We are going to look at market area price determinations for natural gas. We're not going to do necessarily a whole lot of demand and supply by region for natural gas. Our main focus is going to be on Henry Hub prices, looking at the integrated national demand-supply balance for gas. Then we are going to spend a lot of time focusing on how a benchmark price like Henry Hub could be translated into regional spot prices based on the behavior of the basis differentials and factors that affect those. So we're picking out some strategic spot prices for natural gas to do and those will generate our end use prices by region where we need them and hopefully provide key information about the particulars of those markets, whether it be factors that relate to pipeline capacity limits or some other particular factor. Were going to do a pretty detailed look at household activity and particularly heating costs by census division. We collected a lot of that data from our own RECS survey and from other sources of data and we're going to highlight those, too. I think Howard mentioned this but we are hoping to have the testing finished by late fall of this year. We have tasks out there that are going to begin to be used to get the estimation done. We have done a lot of work in getting the database together. We still have a few little things to do there. We hope to be doing the short-term forecast on a limited regional basis, this basis that I just mentioned, in early 2005. I don't know if that means January or February or March. Hopefully one of those three will do but well continue to release it on a monthly basis. That's the goal. Any expanded regional coverage will be in our PC downloadable model, too, and also we'll feature it on the web in as much as detail as we can reasonable do. I think that's it. MR. BREIDT: We have a quick turn- around between our breakout sessions and rather than cut into this next breakout session we can just go directly into it, take some time out at the break as necessary and get back in here at 10:40 for the ASA summaries of those discussions. So as far as breakouts does everybody know which breakouts they are in? So Mark Bernstein, Neha, Nagaraj, and Nick will stay here and the rest of us will be going down the hall. (Recess) MR. TSENG: My name is Phillip Tseng. This morning I will talk about some of the statistical and modeling issues facing the regional short-term energy outlook in model building. So I'll briefly discuss some of the reasons I think Howard and Dave Costello mentioned about the regional model. I'll just list a few reasons why we need a regional model. I'll briefly describe the regional representation of the model and some of the statistical issues in modeling the market and modeling the electricity demand and supply. Why do we need a regional model? I think Dave mentioned earlier national data cannot capture adequately into action soft market demand and supply because we aggregate too many things and sometimes the price variations in a demand response to those differences in prices also are not captured in the national model. So we figured we probably can get more insight when we do the regional model. Many relevant questions cannot be addressed by a national model. For example, when we talk about winter heating fuel demand and supply in the northeast, like Howard mentioned, and differences in regional natural gas prices sometimes we have bottlenecks and we like to capture that as well. Summer gasoline demand and supply in California, so if we understand the regional market we may be able to understand what happens in terms of products, in terms of crude oil supply, and action between different regions. So a regional model provides that kind of information. Also electricity generation and transmission issues, this is a tough one. We were doing the regional basically trying to understand some of the basic elements and hopefully as our understanding of the transmission issues evolve using this regional model we may be able to improve even further in the future. The regional representation of the model, some of this representation we simply use available data and we may aggregate it. So for electricity demand we have four census regions and electricity supply we have seven NERC-based regions, seven or eight. I think we are still debating. For natural gas demand we have four census regions. For natural gas supply we may use six producing regions, looking into the difficulties or implementation issues, and we have three storage regions, inventory regions. For petroleum products we have data for five PADD regions and we may focus on petroleum product demand and supply on maybe east coast and west coast but in general we have information for all five PADD regions and we can probably answer a lot of questions there. This is a kind of simple way to look at it, the four census regions. We have ten regions here but really we are looking at northeast, midwest, west, and south. These are the four regions we try to model. For the electricity regions I mentioned about seven regions and it could be eight. It depends on how we gather the information and how we analyze the results. So the eight regions could be New York, Florida, Texas, California, New England regions, Midwest, east central, mid-Atlantic and southwest as one so we have eight regions. Actually Dave's people started collecting the information. We have developed a database so we can start doing some testing. DR. NEERCHAL: What I notice is that some of the region boundaries are going through the states like Virginia and Iowa. MR. TSENG: That's correct because we follow the NERC definition of the regions. What we may have to do is for some regions and for some technologies we may collect state data and then decide how to fix them. Hydro, actually when we look at states looks pretty good but when we look at the NERC regions we may have to split some of the capacity in generating. So we were looking to the data management issue and making some decisions once we start doing the econometric analysis. So that's the general idea of the census demand and power generation regions. The next topic I'll talk about is statistical issues in modeling the market. Actually my discussion here is very short because when I was trying to understand the modeling structure a lot of issues were not really clear to me. That's why I think the ASA committee can help a lot in terms of clarifying some of the statistical issues, some econometric issues, and some of the modeling issues as well. In the integrated energy model I've got two key elements here. Fewer market representations, that's one level, and sectoral components of demand. That's another level. These elements need to be integrated in a way that optimizes the model in terms of the performance of the model in predicting energy consumption and prices and providing valuable insights in identifying the determinants of energy demand and supply. From the modeling point of view I think Dave's solution algorithm basically indicates you have macro or exogenous variable drive demand and then demand and supply integrate. Then we solved the model. But when we estimate the model there's a statistical issue and econometric issue. When we look at national data, like market data, market demand and supply, and we look at the components, regional demand and supply, somehow there's consistency on how do we estimate the system in a way we can capture some of the constraint within the system. For example, recently we looked at natural gas price and we heard the term "industrial 컴컴 and natural gas demand destruction." Part of the reason is supply is not there and somehow the market system has to allocate available resources to different end users. So there's a market competing mechanism and we hope we can understand the mechanism. So when we estimate the accretion systems we can capture some of the interactions so when we do the forecasts we can actually reflect some of the reality in the framework. So those are the issues we are facing that we are trying to understand the market as well as the meaning of statistics. One example is when we look at a market we simply look at market demand, the fuel market. We look at natural gas, we look at electricity, or we look at petroleum products. So we have demand and we have supply and then we also know the capacity. The capacity information could be in the electricity sector. It could also be in the supply sector like natural gas supply. It has certain limits in terms of what's the boundary in the near term to push the production. That limits determining the allocation of resources within the system in the near term. So the adopted approach to model of fuel market must satisfy factors that determine the market demand and supply of a few so when we look at a fuel market we must capture that aspect. Also if we want to understand prices specification of the demand and supply equations must allow models to identify the equations econometrically. This is an identification issue. Sometimes we say we are trying to estimate equations using some kind of reduced form. Then the question is how do we interpret the meaning of the reduced form equation when we want to do forecasting or do we need to really understand the implied parameters when we use the reduced form and so we say we identify the equations and we meet certain conditions so the reduced form equation will provide the kind of predictability to serve our purposes. But that's the kind of thing I think will be helpful to know. On the demand side we look at the sector demand representation. There are many ways to estimate the demand. For example, if we look at residential demand it could have three different types of demand for residential fuel. For example, we can look at natural gas, electricity, and petroleum. Do they compete? I think some of the comments I heard is in the residential sector in the near term maybe efficiency improvement could be captured in the model specification; however, inter-fuel competition like fuel switching may not. But in the industrial sector or in the electricity sector we could see a lot of fuel switching. So that's another area we're interested to understand and hopefully, we can do some econometric testing and understand for each sector what happens there. Of course, for the residential sector we can also use micro data, can offline do some analysis and then to understand what happens in terms of near- term market demand and fuel switching. So basically I covered some of the issues, why we do the short-term regional modeling and some of the regional representations, some of the statistical issues facing us looking at market demand and supply, and now I'll look at electricity market demand and supply. We want to understand the regional market. Part of the reason is the regional energy demand in the electricity sector can have an impact on natural gas which can actually affect the natural gas storage, which in turn affects winter heating fuel availability for households using natural gas. So that's why we are focusing a little bit more on the electricity and then we are trying to change the representation a little bit. The demand for electricity actually varies by the hour. Peak demand imposes more burden on generation transmission. I think there's some data for some of the California independent system operators. They will release their system load, projected system load, and they would actually plot the load curve and we can see between 12:00 o'clock midnight and 5:00 o'clock in the morning demand for electricity is pretty flat. Then it ramps up and usually it peaks around 3:00 or 5:00 o'clock in the afternoon. So the burden on the system is different when we look at different times of the day. So a regional approach may allow modelers to assess potential bottlenecks in generation and transmission. Given the model structure and the regional representation this is the first attempt. We think once we have a better handle of the regional representation and the flow information we can probably improve the application of this methodology even better in the future. Generation capacity in each region and state can help the estimation of electricity costs. That's another element, I think. If we understand different types of technologies we can actually estimate the margin of cost of producing electricities. Load curves and state or regional electricity generating capacity will be used to determine dispatching. Fewer choices in marginal costs of producing electricity, that's the design of this new modeling structure we are trying to implement. I'll use California as an example to illustrate the importance of load curves on dispatching decisions. But since it's 컴컴 some of the numbers I will just show a few examples here. This is California average hourly sales of electricity by month in 2002. So what I did here is I simply used EIA published data, monthly electricity sales, and that converted to a daily number. For this number I think the important thing is we see the range of hourly sales of electricity derived from monthly data in California just centered around 20.75 gigawatt hours. The level is not very high. The highest hourly demand was in July, about 31 gigawatt hours, and the lowest one was like in November, around 24 gigawatt hours. I'll show you what I've got. This is basically downloaded data from California ISOs and this is in March. The previous chart, you can see, March and November are pretty low but when you look at demand for electricity by the hour even in March it actually exceeded 30 gigawatt hours. So that shows the importance of the hourly demand, that it actually can have an impact on dispatching. Another example here, this is also from California, actual system load in July. Now, the numbers actually exceeded 35 gigawatt hours. So there's a distribution and for some months in July generation actually exceeded like 40 gigawatt hours. I think during the California crisis demand for electricity was higher than that. Also on planned outage, besides the planned outage, on planned outage it also reduced the supply capacity. As a result California experienced some difficulties. So looking at those load curves and looking at California generating capacity by primary energy source in 2002 you can see California has some nuclears, quite a lot of hydroelectric, and a lot of natural gas. They are renewables, dual- fired units, some petroleum and a little bit coal. Its interesting. Coal doesn't play a significant role in California. Yes? MR. BERNSTEIN: I assume this does not include imports into California? MR. TSENG: This is generation capacity in California. For this one when California reports the planned capacity that includes generation capacity plus imports. MR. BERNSTEIN: Right, it particularly includes generation that's owned by California utilities in other states. It's dedicated transmission. So what your histogram is missing is capacity that is actually owned by California utilities that have actual dedicated transmission lines to them. MR. TSENG: That's correct. The information I collected, the total capacity, the number here, if you add up the numbers its about 56 gigawatt capacity. So that's the total capacity in California. But in any given month the available capacity, for example, when I visit the website I look at the numbers in July, especially this one. I actually could see the available generating capacity on March 8, 2004, was 42 gigawatts and that includes imports. So some planned outage in lieu of out of service and as a result I think in terms of the planning part of the reason we see the difference in load curve and difference in seasonal available capacity that all can play a role in terms of meeting the electricity demand. MR. BERNSTEIN: My point is just don't forget when you are doing the regional stuff because you've got generation in other places that is actually owned by so it is imports but it's actually capacity owned by utilities in the state and that is different than importing electricity on a spot basis or a contract basis. MR. TSENG: That's why we have regions and we will try to capture those but still in terms of our definition we will try to say if those power plants are not in California California will be getting it from another region or state. Thanks for pointing that out. It may not be an issue because our region may be more aggregated but for California I think this is a good point and we will definitely pay a little bit more attention to that area. DR. HENGARTNER: What this suggests to the least is that you need to have very careful modeling of the transmission. I mean, it could be 컴컴 as long as the transmission from Oregon to California is well modeled we can account for it. That's one of the difficulties here is the interplay between regions. MR. TSENG: The transmission part, the power flow information is not available anywhere. So that's why we want to use the regional model as a way to understand the potential power flows. Again, when we look at the regional aggregation we integrated some of the issues or assumed away some of the transmission issues. So when you say we want to see what happens, where California will get electricity from different regions, say from Washington State or Oregon through the hydropower plants we may be able to look into that. But there are some other issues which can be interesting such as when California will get the imported electricity. The time of the day affects the type of fuel used to generate electricity. We think in the bigger picture that's one area well be looking at as well. MR. BERNSTEIN: Let's let him finish and then he can answer. MR. TSENG: I'm almost done here and then I can answer questions. So I think when we do regional modeling a lot of times the first question is do you have the data. For the electricity part I think fortunately we do and some of the consultants do, at least like the load curve stuff. So data are available to create load curves. I think that's one thing we know, like the ISOs, the independent system operators and some of the RTOs, like PJMs. They have information and some electricity modeling consultants actually purchase that information so we do have almost like by state. So we plenty utilize that information. So the EIA 컴컴 historic monthly electricity sales set up by region and will be used to create load curves. This is a process we use as typical macro drivers. Exogenous variables drive the demand and then we have the monthly demand and then we convert the monthly demand to a load curve. Then we try to model the dispatching. That's the general approach. There are available to create electricity supply curves. I think EIA actually has very good information and also we can actually get the O&M cost and we can get fuel cost so we can actually create margin of costs of electricity supply from different technologies. Then the dispatching algorithm can be applied to that. So the state regional demand and generation will help us check the flow information. That's the part I think is an attempt. We try to understand the flow information but actually Dr. Hale sitting in the audience is an expert. He just completed a report and he gave the committee a briefing last time. There are a lot of issues in terms of checking the flows, in terms of collecting and processing the information. So what we are doing here is a first step. Were not saying this approach is perfect but at least this approach will allow us to get a handle of looking at the potential flows, looking at the relationship between load curve and generating capacity and possible electricity flow. So a few questions for the committee. The regional short-term forecasting is designed for the short- term forecast so should we use a system of equations approach in our estimation of model parameters? I mentioned earlier because we are looking at demand equations in each region and so exact demand issues in terms of how do we estimate demand. Also there's an overarching market so there's a statistical consistency issue when we estimate the equation system how do we estimate the system and how much do we lose if we simply use an easy approach to estimate the equation just on the demand side? Also I think the supply side we are looking into it and it may take some time and maybe we can raise some of the issues next time. So that's the issue. The second one is how do we handle the linkage between regional demand and supply. Dave's flow chart identifies an algorithm but what we want to be able to solve or estimate equation system is to ensure that the linkage when we estimate a system the top numbers, the market numbers, price, market clearing, demand and supply, are consistent with the regional demand and supply. Even though we are not solving the regional market still we need to understand the consistency issue. In the electricity sector aggregation effectively flatten the regional load curves so the more regions we have the flatter the load curve which be. So we would like to get some guidance in terms of some criteria for the selection of the number of regions even though we look at supply regions with seven or eight and when I started talking to people some people say when you aggregate regions don't include midwest with other regions because mid west has a lot of excess capacity. So if you aggregate midwest with Atlantic States then you don't have any problems in terms of supply and potential flow. So those are the issues we are looking into. Hopefully next time we will have some empirical data to report. Thanks, that concludes my presentation. MR. BERNSTEIN: Thank you. Phillip, did you have a question? MS. KHANNA: About transmission flows I understood that information is not available but I wonder if it should be available with the ISO at least. There must be some place within the grid where someone is tracking flows of electricity from source to end use. Maybe it's not easy to correct but it should be there in the system somewhere. I think that if transmission is the issue then maybe that's what we need to do to try to figure out an approach. MR. TSENG: The transmission flow, we actually had four focus group meetings and Mr. Bradsher-Frederick will give a talk about some of the findings. Actually I was involved in that project. What we found out from the stakeholders is it involves different levels of data collection and analysis. From the stakeholders people say they want to collect information for different power lines. For example, EIA collects 230 KB lines and some 115 KB lines but some stakeholders, especially at the state level, people are looking at 69 KB lines. In some states it's treated as transmission lines but in New York it's distribution lines. So you are talking about a host of information which would be very difficult to process. I think another theme I'm pointing to, Dr. Hale, he has a model. He actually worked with a consortium of professors to develop a power world model basically looking at the flow information. The question is how do we integrate empirical data with the modeling framework. That's another layer. Its a lot more complicated. For the regional model I think one issue is the turn-around time is one month so we need to basically manage the information system and estimate the 컴컴 system in such a way it will not overburden the team who will produce the report every month. DR. NEERCHAL: Looking at the two days of data you showed, the natural question that came to my mind was is the availability the same throughout. I see that the level is different from March to July, the level of load, but is the availability the same? MR. TSENG: The availability is not the same. DR. NEERCHAL: Not the same without the changes or the -- MR. TSENG: Right, I think the interesting pattern we actually have probably five or six years historical data basically looking at hourly information and that I think the burden on the load becomes more severe if you have a heat wave like say five or six days in a row of very hot weather because the first day or second day doesn't impose a load but when the buildings are heated up the demand for cooling will increase. That can really drive up the demand for electricity in the afternoon. That imposes a burden on the power generators and the transmission. But what we are trying to do is we want to first capture some of that element and see how we can improve our understanding of the system. DR. NEERCHAL: Now, for example, the March data and July data, if you look at the largest load minus the smallest load for the 24-hour period of time it is about 4-5 kilowatts, about. MR. TSENG: I used that as an example to show the variation but I actually downloaded another day, like July and other times in California, and actually the actual loads in the afternoon was close to 40 gigawatt hours. MR. BERNSTEIN: We had 42 gigawatt hours two weeks ago with some stage one emergency stuff so it can vary a lot. MR. TSENG: Yes, it can vary a lot. DR. NEERCHAL: The other question or comment I have is that the last bullet in your presentation about the number of regions, one thing I was thinking, if there is 컴컴 until you have very good quantitative data about transmission at least qualitatively when you are doing the regions you should take the transmission flow into account. Are any of the region definitions based on the transmission flow? MR. TSENG: No, actually there's no flow information. Some people requested EIA use the FERC-1 information to create flow information; however, when we talked to some of the users of the data some people say how relevant is that because some analysts look at the congestion issues at control areas, which is very different from the market area or the state level or the region. So the congestion could be very local and the flow information we see is probably just an indicator. Really, there's no way the kind of modeling we are doing at the regional level would pinpoint the congestion point. But the interesting part is a lot of people working on the transmission trying to understand the congestion and some of the implications of reliability and market power don't have any idea about what kind of data would be needed to do the analysis. So what we are doing here is at least one step forward and trying to identify the demand and supply and some potential flow information. MR. BERNSTEIN: I really need to take a step back here and ask a very fundamental question. That's what are you trying to get out of the short-term energy forecasts? Actually in the presentation so far are we trying to get to be able to look out the next month or the next few months prices or are we trying to get reliability? What is the goal of the short-term energy forecasts and therefore the reason for the regional breakdown? Because if you are trying to do anything about whether there will be supplied and the ability to be supplied that's one thing. If you are strictly just saying here are going to be the trends and prices that's another thing altogether. Do you understand the question? MR. TSENG: Yes, I understand the question. Part of the reason we want to understand the field use pattern in the power generation sector is it's a very important element. I was trying to show you the generating capacity, the base load and the peak load. If we don't model the dispatching pattern then we don't know exactly the field usage. MR. BERNSTEIN: Going upstream from that, what are you trying to 컴컴 what do you really want to get out and what is the information that you really need to have out of this for the short-term energy outlook? That's the fundamental question. If we are worried about whether there's going to be sufficient electricity supplied that's one thing. You're not going to do that. That's one thing. Then we need to worry about transmission. But if we're not worried about that then 컴컴 what are you really trying to get at? What's your objective? MR. TSENG: The objective here is really to do a good accounting of demand and supply, especially in the natural gas market. That's why I said well, is there a consistency between the market and demand. So I use electricity as one example. You say well, we don't capture transmission issues but we do capture dispatching decisions. If somehow after we analyze the historical data hydropower is used as both base load and load shaving that could have a lot of impact on demand for electricity. California has a lot of electricity, has combined cycle. So if we can even shave the demand for natural gas slightly looking at the dispatching decision you can change the demand and you can change the underground storage of this in the summer. That could have an impact in the winter in terms of heating fuel demand because in the northeast natural gas and the number two heating fuel will be used to provide heating needs. So what we want is we want to be able to check the flow of demand for fuels and supply of fuels and understand what happens in terms of demand and supply in storage and then look at those relationships in terms of prices and markets. So does that answer your question? MR. BERNSTEIN: Most of the way. MR. CARUSO: One of the things that I think both Howard and I were struck with when we first got here after is the number of questions we were getting both from the executive branch and the congressional branch whenever there was a discontinuity or a potential discontinuity in supply or demand and price spikes. And the fact is we couldn't answer a lot of them because there was no regional detail in the short-term model and, to put it as far upstream as you can go, basically the Secretary wants to know what's going to happen in California, I mean, first starting with the crisis in electricity but in natural gas and now more recently gasoline prices. We have been asked to do analysis on both the west coast and the east coast on gasoline so those are -- MR. BERNSTEIN: So the initial focus appears to be natural gas and gasoline? MR. TSENG: And also heating fuel. However, heating fuel competes with natural gas as well even in the northeast. This is an empirical question and we will do some empirical analysis there. Part of the reason I talk about electricity and you say well, it doesn't answer the question about transmission bottlenecks, it's correct; however, the algorithm, what gave us a better understanding of how natural gas power plants would be used to serve the load, and that information can help us understand how natural gas will be used not only in California but also in other regions because in recent years I think natural gas demand for power generation actually increased by almost 2 quads, the gas combined cycle. A lot of the new gas combined cycles just came on line in the past several years and that increased demand for natural gas and so how natural gas power plants are used especially for intermediate load and peak load can have an impact on natural gas storage because now the market is very tight and sometimes people say well, the forecasting error is maybe one percent or two percent. When we look at a 100 quad, the common energy system, one percent is one quad. If it's in the coal supply or coal demand it may not matter but if it is natural gas then you're talking from $4 or $5 per MCF to $12 per MCF or even higher. MR. BERNSTEIN: So is the vision here to be able to say that this winter if you really have this up and going because of expectations of electricity use and natural gas use for electricity and state of storage that there could be a shortage of natural gas for heating in the wintertime? MR. TSENG: Yes, that could be one outcome because we do a much more rigid accounting of the demand for natural gas. MR. BERNSTEIN: That definitely has to come from a region. That has to be a regional difference? MR. TSENG: Right, yes. MR. BERNSTEIN: Sorry to take up your time with that but it really makes a difference on what it is you are trying to do. You have asked three very specific questions that maybe we should try to address. The first one is should we use a system of equations approach. What's the alternative? Is there an alternative? DR. HENGARTNER: I'm not an economist and that's where my failings are. But I understand this is a very detailed economic trick of modeling that you have done. A statistician would simply look at the data and say I have a vector of prices and a vector of supplies and the question is can we model on the black box without going 컴컴 supplies and demand curve? Simply can I get some model that explains how these prices move maybe in conjunction with the supplies and maybe heat degree days or other exogenous variables? That would be an acceptable approach but it doesn't have the same flavor, the understanding that you are gaining, from the econometric model; however, for forecasting if I need to predict what is going to be the prices a few months down the road that might be a very acceptable solution. So Mark's question, what do you want to do, what is the customer asking you, is fundamental. I mean, that is indeed the first question one has to ask. MR. TSENG: Right. Some of the questions I heard or you read in the press could be are petroleum refiners gouging the customers, why the price is $2.50 per gallon in some areas. So just pure statistic analysis will not give the why and that's the part we are looking, market demand and supply. Sometimes we add additional information in terms of distribution system bottlenecks. For example, a few years ago I think that there was really no natural gas shortage per se but the midwest and northeast had a cold spell. What happened was natural gas producers in the south said well, I'm going to send as much as natural gas as possible. They passed certain processing steps. Normally natural gas coming out of a well will go through a natural gas plant and we call it coming out of a well is wet gas and you strip the liquids and you send the dry gas through the pipeline. But when the demand is very high, what happens is some of the gas producers simply send the natural gas through the pipeline and when you reach north it will liquefy. So it actually jammed the pipeline and so it made the situation even worse. So that's the kind of thing I think especially for our administrator when he has to testify. People will not say what happened. Something is wrong and people are not sending natural gas to the north. That's why prices going through the roof. So the statistic is one thing and also some of the meaning associated with the numbers is important. DR. HENGARTNER: The other question the statistician will ask is how many parameters are you estimating. You say you have 180 equations. One parameter, maybe two, per equation, are we are talking about 600 parameters easily? MR. TSENG: But some of the general form or generic form equations are more or less the same. Demand equations could be very similar. Only the value of the parameter will be different. DR. HENGARTNER: Yes, so you need to estimate those values from the data. MR. TSENG: Yes, we do, right. DR. HENGARTNER: So if you have 600 parameters you need to estimate how many independent "or" observations do you have? Is it even feasible to do it? DR. NEERCHAL: I think econometrics 컴컴 identifiability 컴컴 DR. HENGARTNER: Well, identifiability is one issue but even if it is identifiable do we have enough data? MS. KHANNA: Is it a robust estimate? That's what you're looking at. MR. TSENG: Dave, correct me. Dave knows a lot more about data. I think from '97 we have monthly data, '97 through 2002 or 2003, right? MR. COSTELLO: Just to clarify this, currently we have data going back as far as 1975 by month. But on a consistent basis for the variables that we cover, which are mostly national level flows, stocks, prices, I would say that most of the data begins monthly in about 1987. Now, some of this electricity data that we are talking about, right now we just finished building the first part of the database we are going to use here and it doesn't get into the load curve stuff because that's another matter. But just in terms of the flows, the generation numbers by region and by fuel source, we update it from January of 1989 through today, through the latest month, which is December of 2003. We also have capacity by month, by fuel, by region although, looking at that data, it needs to be cleaned up a little bit. Of course, we maintain everything on a per day basis. We don't put any flows on anything other than a per day basis because it's silly not to. But, anyway, there's a lot of data there and probably more than you usually see on EIA's website because it's hard to get all that historical data and we are putting more into it. But to me one of the questions that this brings up, and I think it's pertinent to what I think Phil was getting at, is let me give you an example. The question of natural gas, in order for us to do a natural gas modeling effort here we've got to have natural gas from the different sectors. That means residential, commercial, and industrial. And the other big sector is electric power. So in order to know what the total amount of natural gas is we've got to obviously do that. So we do it. Now, there are two levels of this question of identification, I think, that are relevant here. One is the question of well, how are we going to get to the equilibrium price, so-called. How do we get a flavor for what that is? The approach that I am trying to sell here is that we ought to look at, first of all, the natural gas market in the United states is pretty well integrated. You have in general the pressure on prices at the wellhead, that is, in the producing region is affected by just about everything else that happens, demand in the northeast, supplies from Alberta, Chicago market demand, all that stuff. It's not perfectly integrated because there are places where there are transmission bottlenecks and so forth. But what we are trying to sell or what I'm trying to sell is we take all these demands. We calculate them by region. We add them all up. Now, the issue I think Phil was focusing on was more the question of how we get the fuel demands appropriately, and there are some transmission issues, too, but the fuel demands appropriately out of all these regions and that includes the electric power sector. We add them all up. That gives us a total requirement for natural gas. We also are tracking inventories. We have to worry about inventory behavior a little bit. But even if we just assume, as seems to be the case, whatever inventories end up at the end of the winter there is a very strong propensity for them to get back close to normal. We notice that, for example, the variance at the beginning of the heating season is much lower than at the end because of the fact that in other words it's not one of these things where its a really cold winter and you wind up with low stocks, oh, we are going to be stuck with low stocks for the year. That's not the way it works. Gas utilities have a lot of requirements and they are basically going to put the 컴컴 so even if we just assumed that well, they are going to get back to normal pretty soon that adds to the amount of flow for natural gas we have to worry about. Anyway, there's a total requirement for new supply that comes out of this. On the other hand, we have to say well, how does new production come about. Well, we look at drilling and so on and so forth and what's available from Canada. At that aggregate level we could just simply say we need to have equilibrium. What will happen is that the price will be the price but equilibrates this aggregate demand for supply. We are looking at the Henry Hub prices as a benchmark price. What's an alternative to that? As an alternative we are just adding up all these things and forcing it to do a demand supply equilibration and give a price, which is essentially what we are doing now. One alternative is to say well, okay, we can characterize the market model as a supply, basically estimate a supply function, and also a demand function in the aggregate and worry about recovering the structural parameters for that system. That gives an answer. Depending on how complicated it is it gives an answer for spot prices. It gives an answer for the actual level of demand and it gives an answer for inventories, probably; however, that's an answer that may not be the same as what we just finished doing at the regional level to add up. It's nice because we can say we have these well-identified specified structural parameters for demand and supply for natural gas in the United states. We could take that and allocate it back in some form to the regions or we could just do the other thing. That's one question, I think, that we are talking about here. The other question has to do with if you are back down at the regional level again and if you are worrying about, lets say, and we do not do this right now and we may not do it but it's another one of Phil's questions, I think, if we look at, say, residential energy demand and leave out gasoline but we are really just talking about electricity and heating fuels we could specify it as a system in which, first of all, we could do an overall sectoral energy demand function as part of a system that says well, the energy demands are one set of inputs in industrial or one set of goods consumers get as opposed to others and there would be some substitutional elasticities in there. Once you determine that then you have to worry about substitutions between fuels which we can figure in the residential sector in the short run there's not a lot of that but there may be some. So there's the question of whether to estimate those functions in that way. The alternative is simply to have an equation that we have for natural gas demand in the residential sector and we know a lot of things that affect this. One of the things is the housing characteristics in the region, which we are going to track, in other words how many of these households have natural gas 컴컴 so on and so forth. We know what the weather is. That's obviously a big factor and these prices will be important, too. So we put those in there. We use ordinarily two-stage least squares, what method. Sometimes it doesn't look like it makes a whole lot of difference. But for me, and this is just because of my focus, for me whatever we do we want to do the regions because we want to know more about particularly how those things change. How do you explain how you got this total gas on hand? I mean, you have an aggregate equation and that's fine and maybe it seems to fit reasonably well but you couldn't tell anybody where it came from. MR. BERNSTEIN: I don't think there's any issue with going 컴컴 the question is how complicated do you need to get. How detailed do you need to get? I think it still comes back to what your goals are and what you are really trying to get out of the model and then asking yourself how do you do it in a way that's going to be understandable but at the same time doable in the time frame that you need to deal with. One of the questions is are you getting too complicated. The other question is well, don't you have to. MS. KHANNA: I'm actually having a hard time trying to answer the first question, whether to use the system of equations 컴컴 versus two-stage least squares because a priori without knowing the data or your structural model I can't answer that. It depends on those 컴컴 do you expect there to be an 컴컴 or simultaneity problem and the second is a statistical question. If so, is it really a statistical issue? So I don't think you can answer those in a vacuum. I mean, people like you who actually deal with the data in the model probably if you give us a little more insight into why you are even asking this question from an econometrics point of view maybe we can give you a more informed answer. MR. BERNSTEIN: I think there's not enough time at the moment to actually provide that. MR. TSENG: I think in terms of asking more specific questions we will have more because right now we are still collecting information. Dave has someone collecting the information. When we actually start estimating the equations well probably know more about the statistical issues and also econometric issues because we could start doing a different kind of statistical test. MR. BERNSTEIN: I think perhaps we may need to wait to try to address some of those questions for the fall after you have actually gotten some 컴컴 DR. HENGARTNER: I have a few more questions about the data. It's beautiful that you can do the electricity on an hourly basis but a lot of the other variables you measure are not on an hourly basis. They are probably not even on a daily basis. I mean you say some of them you have on a daily basis but very few -- MR. COSTELLO: Oh, no, none of the flows that we have are actually day by day observations. When I say daily I mean divide by the number of days in the month. DR. HENGARTNER: That complicates your equation greatly. The only data that's available is what you have in a month and you divide by the number of days in a month. This very detailed modeling might be extremely hard to do because you have this additional source of error. It's