Load Forecasting Case Study th Jan ua ry 15 , 2015 Tao Hong, University of North Carolina at Charlotte and Mohammad Shahidehpour, Illinois Institute of Technology For EISPC and NARUC Funded by the U.S. Department of Energy i Acknowledgement This material is based upon work supported by the Department of Energy, National Energy Technology Laboratory, under Award Number DE-OE0000316. On behalf of the members of this project, we would like to thank the ESPIC and NARUC for making this work possible. The many tasks of this work could not have been completed without the dedicated effort of the EISPC studies and white papers work group members. Several key project members provided tremendous support to this project. Dr. Zuyi Li from Illinois Institute of Technology was heavily involved in most of the writing. Jingrui (Rain) Xie and Bidong Liu from the Big Data Energy Analytics Laboratory (BigDEAL) at the University of North Carolina at Charlotte made great contributions in conducting the three case studies, from data processing to model building and result presentation. Two other BigDEAL students, Jiali Liu and Lili Zhang have made major contributions in the revisions of the report. The EISPC studies and white papers work group members helped thoroughly review the draft report and offered many valuable comments. In addition, we received many useful comments and inputs from many members of the IEEE Working Group on Energy Forecasting and colleagues in the utility industry. Below is an incomplete list of the contributors: • Parveen Baig, Iowa Utilities Board • Jonathan Black, ISO New England • Tim Fairchild, SAS • Shu Fan, Monash University, Australia • Mário Bruno Ferreira, REN Rede Eléctrica Nacional, S.A., Portugal • Douglas Gotham, Purdue University • David Hamilton, Old Dominion Electric Cooperative • Mary Hayes, Lakeland Electric • Rob Hyndman, Monash University, Australia • Tom Laing, North Carolina Electric Membership Corporation • Brad Lawson, SAS • Stuart McMenamin, Itron • Wayne Moodie, PJM Interconnection • George Novela, El Paso Electric Company • Jonathan Nunes, Leidos Engineering • Tom Osterhus, Integral Analytics • Bob Pauley, Indiana Utility Regulatory Commission • Paul Preckel, Purdue University • Adam Rue, Eugene Water & Electric Board i • Xiaoyu Shi, Northern Virginia Electric Cooperative • Scott Smith, Integral Analytics • Tom Stanton, National Regulatory Research Institute • Sharon Thomas, National Association of Regulatory Utility Commissioners • Gareth Thomas, IHS Eviews • Laura White, North Carolina Electric Membership Corporation • Jason Wilson, North Carolina Electric Membership Corporation • Kyle Wood, Seminole Electric Cooperative Tao Hong, PhD Assistant Professor and Director Big Data Energy Analytics Laboratory (BigDEAL) Energy Production and Infrastructure Center (EPIC) University of North Carolina at Charlotte Charlotte, NC 28223 Mohammad Shahidehpour, PhD Bodine Chair Professor and Director Robert W. Galvin Center for Electricity Innovation Illinois Institute of Technology Chicago, IL 60616 ii Disclaimer This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. The information and studies discussed in this report are intended to provide general information to policy- makers and stakeholders but are not a specific plan of action and are not intended to be used in any State electric facility approval or planning processes. The work of the Eastern Interconnection States’ Planning Council or the Stakeholder Steering Committee does not bind any State agency or Regulator in any State proceeding. iii Executive Summary University of North Carolina at Charlotte (UNCC) teamed with Illinois Institute of Technology (IIT), ISO-New England, and North Carolina Electric Membership Corporation (NCEMC) to prepare a Load Forecasting Case Study for the Eastern Interconnection States’ Planning Council (EISPC) in response to the NARUC solicitation NARUC-2014-RFP042–DE0316. The work was supported by the Department of Energy, National Energy Technology Laboratory, under Award Number DE-OE0000316. The study includes two parts: 1) A comprehensive review of load forecasting topics for states, planning coordinators, and others. This was covered in Chapters 1 through 6. In addition, a list of recommended actions is summarized in Chapter 8. 2) Three case studies in three regions to assist Planning Coordinators and their relevant states with applying state-of-the-art concepts, tools, and analysis to their forecasting regime. The case study is presented in Chapter 7 and a glossary of terms can be found in Chapter 9. This study is intended to be both a primer on load forecasting as well as provide an in-depth discussion of load forecasting topics with a real-world demonstration that will be useful to state commissioners, planning coordinators, utilities, legislators, researchers, and others. This study is also intended to simplify and demystify the many complex concepts, terms, and statistics used in load forecasting. A few key takeaways from this study include: 1) Load forecasting is the foundation for utility planning and it is a fundamental business problem in the utility industry. Especially with the extraordinary risks confronting the electric utility industry due to a potentially significant change in the resource mix resulting from environmental regulation, aging infrastructure, the projected low cost of natural gas, and decreasing costs of renewable technologies, it is crucial for utilities to have accurate load forecasts for resource planning, rate cases, designing rate structures, financial planning, and so forth. 2) The states have varying degrees of authority to foster improvements in the databases, the forecasting tools, and the forecasting processes. A comprehensive load forecasting process often involves complicated data requirements, reliable software packages, advanced statistical methods, and solid documentation to construct credible narratives to explain the potential future energy use of customers. Load forecasting is not a static process. Rather, utilities and policymakers should be continually looking for ways to improve the process, the databases, and advance the state-of-the- art in forecasting tools. It is imperative that utilities devote substantial time and resources to the effort to develop credible load forecasts. 3) Deployment of smart grid technologies has made high granular data available for load forecasting. An emerging topic, hierarchical load forecasting, which produces load forecasts with various hierarchies, such as geographic and temporal hierarchies, is of great importance in the smart grid era. While customizing the models for each sub-region or utility would enhance the iv forecasting accuracy at the sub-regional level or utility level, the accuracy gained at a lower level can be often translated to the enhanced forecasts at the aggregated levels. 4) Many factors influence the load forecasting accuracy, such as geographic diversity, data quality, forecast horizon, forecast origin, and customer segmentation. The same model that works well in one utility may not be the best model for another utility. Even within the same utility, a model that forecasts well in one year may not generate a good forecast for another year. In order to establish the credibility in load forecasting, utilities have to follow forecasting principles to develop a consistent load forecasting methodology. 5) The recent recession has brought many utilities a paradigm change in how customers use electricity and how much they use. The North Carolina Electric Membership Corporation case study in Chapter 7 was designed to show how the same forecasting methodology would lead to different results and varying degrees of forecasting accuracy in three supply areas of the same state (North Carolina). 6) It is inappropriate to evaluate long-term load forecasts based on ex ante point forecasting accuracy. Long term load forecasts should be probabilistic rather than point estimates. The evaluation should also be based on probabilistic scoring rules. 7) All forecasts are wrong. While the ability to predict the future with as much accuracy as possible would be ideal, a more realistic expectation, especially for long-term forecasts, is the insights on the various risks that may confront a utility. v Scope of the Work This work includes two parts: Part I White Paper and Part II Case Study. Part I White Paper • Task 1: History, requirements, and uses of state-of-the-art load forecasting: o Why load forecasting is important o Brief historical perspective on the evolution of load forecasting (e.g. the NERC Fan) o How load forecasts are used for financial forecasts o How load forecasts are used for transmission and other resource planning