Best Practices in Electricity Load Modeling and Forecasting for Long-Term Power System Planning

To support long-term power sector planning, the National Renewable Energy Laboratory (NREL) and the Lawrence Berkeley National Laboratory (LBNL) conducted a study on best practices in electricity load modeling and forecasting. The study aimed to identify effective methods for developing accurate load models and forecasts, which are essential for informing investment decisions and ensuring a reliable and efficient power system. The study involved a comprehensive review of existing literature and case studies from various countries, including the Philippines, India, Indonesia, and Mexico. The researchers identified several key challenges in load modeling and forecasting, including the lack of reliable data, the need for accurate representation of distributed energy resources, and the importance of considering the impact of energy efficiency measures. To address these challenges, the researchers developed a framework for load modeling and forecasting that incorporates the use of proxy or simulated data, surveys, web crawling, and telematics. The framework was applied to several case studies, including the development of load baselines in Indonesia, the modeling of vehicle electrification at a macro scale, and the analysis of energy efficiency opportunities in South Africa's residential sector. The study found that the use of proxy or simulated data can be an effective approach when country-specific data are unavailable. For example, in Mexico, the researchers used proxy data to model distributed generation, which helped to identify the potential for increased renewable energy penetration. In India, the researchers used surveys to collect data on building energy use, which enabled the development of accurate load models. The study also highlighted the importance of considering the impact of energy efficiency measures on load modeling and forecasting. For example, in South Africa, the researchers found that energy efficiency opportunities in the residential sector could reduce peak demand by up to 10%. In Indonesia, the researchers found that the development of load baselines could help to identify areas where energy efficiency measures could be most effectively implemented. The study concluded that accurate load modeling and forecasting are critical for supporting long-term power sector planning. The researchers identified several key takeaways, including the need for reliable data, the importance of considering the impact of energy efficiency measures, and the value of using proxy or simulated data when country-specific data are unavailable. The study's findings and recommendations are expected to inform the development of more accurate load models and forecasts, which will help to ensure a reliable and efficient power system in the Philippines and other countries. The study's methodology involved a comprehensive review of existing literature and case studies, as well as the development of a framework for load modeling and forecasting. The framework was applied to several case studies, including the development of load baselines in Indonesia, the modeling of vehicle electrification at a macro scale, and the analysis of energy efficiency opportunities in South Africa's residential sector. The study's results were disseminated through a series of reports and presentations, which were shared with stakeholders in the Philippines and other countries. The study's findings and recommendations are expected to inform the development of more accurate load models and forecasts, which will help to ensure a reliable and efficient power system in the Philippines and other countries. The study's broader crosscutting considerations included the need for a more comprehensive understanding of the energy sector, the importance of considering the impact of energy efficiency measures, and the value of using proxy or simulated data when country-specific data are unavailable. The study's conclusions and main takeaways are expected to inform the development of more accurate load models and forecasts, which will help to ensure a reliable and efficient power system in the Philippines and other countries. The study's references included a comprehensive list of sources cited in the report, including academic articles, technical reports, and government documents. The study's list of figures and tables included a series of illustrations and data tables that supported the study's findings and recommendations. The study's list of acronyms included a comprehensive list of abbreviations used in the report, including ADOPT, BUENAS, DER, DPV, EE, EV, GDP, IDEA, LBNL, LEAP, LOADM, MEPS, NREL, PDOE, PV, RDMA, RE, SAM, TWh, USAID, and ZPAC.