A Review of Methods for Long-Term Electric Load Forecasting

Abstract

Long-term load forecasting (LTLF) has been a fundamental least-cost planning tool for electric utilities. In the past, utilities were monopolies and paid less attention to uncertainty in their LTLF methodologies. Nowadays, such casualness is pricey in competitive markets because utilities need to examine the financial implications of forecast uncertainty for survival. Hence, the aim of this paper is to critique the LTLF research trends with a focus on uncertainty quantification (UQ). For this purpose, we examined 40 LTLF articles published between January 2003 and February 2021. We found that UQ is a nascent area of LTLF research. Our review found two approaches to UQ in LTLF: probabilistic scenario analysis and direct probabilistic methods. The former approach is more helpful to risk analysts but has major caveats in addressing interdependencies of socioeconomic and climate scenarios. We identified very little LTLF research that examines uncertainties associated with climate extremes, distributed generation resources, and demand-side management. Lastly, there is enormous potential for mitigating financial risks by embracing asymmetric cost functions in LTLF research. Future LTLF researchers can work on these identified gaps to help utilities in risk estimation, cost-reliability balancing, and estimation of reserve margin under climate change.