On the long-term density prediction of peak electricity load with demand side management in buildings

Abstract

Long-term daily peak demand forecast plays an important role in the effective and economic operations and planning of power systems. However, many uncertainties and building demand variability, which are associated with climate and socio-economic changes, complicate demand forecasting and expose power system operators to the risk of failing to meet electricity demand. This study presents a new approach to provide the long-term density prediction of the daily peak demand. Specifically, we make use of temperature projections from physics-based global climate models and calibrate the projections to address possible biases. In addition, the effects of population growth and demand side management efforts in buildings are taken into consideration. Finally, the daily peak demands are modeled with the nonhomogeneous generalized extreme value distribution where the parameters are allowed to vary, depending on the predicted temperature and population. A case study using actual building use data in the south-central region in Texas demonstrates that the proposed approach can quantify the uncertainties in an integrative framework and provide useful insights into the long-term evolution of peak demand density. A well-established building demand saving strategy is predicted to buffer against the growing needs of long-term peak electricity demand.