A novel stochastic model for hourly electricity load profile analysis of rural districts in Fujian, China. (27th September 2022)

• Record Type: Journal Article
• Authors: Zhou, Bing; Wang, Xiao; Yan, Da; Xu, Jieyan; Kang, Xuyuan; Chen, Zheng; Hao, Tianyi
• Is Part Of: Science and technology for the built environment. Volume 28:Number 9(2022)
• Page Start: 1166
• Page End: 1183
• Publication Date: 2022-09-27
• Subjects: Heating -- Periodicals; Ventilation -- Periodicals; Air conditioning -- Periodicals; Refrigeration and refrigerating machinery -- Periodicals; Indoor air quality -- Periodicals; Indoor air quality; Air conditioning; Heating; Refrigeration and refrigerating machinery; Ventilation; Periodicals; 697
• DOI: 10.1080/23744731.2022.2091357
• Languages: English
• ISSNs: 2374-474X

Abstract

Abstract : Renewable energy is important for achieving carbon neutralization. However, power generation from renewable energy sources can be uncertain and uncontrollable. Therefore, understanding the features of energy demand is pivotal for integrating renewable energy sources and storage systems within the entire energy network. Traditional load profiles are depicted by fixed typical load curves, which cannot support detailed dynamic simulations of annual hourly electricity consumption. Here, a novel stochastic model for hourly electricity load profile analysis is proposed. A clustering-based model of hourly load was constructed to depict load profiles of typical days, while a nonlinear regression method determined the temperature-related factors within annual daily load consumptions, based on which a stochastic simulation model was established for hourly electricity load profiles. The model's performance was tested with electricity data of rural districts in Fujian Province, China. The proposed model achieved a coefficient of variation of the mean absolute error of 15.7%, which was significantly lower than that of the traditional model. Further, a simplified case was employed to analyze the application of the proposed stochastic model in the design of energy storage systems. The proposed method enables the optimal design of integrated energy networks with renewable energy sources and energy storage systems. HIGHLIGHTS: A two-step clustering method was used for typical load profiles from 4, 053 rural districts. Probability distribution models were developed for stochastic simulation of annual hourly electricity load. The correlation between temperature and electricity consumption was described using a nonlinear regression model.