A hierarchical multi-resolution agent-based modeling and simulation framework for household electricity demand profile. (August 2020)

• Record Type: Journal Article
• Authors: Mahmood, Imran; Quair-tul-ain, ; Nasir, Hasan Arshad; Javed, Fahad; Aguado, José A
• Is Part Of: Simulation. Volume 96:Number 8(2020)
• Page Start: 655
• Page End: 678
• Publication Date: 2020-08
• Subjects: Agent-based modeling and simulation -- multi-resolution modeling -- electricity demand profiles -- electricity consumption behavior -- urban electricity use -- occupancy patterns -- electricity appliances -- model validation; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549720923401
• Languages: English
• ISSNs: 0037-5497

Abstract

Analyzing demand behavior of end consumers is pivotal in long term energy planning. Various models exist for simulating household load profiles to cater different purposes. A macroscopic viewpoint necessitates modeling of a large-scale population at an aggregate level, whereas a microscopic perspective requires measuring loads at a granular level, pertinent to the individual devices of a household. Both aspects have lucrative benefits, instigating the need to combine them into a modeling framework which allows model scalability and flexibility, and to analyze domestic electricity consumption at different resolutions. In this applied research, we propose a multi-resolution agent-based modeling and simulation (ABMS) framework for estimating domestic electricity consumption. Our proposed framework simulates per minute electricity consumption by combining large neighborhoods, the behavior of household individuals, their interactions with the electrical appliances, their sociological habits and the effects of exogenous conditions such as weather and seasons. In comparison with the existing energy models, our framework uniquely provides a hierarchical, multi-scale, multi-resolution implementation using a multi-layer architecture. This allows the modelers flexibility in order to model large-scale neighborhoods at one end, without any loss of expressiveness in modeling microscopic details of individuals' activities at house level, and energy consumption at the appliance level, at the other end. The validity of our framework is demonstrated using a case study of 264 houses. A validated ABMS framework will support: (a) Effective energy planning; (b) Estimation of the future energy demand; (c) and the analysis of the complex dynamic behavior of the consumers.