Data Model for Residential and Commercial Buildings. Load Flexibility Assessment in Smart Cities

• Published in: Sustainability Vol. 13; no. 4; p. 1736
• Main Authors: Oprea, Simona-Vasilica, Bâra, Adela, Marales, Răzvan Cristian, Florescu, Margareta-Stela
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 05.02.2021
• Subjects: Account aggregation; Alternative energy sources; Archives & records; Automation; Big Data; Commercial buildings; Consumption; Cost control; Data collection; Data models; Data processing; Datasets; Demand side management; Electric power demand; Electric power systems; Electric vehicles; Electricity; Energy resources; Energy sources; Flexibility; Geographical coordinates; Heating; HVAC; Load; Modernization; New technology; Peak load; Relational data bases; Renewable energy sources; Renewable resources; Smart sensors; Sustainability; Tariffs; Water heaters; United States > US
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su13041736

Demand response (DR) programs were usually designed to provide load peak reduction and flatten the load curve, but in the context of rapid adoption of emerging technologies, such as smart metering and sensors, load flexibility will address current trends and challenges (such as grid modernization, demand, and renewables growth) encountered by the evolving power systems. The uncertainty of the renewable energy sources (RES) and electric vehicle (EV) fleet operation has increased the importance of load flexibility that can be managed to provide more support for the stable operation of power systems, including balancing. In this paper, we propose a data model to handle load flexibility and take advantage of its benefits. We also develop a methodology to collect and organize data, combining the consumption profile with several auxiliary datasets such as climate characteristics of the location, independent system operator (ISO) to which the consumer is affiliated, geographical coordinates, assessed flexibility coefficients, tariff rates, weather forecast for day-ahead flexibility forecast, DR-enabling technology costs, and DR programs. These multiple features are stored into a flexibility relational database and NoSQL database for large consumption data collections. Then, we propose a data processing flow to obtain valuable insights from numerous .csv files and an algorithm to assess the load flexibility using large residential and commercial profile datasets from the USA, estimating plausible values of the flexibility provided by two categories of consumers.