Data-Driven Load Modeling to Analyze the Frequency of System Including Demand Response: A Colombian Study Case

IEEE Access

卷:9

Luis Lopez¹  Juan Ramirez-Ortiz²  Adriana Arango-Manrique²  Ingrid Oliveros² 

[1]Center for Energy Science and Technology (CEST), Skolkovo Institute of Science and Technology, Moscow, Russia

[2]|Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla, Colombia

关键词: Demand response;    load management;    frequency analysis;    renewable energy;    DR strategies;   

DOI  :  10.1109/ACCESS.2021.3069006

来源: DOAJ

【 摘 要 】

This paper analyzes the potential impact of implementing demand response strategies in a power system. This work aims to present a methodology to evaluate three demand response models to reduce frequency variations in the system. The method starts with the modeling of the system load and the demand response strategies. The power loads are modeled through active power and reactive power measurements in the system’s different buses. A data-driven methodology is proposed to obtain three profiles that simulate residential, commercial, and industrial users’ behavior. Mathematical modeling is proposed for demand response strategies. Time of Use tariff, Solar PV Distributed Generation, and Load Curtailment are the strategies used for residential, commercial, and industrial users, respectively. A brand-new combination of scenarios is developed in this paper with different penetration levels of the demand response strategy. Besides, a novel analysis of the frequency profile is performed for the proposed scenarios. A modified IEEE-39 power system is proposed, adjusting generation and demand using the Colombian demand profile and the generating units’ energy mix. The results indicate that the implementation of demand response strategies improves the system’s frequency profile. The frequency drop was reduced by 11.4 %, and power generator units released up to 2.1 GWh through the day with the implementation of the DR strategies.

【 授权许可】

Unknown