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Data analytics-based methods to estimate the rooftop-PV hosting capacity in distribution systems using smart meters

Grant number: 23/07072-9
Support Opportunities:Scholarships in Brazil - Master
Effective date (Start): September 01, 2023
Effective date (End): August 31, 2025
Field of knowledge:Engineering - Electrical Engineering - Power Systems
Principal Investigator:Walmir de Freitas Filho
Grantee:Augusto Janssen Harger da Silva
Host Institution: Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:21/11380-5 - CPTEn - São Paulo Center for the Study of Energy Transition, AP.CCD


The efficient management of rooftop photovoltaic interconnection is one of the main challenges faced by distribution energy utilities worldwide. In Brazil, considering business hours, a new photovoltaic generator was connected to a distribution system every 10 seconds in 2022. To facilitate this assessment, several hosting capacity analysis methods have been investigated in the literature, which are based on: (a) extensive probabilistic or deterministic simulations, (b) optimization methods, and (c) data analytics and estimation using electrical measurements. In this context, the objective of this M.Sc. Project is to develop data analytics methods to periodically estimate rooftop photovoltaic hosting capacity on distribution systems, facilitating the management by distribution utilities of interconnection applications (customer requests). This project will focus on the determination of hosting capacity assuming that distribution system models are unknown (model-free) or inaccurate and will be mainly based on customer smart meter measurements. First, the customers will be classified on groups that have voltage variations most related to each other. Then, the aggregated hosting capacity of each group and the individual hosting capacity of each customer will be determined. The main advantage of the proposed methods is that the accuracy is not impacted by errors of distribution system models that are commonly found on utilities database.

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