Resilience analysis of distribution systems using probabilistic networks

• Grant number: 21/12220-1
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• Start date: April 01, 2022
• End date: March 18, 2027
• Field of knowledge: Engineering - Electrical Engineering - Power Systems
• Principal Investigator: Carlos Dias Maciel
• Grantee: Henrique de Oliveira Caetano
• Host Institution: Escola de Engenharia de São Carlos (EESC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated scholarship(s): 23/07634-7 - Using probabilistic networks to allocate resources in a power distribution system, BE.EP.DD

Abstract

The resilience and reliability analysis of electric power systems in response to failures and disruptive events is an area of growing interest today, as these systems are considered Critical Infrastructures (CIs), whose full functioning is essential for society. In particular, given the interest in analyzing how a given variable affects both other variables and the response of the system as a whole, there is a need to model causality relations in a power system. One of the approaches to this problem is the usage of causal inference using Bayesian networks, given its ability to deal with uncertainties, as well as its differentiation between conditional probabilities and interventions. This project aims to model a dynamic Bayesian network, capable of analyzing and structuring the evolution of a distribution system over time, considering its response to failures. For this, a two-layer model will be used: the operational layer, responsible for simulating the system dynamics and extracting data from its variables; and the management layer, which will establish different resilience metrics for the system, based on data extracted from the operational layer, using them to build the causal network itself. (AU)

Scientific publications

(9)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

CAETANO, HENRIQUE O.; FOGLIATTO, MATHEUS DE S. S.; DESUO, N. LUIZ; PEREIRA, J. BENVINDO R.; MACIEL, CARLOS D.; AIELLO, MARCO. Intelligent Resource Allocation in Power Grids: Leveraging Variable Neighborhood Search for Optimal Switch Allocation. IEEE CONFERENCE ON EVOLVING AND ADAPTIVE INTELLIGENT SYSTEMS 2024, IEEE EAIS 2024, v. N/A, p. 8-pg., 2024-01-01. (23/07634-7, 21/12220-1)

CAETANO, HENRIQUE O.; DESUO, N. LUIZ; FOGLIATTO, MATHEUS DE S. S.; RIBEIRO, VITOR P.; BALESTIERI, JOSE A. P.; MACIEL, CARLOS D.. A Bayesian Hierarchical Model to create synthetic Power Distribution. Electric Power Systems Research, v. 235, p. 8-pg., 2024-07-02. (23/07634-7, 21/12220-1)

CAETANO, HENRIQUE O.; DESUO, N. LUIZ; FOGLIATTO, MATHEUS S. S.; MACIEL, CARLOS D.. Distribution systems cost reduction based on switches upgrades: A stochastic. Electric Power Systems Research, v. 216, p. 6-pg., 2023-01-12. (14/50851-0, 21/12220-1, 19/06531-4, 18/19150-6)

CAETANO, HENRIQUE O.; DESUO, N. LUIZ; AIELLO, MARCO; MACIEL, CARLOS D.. Integrating structural and operational knowledge into multi-state system modeling: Application in urban infrastructures. KNOWLEDGE-BASED SYSTEMS, v. 329, p. 17-pg., 2025-11-04. (23/07634-7, 21/12220-1)

CAETANO, HENRIQUE O.; DESUO, N. LUIZ; FOGLIATTO, MATHEUS S. S.; MACIEL, CARLOS D.. Resilience assessment of critical infrastructures using dynamic Bayesian networks and evidence propagation. RELIABILITY ENGINEERING & SYSTEM SAFETY, v. 241, p. 22-pg., 2023-09-29. (19/07665-4, 14/50851-0, 21/12220-1)

NETO, LUIZ DESUO; CAETANO, HENRIQUE DE OLIVEIRA; FOGLIATTO, MATHEUS DE SOUZA SANT'ANNA; MACIEL, CARLOS DIAS. Predicting carbon footprint in stochastic dynamic routing using Bayesian Markov random fields. EXPERT SYSTEMS WITH APPLICATIONS, v. 276, p. 16-pg., 2025-03-12. (21/12220-1, 18/19150-6, 19/07665-4, 14/50851-0)

DESUO, N. LUIZ; FOGLIATTO, MATHEUS S. S.; CAETANO, HENRIQUE O.; PEREIRA, J. BENVINDO R.; LONDON J, J. JOAO B. A.; MACIEL, CARLOS D.. A Multilayer System and Optimization Framework for Team Dispatch Towards Service Recovery. IEEE TRANSACTIONS ON RELIABILITY, v. N/A, p. 14-pg., 2023-09-19. (19/07665-4, 21/12220-1, 14/50851-0, 18/19150-6)

FOGLIATTO, M. S. S.; CAETANO, H. O.; DESUO N, L.; MASSIGNAN, J. A. D.; FANUCCHI, R. Z.; LONDON, J. B. A.; PEREIRA, B. R.; BESSANI, M.; MACIEL, C. D.. Power distribution system interruption duration model using reliability. Electric Power Systems Research, v. 211, p. 12-pg., 2022-10-01. (18/19150-6, 21/12220-1, 14/50851-0)

BARTH, VITOR O.; CAETANO, HENRIQUE O.; MACIEL, CARLOS D.; AIELLO, MARCO. Quantifying uncertainty in Bayesian Networks structural learning. IEEE CONFERENCE ON EVOLVING AND ADAPTIVE INTELLIGENT SYSTEMS 2024, IEEE EAIS 2024, v. N/A, p. 8-pg., 2024-01-01. (23/07634-7, 21/12220-1)