Development of a methodology for the optimal restoration switching sequence in smart electricity distribution networks

• Grant number: 16/21496-2
• Support Opportunities: Scholarships abroad - Research Internship - Scientific Initiation
• Effective date (Start): January 15, 2017
• Effective date (End): May 14, 2017
• Field of knowledge: Engineering - Electrical Engineering - Power Systems
• Principal Investigator: Marcos Julio Rider Flores
• Grantee: Bárbara Resende Rosado
• Supervisor: Bala Venkatesh
• Host Institution: Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Research place: Ryerson University, Canada
• Associated to the scholarship: 16/10010-1 - Development of a methodology for the restoration of electricity distribution networks, BP.IC

Abstract

The restoration of electricity distribution networks determines the switches operations needed to isolate one area or zone of the network under fault, in order to minimize operating costs related to the disconnection of loads and the switching number of the switches in the restorative state. Additionally, the final topology of the restoration must ensure that the system operational constraints are respected, in other words, the voltage magnitude limits in nodes, the current magnitude limits in branches, the capacities of the feeders and the radial operation of the distribution network. This scientific research work aims to develop a methodology based on a mathematical model of mixed-integer linear programming (MILP) to solve the problem of restoration switching sequence of electricity distribution networks. The proposed methodology uses linearization techniques to obtain a MILP model with a good approximation of mixed-integer nonlinear programming model (MINLP) original. A MILP model has the following benefits: (a) a robust mathematical model, generic and flexible; (b) an effective solution using commercial computational solvers; and (c) the convergence to the optimal solution is guaranteed using classical optimization techniques. The proposed model will be implemented in the mathematical modeling language AMPL and solved using the commercial solver CPLEX. (AU)