Optimal Sizing and Allocation of Distributed Energy Resources in Microgrids

• Grant number: 22/12677-4
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Effective date (Start): April 01, 2023
• Effective date (End): June 30, 2024
• Field of knowledge: Engineering - Electrical Engineering - Power Systems
• Principal Investigator: Marcos Julio Rider Flores
• Grantee: Marcelo Montandon Magalhães
• 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
• Associated scholarship(s): 23/13121-2 - A python framework for optimal sizing and allocation of distributed energy resources, BE.EP.IC

Abstract

Microgrids are playing an ever-growing role in the conceptual evolution of the traditional electrical system into a more modern and decentralized grid. The greatest advantage enabled by microgrid is the integration of different technologies, such as: distributed renewables (solar, wind, hydrogen, biogas), energy storage (batteries, flywheels), demand management (energy efficiency, demand response), smart measuring, electrical vehicles, advanced communications systems, optimization of distributed resources, among others. Microgrids emerge with the main objective of coordinating and merging different technologies (with different levels of technical and commercial maturity) in a single platform. It is in this context that this research project is built, with the goal of developing a mathematical model of mixed-integer linear programming (MILP) for both the project and microgrid dimensioning, considering the array of available technologies of distributed energy resources and the technical and strategic definitions of the energy concessionaire. A MILP model has the following benefits: (a) a robust, generic and flexible mathematical model; (b) an efficient computational solution with conventional solvers; (c) the convergence to the optimal solution is guaranteed utilizing classical optimization techniques. The MILP model will be applied using the mathematical modeling language AMPL and using the commercial solver CPLEX.