Name: FELIPE ZAMBORLINI DA SILVA
Type: MSc dissertation
Publication date: 18/10/2021
Advisor:
Name |
Role |
|---|---|
| AUGUSTO CÉSAR RUEDA MEDINA | Advisor * |
Examining board:
| Name |
Role |
|---|---|
| AUGUSTO CÉSAR RUEDA MEDINA | Advisor * |
| JUSSARA FARIAS FARDIN | Internal Examiner * |
| WALBERMARK MARQUES DOS SANTOS | Internal Examiner * |
Summary: The greater yearn for less polluting and sustainable energy sources has fostered the search for electric vehicles as a mean to mitigate the pollution intrinsic to the current transport system, which is a high consumer of fossil fuels. Nonetheless, the increase in the number of electric vehicles will result on an equal increase on power demand from the distribution system. Thus, investments in renewable generation systems, implemented through distributed generation, are necessary to deal with these loads, otherwise the polluting source would only be changing. The insertion of distributed generators added to the loads of electric vehicles, which are extremely stochastic, impacts in the dynamics of the network and requires the application of optimization techniques to ensure the best use of these assets. Therefore, the present work proposes two hybrid optimization methods, the Genetic Algorithms-Interior Points method and the Grey Wolves-Interior Points method, two techniques that combine metaheuristic methods, responsible for realizing the allocation of electric vehicle charging stations and distributed generators on the grid, with a classic method, responsible for defining the economic dispatch of the generators, aiming at minimizing the system`s operational losses. Both methods proved to be effective with similar results, reducing operating cost by 13.107% and 13.113%, respectively.
Keywords: Nonlinear Programming; Genetic Algorithm; Economic Dispatch; Grey Wolf; Distributed Generation; Electrical Vehicles.
