New Strategies for Power Companies in Brazil

Tolmasquim, Maurício Tiommo; Pires, Jose Claudio Linhares; Rosa, Luis Pinguelli

doi:10.1016/b978-008043631-9/50010-7

Cited by 2 publications

(1 citation statement)

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“…The radial configuration of these networks gives rise to large energy losses values, which are 3-9-times the percentage of the energy losses present in large-scale transmission networks [6]. These losses are mostly transferred to the end user via an electricity fee [7]; however, most electricity service companies are currently interested in buying green energy on the spot market to help with global warming objectives and make their grids more efficient and sustainable [8].…”

Section: Introductionmentioning

confidence: 99%

Optimal Placement and Sizing of PV Sources in Distribution Grids Using a Modified Gradient-Based Metaheuristic Optimizer

2022

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The problem of the optimal placement and sizing of renewable generation sources based on photovoltaic (PV) technology in electrical distribution grids operated in medium-voltage levels was studied in this research. This optimization problem is from the mixed-integer nonlinear programming (MINLP) model family. Solving this model was achieved by implementing a master–slave optimization approach, where the master–slave corresponded to the application of the modified gradient-based metaheuristic optimizer (MGbMO) and the slave stage corresponded to the application of the successive approximation power flow method. In the master stage, the problem of the optimal placement and sizing of the PV sources was solved using a discrete–continuous codification, while the slave stage was used to calculate the objective function value regarding the energy purchasing costs in terminals of the substation, as well as to verify that the voltage profiles and the power generations were within their allowed bounds. The numerical results of the proposed MGbMO in the IEEE 34-bus system demonstrated its efficiency when compared with different metaheuristic optimizers such as the Chu and Beasley genetic algorithm, the Newton metaheuristic algorithm, the original gradient-based metaheuristic optimizer, and the exact solution of the MINLP model using the general algebraic modeling system. In addition, the possibility of including meshed distribution topologies was tested with excellent numerical results.

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Section: Introductionmentioning

confidence: 99%