Voltage control in microgrids with minimum adjustment in distributed generation units
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New challenges are emerging with the insertion of renewable energy sources into Brazilian electricity grids. An example of this is the integration of distributed generation in the distribution networks, causing changes in the management and mode of operation of the system. The increase in the complexity and uncertainties resulting from this new trend in the electric sector results in a series of impacts, with emphasis on the occurrence of inadequate voltage levels and constant voltage variations, which can often violate the constructive limits of the equipment, impairing normal operation of the system. Thus, analyzes of different operating scenarios are necessary aiming to develop control strategies for answer to this new paradigm. This paper presents an optimization method to automatically coordinate voltage regulation resources in a microgrid considering the minimum adjustment in distributed generation units, contributing to the microgrids operation in real time. The implementation of Python logic and computational intelligence application using the PSO, Particle Swarm Optimization, technique will allow the evaluation of input data and, within system constraints and user configurations, control the energy utilization, contributing to voltage control of microgrids. ...learn more
Project status: Published/In Market
Intel Technologies
Other
Overview / Usage
The problem to be solved is the significant penetration of generation distributed in the distribution network, especially photovoltaic systems, which has been causing violations of the voltage levels allowed by standards. This work aims to solve this problem through the application of optimization techniques, being a proposal of voltage control methodology for real time operation.
Methodology / Approach
In this work the PSO (Particle Swarm Optimization) computational intelligence technique is applied, aiming the analysis of voltage control with single objective and MOPSO for the multiobjective analysis of the problem.
Technologies Used
In this project it was used to compare processing time the Intel Distribution for Python.