Resumo:
The evolution of current electrical systems is notorious when compared in the last years.
This evolution is mainly due to the emergence of the smart-grids concept, giving increased
access to the penetration of distributed resources at the level of energy distribution. In this
context, microgrids are increasingly emerging as a potential solution to combine reliability,
quality, and sustainability in the supply of electricity. Since there is a possibility of a microgrid
operates in isolation from the main network, special attention should be given to this issue. In
this way, the MGCC plays a vital role in the survival of the system, managing all the resources
of the agents to guarantee safe operation and within the standards of supply demanded by the
regulatory agencies.
In this Ph.D. thesis, the whole theoretical basis of microgrids is presented, giving
evidence mainly to the problem of islanding regarding the variation of demand, which in many
cases, can cause a point of operation unfeasible due to the inherent variability of demand. To
correct the operation, a unified secondary voltage and frequency control strategy in the process
of power flow convergence is proposed. In addition, taking into account the limitation of the
available resources, the load shedding becomes an emergency approach, preserving the
operation limits and the continuity in the supply of electricity to essential services. Finally, an
analysis within the concept of voltage stability is also presented with the purpose of assisting
in decision making. This analysis shows the relation of the degree of system supportability
related to the converters operation mode and their generation limits, both in the connected and
islanded systems operation.
The results from the tools proposed here are validated and discussed based on the IEEE
37-Nodes Test Feeder system, when modifications are made to make it equivalent to a micro
network.
Thus, in an islanding scenario, it is expected that MGCC with the tools developed here
will be to manage all resources by predicting demand variability and before the system
experiences any non-feasible operating point, regardless of the load and penetration scenario.
renewable resources
