Investigating The Impact of Renewable Energy Sources on Networked Microgrids Using Probabilistic Load Flow
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Keywords:
Microgrids (MG), Networked Microgrids (Mgs), Monte Carlo Simulation (MCS), Probabilistic Power Flow (PPF), Photovoltaic (PV), Wind Turbine (WT)Abstract
Efforts to reduce the environmental impact of fossil fuels have led to the proliferation of
renewable energy sources in the power grid. As a result of the increase in renewable energy sources such
as solar and wind, uncertainties in the grid have increased. It is not possible to observe these increasing
uncertainties with deterministic methods. Therefore, it is necessary to observe the output values for different
input conditions using stochastic methods. Probabilistic power flow (PPF) was used as a stochastic method
in our study. We performed the study on a micro-grid (MG) test system with Monte Carlo Simulation
(MCS), which gives more accurate results than other methods. This method records the results by
performing the deterministic power flow repeatedly with high repetition numbers. With the increasing
processing power of computers, Monte Carlo simulations can be performed much faster. The test system
used utilizes multiple microgrids (MGs). There are no studies in the literature using MGs of this complexity.
In the test system used, four microgrids are connected to each other. In the results obtained, we can see the
total losses region by region. Only the lines between the microgrids are approaching their limits. The aim
of this study was to produce a program that can perform probabilistic power flow analysis in MGs. By
making improvements in the developed program, probabilistic load flow was performed in a very high
busbar system in a short time.
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