Abstract:
It is well known fact that the number of populations increase from time to time throughout the
world. It increases the energy demand. It can be balanced using replenished energy sources that
are known as renewable energy source. Due to environmentally friendly nature and unlimited
existence, they are highly applicable for generation of power. Solar and wind energy sources are
the basic types of renewable energy. To obtain better energy service and improve reliability, a
hybrid system is recommended that standalone system. The fitful nature of solar and wind energy
sources causes a power quality and sustainability problem. As a result, a continuous monitoring,
controlling, and optimization of generation system performance is required using different
software’s and algorithms. This process is known as energy management system (EMS). Basically,
the required power demand is efficiently supplied by a good management system. In this thesis, a
droop control strategy and synchronization of wind and solar hybrid microgrid EMS is designed
and presented using adaptive Neuro-fuzzy inference control system as a case study at debire birhan
referral Hospital energy distribution system. In solar energy source, an adaptive neural fuzzy
inference system (ANFIS) technique is used to attain a maximum power point tracking of
photovoltaic panels. Whereas, proportional integral (PI) controller controls the wind energy.
Moreover, a fuel cell is used as a battery for storage of charges from solar panels. The simulation
results show that an effective stability and transmission of power without any interruption is
obtained by using PSO optimized ANFIS algorithm. Finally, the effectiveness of PSO and ANFIS
on fuel cell and PV system is compared with and without PSO.As a result the PSO optimization
have good effectiveness with ANFIS controller. Hence, the required power demand is supplied
effectively with an increase of reliability to the users.
