Energy Management and Voltage Control in Microgrids Using Artificial Neural Networks, PID, and Fuzzy Logic Controllers
Abstract
:1. Introduction
 Voltage and frequency profiles are regulated in both islanded and networked operation modes;
 Grid synchronization with the main grid;
 MG’s entire energy management system is optimized;
 As much as feasible, cut the MG’s operational costs.
2. Overview of Local Control in Microgrids
2.1. Proportional–Integral–Derivative (PID) Technique
2.2. Neural Networks for Microgrid Control
2.3. Fuzzy Logic in Power Systems
3. Microgrid Modeling
3.1. Photovoltaic System
3.2. Wind Turbine System
3.3. Battery Energy Storage Systems (BESSs)
3.4. Energy Management System (EMS)
 To extract the greatest power from the photovoltaic solar system, it is connected to the DC bus via a DC/DC converter controlled by an MPPT block;
 To draw the most power from a wind turbine, multiple converters and an MPPT block are used to link it to the DC bus;
 The battery system is connected to the microgrid via BDC, and several controllers are used to operate it (PID, ANNC, and FL);
 The main grid, connected to the DC bus via an AC/DC converter, will only be used in an emergency (when renewable energy is insufficient), and the battery state of charge is less than 20%.
 To supply the energy demand;
 To increase the renewable energy produced;
 To maintain the microgrid bus’s balance, keep the DC bus’s voltage constant at 300 V and the AC bus’s frequency constant at 50 Hz;
 To avoid overcharging or discharging of the battery.
4. Control Design for the Microgrid
4.1. PID Controller Design
4.2. Fuzzy Logic Controller Design
4.3. ANN Controller Design
5. Simulation and Results
6. Conclusions
 Hardware implementation of the three methods (PID, ANN, and FL) to validate the performance in experimental processes;
 The integration of energy optimization (electricity, price, and CO_{2} emissions) in global management of the energy management system.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ANN  Artificial neural network 
BESS  Battery energy storage system 
CCP  Common coupling point 
CC  Central controller 
DG  Distributed generation 
ESS  Energy storage system 
EMS  Energy management system 
FL  Fuzzy logic 
LC  Local controller 
MG  Microgrid 
MPPT  Maximum power point tracking 
RES  Renewable energy resource 
RBFN  Radial basis function network 
PID  Proportionalintegralderivative 
PV  Photovoltaic 
PWM  Pulsewidth modulation 
SOC  State of charge 
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Techniques  Advantages  Disadvantages 

PID Controller 








Parameters  Values 

Maximum power  250 W 
Maximum power voltage  42.8 V 
Maximum power current  5.84 A 
Open circuit voltage  50.93 V 
Current courtcircuit  6.2 A 
Cellule numbers  72 
Temperature coefficient of opencircuit voltage  −0.29103%/°C 
Temperature Coefficient of current courtcircuit  0.013306%/°C 
Shunt resistance  448.6949 ohms 
Series resistance  0.37759 
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Al Sumarmad, K.A.; Sulaiman, N.; Wahab, N.I.A.; Hizam, H. Energy Management and Voltage Control in Microgrids Using Artificial Neural Networks, PID, and Fuzzy Logic Controllers. Energies 2022, 15, 303. https://doi.org/10.3390/en15010303
Al Sumarmad KA, Sulaiman N, Wahab NIA, Hizam H. Energy Management and Voltage Control in Microgrids Using Artificial Neural Networks, PID, and Fuzzy Logic Controllers. Energies. 2022; 15(1):303. https://doi.org/10.3390/en15010303
Chicago/Turabian StyleAl Sumarmad, Khaizaran Abdulhussein, Nasri Sulaiman, Noor Izzri Abdul Wahab, and Hashim Hizam. 2022. "Energy Management and Voltage Control in Microgrids Using Artificial Neural Networks, PID, and Fuzzy Logic Controllers" Energies 15, no. 1: 303. https://doi.org/10.3390/en15010303