TY - JOUR
T1 - Two-Stage experimental intelligent dynamic energy management of microgrid in smart cities based on demand response programs and energy storage system participation
AU - Sepehrzad, Reza
AU - Hedayatnia, Atefeh
AU - Amohadi, Mahdi
AU - Ghafourian, Javid
AU - Al-Durra, Ahmed
AU - Anvari-Moghaddam, Amjad
N1 - Publisher Copyright:
© 2023 The Author(s)
PY - 2024/1
Y1 - 2024/1
N2 - Power variations due to uncertainties create fluctuations in voltage/frequency (V/F). Most critical microgrid's (MG) challenge in smart cities is V/F stability considering uncertainties in different operating conditions. This study proposes an energy management platform based on an intelligent probabilistic wavelet petri neuro-fuzzy inference algorithm (IPWPNFIA) to control the V/F index in the presence of renewable energy sources (RESs) and battery energy storage system (BESS) facing with various uncertainties. The suggested approach is programmed at two central and local controller stages based on the communication system and time-of-use demand response programs execution. The suggested approach is modeled by considering asymmetric membership functions based on the BESS optimal participation to control uncertainties caused by RESs, plug-and-play operations, and load fluctuations. The proposed platform's performance is verified and compared in different scenarios with different methods. The experimental setup and results are based on the rapid control prototyping of the micro-grid platform, MATLAB/Simulink and RT-LAB software, and hardware infrastructure such as the OPAL-RT (OP5600/OP8660) System. The most important highlights of this research are: fast dynamic response, real-time control based on real data, reducing the calculation time and burden based on learning algorithms, and the suitable coordination to adjust the protection equipment pick-up time.
AB - Power variations due to uncertainties create fluctuations in voltage/frequency (V/F). Most critical microgrid's (MG) challenge in smart cities is V/F stability considering uncertainties in different operating conditions. This study proposes an energy management platform based on an intelligent probabilistic wavelet petri neuro-fuzzy inference algorithm (IPWPNFIA) to control the V/F index in the presence of renewable energy sources (RESs) and battery energy storage system (BESS) facing with various uncertainties. The suggested approach is programmed at two central and local controller stages based on the communication system and time-of-use demand response programs execution. The suggested approach is modeled by considering asymmetric membership functions based on the BESS optimal participation to control uncertainties caused by RESs, plug-and-play operations, and load fluctuations. The proposed platform's performance is verified and compared in different scenarios with different methods. The experimental setup and results are based on the rapid control prototyping of the micro-grid platform, MATLAB/Simulink and RT-LAB software, and hardware infrastructure such as the OPAL-RT (OP5600/OP8660) System. The most important highlights of this research are: fast dynamic response, real-time control based on real data, reducing the calculation time and burden based on learning algorithms, and the suitable coordination to adjust the protection equipment pick-up time.
KW - Demand Response Program
KW - Microgrid
KW - Power and Energy Management
KW - Smart Cities
KW - Voltage/Frequency Control
UR - http://www.scopus.com/inward/record.url?scp=85176346838&partnerID=8YFLogxK
U2 - 10.1016/j.ijepes.2023.109613
DO - 10.1016/j.ijepes.2023.109613
M3 - Article
AN - SCOPUS:85176346838
SN - 0142-0615
VL - 155
JO - International Journal of Electrical Power and Energy Systems
JF - International Journal of Electrical Power and Energy Systems
M1 - 109613
ER -
