TY - GEN
T1 - Online OPF Using Combined MOGA-ETS to Minimize Losses and Extend Battery Lifetime in Micro-Grid
AU - Sulistijono, Primaditya
AU - Soeprijanto, Adi
AU - Riawan, Dedet Candra
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/7/21
Y1 - 2021/7/21
N2 - In this paper, an Optimal Power Flow in Micro-Grid Operation is proposed. It is based on a learning algorithm combining prediction and optimization methods (Multi-objective Genetic Algorithm-Evolving Takagi-Sugeno) for implementing two objective functions i.e. minimizing losses and extending battery lifetime in online condition. This Micro-Grid operates in DC including the interest of redundancy i.e. parallel circuits for supplying loads from photovoltaic panels and batteries. The batteries use two way operations as energy generation and energy storage. It has been tested using PV power generation data and load data in a region. It is also demonstrated the comprehensive comparisons with some other learning algorithms. The results illustrate a higher online performance with optimal solution in many cases with the efficiency are higher than 97%. Moreover, reducing a high amount of CPU-time and large disk space for saving data can be achieved by the proposed approach.
AB - In this paper, an Optimal Power Flow in Micro-Grid Operation is proposed. It is based on a learning algorithm combining prediction and optimization methods (Multi-objective Genetic Algorithm-Evolving Takagi-Sugeno) for implementing two objective functions i.e. minimizing losses and extending battery lifetime in online condition. This Micro-Grid operates in DC including the interest of redundancy i.e. parallel circuits for supplying loads from photovoltaic panels and batteries. The batteries use two way operations as energy generation and energy storage. It has been tested using PV power generation data and load data in a region. It is also demonstrated the comprehensive comparisons with some other learning algorithms. The results illustrate a higher online performance with optimal solution in many cases with the efficiency are higher than 97%. Moreover, reducing a high amount of CPU-time and large disk space for saving data can be achieved by the proposed approach.
KW - evolving takagi sugeno
KW - genetic algorithm
KW - micro-grid operation
KW - multiobjective
KW - online learning
KW - optimal power flow
UR - http://www.scopus.com/inward/record.url?scp=85114635761&partnerID=8YFLogxK
U2 - 10.1109/ISITIA52817.2021.9502201
DO - 10.1109/ISITIA52817.2021.9502201
M3 - Conference contribution
AN - SCOPUS:85114635761
T3 - Proceedings - 2021 International Seminar on Intelligent Technology and Its Application: Intelligent Systems for the New Normal Era, ISITIA 2021
SP - 52
EP - 57
BT - Proceedings - 2021 International Seminar on Intelligent Technology and Its Application
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Seminar on Intelligent Technology and Its Application, ISITIA 2021
Y2 - 21 July 2021 through 22 July 2021
ER -
