TY - JOUR
T1 - Load Frequency Control in Microgrids Based on a Stochastic Non-Integer Controller
AU - Khooban, Mohammad Hassan
AU - Niknam, Taher
AU - ShaSadeghi, Mokhtar
AU - Dragicevic, Tomislav
AU - Blaabjerg, Frede
PY - 2018
Y1 - 2018
N2 - In this paper, an adaptive multi-objective Fractional-Order Fuzzy proportional-integral-derivative (MOFOFPID) controller is proposed for the load frequency control (LFC) of islanded Microgrids (MGs), while benefiting from the assets of electric vehicles (EVs) in this respect. Although the use of battery energy storage systems (BESS) can solve the unbalance effects between the load and supply of an isolated MG, their high cost and tendency toward degradation are restrictive factors, which call for the use of alternative power balancing options. In recent years, the concept of utilizing the BESSs of EVs, also known as vehicle-to-grid (V2G) concept, for frequency support of MGs has attracted a lot of attention. In order to allow the V2G controller operate optimally under a wide range of operation conditions caused by the intermittent behavior of renewable energy resources (RESs), a new multi-objective fractional-order control strategy for the EVs in V2G scenarios is proposed in this paper. Moreover, since the performance of the controller depends on its parameters, optimization of these parameters can play a significant role in promoting the output performance of the LFC control; hence, a modified black hole optimization algorithm (MBHA) is utilized for the adaptive tuning of the non-integer fuzzy PID controller coefficients. The performance of the proposed LFC is evaluated by using real world wind and solar radiation data. Finally, the extensive studies and hardware-in-the-loop (HIL) simulations are presented to prove that the proposed controller tracks frequency with lower deviation and fluctuation and is more robust in comparison with the prior-art controllers used in all the case studies.
AB - In this paper, an adaptive multi-objective Fractional-Order Fuzzy proportional-integral-derivative (MOFOFPID) controller is proposed for the load frequency control (LFC) of islanded Microgrids (MGs), while benefiting from the assets of electric vehicles (EVs) in this respect. Although the use of battery energy storage systems (BESS) can solve the unbalance effects between the load and supply of an isolated MG, their high cost and tendency toward degradation are restrictive factors, which call for the use of alternative power balancing options. In recent years, the concept of utilizing the BESSs of EVs, also known as vehicle-to-grid (V2G) concept, for frequency support of MGs has attracted a lot of attention. In order to allow the V2G controller operate optimally under a wide range of operation conditions caused by the intermittent behavior of renewable energy resources (RESs), a new multi-objective fractional-order control strategy for the EVs in V2G scenarios is proposed in this paper. Moreover, since the performance of the controller depends on its parameters, optimization of these parameters can play a significant role in promoting the output performance of the LFC control; hence, a modified black hole optimization algorithm (MBHA) is utilized for the adaptive tuning of the non-integer fuzzy PID controller coefficients. The performance of the proposed LFC is evaluated by using real world wind and solar radiation data. Finally, the extensive studies and hardware-in-the-loop (HIL) simulations are presented to prove that the proposed controller tracks frequency with lower deviation and fluctuation and is more robust in comparison with the prior-art controllers used in all the case studies.
KW - Load frequency control (LFC), modified black hole algorithm (MBHA), microgrid (MG), fractional controller, electric-vehicle (EV).
KW - electric-vehicle (EV)
KW - Load frequency control (LFC)
KW - modified black hole algorithm (MBHA)
KW - fractional controller
KW - microgrid (MG)
UR - http://www.scopus.com/inward/record.url?scp=85044518669&partnerID=8YFLogxK
U2 - 10.1109/TSTE.2017.2763607
DO - 10.1109/TSTE.2017.2763607
M3 - Journal article
SN - 1949-3029
VL - 9
SP - 853
EP - 861
JO - I E E E Transactions on Sustainable Energy
JF - I E E E Transactions on Sustainable Energy
IS - 2
ER -