TY - JOUR
T1 - Coordinated control scheme for provision of frequency regulation service by virtual power plants
AU - Oshnoei, Arman
AU - Kheradmandi, Morteza
AU - Blaabjerg, Frede
AU - Hatziargyriou, Nikos
AU - Muyeen, S.M.
AU - Anvari-Moghaddam, Amjad
PY - 2022/11
Y1 - 2022/11
N2 - This paper proposes a coordinated control strategy for a Virtual Power Plant (VPP) contribution to load frequency control. The considered VPP comprises distributed Battery Energy Storage Systems (BESSs) and Heat Pump Water Heaters (HPWHs). The frequency regulation signal is distributed between thermal generator and the VPP based on distribution coefficients which are calculated through conducting a multi-objective optimization problem. The optimization framework incorporates the dynamic regulation performance as well as the total regulation cost. A fuzzy strategy is adopted to obtain the final solution according to user-defined conditions. The regulation signal of VPP is dispatched based on the speed and the available power capacity of VPP components. The performance of the proposed coordination scheme is compared to the scheme without coordination and that with no involvement of VPP in frequency regulation. The regulation performance is also evaluated for varying time delays expected in the communication channels. An approach based on brain emotional learning is developed to coordinate the VPP and conventional generation unit to avoid large frequency deviations caused by the communication delays. Case studies are conducted on a multi-area power system in MATLAB/Simulink environment, and the results are verified by the OPAL-RT real-time simulator.
AB - This paper proposes a coordinated control strategy for a Virtual Power Plant (VPP) contribution to load frequency control. The considered VPP comprises distributed Battery Energy Storage Systems (BESSs) and Heat Pump Water Heaters (HPWHs). The frequency regulation signal is distributed between thermal generator and the VPP based on distribution coefficients which are calculated through conducting a multi-objective optimization problem. The optimization framework incorporates the dynamic regulation performance as well as the total regulation cost. A fuzzy strategy is adopted to obtain the final solution according to user-defined conditions. The regulation signal of VPP is dispatched based on the speed and the available power capacity of VPP components. The performance of the proposed coordination scheme is compared to the scheme without coordination and that with no involvement of VPP in frequency regulation. The regulation performance is also evaluated for varying time delays expected in the communication channels. An approach based on brain emotional learning is developed to coordinate the VPP and conventional generation unit to avoid large frequency deviations caused by the communication delays. Case studies are conducted on a multi-area power system in MATLAB/Simulink environment, and the results are verified by the OPAL-RT real-time simulator.
KW - Battery Energy Storage System (BESS)
KW - learning algorithm
KW - Load Frequency Control (LFC)
KW - Heat pump water heater (HPWH)
KW - Virtual Power Plant (VPP)
UR - http://www.scopus.com/inward/record.url?scp=85136766918&partnerID=8YFLogxK
U2 - 10.1016/j.apenergy.2022.119734
DO - 10.1016/j.apenergy.2022.119734
M3 - Journal article
SN - 0306-2619
VL - 325
SP - 1
EP - 14
JO - Applied Energy
JF - Applied Energy
M1 - 119734
ER -