TY - JOUR
T1 - Encryption-based Coordinated Volt/Var Control for Distribution Networks With Multi-Microgrids
AU - Sun, Xianzhuo
AU - Qiu, Jing
AU - Ma, Yuan
AU - Tao, Yuechuan
AU - Zhao, Junhua
AU - Dong, Zhaoyang
PY - 2023/11
Y1 - 2023/11
N2 - This paper proposes a data-driven coordinated volt/var control (VVC) strategy for active distribution networks (ADN) with multi-microgrids, which can achieve online economic and secure operations under false data injection attacks (FDIA). Based on voltage and power measurements, the microgrid central controller (MGCC) obtains optimal reactive power supports at the point of common coupling (PCC) and sends them to the distribution system operator (DSO). The MGCC is formulated with a convolution neural network (CNN) to emulate the optimal behaviors in microgrids (MGs), which can reduce computational burdens and facilitate its online application. The DSO then performs centralized optimization to dispatch VVC devices and update voltages at PCC. A voltage sensitivity-based reactive power adjustment method is also developed to simplify the iterative optimization process between ADN and MGs without deteriorating the VVC performance in each MG. Finally, data integrity and privacy are protected through an encrypted communication process against FDIA. The GGH (Goldreich-Goldwasser-Halevi) encryption algorithm directly prevents attackers from accessing the original transmitted data, while the RSA (Rivest-Shamir-Adleman) digital signature algorithm helps detect malicious tampering with the ciphertext during communication. Numerical simulations on a modified IEEE 33-bus ADN with three EU 16-bus MGs verify the effectiveness of the proposed method in mitigating voltage violations, reducing voltage regulation costs and protecting data security. © 2022 IEEE.
AB - This paper proposes a data-driven coordinated volt/var control (VVC) strategy for active distribution networks (ADN) with multi-microgrids, which can achieve online economic and secure operations under false data injection attacks (FDIA). Based on voltage and power measurements, the microgrid central controller (MGCC) obtains optimal reactive power supports at the point of common coupling (PCC) and sends them to the distribution system operator (DSO). The MGCC is formulated with a convolution neural network (CNN) to emulate the optimal behaviors in microgrids (MGs), which can reduce computational burdens and facilitate its online application. The DSO then performs centralized optimization to dispatch VVC devices and update voltages at PCC. A voltage sensitivity-based reactive power adjustment method is also developed to simplify the iterative optimization process between ADN and MGs without deteriorating the VVC performance in each MG. Finally, data integrity and privacy are protected through an encrypted communication process against FDIA. The GGH (Goldreich-Goldwasser-Halevi) encryption algorithm directly prevents attackers from accessing the original transmitted data, while the RSA (Rivest-Shamir-Adleman) digital signature algorithm helps detect malicious tampering with the ciphertext during communication. Numerical simulations on a modified IEEE 33-bus ADN with three EU 16-bus MGs verify the effectiveness of the proposed method in mitigating voltage violations, reducing voltage regulation costs and protecting data security. © 2022 IEEE.
KW - Convolution neural networks
KW - coordinated volt/var control
KW - cyber security
KW - encrypted communication
KW - multi-microgrids
UR - http://www.scopus.com/inward/record.url?scp=85146245636&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85146245636&origin=recordpage
U2 - 10.1109/TPWRS.2022.3230363
DO - 10.1109/TPWRS.2022.3230363
M3 - RGC 21 - Publication in refereed journal
SN - 0885-8950
VL - 38
SP - 5909
EP - 5921
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
IS - 6
ER -