Encryption-based coordinated volt/var control for distribution networks with multi-microgrids

• Main Authors: Sun, Xianzhuo, Qiu, Jing, Ma, Yuan, Tao, Yuechuan, Zhao, Junhua, Dong, Zhao Yang
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Electrical and electronic engineering; Convolution Neural Networks; Encrypted Communication
• Online Access: https://hdl.handle.net/10356/172720
• Institution: Nanyang Technological University

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.