  This paper proposes a cyber-physical architecture for the secured social
operation of isolated hybrid microgrids (HMGs). On the physical side of the
proposed architecture, an optimal scheduling scheme considering various
renewable energy sources (RESs) and fossil fuel-based distributed generation
units (DGs) is proposed. Regarding the cyber layer of MGs, a wireless
architecture based on low range wide area (LORA) technology is introduced for
advanced metering infrastructure (AMI) in smart electricity grids. In the
proposed architecture, the LORA data frame is described in detail and designed
for the application of smart meters considering DGs and ac-dc converters.
Additionally, since the cyber layer of smart grids is highly vulnerable to
cyber-attacks, t1his paper proposes a deep-learning-based cyber-attack
detection model (CADM) based on bidirectional long short-term memory (BLSTM)
and sequential hypothesis testing (SHT) to detect false data injection attacks
(FDIA) on the smart meters within AMI. The performance of the proposed energy
management architecture is evaluated using the IEEE 33-bus test system. In
order to investigate the effect of FDIA on the isolated HMGs and highlight the
interactions between the cyber layer and physical layer, an FDIA is launched
against the test system. The results showed that a successful attack can highly
damage the system and cause widespread load shedding. Also, the performance of
the proposed CADM is examined using a real-world dataset. Results prove the
effectiveness of the proposed CADM in detecting the attacks using only two
samples.
