  With the proliferation of distributed energy resources (DERs) in the
distribution grid, it is a challenge to effectively control a large number of
DERs resilient to the communication and security disruptions, as well as to
provide the online grid services, such as voltage regulation and virtual power
plant (VPP) dispatch. To this end, a hybrid feedback-based optimization
algorithm along with deep learning forecasting technique is proposed to
specifically address the cyber-related issues. The online decentralized
feedback-based DER optimization control requires timely, accurate voltage
measurement from the grid. However, in practice such information may not be
received by the control center or even be corrupted. Therefore, the long
short-term memory (LSTM) deep learning algorithm is employed to forecast
delayed/missed/attacked messages with high accuracy. The IEEE 37-node feeder
with high penetration of PV systems is used to validate the efficiency of the
proposed hybrid algorithm. The results show that 1) the LSTM-forecasted lost
voltage can effectively improve the performance of the DER control algorithm in
the practical cyber-physical architecture; and 2) the LSTM forecasting strategy
outperforms other strategies of using previous message and skipping dual
parameter update.

Systems and Control;Systems and Control