Decoding the `Nature Encoded' Messages for Distributed Energy Generation Control in Microgrid

TL;DR

This paper proposes novel decoding methods for real-time microgrid energy control messages by leveraging system dynamics as a form of 'nature encoding', enhancing reliability without heavy channel coding.

Contribution

It introduces two decoding schemes based on Kalman filtering and Belief Propagation that utilize system dynamics as a form of natural redundancy for reliable message decoding.

Findings

Kalman filtering-based decoding improves message reliability.

Belief Propagation-based decoding enhances robustness in microgrid control.

Numerical simulations validate the effectiveness of the proposed schemes.

Abstract

The communication for the control of distributed energy generation (DEG) in microgrid is discussed. Due to the requirement of realtime transmission, weak or no explicit channel coding is used for the message of system state. To protect the reliability of the uncoded or weakly encoded messages, the system dynamics are considered as a `nature encoding' similar to convolution code, due to its redundancy in time. For systems with or without explicit channel coding, two decoding procedures based on Kalman filtering and Pearl's Belief Propagation, in a similar manner to Turbo processing in traditional data communication systems, are proposed. Numerical simulations have demonstrated the validity of the schemes, using a linear model of electric generator dynamic system.