Spike Talk: Genesis and Neural Coding Scheme Translations

TL;DR

This paper introduces Spike Talk, a novel neural-inspired architecture for microgrid control that uses spike-based data normalization to improve coordination, reduce reliance on ICT, and enhance system performance.

Contribution

It presents a new decentralized control scheme inspired by neuroscience, translating power and information into spikes for efficient microgrid management.

Findings

Spike Talk simplifies cyber-physical architecture by removing ICT layer.

The scheme enables decentralized control among DERs using power flow interactions.

Performance varies with different neural coding schemes for spike generation.

Abstract

Although digitalization of future power grids offer several coordination incentives, the reliability and security of information and communication technologies (ICT) hinders its overall performance. In this paper, we introduce a novel architecture Spike Talk via a unified representation of power and information as a means of data normalization using spikes for coordinated control of microgrids. This grid-edge technology allows each distributed energy resource (DER) to execute decentralized secondary control philosophy independently by interacting among each other using power flow along the tie-lines. Inspired from the field of computational neuroscience, Spike Talk basically builds on a fine-grained parallelism on the information transfer theory in our brains, particularly when neurons (modeled as DERs) transmit information (inferred from power streams measurable at each DER) through synapses (modeled as tie-lines). Not only does Spike Talk simplify and address the current bottlenecks of the cyber-physical architectural operation by dismissing the ICT layer, it provides intrinsic operational and cost-effective opportunities in terms of infrastructure development, computations and modeling. Hence, this paper provides a pedagogic illustration of the key concepts and design theories. Since we focus on coordinated control of microgrids in this paper, the signaling accuracy and system performance is studied for several neural coding schemes responsible for converting the real-valued local measurements into spikes.