Hybrid analog/digital circuit for solving non-linear programming problems in real-time

Edge optimization is broadly needed to ensure the efficient, reliable, and optimal operation of a variety of energy systems, including distributed energy resources (DERs) installed at the grid edge, EV charging infrastructure, and building HVAC equipment. However, it is becoming clear that conventional digital microcontrollers (MCUs) are largely insufficient for implementing edge optimization techniques because they greatly lack the needed performance-per-watt for edge applications.

Addressing these issues, this invention is a fast and energy-efficient computational platform suitable for edge optimization of distributed energy systems. The core technology is a mixed-signal computing system composed of a digitally-assisted analog circuit that computes solutions to constrained nonlinear optimization problems in real-time. The technology is fundamentally unlike any commercial MCU currently on the market and can enable orders-of-magnitude improvements in the energy efficiency and computational speed of edge optimization techniques. A hardware prototype of the concept has demonstrated a 10X reduction in power consumption and a 200X speed improvement compared to conventional MCUs for a solar inverter edge optimization application.

The invention enables fast and efficient edge optimization, and comprises a mixed-signal computing chip programmable through an easy-to-use Python API.

Stage of Development