Quantum Hardware-in-the-Loop for Optimal Power Flow in Renewable-Integrated Power Systems

TL;DR

This paper demonstrates the integration of quantum hardware with real-time simulation for power flow and optimal power flow analysis in renewable-rich power systems, showing promising accuracy and stability improvements.

Contribution

It introduces adiabatic quantum algorithms for power flow and optimal power flow, validated on standard test systems with renewable sources, advancing quantum applications in power system optimization.

Findings

Quantum algorithms closely match classical solutions.

Robust convergence under variable renewable generation.

Effective integration of renewable sources in quantum-based optimization.

Abstract

This paper presents a proof-of-concept for integrating quantum hardware with real-time digital simulator (RTDS) to model and control modern power systems, including renewable energy resources. Power flow (PF) analysis and optimal power flow (OPF) studies are conducted using RTDS coupled with Fujitsu's CMOS Digital Annealer and D-Wave's Advantage quantum processors. The adiabatic quantum power flow (AQPF) and adiabatic quantum optimal power flow (AQOPF) algorithms are used to perform PF and OPF, respectively, on quantum and quantum-inspired hardware. The experiments are performed on the IEEE 9-bus test system and a modified version that includes solar and wind farms. The findings demonstrate that the AQPF and AQOPF algorithms can accurately perform PF and OPF, yielding results that closely match those of classical Newton-Raphson (NR) solvers while also exhibiting robust convergence. Furthermore, the integration of renewable energy sources (RES) within the AQOPF framework proves effective in maintaining system stability and performance, even under variable generation conditions. These findings highlight the potential of quantum computing to significantly enhance the modeling and control of future power grids, particularly in systems with high renewable energy penetration.