Quantum Computing Enhances Power Grid Modelling and Renewable Energy Control.

Researchers successfully integrated quantum hardware with real-time digital simulators to model power systems, including renewables. Adiabatic quantum algorithms accurately performed power flow and optimal power flow analysis on a standard nine-bus test system, matching classical solver performance and maintaining stability with fluctuating renewable generation.

The increasing complexity of modern power grids, driven by the integration of intermittent renewable energy sources, presents substantial computational challenges for real-time system analysis and control. Researchers are now exploring the potential of quantum and quantum-inspired computing to address these demands. A collaborative team, comprising Zeynab Kaseb, Rahul Rane, Aleksandra Leki´c, Matthias Möller, Amin Khodaei, Peter Palensky, Pedro P. Vergara, and representatives from D-Wave Systems, detail their work in the paper “Quantum Hardware-in-the-Loop for Optimal Power Flow in Renewable-Integrated Power Systems”. They present a proof-of-concept integrating quantum hardware with a real-time digital simulator to model and control power systems, demonstrating the accurate performance of adiabatic quantum power flow and optimal power flow algorithms on both CMOS digital annealers and quantum processors using a standard nine-bus test system augmented with renewable generation.

Quantum Computation Advances Power System Modelling

The integration of quantum hardware with real-time digital simulators (RTDS) is facilitating advances in power system modelling and control, particularly for contemporary grids incorporating increasing levels of renewable energy. Researchers have validated both adiabatic quantum power flow (AQPF) and adiabatic quantum optimal power flow (AQOPF) algorithms on both conventional CMOS Digital Annealers – specialised hardware designed to solve optimisation problems – and nascent quantum processors. These validations demonstrate the capability of these algorithms to perform power flow (PF) and optimal power flow (OPF) calculations.

Power flow studies determine the electrical state of a power system for a given load and generation profile, while optimal power flow aims to find the most efficient and cost-effective operating point, considering system constraints. The researchers confirmed the algorithms’ accuracy using the standard IEEE 9-bus test system, a benchmark for power system analysis, and a modified version incorporating solar and wind farms. Results achieved convergence – a solution was found – and closely matched those obtained from conventional Newton-Raphson (NR) solvers, the industry standard for these calculations.

Classical optimisation techniques, including linear and quadratic programming, traditionally underpin power system analysis. However, these methods encounter limitations when applied to increasingly complex, large-scale systems. Prior research has highlighted the inherent non-convexities within OPF problems – meaning the solution landscape contains many local minima – posing significant computational challenges. The presented quantum-inspired approach offers a potential pathway to address these complexities.

This work builds upon a growing body of research exploring machine learning applications to OPF, including physics-informed neural networks and graph neural networks. However, the integration of quantum computing represents a novel approach to enhance both the accuracy and efficiency of power system modelling and control.

Results indicate that AQPF and AQOPF accurately determine power flow and optimise system performance, while also exhibiting robust convergence characteristics. The investigation confirms the effectiveness of incorporating renewable energy sources within the AQOPF framework, maintaining system stability and performance even under fluctuating generation conditions, a critical requirement for grids with high renewable penetration.

The study confirms the efficacy of AQOPF in maintaining system stability and performance when integrating variable renewable energy generation. This is a particularly significant achievement given the increasing prevalence of intermittent sources like wind and solar in modern power grids. Researchers demonstrate the potential for quantum algorithms to deliver accurate solutions, even for complex, non-convex optimisation problems inherent in optimal power flow.