Quantum Optimisation for Sustainable Energy Applications

The increasing adoption of renewable energy systems and electric vehicles (EVs) is creating new challenges for modern electricity grids. One of the most significant challenges is the “duck curve” phenomenon, where excess solar energy generation during the daytime is followed by steep increases in electricity demand during the evening. Managing this imbalance efficiently is becoming increasingly important for future sustainable energy systems.

This project explores how quantum optimisation algorithms may provide an alternative avenue to traditional classical optimisation approaches for solving future energy scheduling problems. Rather than focusing on large-scale real-world deployment, this short research project will investigate small-scale simulated optimisation problems to understand how hybrid quantum-classical approaches can be applied to smart energy coordination challenges.

The project is intentionally designed with a realistic and achievable scope suitable for a 4-week Winter Scholarship program. Students will work with simplified energy scheduling scenarios involving electric vehicle charging and household energy demand simulations. The goal is not to outperform classical systems, but rather to explore how emerging quantum optimisation methods can model and solve these problems differently compared to conventional techniques.

Students will gain introductory exposure to:

1. Quantum computing fundamentals
2. Quantum optimisation concepts
3. Human-Centred Computing approaches for sustainable technologies
4. Python programming and simulation tools
5. Qiskit quantum development framework
6. Energy scheduling and optimisation problems

The project also includes a Human-Centred Computing (HCC) perspective by considering how future intelligent energy systems should remain understandable, explainable, and user-friendly for communities and households. Students will explore how optimisation decisions can balance technical efficiency with human preferences and usability considerations.

Over the 4-week scholarship, students will:

1. Learn the fundamentals of quantum computing and quantum optimisation
2. Explore the duck curve problem and its impact on renewable energy systems
3. Develop a small-scale EV charging optimisation model
4. Implement a simple classical optimisation approach
5. Implement a small hybrid quantum optimisation example using Qiskit
6. Compare the behaviour of classical and quantum-inspired optimisation methods
7. Visualise energy scheduling outcomes using Python-based simulations
8. Produce a short research-style report and presentation summarising findings

The practical scope of the project is intentionally lightweight and simulation-based to ensure completion within the scholarship duration. The focus is on learning, experimentation, and exploring emerging computational approaches rather than building production-ready systems.

This project provides students with hands-on exposure to cutting-edge emerging technologies while addressing an important sustainability challenge. It introduces students to interdisciplinary research at the intersection of quantum computing, optimisation, sustainability, and human-centred system design.