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arXiv:2003.00254 · Energy

Quantum computing for energy systems optimization: Challenges and opportunities

Akshay Ajagekar, Fengqi You

Published May 2, 2019• 290 citations
arXivHTMLPDF
DOI
10.1016/j.energy.2019.04.186

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Developing high-performance materials is critical for diverse energy applications to increase efficiency, improve sustainability and reduce costs. Classical computational methods have enabled important breakthroughs in energy materials development, but they face scaling and time-complexity limitations, particularly for high-dimensional or strongly correlated material systems. Quantum computing (QC) promises to offer a paradigm shift by exploiting quantum bits with their superposition and entanglement to address challenging problems intractable for classical approaches. This perspective discusses the opportunities in leveraging QC to advance energy materials research and the challenges QC faces in solving complex and high-dimensional problems. We present cases on how QC, when combined with classical computing methods, can be used for the design and simulation of practical energy materials. We also outline the outlook for error-corrected, fault-tolerant QC capable of achieving predictive accuracy and quantum advantage for complex material systems.
Optimized power system planning and operation are core to delivering a low-cost and high-reliability transition path to net-zero carbon emissions. The major technological changes associated with net zero, including the rapid adoption of renewables, electrification of transport and heating, and system-wide digitalization, each increase the scope for optimization to create value, but at the cost of greater computational complexity. Although power system optimization problems are now posing challenges for even the largest exa-scale supercomputers, a new avenue for progress has been opened by recent breakthroughs in quantum computing. Quantum computing offers a fundamentally new computational infrastructure with different capabilities and trade-offs and is reaching a level of maturity where, for the first time, a practical advantage over classical computing is available for specific applications. In this review, we identify significant and wide-ranging opportunities for quantum computing to offer value for power system optimization. In addition to reviewing the latest work on quantum computing for simulation-based and combinatorial power system optimization applications, we also review state-of-the-art theoretical work on quantum convex optimization and machine learning and map this to power system optimization applications where quantum computing is underexplored. Based on our review, we analyze challenges for industry implementation and scale-up and propose directions for future research.
The pursuit of energy transition necessitates the coordination of several technologies, including more efficient and cost-effective distributed energy resources, smart grids, carbon capture, utilization, and storage, energy-efficient technologies, Internet of Things, edge computing, artificial intelligence (AI), and nuclear energy, among others. Quantum computing is an emerging paradigm for information processing at both hardware and software levels by exploiting quantum mechanical properties to solve certain computational tasks exponentially faster than classical computers. This chapter will explore the opportunities and challenges of using quantum computing for energy management applications, enabling the more efficient and economically optimal integration of DERs such as solar photovoltaic rooftops, energy storage systems, electric vehicles (EVs), and EV charging stations into the grid.