Bridging the Gap to Next Generation Power System Planning and Operation with Quantum Computation
<|reference_start|>Bridging the Gap to Next Generation Power System Planning and Operation with Quantum Computation: Innovative solutions and developments are being inspected to tackle rising electrical power demand to be supplied by clean forms of energy. The integration of renewable energy generations, varying nature loads, importance of active role of distribution system and consumer participation in grid operation has changed the landscape of classical power grids. Implementation of smarter applications to plan, monitor, operate the grid safely are deemed paramount for efficient, secure and reliable functioning of grid. Although sophisticated computations to process gigantic volume of data to produce useful information in a time critical manner is the paradigm of future grid operations, it brings along the burden of computational complexity. Advancements in quantum technologies holds promising solution for dealing with demanding computational complexity of power system related applications. In this article, we lay out clear motivations for seeking quantum solutions for solving computational burden challenges associated with power system applications. Next we present an overview of quantum solutions for various power system related applications available in current literature and suggest future topics for research. We further highlight challenges with existing quantum solutions for exploiting full quantum capabilities. Additionally, this paper serves as a bridge for power engineers to the quantum world by outlining essential quantum computation fundamentals for enabling smoother transition to future of power system computations.<|reference_end|>
@article{ganeshamurthy2024bridging,
    title={Bridging the Gap to Next Generation Power System Planning and Operation
  with Quantum Computation},
    author={Priyanka Arkalgud Ganeshamurthy, Kumar Ghosh, Corey O'Meara, Giorgio
  Cortiana, Jan Schiefelbein-Lach},
    journal={arXiv preprint arXiv:2408.02432},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02432},
    primaryClass={quant-ph cs.SY eess.SY}
}