The rising need for exploiting a novel and evolved computation is an increasing concern in the power distribution system to address the exponential growth of distribution-connected devices. Scheduling numerous battery energy storage systems in an optimal way is one of the emerging challenges that will be more noticeable as the number of batteries, including residential, community, and vehicle batteries, increases in the grid. This thesis focuses on this topic and offers a necessary component in building the quantum-compatible distribution system of the future. Using a constrained quadratic model (CQM) on D-Wave’s hybrid solver as well as a binary quadratic model (BQM), this thesis solves the optimal battery scheduling problem for a large number of batteries. To formulate the BQM, a quadratic unconstrained binary optimization (QUBO) format was chosen and in order to fine-tune the QUBO model parameters, a sensitivity analysis was conducted. Numerical simulations, using Tesla Powerwalls, demonstrate promising results of model scalability for a large number of batteries. Additionally, the trend of computational time shows a linear pattern whereas in classical solvers this is exponential.
","abstract_html":"<p>The rising need for exploiting a novel and evolved computation is an increasing concern in the power distribution system to address the exponential growth of distribution-connected devices. Scheduling numerous battery energy storage systems in an optimal way is one of the emerging challenges that will be more noticeable as the number of batteries, including residential, community, and vehicle batteries, increases in the grid. This thesis focuses on this topic and offers a necessary component in building the quantum-compatible distribution system of the future. Using a constrained quadratic model (CQM) on D-Wave’s hybrid solver as well as a binary quadratic model (BQM), this thesis solves the optimal battery scheduling problem for a large number of batteries. To formulate the BQM, a quadratic unconstrained binary optimization (QUBO) format was chosen and in order to fine-tune the QUBO model parameters, a sensitivity analysis was conducted. Numerical simulations, using Tesla Powerwalls, demonstrate promising results of model scalability for a large number of batteries. Additionally, the trend of computational time shows a linear pattern whereas in classical solvers this is exponential.</p>","abstract_has_math":false,"creators":["Ehsani, Diba"],"institution":null,"degree_name":"M.S.","degree_level":"Masters Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Amin Khodaei","Yun-Bo Yi","Mohammad Matin","Rui Fan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-03-01T08:00:00Z","date_published":"2024-03-01T08:00:00Z","updated_at":"2026-07-24T02:01:48Z","subjects":["Power distribution systems","Battery","Energy storage","Binary quadratic model (BQM)","Electrical and Computer Engineering","Energy Systems","Engineering","Power and Energy"],"languages":["English (eng)"],"rights":["Copyright is held by the author. User is responsible for all copyright compliance.
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