Degree

Doctor of Philosophy (PhD)

Department

Electrical and Computer Engineering

Document Type

Dissertation

Abstract

The reliable and efficient operation of power systems is crucial for the proper functioning of modern society. However, the large and complex nature of power systems presents significant computational challenges for their operation and planning. Traditional computational techniques are insufficient in addressing these complexities, and novel approaches are necessary to enhance the efficiency and effectiveness of power system operations and planning. Although quantum computing algorithms have shown the potential in completing significant computing tasks faster than traditional computers, their application to power systems remains largely unexplored. This dissertation aims to investigate advanced solution techniques for power system optimization problems by proposing and implementing hybrid distributed-quantum computing-based optimization approaches. To address the current limitations of noisy quantum computers and their practical application, a multi-block alternating direction method of multiplier (ADMM) is proposed for solving mixed-integer linear programming (MILP) problems. The proposed algorithm is applied to a large-scale transmission expansion planning problem to demonstrate its performance. Furthermore, the quantum approximate optimization algorithm (QAOA) is integrated into the ADMM algorithm as a hybrid quantum-classical method for solving the MILP problem. The effectiveness of this approach is demonstrated through simulations and numerical experiments on a generation scheduling problem. To reduce the quantum-classical iteration of the QAOA algorithm, a recurrent neural network is designed to estimate the variational parameters of the quantum circuit with an appropriate sampling technique. The simulation results exhibit the effectiveness of the proposed model.

Date

8-22-2024

Committee Chair

Amin Kargarian

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Available for download on Sunday, August 22, 2027

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