A quantum-classical hybrid framework for optimal energy storage systems planning

Area of Science:

• Electrical Engineering
• Quantum Computing
• Optimization

Background:

• Power-electronic deployments cause grid variability, impacting voltage quality and reliability.
• Energy Storage Systems (ESS) can mitigate variability but require coordinated siting and sizing.
• Current optimization methods for ESS planning are computationally intensive and may not yield optimal solutions.

Purpose of the Study:

• To develop a novel two-stage hybrid quantum-classical framework for ESS planning.
• To address computational challenges in large-scale mixed-integer optimization problems.
• To improve the efficiency and optimality of ESS siting and sizing.

Main Methods:

• Stage I: Reformulates ESS siting as a Quadratic Unconstrained Binary Optimization (QUBO) problem solved using a hybrid quantum workflow.
• Utilizes stochastic sampling as a 'quantum sieve' to identify diverse candidate site combinations.
• Stage II: Employs a classical Second-Order Cone Programming (SOCP) solver for optimal ESS capacity and operational setpoint computation.

Main Results:

• The hybrid framework achieves grid-standard accuracy comparable to classical solvers.
• Demonstrates the potential of quantum computing for enhancing ESS planning.
• Identifies hardware latencies as a current limitation in the NISQ era.

Conclusions:

• The proposed two-stage hybrid quantum-classical framework offers an effective approach to ESS planning.
• Quantum computing, despite current limitations, shows promise for solving complex grid optimization problems.
• Further research is needed to overcome practical hurdles for large-scale quantum implementation.