Optimal Design of High-Critical-Current SMES Magnets: From Single to Multi-Solenoid Configurations.

Area of Science:

• Materials Science
• Electrical Engineering
• Energy Systems

Background:

• High-penetration renewable energy integration necessitates advanced energy storage to stabilize power grids.
• Superconducting Magnetic Energy Storage (SMES) offers rapid response, high efficiency, and longevity but faces design challenges.
• Complex critical current modeling and computationally intensive optimization hinder the commercialization of megajoule-class SMES magnets.

Purpose of the Study:

• To develop an integrated and computationally efficient design framework for megajoule-class SMES magnets.
• To address challenges in modeling anisotropic critical current behavior and high-dimensional optimization.
• To create a user-friendly tool supporting the development of large-scale High-Temperature Superconducting (HTS) magnets for smart grids.

Main Methods:

• Synergistic integration of a 2D axisymmetric magnetic field model (Conway's current-sheet theory) and a critical current anisotropy model.
• Implementation of an adaptive genetic algorithm (AGA) with a dual-module chromosome encoding strategy (discrete gap index + nonlinear increment).
• Development of parallel acceleration techniques and co-calculation of electromagnetic parameters for magnet optimization.

Main Results:

• Achieved efficient optimization of megajoule-class SMES magnets.
• For a single solenoid, critical current increased by 22.6% (915 A) and energy storage capacity by 41.8% (1.12 MJ).
• A 20-unit array (20 MJ) demonstrated matched inductance/current (0.15 H/827 A), enhancing transient stability control for smart grids.

Conclusions:

• The proposed integrated design framework provides a computationally efficient paradigm for high-current SMES magnet design.
• The developed method and software tool support the advancement of large-scale HTS magnets for smart grid applications and high-field uses.
• This research facilitates the commercialization of SMES technology by overcoming critical current modeling and optimization inefficiencies.