Dimensionality-reduced multi-objective framework for resilient power systems with electric vehicles and...

Area of Science:

• Electrical Engineering
• Power Systems Engineering
• Optimization Theory

Background:

• Modern power systems face increasing complexity and vulnerability.
• System resilience is crucial for withstanding disruptive events.
• Integrating distributed energy resources (DERs) and energy storage is key to enhancing grid stability.

Purpose of the Study:

• To propose a resilience-oriented framework coordinating DERs with hydrogen-based energy storage (HESS).
• To integrate planning for transmission lines, electric vehicles (EVs), and flexible AC transmission systems (FACTS).
• To develop a multi-objective optimization model minimizing resilience strategy costs and maximizing DER contribution to resilience.

Main Methods:

• Formulated a multi-objective optimization problem with numerous decision variables.
• Applied dimensionality reduction strategies: power dispatch/load-shedding reduction and transmission line/FACTS-based strategy.
• Employed multi-objective metaheuristic algorithms to solve the reduced-scale problem.

Main Results:

• The proposed strategies significantly improved system robustness and solution quality across IEEE 9-bus, 30-bus, and 53-bus systems.
• HESS demonstrated advantages over conventional battery storage, reducing planning costs by 29.7-55.2% (9-bus) and 36.3-57.66% (30-bus).
• Robustness gains ranged from 10-30%, and solution quality improved by 3-38.85% depending on the system.

Conclusions:

• The developed resilience framework effectively enhances power system stability and reliability.
• HESS offers a superior alternative to battery storage for improving grid resilience and cost-efficiency.
• The dimensionality reduction techniques successfully addressed computational challenges in large-scale optimization.