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Optimization of cascade-resilient electrical infrastructures and its validation by power flow modeling
Yiping Fang1, Nicola Pedroni1, Enrico Zio1,2
1Chair on Systems Science and the Energetic Challenge, Ecole Centrale Paris and Supelec, France.
Summary
This study optimizes power grid connections to enhance resilience against cascading failures while minimizing costs. The findings demonstrate improved grid stability through strategic generator-distributor rewiring using advanced algorithms.
Area of Science:
- Electrical Engineering
- Complex Systems Science
- Network Theory
Background:
- Large-scale power outages pose significant risks to critical infrastructures.
- Optimizing generator-distributor connections is crucial for network resilience and cost-efficiency.
Purpose of the Study:
- To develop and validate a method for optimizing power transmission network resilience against cascading failures.
- To balance network resilience with investment costs in generator-distributor allocation.
Main Methods:
- Utilized a nondominated sorting binary differential evolution (NSBDE) algorithm for combinatorial multiobjective optimization.
- Employed the computationally inexpensive Motter-Lai (ML) topological model to simulate cascading failures.
- Validated results using a more computationally intensive optimal power flow (ORNL-Pserc-Alaska) model.
Main Results:
- Identified optimal generator-distributor connection patterns for the French 400 kV power transmission network.
- Demonstrated significant improvements in cascading resilience at acceptable investment costs.
- Confirmed the consistency between topological and power flow models for failure analysis.
Conclusions:
- Topological complex network models offer a simple, scalable, and computationally efficient approach for infrastructure analysis and optimization.
- The proposed NSBDE method effectively identifies robust and cost-effective network configurations.
- Findings support the use of network theory for enhancing the resilience of critical infrastructures.
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