Bio-inspired design for robust power networks | Conference Paper individual record
abstract

© 2019 IEEE. Extreme events continue to show that existing power grid configurations can be vulnerable to disturbances. Drawing inspiration from naturally robust biological ecosystems presents a potential source of robust design guidelines for modern power grids. The robust network structure of ecosystems is partially derived from a unique balance between pathway efficiency and redundancy. Structural and basic-functional similarities support the application of ecological properties and analysis techniques to power grid design. The work presented here quantitatively investigates the level of similarity between ecosystems and power grids by applying ecological network metrics to a basic, realistic hypothetical 5-bus power system. A comparison between the power grid's performance and average ecosystem performance substantiates the use of the ecological robustness metric for the development of a bio-inspired power grid optimization model. The bio-inspired optimization model re-configures the five bus grid to mimic ecosystem robustness. The results demonstrate the potential of ecosystems to provide new robust design principles for power grids.

author list (cited authors)
Panyam, V., Huang, H., Pinte, B., Davis, K., & Layton, A.
publication date
2019
publisher
IEEE Publisher
keywords
  • Pathway Efficiency
  • Power Grids
  • Ecological Robustness
  • Ecological Properties
  • Optimisation
  • Ecosystem Robustness
  • Robust Design Guidelines
  • Ecological Network Metrics
  • Naturally Robust Biological Ecosystems
  • Environmental Science Computing
  • Robustness
  • Ecology
  • Power Engineering Computing
  • Potential Source
  • Extreme Events
  • Robust Network Structure
  • Bus Grid
  • Redundancy
  • Ecosystems
  • Bio-inspired Design
  • Modern Power Grids
  • Biological System Modeling
  • Bus Power System
  • Robust Power Networks
  • Generators
  • Functional Similarities
  • Power Grid Design
  • Bio-inspired Power Grid Optimization Model
  • Power Grid Configurations
citation count

4