학술논문

Preventive framework for resilience enhancement in networked microgrids: A focus on hydrogen integration and optimal energy management
Document Type
article
Source
IET Generation, Transmission & Distribution, Vol 18, Iss 8, Pp 1653-1662 (2024)
Subject
energy management systems
renewable energy sources
safety
Distribution or transmission of electric power
TK3001-3521
Production of electric energy or power. Powerplants. Central stations
TK1001-1841
Language
English
ISSN
1751-8695
1751-8687
Abstract
Abstract The integration of distributed energy resources and smart grid technologies has increased the vulnerability and complexity of modern power systems, especially in the face of extreme events. Enhancing the resilience of power systems is crucial to ensure continuous and reliable electricity supply during and after such events. This paper proposes a preventive framework to enhance the resilience of networked microgrids (NMGs) during extreme events. The framework integrates optimal energy management strategies and preventive resilience enhancement measures, focusing on the coordination and utilization of hydrogen (H2) energy systems and controllable distributed generators (CDGs). The framework leverages capacity‐based signals to prompt H2 system owners and CDG operators to prepare for disruptions by fully charging H2 storage tanks and pre‐scheduling CDG commitments. By utilizing this proactive energy scheduling, critical loads can be supplied through stationary fuel cells and CDGs, improving overall network resilience. The proposed resilient energy management approach combines optimization techniques to improve the resilience of NMGs and reduce their dependence on the main grid. Numerical simulations demonstrate the effectiveness of the proposed linear model in enhancing the resilience of NMGs and improving their operational performance during extreme events. The contributions of this paper include advancements in understanding NMGs, the development of a preventive framework, integration of H2 energy systems and CDGs, and proposal of effective signalling models.