Emergency electric service restoration in the aftermath of a natural disaster

• Published in: 2015 IEEE Global Humanitarian Technology Conference (GHTC) pp. 183 - 190
• Main Authors: Choobineh, Moein, Mohagheghi, Salman
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2015
• Subjects: Batteries; demand response; Density estimation robust algorithm; Distributed energy resource; electric service restoration; Fuels; Indexes; Microgrid; Microgrids; natural disaster
• DOI: 10.1109/GHTC.2015.7343971

The colossal amount of energy released by natural disaster events can devastate the critical infrastructure of affected cities and rural regions. Possible damages to the electric power grid can lead to large-scale interruption in electric service, which could greatly impede post-disaster relief efforts. To make communities resilient against natural hazards, the power grid must have post-disaster self-healing capability, allowing it to restore power to as many sections of the network as possible within a reasonably short timeframe. Traditionally, electric service restoration is performed by first identifying alternative substations and possible routes, followed by network reconfiguration, so that the outage area can be re-energized via these substations. However, this approach may not be possible in the aftermath of a natural disaster. This is because many parts of the network may already have become non-operational due to direct or indirect damages incurred by the event. Here, service restoration can be achieved through a decentralized approach where one or more Microgrids are formed in order to supply the loads locally. A Microgrid dispatch solution is proposed in this paper for emergency electric service restoration in the aftermath of a natural disaster event. A nonlinear mixed-integer optimization problem is formulated that finds the optimal dispatch of the energy resources within the Microgrid subject to capacity and fuel availability constraints. To demonstrate the applicability of the solution, a case study is provided using the IEEE 123-bus test distribution system.