Assessing Resilience in Power Grids as a Particular Case of Supply Chain Management
AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH DEPT OF OPERATIONAL SCIENCES
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Electrical power grids represent a critical infrastructure for a nation as well as strategically important. Literature review identified that power grids share basic characteristics with Supply Chain Management. This thesis presents a linear programming model to assess power grid resilience as a particular case of Supply Chain Management. Since resilient behavior is not an individual or specific systems attribute but a holistic phenomenon based on the synergic interaction within complex systems, resilience drivers in power grids were identified. Resilience is a function of Reliability, Recovery Capability, Vulnerability and Pipeline Capacity. In order to embed heterogeneous variables into the model, parameterization of resilience drivers were developed. A principle of improving resilience through redundancy was applied in the model by using a virtual redundancy in each link which allows reliability improvement throughout the entire network. Vulnerability was addressed through the standard MIL-STD 882D, and mitigated through security allocation. A unique index R integrates the resilience complexity to facilitate alternate scenarios analysis toward strategic decision making. Decision makers are enabled to improve overall power grid performance through reliability development as well as security allocation at the more strategic links identified by the optimal solutions. Moreover, this tool lets decision makers fix grid variables such as reliability, reduced pipeline capacity, or vulnerabilities within the model in order to find optimal solutions that withstand disruptions. The model constitutes an effective tool not only for efficient reliability improvement but also for rational security allocation in the most critical links within the network. Finally, this work contributes to the federal government mandates accomplishment, intended to address electrical power-related risks and vulnerabilities.
- Administration and Management
- Electric Power Production and Distribution
- Operations Research