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Smart city article details
| Title | Mpc-Based Framework Incorporating Pre-Disaster And Post-Disaster Actions And Transportation Network Constraints For Weather-Resilient Power Distribution Networks |
|---|---|
| ID_Doc | 38005 |
| Authors | Souto L.; Parisio A.; Taylor P.C. |
| Year | 2024 |
| Published | Applied Energy, 362 |
| DOI | http://dx.doi.org/10.1016/j.apenergy.2024.123013 |
| Abstract | Enhancing power system resilience to extreme weather requires an effective coordination of pre-disaster preventive response actions and post-disaster repair and restoration services. In this context, this article presents a two-stage framework to enhance resilience of power distribution networks to severe weather events. It considers operating and resource constraints in interdependent power and transportation networks based on official guidance from transport appraisal studies. It also incorporates sources of information available in a smart city context, such as energy measurements, traffic flows, and geographical information systems. In the first stage, a pre-disaster operational planning strategy is defined to prepare the grid for a high-risk outage scenario with the objective of minimizing the value of lost loads, formulated as a mixed-integer quadratic programming model. In the second stage, a post-disaster corrective strategy is implemented over a receding time horizon to compensate for deviations between the actions computed in the first stage and the actions required to minimize power outages, embedded into a model predictive control scheme. The effectiveness of the proposed framework is demonstrated on a real-world large-scale distribution network in the United Kingdom over a range of outage scenarios. Simulation results show that the proposed framework is capable of effectively minimizing first-stage operational costs and second-stage deviations between the projected and actual load demand supply. Numerical results obtained with the proposed framework indicate that the load energy unserved is at least twice smaller than with typical practices adopted by distribution network operators and computational times take two minutes or less. Therefore, it can be effectively used by distribution network operators to ensure an appropriate level of preparedness to power outages caused by extreme weather along with prompt restoration and repair services. © 2024 The Author(s) |
| Author Keywords | Extreme weather; Mixed-integer quadratic programming; Model predictive control; Power distribution networks; Power system resilience; Power system restoration; Smart grids |
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