Abstract
For several years, the increase of extreme meteorological events due to climate change, especially in unusual areas, has focused authorities and stakeholders attention on electric power systems’ resilience. In this context, the authors have developed a simulation model for managing the resilience of electricity distribution grids with respect to the main threats to which these infrastructures may be exposed (i.e., ice sleeves, heat waves, water bombs, floods, tree falls). The simulator identifies the more vulnerable network assets by means of probabilistic indexes, thus suggesting the best corrective actions to be implemented for resilience improvement. The fulfillment of grid constraints, i.e., loading limits for branches and voltage limits for buses, under actual operating conditions, is taken into account. Load scenarios extracted from available measurements are evaluated by means of load flow analyses in order to choose, among the best solutions identified, those compatible with the constraints. The proposed tool can assist Distribution System Operators (DSOs) in drawing up the Action Plan to improve, on one hand, the resilience of the network and, on the other hand, to remove any possible limitation for the adoption of the best solutions to ensure maximum operational continuity during extreme weather events.
Highlights
PDNs are the part of the electric power system in charge of supplying final users, being one of the main providers of goods and services to citizens
This suggests the possibility to operate all of the PDN at a 20 kV voltage level as a corrective action able to increase resilience
This paper presented a procedure based on probabilistic indexes able to assess the resilience of distribution networks against threats such as ice sleeves formation, heat waves, floods and tree fall
Summary
PDNs are the part of the electric power system in charge of supplying final users, being one of the main providers of goods and services to citizens. A time-dependent resilience metric is presented in [8] for urban infrastructure systems, which has been used by [9] for the probabilistic assessment of PDN resilience, taking into account different weather scenarios by means of Monte Carlo simulations.
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