Chance-Constrained Service Restoration For Distribution Networks With Renewables

Keywords

Chance constrained optimization; Distribution service restoration; Markov's inequality; Mixed integer convex programming; PV generation uncertainty

Abstract

Power systems operation is facing great challenges from natural disasters and cyber-attacks. It is critical but also difficult to enhance the reliability and resilience against extreme events. To better response to inevitable outages or blackouts, service restoration in distribution networks is important to minimize the disastrous impacts of catastrophic events. The increasing penetration of distributed energy resources (DERs) provides new opportunities to expedite the restoration process. However, the coordination with conventional distribution system control devices and the uncertainty and variability of intermittent renewable energy resources requires new operation and control strategies for distribution service restoration (DSR). This paper develops an optimal bottom-up DSR strategy by coordinating DERs with voltage regulators and capacitor banks. The chance-constrained (CC) programming approach is used to model the probabilistic output limit of solar radiation and PV generation. The Markov's inequality and Latin hypercube sampling techniques are applied to convert and incorporate the chance constraints into the DSR optimization problem. The CC-DSR problem is formulated as a mixed integer convex programming problem, considering various operational cost functions and bidirectional three-phase unbalanced load flow. Simulation results on the modified IEEE 13-node test feeder system demonstrate the effectiveness and flexibility of the bottom-up DSR strategy.

Publication Date

8-17-2018

Publication Title

2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/PMAPS.2018.8440520

Socpus ID

85053159938 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85053159938

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