A Robust Integrated Approach for Optimal Management of Power Networks Encompassing Wind Power Plants


This paper basically concentrates on providing some significant steps for congestion management of the power systems based on an interval-based robust chance constrained transmission switching (IBRCC-TS) approach for decreasing the congestion of the system while increasing the robustness of the system against uncertainties of the wind turbines. However, the utilization of TS approach in the power system is a severe challenge, since there is no limitation over the switching rate during a certain timespan in the network as well as the power switches' failure uncertainty. Besides, the frequent switching by the TS method decreases the switch maintenance and puts the network reliability at risk. To overcome this problem, the reliability of the circuit breakers (CBs) is considered in the studied model aiming to determine the optimal number of the switching of the CBs. In addition, due to the nonlinear relation of CBs' reliability, a linearization technique is performed to linearize the CBs' reliability relation. Another step which is pursued in this paper is the utilization of energy storage system (ESS) to increase the reliability and decrease the congestion of the system. The effectiveness of the algorithm is compared with some of the well-known meta-heuristic algorithms and the proposed model is implemented on the IEEE 6-bus and 24-bus test systems. The obtained results proved the authenticity and validity of the work.


Electrical and Computer Engineering

Keywords and Phrases

Allocation Algorithm; Copper; Energy Storage System; Interval Based Robust Chance Constraint (IBRCC); Power System Reliability; Power Systems; Reliability; Reliability; Resource Management; Switches; Uncertainty; Wind Power

International Standard Serial Number (ISSN)

0093-9994; 1939-9367

Document Type

Article - Journal

Document Version


File Type





© 2020 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

29 Jun 2020