Constrained Fuzzy Power Flow Applied to Transmission Congestion

Abstract

The deregulation of the electricity industry, the increasing penetration of renewable energy resources, and the presence of new stakeholders, such as Electric Vehicles (EV), make managing transmission system congestion critical. In this context, load flow methodologies that consider non-probabilistic uncertainty due to a lack of data (particularly concerning EV) seem to be a handy tool to analyze network congestion situations and to support electrical network planning. The congestion of the branches is related to the load and generation of the network buses. Still, the congestion of a specific branch may not be related to the load and generation on the neighbouring buses. In this line, Fuzzy Power Flow (FPF) is suitable for identifying congestion situations in the transmission system and the branches responsible for such cases when probabilistic models may not describe uncertainty. This paper uses an FPF model, a non-linearized Constrained Fuzzy Power Flow (CFPF), to characterize congestion situations in transmission network planning. The application of the CFPF to analyze congestion situations is a novelty. The results of that application may be used to define the reinforcement of the most appropriate branches to achieve specific suitability of the network in the face of a diverse set of uncertainties. Furthermore, the dual values provided by Lagrange multipliers resulting from the CFPF optimization problem are used to identify the most promising options to mitigate congestion situations.
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