Chance-constrained allocation of UFLS candidate feeders under high penetration of distributed generation

TL;DR

This paper introduces a chance-constrained method for allocating UFLS feeders in power systems with high distributed generation, explicitly accounting for forecast uncertainty and correlations to improve reliability.

Contribution

It proposes a novel chance-constrained approach for UFLS feeder selection that incorporates forecast uncertainty and correlations, enhancing traditional deterministic methods.

Findings

The method guarantees load disconnection with a predefined probability.

Considering forecast correlation improves UFLS reliability.

Case studies validate the effectiveness of the proposed approach.

Abstract

Under-Frequency Load Shedding (UFLS) schemes are the last resort to contain a frequency drop in the grid by disconnecting part of the demand. The allocation methods for selecting feeders that would contribute to the UFLS scheme have traditionally relied on the fact that electric demand followed fairly regular patterns, and could be forecast with high accuracy. However, recent integration of Distributed Generation (DG) increases the uncertainty in net consumption of feeders which, in turn, requires a reformulation of UFLS-allocation methods to account for this uncertainty. In this paper, a chance-constrained methodology for selecting feeders is proposed, with mathematical guarantees for the disconnection of the required amount of load with a certain pre-defined probability. The correlation in net-load forecasts among feeders is explicitly considered, given that uncertainty in DG power output is driven by meteorological conditions with high correlation across the network. Furthermore, this method is applicable either to systems with conventional UFLS schemes (where relays measure local frequency and trip if this magnitude falls below a certain threshold), or adaptive UFLS schemes (where relays are triggered by control signals sent in the few instants following a contingency). Relevant case studies demonstrate the applicability of the proposed method, and the need for explicit consideration of uncertainty in the UFLS-allocation process.