A Novel Dynamic Load Modeling for Power Systems Restoration: An Experimental Validation on Active Distribution Networks

Abstract

In this paper, a novel approach to assess the power demand of distribution networks during a restoration process following an outage is presented. The possibility of correctly estimating such power demand represents a very important support in the choice and management of reliable restoration paths which significantly contributes to increase the resilience of the power system. In fact, due to the ever-growing penetration of renewable energy sources in the worldwide networks, electrical systems are often pushed to operate closer to their design limits and so, in particular conditions, black outs can be more frequent compared to the past. In this context, the aim of this work is to develop a general expression which can consider and represent all the key elements which affect the active and reactive power demand of distribution grids during the restoration process. The proposed method considers also the relationship between distributed generation and frequency behaviour in the power demand estimation. This aspect is generally neglected in literature although it has a significative impact. The effectiveness of the present approach is experimentally demonstrated on field. More specifically, the developed tool is applied to estimate the power demand of two real distribution networks and the tool results are compared with on-field recordings. This comparison demonstrates that the proposed approach represents a promising tool which, together with the restoration tests, could be an important ally in the design and management of the restoration plan of electrical networks.INDEX TERMS Load modelling, Cold load pick up, Black start, power system restoration, modelling of distributed generation.

Extraction of amplitude-domain characteristics from dynamic electric signals that cause large errors in electricity metering

“…For example, based on load power data recorded every 15 min, characteristic parameters have been extracted such as daily load rate, maximum utilization hour rate, daily peak-to-valley difference rate, peak period load rate, flat load rate, and valley load rate [15,16]. The above power characteristics were analyzed on large timescales, such as years, months, and day [17][18][19]. Furthermore, these characteristics have been applied to load prediction and power system operational planning [20,21].…”