Load models for modeling dynamic behaviors of reactive loads: Evaluation and comparison

Li, Yinhong; Chiang, Hsiao‐Dong; Choi, Byoung-Kon; Chen, Yung‐Tien; Huang, Der-Hua; Lauby, M. G.

doi:10.1016/j.ijepes.2008.04.007

Cited by 34 publications

(13 citation statements)

References 11 publications

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“…An equation that can describe the load power dependency as a function of system voltage and frequency under the static exponential load model is as follows [30,31]:…”

Section: The Load Modelmentioning

confidence: 99%

Distributed Generation Islanding Effect on Distribution Networks and End User Loads Using the Load Sharing Islanding Method

et al. 2016

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Abstract:In this paper a realistic medium voltage (MV) network with four different distributed generation technologies (diesel, gas, hydro and wind) along with their excitation and governor control systems is modelled and simulated. Moreover, an exponential model was used to represent the loads in the network. The dynamic and steady state behavior of the four distributed generation technologies was investigated during grid-connected operation and two transition modes to the islanding situation, planned and unplanned. This study aims to address the feasibility of planned islanding operation and to investigate the effect of unplanned islanding. The load sharing islanding method has been used for controlling the distributed generation units during grid-connected and islanding operation. The simulation results were validated through various case studies and have shown that properly planned islanding transition could provide support to critical loads at the event of utility outages. However, a reliable protection scheme would be required to mitigate the adverse effect of unplanned islanding as all unplanned sub-cases returned severe negative results.

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“…An equation that can describe the load power dependency as a function of system voltage and frequency under the static exponential load model is as follows [30,31]:…”

Section: The Load Modelmentioning

confidence: 99%

Distributed Generation Islanding Effect on Distribution Networks and End User Loads Using the Load Sharing Islanding Method

et al. 2016

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“…ccurate dynamic load modeling is critical for power system transient stability analysis and various simulationbased studies [1]- [2]. It is also known to improve the power system operation flexibility, reduce system operating costs, and better determine the corridor transfer limits [3]- [4]. In the past few decades, both industry and academic researchers have widely used ZIP and induction motors (ZIP + IM) as the composite load model (CLM) for quantifying load characteristics [5]- [7], in which ZIP approximates the static load transient behaviors and the IM approximates the dynamic load transient behaviors.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage WECC Composite Load Modeling: A Double Deep Q-Learning Networks Approach

Wang

Shi

et al. 2020

IEEE Trans. Smart Grid

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With the increasing complexity of modern power systems, conventional dynamic load modeling with ZIP and induction motors (ZIP + IM) is no longer adequate to address the current load characteristic transitions. In recent years, the WECC composite load model (WECC CLM) has shown to effectively capture the dynamic load responses over traditional load models in various stability studies and contingency analyses. However, a detailed WECC CLM model typically has a high degree of complexity, with over one hundred parameters, and no systematic approach to identifying and calibrating these parameters. Enabled by the wide deployment of PMUs and advanced deep learning algorithms, proposed here is a double deep Q-learning network (DDQN)-based, two-stage load modeling framework for the WECC CLM. This two-stage method decomposes the complicated WECC CLM for more efficient identification and does not require explicit model details. In the first stage, the DDQN agent determines an accurate load composition. In the second stage, the parameters of the WECC CLM are selected from a group of Monte-Carlo simulations. The set of selected load parameters is expected to best approximate the true transient responses. The proposed framework is verified using an IEEE 39-bus test system on commercial simulation platforms.Index Terms-Load modeling, double deep Q-learning network, load component identification, measurement-based.

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“…Noticeably, in the varying reactive load scenario [12], where the stability of voltage profile is necessary [13], the addition of the reactive components of a desired bus or line has a significant from the perspective of efficient power system design. As the result, it is obligatory to choose a finite value of the capacitance and reactor to keep voltage in the stability limit [13,14].…”

Section: Introductionmentioning

confidence: 99%

Intrinsic geometric analysis of the network reliability and voltage stability

Gupta

Tiwari

Bellucci

2013

International Journal of Electrical Power & Energy Systems

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a b s t r a c tThis paper presents the intrinsic geometric model for the solution of power system planning and its operation. This problem is large-scale and nonlinear, in general. Thus, we have developed the intrinsic geometric model for the network reliability and voltage stability, and examined it for the two and three parameter systems. The robustness of the proposed model is illustrated by introducing variations of the network parameters. Exact analytical results show the accuracy as well as the efficiency of the proposed solution technique.

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Load models for modeling dynamic behaviors of reactive loads: Evaluation and comparison

Cited by 34 publications

References 11 publications

Distributed Generation Islanding Effect on Distribution Networks and End User Loads Using the Load Sharing Islanding Method

Distributed Generation Islanding Effect on Distribution Networks and End User Loads Using the Load Sharing Islanding Method

Two-Stage WECC Composite Load Modeling: A Double Deep Q-Learning Networks Approach

Intrinsic geometric analysis of the network reliability and voltage stability

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