Static load models, including ZIP coefficients, are an established facet of power systems modelling. Despite the rising penetration of non-linear load types across modern networks, the characterisation of load behaviours by such means remains effective. However, the accuracy of ZIP load based methods is shown to decline in the midst of high levels of harmonic current and/or voltage distortion. This observation is validated by making reference to the definitions set out in IEEE standard 1459-2010. Various power systems optimization strategies, such as Conservation Voltage Reduction, rely on load models to faithfully reproduce network conditions in simulation. Thus, an extension to the conventional ZIP model approach is proposed, which incorporates the effects of harmonic current distortion, attributable to particular load types, and harmonic voltage distortion.

This paper investigates the voltage level dependencies of different modern lighting types.Experiments are undertaken and the results used to formulate polynomial load models, presented in a ZIP format. These characterize active power, reactive power and harmonic current behaviors; are compatible with established power systems simulation practice; and are generally demonstrated to muster a good fit to the raw experimental data. Despite exceedingly high levels of current distortion being noted for several lamp instances, the attenuating effects of lowering voltage and harmonic diversity, as captured within a pair of diversity factor terms, are shown to help significantly alleviate the associated harmonic pollution impact on low voltage networks.

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Abstract. Loads are often represented as a weighted combination of constant impedance (Z), current (I) and power (P) components, so called ZIP models, by various power systems network simulation tools. However, with the growing need to model nonlinear load types, such as LED lighting, ZIP models are increasingly rendered inadequate in fully representing the voltage dependency of power consumption traits. In this paper we propose the use of small-signal ZIP models, derived from a neural network model of appliance level consumption profiles, to enable better characterizations of voltage dependent load behavior. Direct and indirect approaches to small-signal ZIP model parameter estimation are presented, with the latter method shown to be the most robust to neural network approximation errors. The proposed methodology is demonstrated using both simulation and experimentally collected load data.