Modelling the electrical loads of UK residential energy users

Tsagarakis, George; Collin, Adam J.; Kiprakis, Aristides

doi:10.1109/upec.2012.6398593

Cited by 16 publications

(12 citation statements)

References 4 publications

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“…More specific differentiation is needed in case of lighting and cycling devices based on [9]. The total energy for hot water is distributed into the three time frames with 35% in the morning, 20% at noon and the majority of 45% in the evening hours [10].…”

Section: B Initialize Household Structurementioning

confidence: 99%

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Approaching the diversity of unbalanced residential load in low-voltage grids by probabilistic load-flow simulation of cross-sectional data

Huppertz

Kopczynski

Zeise

et al. 2015

2015 IEEE Eindhoven PowerTech

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For the approximation of low-voltage grid dynamics we present an energy based bottom-up model to create highly diverse household load profiles. In an object-oriented approach, nodal loads are represented by classifications of appliances and distributed to the phases in order to consider the characteristically unbalanced load of residential customers over time. Probability parameters, regarding time of use, corresponding duration of use and the magnitude of power consumption, are derived from a combination of statistical data and global load profiles. These parameters build probability models from crosssectional data for specific points in time which are processed by a probabilistic load-flow along the time axis. A simulation of a rural low-voltage grid using detailed information about the topology, renewable energy sources and residential energy consumption is performed. Results are validated and compared to high-resolution measurements of the real grid. The proposed model -embedded in an appropriate simulation scheme -will support the further development of planning, operational and control strategies for today's and future low-voltage grids.

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Section: B Initialize Household Structurementioning

confidence: 99%

“…II). Lighting has a significant seasonal variation and is modeled according to the results [9] for the morning and evening hours. In case of hot water supply by electrical power the profiles of [12] are used.…”

Section: B Initialize Household Structurementioning

confidence: 99%

Approaching the diversity of unbalanced residential load in low-voltage grids by probabilistic load-flow simulation of cross-sectional data

Huppertz

Kopczynski

Zeise

et al. 2015

2015 IEEE Eindhoven PowerTech

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“…The parameters considered in this work are shown in Table 1. Column % indicates the percentage of users with a similar residential load according to UK National Statistics [10]. [11], [12]…”

Section: B Zip Modelmentioning

confidence: 99%

Estimating the load response to voltage changes at UK primary substations

Lamberti

Dong

Calderaro

et al. 2013

IEEE PES ISGT Europe 2013

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The development of Smart Grid technologies and the need to defer investments for expanding and reinforcing distribution networks have attracted new attention to aspects of Conservation Voltage Reduction (CVR). Based on the fact that the demand of certain loads can change with voltage, it is plausible to envisage the active management of voltage regulation devices in order to increase/decrease the demand during certain periods for the benefit of the distribution network (e.g., peak shaving) or the whole system (e.g., fast reserves). The extent of this voltage-driven demand response is however entirely dependent on the instantaneous load composition. In order to quantify the benefits of such a scheme, this work proposes a methodology to estimate the time-varying residential load response to primary substation voltage changes whilst complying with voltage limits at low voltage. This is applied to a small UK distribution network downstream an OLTC-enabled 33kV/6.6kV substation with 351 residential, 2 commercial and 1 industrial customers (total peak demand of 932 kW). The results, only considering part of the residential loads modeled with time-varying ZIP parameters, suggest thateven considering voltage constraints-there is considerable load response that can be unlocked from residential loads. However, this is highly dependent on the time of the day.Index Terms-active load management, CVR, demand response, ZIP model, voltage management, voltage regulation, smart grids I.

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“…[18,19], is used to determine the probability of the activity being converted to an electrical load. Further details are given in reference [20].…”

Section: E Appliance Usementioning

confidence: 99%

“…[18,19], were analysed to ensure that the appliance use conversion retains the overall characteristics of the UK population. Additional details are given in [20].…”

Section: F Electrical Load Model 1) Electricity Profilementioning

confidence: 99%

Multi-scale electrical load modelling for demand-side management

Collin

Tsagarakis

Kiprakis

et al. 2012

2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe)

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This paper presents a Markov chain approach for developing low-voltage (LV) residential load models for use in the analysis of future smart grids. The motivation is to obtain a better understanding of load use and user behaviour at the LV level which will allow future electricity network performance to be improved by understanding the impact of energy demand transformations through the application of demand-side management (DSM) strategies at the LV side. The developed load models highlight the variations between individual and aggregate load model representation and show that the composition of LV residential users should be carefully considered when analysing LV networks.Index Terms-demand-side management, load modelling, markov processes, power demand, smart grids.

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Modelling the electrical loads of UK residential energy users

Cited by 16 publications

References 4 publications

Approaching the diversity of unbalanced residential load in low-voltage grids by probabilistic load-flow simulation of cross-sectional data

Approaching the diversity of unbalanced residential load in low-voltage grids by probabilistic load-flow simulation of cross-sectional data

Estimating the load response to voltage changes at UK primary substations

Multi-scale electrical load modelling for demand-side management

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