Feeder load composition tracking for smart metered low voltage circuits

Research output: Contribution to conferencePaper

  • 1 Citations

Abstract

As Distributed Generation penetrates Low Voltage networks in greater quantities, the behaviour of the loads on these circuits, typically small and residential customers, must be better understood to avoid unnecessary investment and capitalize on increased efficiencies. Estimating thermal constraints as well as islanding capabilities hinges on accurate and representative load profiling, which requires periods of typical behaviour to be gathered through metering. The increasing availability of Smart Meters offers a solution to this with high frequency load measurements that align with generation dispatch periods. Prior work has taken steps to develop finer grained load profiles than those developed at the national level but given the high propensity for variability in residential customers, such metrics are overly general for small power systems. This paper takes previous work on load profiling at MV and LV levels and uses it to generate load profile compositions for learning the composition of customers that make up an LV feeder load and how it evolves over time. A simplification of a residential load profile model is applied to a set of real AMI data on a simulated feeder, resulting in 3 categories of user, a stratification learned from historical metering data. This yields an abstracted disaggregation of the loads on the feeder that accommodates the high variability and the heterogeneous profiles within the residential loads. Since compositional data is defined over the simplex rather than a real space, this restricts the statistical tools available for modelling to an inflexible subset. The solution presented circumvents this problem by utilising transforms that map the contributions to the aggregated feeder load into real space permitting a wider selection of analysis tools to be applied. A demonstration using a year’s worth of smart meter data is provided to show how latent traits in load composition and forecasted changes can be tracked over time using a modified linear dynamical system. This technique can be used in system planning to anticipate reversed power flows on LV networks where demand is insufficient to absorb distributed generation this avoiding the need for expensive tap changer upgrades or up rating the thermal limit on the lines.

Conference

ConferenceCIRED 2013, 22nd International Conference on Electricity Distribution
CountrySweden
CityStockholm
Period10/06/1313/06/13

Fingerprint

Smart meters
Distributed power generation
Networks (circuits)
Electric potential
Chemical analysis
Hinges
Dynamical systems
Demonstrations
Availability
Planning
Hot Temperature

Keywords

  • smart metered
  • low voltage circuits
  • feeder load composition tracking

Cite this

Stephen, B., & Galloway, S. (2013). Feeder load composition tracking for smart metered low voltage circuits. Paper presented at CIRED 2013, 22nd International Conference on Electricity Distribution, Stockholm, Sweden.
Stephen, Bruce ; Galloway, Stuart. / Feeder load composition tracking for smart metered low voltage circuits. Paper presented at CIRED 2013, 22nd International Conference on Electricity Distribution, Stockholm, Sweden.4 p.
@conference{4a059f18cd174924a7f3002bdf9eee96,
title = "Feeder load composition tracking for smart metered low voltage circuits",
abstract = "As Distributed Generation penetrates Low Voltage networks in greater quantities, the behaviour of the loads on these circuits, typically small and residential customers, must be better understood to avoid unnecessary investment and capitalize on increased efficiencies. Estimating thermal constraints as well as islanding capabilities hinges on accurate and representative load profiling, which requires periods of typical behaviour to be gathered through metering. The increasing availability of Smart Meters offers a solution to this with high frequency load measurements that align with generation dispatch periods. Prior work has taken steps to develop finer grained load profiles than those developed at the national level but given the high propensity for variability in residential customers, such metrics are overly general for small power systems. This paper takes previous work on load profiling at MV and LV levels and uses it to generate load profile compositions for learning the composition of customers that make up an LV feeder load and how it evolves over time. A simplification of a residential load profile model is applied to a set of real AMI data on a simulated feeder, resulting in 3 categories of user, a stratification learned from historical metering data. This yields an abstracted disaggregation of the loads on the feeder that accommodates the high variability and the heterogeneous profiles within the residential loads. Since compositional data is defined over the simplex rather than a real space, this restricts the statistical tools available for modelling to an inflexible subset. The solution presented circumvents this problem by utilising transforms that map the contributions to the aggregated feeder load into real space permitting a wider selection of analysis tools to be applied. A demonstration using a year’s worth of smart meter data is provided to show how latent traits in load composition and forecasted changes can be tracked over time using a modified linear dynamical system. This technique can be used in system planning to anticipate reversed power flows on LV networks where demand is insufficient to absorb distributed generation this avoiding the need for expensive tap changer upgrades or up rating the thermal limit on the lines.",
keywords = "smart metered , low voltage circuits, feeder load composition tracking",
author = "Bruce Stephen and Stuart Galloway",
year = "2013",
month = "6",
language = "English",
note = "CIRED 2013, 22nd International Conference on Electricity Distribution ; Conference date: 10-06-2013 Through 13-06-2013",

}

Stephen, B & Galloway, S 2013, 'Feeder load composition tracking for smart metered low voltage circuits' Paper presented at CIRED 2013, 22nd International Conference on Electricity Distribution, Stockholm, Sweden, 10/06/13 - 13/06/13, .

Feeder load composition tracking for smart metered low voltage circuits. / Stephen, Bruce; Galloway, Stuart.

2013. Paper presented at CIRED 2013, 22nd International Conference on Electricity Distribution, Stockholm, Sweden.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Feeder load composition tracking for smart metered low voltage circuits

AU - Stephen,Bruce

AU - Galloway,Stuart

PY - 2013/6

Y1 - 2013/6

N2 - As Distributed Generation penetrates Low Voltage networks in greater quantities, the behaviour of the loads on these circuits, typically small and residential customers, must be better understood to avoid unnecessary investment and capitalize on increased efficiencies. Estimating thermal constraints as well as islanding capabilities hinges on accurate and representative load profiling, which requires periods of typical behaviour to be gathered through metering. The increasing availability of Smart Meters offers a solution to this with high frequency load measurements that align with generation dispatch periods. Prior work has taken steps to develop finer grained load profiles than those developed at the national level but given the high propensity for variability in residential customers, such metrics are overly general for small power systems. This paper takes previous work on load profiling at MV and LV levels and uses it to generate load profile compositions for learning the composition of customers that make up an LV feeder load and how it evolves over time. A simplification of a residential load profile model is applied to a set of real AMI data on a simulated feeder, resulting in 3 categories of user, a stratification learned from historical metering data. This yields an abstracted disaggregation of the loads on the feeder that accommodates the high variability and the heterogeneous profiles within the residential loads. Since compositional data is defined over the simplex rather than a real space, this restricts the statistical tools available for modelling to an inflexible subset. The solution presented circumvents this problem by utilising transforms that map the contributions to the aggregated feeder load into real space permitting a wider selection of analysis tools to be applied. A demonstration using a year’s worth of smart meter data is provided to show how latent traits in load composition and forecasted changes can be tracked over time using a modified linear dynamical system. This technique can be used in system planning to anticipate reversed power flows on LV networks where demand is insufficient to absorb distributed generation this avoiding the need for expensive tap changer upgrades or up rating the thermal limit on the lines.

AB - As Distributed Generation penetrates Low Voltage networks in greater quantities, the behaviour of the loads on these circuits, typically small and residential customers, must be better understood to avoid unnecessary investment and capitalize on increased efficiencies. Estimating thermal constraints as well as islanding capabilities hinges on accurate and representative load profiling, which requires periods of typical behaviour to be gathered through metering. The increasing availability of Smart Meters offers a solution to this with high frequency load measurements that align with generation dispatch periods. Prior work has taken steps to develop finer grained load profiles than those developed at the national level but given the high propensity for variability in residential customers, such metrics are overly general for small power systems. This paper takes previous work on load profiling at MV and LV levels and uses it to generate load profile compositions for learning the composition of customers that make up an LV feeder load and how it evolves over time. A simplification of a residential load profile model is applied to a set of real AMI data on a simulated feeder, resulting in 3 categories of user, a stratification learned from historical metering data. This yields an abstracted disaggregation of the loads on the feeder that accommodates the high variability and the heterogeneous profiles within the residential loads. Since compositional data is defined over the simplex rather than a real space, this restricts the statistical tools available for modelling to an inflexible subset. The solution presented circumvents this problem by utilising transforms that map the contributions to the aggregated feeder load into real space permitting a wider selection of analysis tools to be applied. A demonstration using a year’s worth of smart meter data is provided to show how latent traits in load composition and forecasted changes can be tracked over time using a modified linear dynamical system. This technique can be used in system planning to anticipate reversed power flows on LV networks where demand is insufficient to absorb distributed generation this avoiding the need for expensive tap changer upgrades or up rating the thermal limit on the lines.

KW - smart metered

KW - low voltage circuits

KW - feeder load composition tracking

UR - http://www.cired2013.org/

UR - http://www.cired.net/publications/cired2013/pdfs/CIRED2013_0254_final.pdf

M3 - Paper

ER -

Stephen B, Galloway S. Feeder load composition tracking for smart metered low voltage circuits. 2013. Paper presented at CIRED 2013, 22nd International Conference on Electricity Distribution, Stockholm, Sweden.