Through the planned monitoring activities and the related modelling work, TVV intend to understand solutions that minimise the need for extensive monitoring on all parts of the network that will be both costly and time consuming to achieve.

This phase of the project will explore:
What do we need to know about customer behaviour in order to optimise network investment?
What is the optimum level and location of network monitoring?
To what extent can customers be categorised in order to better understand their behaviour?

The trials listed below will ensure that we provide answers to these questions.

End Points

The TVV project has installed over 250 end point monitors in customers houses and small businesses in Bracknell to understand the energy utilised by different groups of energy consumers.

The trials that the project are conducting relating to end point monitoring will help electricity distribution companies and wider utility providers understand where the optimum locations are, and the suitability and relevance of installing end point monitoring devices.

The trials will also assess initial effectiveness of end point monitoring and evaluate end use and network monitoring by measuring system performance and benchmark against potential alternatives.

The end point monitoring trials will assist the evaluation of the performance of smart meter deployments and the relevance to electricity distribution companies.

The data gathered by the installed end point monitors will allow statistical analysis and allow for the characterisation of customers to determine the optimal balance of installing end point monitors. This trial will also assess whether buddying customers using statistical methodologies are a viable alternative to installing end point monitors.

The image below shows the domestic end point monitor being used as part of the TVV project.

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Low-voltage substation monitors

The TVV project requires half hourly energy data to be captured from substations on the low voltage network so that energy usage patterns can be identified, categorised and forecast, and compared with aggregated data from end points. With this information it is expected that meaningful forecasts can be made regarding the future loading of the low voltage network.

The substation monitors are GE Digital Energy Multilin DGCM devices. These have been configured for use at distribution substations with up to 6 Low-Voltage feeders, with communications achieved using a GPRS modem, and GPRS/3G SIM card connected to the Vodafone network.

The initial trials for substation monitoring will assess the effectiveness of substation monitoring and lead to an optimised strategy for any future substation monitoring deployment. The first tranche of deployments has seen over 100 substation monitoring devices installed in Bracknell to monitor the energy data at those sites.

The data gathered and analysed from the first 100 substations will inform whether additional devices are needed to be installed on the Low-Voltage network in the Bracknell area and will inform the final evaluation regarding the effectiveness of LV substation monitoring.

DGCM meter

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ICT requirements

The TVV project has installed 251  single-phase end point monitors in the Bracknell area. These end point monitors are collecting the energy used by project participants at half-hourly intervals. The end point monitor data is sent to a meter data management system known as SMOS.

SMOS is a data aggregator, meter commissioning and reporting tool. All half hourly readings from our meters are aggregated through SMOS for onward delivery to our Data Historian. Data is used to look at energy usage and create energy usage profiles, which are used to inform future electrical network planning and design. SMOS also provides standard meter commissioning programs, to ensure that all previous data is wiped from meters if they are reused.

The diagram below shows the features of the SMOS system.

SMOS diagram

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SMOS is deployed on an Oracle Applications Server. In this respect it can be delivered with various operating systems and can make use of standard Relational Database Management systems, such as Oracle or SQL Server, as well as high-speed in-memory databases, such as TimesTen.

Further reading : https://www.gedigitalenergy.com/SmartMetering/catalog/smos.htm

X88

A trial of the TVV project relates to ensuring that new and existing data is validated before being analysed by the project team or the University of Reading.

X88 Pandora provides a pattern-based data quality management tool. This can be used to:

  • Determine when a customer’s energy usage drops to 0 on a sustained basis, allowing the project to investigate whether the customer has moved out.
  • Find gaps in our data , for instance, missing or incomplete postcodes
  • Find duplicate records

All of which helps TVV maintain their commitment to high-quality data management.

The image below shows a sample data quality assessment.

X88 Pandora

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X88 Pandora Data Quality Edition uses Java code and a bespoke back-end database. It connects to most other data sources, including Microsoft Excel, PI System, SQL Server and Oracle database servers, via JDBC.

Further Reading : http://www.x88.com/pandora/x88_pandora_overview.shtml

Other trials of the ICT work stream are to compare various communications providers to ensure that a recommendation can be made regarding the performance, cost, reliability and ability to be deployed at scale across the UK.

Characterisation

The TVV project is collecting half-hourly energy data from both end point monitors and LV substation monitors. This data will allow the project to conduct a ‘buddying’ technique to categorise customers based on their energy use by the creation of customer energy demand profiles.

This detailed data analysis will help us determine whether meaningful demand categories can be identified among domestic and small business customers by identifying the behaviours and usage patterns associated with each category of customer. The trial for the characterisation of consumers based on their energy usage will ensure that we are able to identify the input data required to inform categorisation and to assess the limits of the categorisations (including one off events).

The TVV project will demonstrate the applicability of the statistical modelling techniques used in this trial to a diverse small business customer base.

The use of customer categorisation will assist the project to inform network operators how best to improve and target network monitoring and demand prediction using statistical methods, leading to a network monitoring deployment strategy for dissemination to other Distribution Network Operators.