Improving quality of service information and incentives in the gb electricity industry
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Mott MacDonald. Improving Quality of Service – Information and Incentives in the GB Electricity Industry. Alan Friday (ERA Technology Ltd), Chris Watts (Ofgem). Contents. Overview of IIP Audit framework Statistical work Overall impressions Results of the audit. Overview of IIP.

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Improving Quality of Service – Information and Incentives in the GB Electricity Industry

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Improving quality of service information and incentives in the gb electricity industry

Mott

MacDonald

Improving Quality of Service – Information and Incentives in the GB Electricity Industry

Alan Friday (ERA Technology Ltd), Chris Watts (Ofgem)


Contents

Contents

  • Overview of IIP

  • Audit framework

  • Statistical work

  • Overall impressions

  • Results of the audit


Overview of iip

Overview of IIP

  • Address known weaknesses of RPI-X regulation for the DNOs

    • Improve quality of service data

    • Improve balance between incentives for cost efficiency and quality of service

  • Key quality of service measures identified as being:

    • Frequency and duration of supply interruptions

    • Speed and quality of telephone response


Overview of iip 2

Overview of IIP (2)

  • Over past 2 years Ofgem and the industry have been working to ensure that quality of supply information is

    • accurate

    • as consistent as possible across companies

  • To achieve these goals Ofgem has put in place:

    • standard definitions and guidance

    • minimum levels of accuracy for reporting interruptions

    • annual audits of data


Overview of iip 3

Overview of IIP (3)

  • New quality of service incentive scheme in place on DNOs from 1 April 2002 to 31 March 2005

  • Up to 2 % revenue at risk (£4 million)

  • Rewards and penalties for performance against own targets for number and duration of interruptions

  • Relative scheme for quality of telephone response


Audit framework

Stage 1

MPAN’s

Connectivity

Model

Ofgem’s requirements

Stage 2

Statistical

Analysis

Accuracy

Stage 3

Audit of

Reporting

Sample Size & Make-up

Sample Accuracy

Final Accuracy

Audit framework


Statistical analysis nature of interruption data

Statistical analysis-nature of interruption data

  • Data is highly skewed

    Mean = 191

    Median = 12

     = 817

  • Usual method using sampling charts based on normal distribution not applicable


Statistical analysis describing the data

Statistical analysis-describing the data

  • Data fitting software used to investigate which distribution best describes the data

  • Lognormal provided the best fit

  • Characterised by 50% value (M) and slope (S)

  • Iterative process used to define sample size


Audit process

Audit process

  • Data from each DNO analysed to determine sample sizes for low voltage and higher voltage interruptions

  • Specific incidents selected and communicated to each DNO to allow preparation

  • Each incident scrutinised by audit team to determine appropriate CIs and CMLs

  • Audited and reported values recorded in incident work book

  • Workbook for each DNO used to determine accuracy of measurement system


Results of the audit

Results of the audit

  • Main sources of inaccuracy were:

    • inaccurate reporting of customer numbers before new connectivity systems were introduced

    • manual intervention in incident reporting

    • inaccurate reporting of customer numbers for LV faults affecting only part of a feeder

    • missing restoration stages

    • incorrect incident start times


Overall impressions

Overall impressions

  • Impressive amount of work has been done by all DNOs in developing their systems to meet accuracy targets

  • Welcome the commitment that DNOs have made to the audit process

    • preparation of audit trail

    • availability of appropriate staff

  • Culture change in many DNOs emphasising importance of correct information


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