Dorinda Blades

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The impact of inaccurate process variables on
Energy Balancing

• Dorinda Blades
• Maboe Maphaka

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Table of Contents

• Introduction
• Desktop Research on process variables (Theory)
• Field Results (Practice)
• Customer Network Links (CNL)
• Lessons Learnt

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INTRODUCTION

• Background to the Distribution Energy Loss
  Problem
• Total Distribution Energy Losses (technical non
  technical) have been increasing in the past few
  years (currently estimated at 6 of total energy
  purchased)
• Losses are gaining prominence due to increasing
  load growth and diminishing excess generation
  capacity
• In response to this problem Eskom Distribution
  initiated a number of projects to reduce or
  manage Losses
• One of the critical tool used by Revenue
  Protection in the endeavor to reduce losses is
  area based targeting . based on the Energy
  Balancing Report .

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Desktop Research on process variables

• In a paper presented at the SARPA Convention in
  Durban in 2004, the following were tabled as
  variables impacting the Energy Balancing Process
  and hence the results thereof
• Accurate and working meters
• Accurate and timeous statistical and customer
  meter readings
• Accurate and timeous Credit Dispensing Units
  (CDUs) uploads
• Accurate linking of customer to the network
• Accurate knowledge of network configuration
  during normal and abnormal conditions

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Desktop Research on process variables
How are these variables inter-relate?
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Desktop Research on process variables
Energy Balancing Report
Loss Reduction Action Plan
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Field Results (Practice)

• We found that in deed the identified process
  variables have an impact on the Energy Balancing
  Process. However the degree will differ from
  variable to variable depending on its severity
  in a particular area
• In Eskom we found data management to be a
  critical weak link in this regard, in particular
  the following highlighted
• Accurate and working meters
• Accurate and timeous statistical and customer
  meter readings
• Meter management process
• Accurate and timeous Credit Dispensing Units
  (CDUs) uploads
• Accurate linking of customer to the network
• Accurate knowledge of network configuration
  during normal and abnormal conditions

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CRITICAL DATA FOR ENERGY BALANCING
CRITICAL DATA FOR ENERGY BALANCING

Energy Delivered
KWh sales
CUSTOMER NETWORK LINK (CNL)
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Customer Network Links

• Approximately 95 of all customers have a CNL
  with the confidence
• level of accuracy of the CNL at approximately 70
• CNL Data KPI measures for
• Completeness (number of customers linked to a
  transformer)
• Accuracy (work orders not affected by locations /
  outage notification correct)
• Business Rules
• Total number of LPU, SPU and PP premises
• Total number of LPU, SPU and PP premises added
• of premises that links to the Engineering
  database
• Total number of premises not linked to the
  Engineering database
• Premises incorrectly linked
• Premises linked to a duplicate Transformer or
  Pole Number
• Transformers / pole unknown
• Technical Service Areas incorrectly linked

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CNL Data Measurements
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Data Reports for Revenue Protection

• No Sales / Low consumption reports
• Advances on terminated service points
• Tamper fees raised and not yet paid
• Faulty meters faulty, replaced, not replaced

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Feeder Balancig Data Challenges

• No Sales / Low consumption reports
• Advances on terminated service points
• Tamper fees raised and not yet paid
• Faulty meters faulty, replaced, not replaced

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Feeder Balancig Data Challenges

• Meter change outs
• Unallocated customers
• Unidentified customers
• Consistency - whether the values of attributes
  managed or presented in multiple locations are
  the same such as network changes

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Lessons Learnt

• Utilities should have robust systems to keep
  accurate records to facilitate effective loss
  management programs
• The systems should be integrated to ensure smooth
  flow of information from one to the next
• Input data into the systems should be tightly
  managed
• Perform desk top audits before going out in the
  field this could save you a fruitless field
  visit
• Avoid using one months information for decision
  making, especially in areas where estimations are
  high in this case trends are more reliable than
  monthly figures
• GIS linked systems have an advantage over most
  traditional systems when coming to locating losses

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Thank You