NTLs and the Supply Challenge Proving the Yelland Thesis William C. Erasmus CEM (FSAIEE)

Introduction In South Africa we have adequate energy (kWhs) but we do not have the power capacity (MW or GW) to meet the demand for energy This status unequivocally creates an opportunity to unlock power capacity by reducing energy demand through DR and reducing NTLs thereby deferring new investment into generation, transmission and distribution

What are Non-Technical Losses – NTL’s ? Commercial Administrative Receivables Fraud may be: –Energy theft (illegal connections to the network) –Meter fraud or by-passes Meter errors Fraud may be implemented by the customer or by professionals

NTL’s : Commercial High cost of managing aging accounts –Default Customers – Delayed Payments and no payment Defaulter consumer (*)  some will delay, some will not pay Volume and aging of receivable accounts has HIGH CO$T IMPACT  consumer used energy but did not pay on time, which increase company’s financial expenses * Usually not accounted as NTL. Administrative Receivables

NTL’s : Commercial Internal process flaws and database inconsistencies (Average 20% of all problems identified by C&I Audits in SA) –Human and system errors (*) –Incorrect tariff (*) –Internal fraud (*) * Usually not accounted. Administrative Receivables

Non-Technical Loss NTLs and the Supply Challenge NTL detection Revenue Increase Energy Consumption Reduction Time Energy Energy paid Energy consumption 32%* 68%* * Figures from recent studies in Brazil Cost-free DSM

Advantages of NTL Reduction NTLs decrease  double benefit –Significant energy consumption reduction - unlocked significant additional power capacity –Revenue increase Energy consumption reduction –Diminishes total supply –Reduces the demand-peak Therefore, reducing NTLs has a huge benefit in demand management without additional investments –Cost-free DSM benefits

Brazilian Case Studies Northeast Brasil Southeast Brasil Brazil SAELPA Area: Km 2 Consumers : 837 thousand NTLs : 9% Area: Km 2 Consumers : 131 thousand NTLs: 4% Area: Km 2 Consumers : 436 thousand NTLs: 4% Area: Km 2 Consumers : 378 thousand NTLs: 2% Date: April / 04

Energisa (SAELPA) Energisa implemented solutions to reduce NTLs from Feb-03 to Nov-04 at their following distribution companies:  ENERGIPE – Feb/04  SAELPA/CELB – May/04  CFLCL/ CENF – Nov/04 RESULTS AFTER 1-Year –SAELPA has 850 thousand customers in one of poorest states of Brazil –Productivity increase = 90.25% –Energy consumption reduction = 40 GWh –Additional net revenue in 1 year = US$ 2,9 million

CEMAR implemented a solution in 2006 and results for 2007 are: –Cemar has 1,35 million customers –Productivity increase = % –Energy consumption reduction = 227 GWh –Additional net revenue in 1 year = US$ 16,9 million Cemar

Prod GES = { [  i n = 1 (  j m = 1 Ebc A i C j + Ei A i C j ) ] + Ecr GES } _________________________________________________ [  i n = 1 (  j m = 1 Cost A i C j ) ] Measurement of Productivity Non-Technical Loss NTL Detection Revenue Increase Energy Consumption Reduction Time Energy Energy Paid Energy Consumption

Opportunity Cost/Benefit Differentiator Cost to implement a solution to identify sources of NTLs for, say, Jhb Metro (1.4 million consumers) = 20 million USD and deliver result in 1-year with a ROI in excess of 400% New thermal plant of equivalent unlocked capacity would cost in the region of between 10 to 20 billion USD and take nearly 9-years to complete The opportunity for the economy is a no-brainer

The Important ? Business/Economy needs to ask Government/Eskom why the economy is been held to ransom a situation which is critically effecting employment, job creation and the countries ability to remain globally competitive

Thank You

Optimization Problem There are several activities to reduce NTLs such as inspections, anti-theft cable-box-seal, AMR, “SMART” meters etc. Activities can’t be applied to all customers because of economic restrictions hence, distribution companies only have budget for a portion of consumer base The consequential questions are: –What is the most-effective (ROI) activity for each customer ? –Who are the top customers to apply each activity ? As a result we are confronted with an optimization problem