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CEYLON ELECTRICITY BOARD Determination of Electricity Demand Forecast by combining Medium Term Time Trend and Long Term Econometric Modelling Eng. Buddhika Samarasekara Chief Engineer (Generation Planning) Transmission Division Ceylon

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CEYLON ELECTRICITY BOARD

  • Eng. Buddhika Samarasekara

Chief Engineer (Generation Planning)

Transmission Division Ceylon Electricity Board Sri Lanka August 2017

Determination of Electricity Demand Forecast by combining Medium Term Time Trend and Long Term Econometric Modelling

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OUTLINE OF THE PRESENTATION

2

  • Introduction
  • Methods of Electricity Demand Forecasting
  • Methodology adopted for Electricity Demand Forecast 2018-2042
  • Medium Term Time Trend Forecast 2017-2020
  • Medium Term Time Trend Forecast comparison with Distribution

Division Forecast

  • Long Term Econometric Forecast 2021-2042

Limitations/assumptions on Socio-Economic Variables Regression equations of model Forecast of significant variables

  • Combination of Medium Term Time Trend and Long Term

Econometric Results

  • Analysis of changes in demand profile
  • Base Demand Forecast 2018-2042
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INTRODUCTION

  • Ceylon Electricity Board Act : Section 11 - 1

“It shall be the duty of the Ceylon Electricity Board to develop and maintain an efficient, coordinated and economical system of electricity supply in accordance with any appropriate license issued by the Public Utilities Commission of Sri Lanka (PUCSL)”

  • Policies and Guidelines for Electricity Demand Forecast

– National Energy Policy and Strategies of Sri Lanka in 2008 – General Policy Guidelines on the Electricity Industry for the Public Utilities Commission of Sri Lanka (PUCSL) in 2009

  • Electricity Demand Forecast for Long Term Generation Expansion

Plan (LTGEP) 2018-2037

– Time Trend Modelling Medium Term (Year 2017 to 2020) – Econometric Modelling Long Term (Year 2021 to 2042)

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METHODS OF ELECTRICITY DEMAND FORECASTING

  • Time Trend Analysis

– Analysis of historical demand data and trends

  • Econometric Analysis

– Statistically

quantify the relationship between the electricity demand and significant factors that affect the demand

  • End Use (Bottom Up) Approach

– Looking at individual users, their operating patterns, end

used devices, efficiencies etc.

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METHODOLOGY ADOPTED FOR ELECTRICITY DEMAND FORECAST 2018 - 2042

  • Combination
  • f

Time Trend modelling and Econometric approach for the preparation of 25 year electricity demand forecast

Time Trend Modelling Medium Term Forecast (Year 2017 to 2020) Econometric Modelling Long Term Forecast (Year 2021 to 2042)

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MEDIUM TERM TIME TREND FORECAST 2017 - 2020

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MEDIUM TERM TIME TREND FORECAST

  • Fit the best curve to the historical demand data (Last

4 years from 2013 to 2016) and assume that the future will follow that line

  • Considered the coefficient of determination for the

regression equation (R²)

  • Captures the recent variations in the electricity

demand with the present socio economic factors of the country

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MEDIUM TERM TIME TREND FORECAST

Determination of best fit curve

y = 9817.6e0.066x R² = 0.9667 2000 4000 6000 8000 10000 12000 14000 2013 2014 2015 2016 Demand (GWh) Year Electricity Demand

Coefficient of determination is 0.97 and exponential trend reflects the recent demand variation

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MEDIUM TERM TIME TREND FORECAST

Electricity Demand Forecast 2017-2020 from Time Trend Analysis

2000 4000 6000 8000 10000 12000 14000 16000 18000 2017 2018 2019 2020 Demand (GWh) Year Electricity Demand

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MEDIUM TERM TIME TREND FORECAST

  • Average annual demand growth rate : 6.8%

2000 4000 6000 8000 10000 12000 14000 16000 18000 2013 2014 2015 2016 2017 2018 2019 2020 Demand (GWh) Year Electricity Demand Forecast Actual

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MEDIUM TERM TIME TREND FORECAST COMPARISON WITH DISTRIBUTION DIVISION FORECAST

  • Compared the immediate 5 year sales forecasts from

CEB Distribution Divisions and LECO (Private Distribution Company) with Time Trend Forecast

14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 2017 2018 2019 2020 2021

Generation (GWh) Year All Distribution Divisions (GWh) Time Trend Forecast (GWh)

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MEDIUM TERM TIME TREND FORECAST COMPARISON WITH DISTRIBUTION DIVISION FORECAST

  • Year 2020 shows the deviation between two forecasts

due to;

▪ Distribution

divisions have considered full demand requirement

  • f

Megapolis Projects and Other new developments in Sri Lanka

▪ Consideration of full demand (MW) and load factor (%) will

result for overestimated energy demand

Average 6.8 % growth will be reasonable to represent medium term demand growth of Sri Lanka

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LONG TERM ECONOMETRIC FORECAST 2021 - 2042

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LONG TERM ECONOMETRIC FORECAST

  • Statistical analysis of the relationship between the

electricity demand and several factors which affect to the demand

  • Consider sector wise electricity demand :
  • Domestic
  • Industrial
  • Commercial (General Purpose + Hotel + Government)
  • Equation for Econometric model;

Yi = b1+b2X2i+…………. +bkiXki+ ei

Where, b₁ = Constant , Yi = Dependent variable (Electricity Demand), Xi = Independent variables, ei = Error term

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LONG TERM ECONOMETRIC FORECAST

  • Variables/factors considered for the econometric

modelling:

Sector Domestic Industrial Commercial Variables GDP GDP GDP GDP Per Capita Previous Year GDP Previous Year GDP Population Population Population

  • Avg. Electricity Price
  • Avg. Electricity Price
  • Avg. Electricity Price

Previous Year Demand Previous Year Demand Previous Year Demand Domestic Consumer Accounts Agriculture Sector GDP Agriculture Sector GDP Previous Year Dom. Consumer Accounts Industrial Sector GDP Industrial Sector GDP Service Sector GDP Service Sector GDP

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LONG TERM ECONOMETRIC FORECAST

Limitations/assumptions on Socio-Economic Variables

  • Consideration of Total GDP and Sector wise GDP as variables

–

Total GDP is the combination of following main four sectors

–

Electricity consumption for the agriculture sector in Sri Lanka is very low and therefore the consideration of total GDP doesn’t reflect the actual situation

–

Therefore, additionally considered the main two sectorial GDP for the analysis;

  • Industrial Sector GDP
  • Service Sector GDP

8% 29% 56% 7% Agriculture Industry Services Taxes Less Subsidies on Products

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LONG TERM ECONOMETRIC FORECAST

  • Analysis of Industrial and Service Sector GDP with CEB Tariff

categories

–

Industrial and Service Sector GDP further analyzed to investigate the discrepancies between CEB tariff categories

Industries Mining and Quarrying Manufacture of Food, Beverages & Tobacco Products Manufacture of Textiles, Wearing Apparel and Leather Related Products Manufacture of Wood and of Products of Wood and Cork Manufacture of Paper Products, Printing and Reproduction of Media Products Manufacture of Coke and Refined Petroleum Products Manufacture of Chemical Products and Basic Pharmaceutical Products Manufacture of Rubber and Plastic Products Manufacture of Other Non- metallic Mineral Products Manufacture of Basic Metals and Fabricated Metal Products Manufacture of Machinery and Equipment Manufacture of Furniture Other Manufacturing, and Repair and Installation of Machinery and Equipment Electricity, Gas, Steam and Air Conditioning Supply Water Collection, Treatment and Supply Sewerage, Waste, Treatment and Disposal Activities Construction Services Wholesale and Retail Trade Transportation of Goods and Passengers including Warehousing Postal and Courier Activities Accommodation, Food and Beverage Service Activities Programming and Broadcasting Activities and Audio Video Productions Telecommunication IT Programming Consultancy and Related Activities Financial Service Activities and Auxiliary Financial Services Insurance, Reinsurance and Pension Funding Real Estate Activities, including Ownership of Dwelling Professional Services Public Administration and Defence; Compulsory Social Security Education Human Health Activities, Residential Care and Social Work Activities Other Personal Service Activities

In line with Industrial Tariff In line with Commercial Tariff

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LONG TERM ECONOMETRIC FORECAST

  • Past population variation in Sri Lanka

–

Considered end year population and drop was observed in 2001 and 2011, where actual census was carried out

–

Analyzed and adjusted based on avg. annual growth rate

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LONG TERM ECONOMETRIC FORECAST

  • Derive the Regression equations for each sector using

SPSS (Statistical Package for Social Science) software

  • Considered statistical tests;
  • T statistic
  • Durbin Watson test
  • Coefficient of determination (R²)
  • F value
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LONG TERM ECONOMETRIC FORECAST

Regression equations with most significant variables

Domestic Sector Ddom (t) I = 203.55 + 1.36 GDPPC (t) i + 0.71 CAdom (t-1)

Where, Ddom (t) - Electricity demand in domestic consumer category (GWh) GDPPC (t)- Gross Domestic Product Per Capita (’000s LKR) CAdom (t-1)- Domestic Consumer Accounts in previous year (in ’000s)

Industrial Sector Di (t) i = 11.35 + 0.29 GDPi (t) i + 0.87 Di (t-1)

Where, Di (t)

  • Electricity demand in Industrial consumer categories (GWh)

GDPi

  • Industrial Sector Gross Domestic Product (in ’000 LKR)

Di (t-1)

  • Previous year Electricity demand in Industrial consumer

category (GWh)

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LONG TERM ECONOMETRIC FORECAST

Regression equations with most significant variables

Commercial Sector (General Purpose, Hotel & Government) Dcom (t) i = -104.41 + 0.16 GDPser (t) i + 0.83 Dcom (t-1)

Where, Dcom (t) - Electricity demand in Commercial consumer categories (GWh) GDPser

  • Service Sector Gross Domestic Product (in ’000 LKR)

Dcom (t-1)- Previous year Electricity demand in Commercial consumer category (GWh) Religious purpose and Street Lighting were considered in the ‘Other Sector’. This category was analysed without any links to social or demographic variables due to the diverse nature of the consumers included in this category, . Hence, the time-trend analysis was performed to predict the demand in this sector. ln (Dos(t)) = -103.30 + 0.055 t Where, t

  • Year
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LONG TERM ECONOMETRIC FORECAST

Forecast of significant variables

  • GDP Growth Rate

–

GDP growth rate projection based on the Annual Reports of Central Bank

  • f Sri Lanka (CBSL) and discussions had with them

–

Higher growth up to 7.5% and saturate in 5% beyond 2032

Year Source GDP Growth Projection (%) 2016 Annual Report 2015, Central Bank of Sri Lanka 5.8 2017 6.3 2018 7.0 2019 7.0 2020-2024 Assumptions 7.5 2025-2026 7.0 2027-2029 6.5 2030 6.0 2031 5.5 2032-2042 5.0

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LONG TERM ECONOMETRIC FORECAST

Forecast of significant variables

  • GDP Structure Change and Sector wise GDP

–

change in the GDP sector percentage was considered by assuming Industrial sector development inline with proposed government developments by compensating the Service and Agriculture sectors throughout the forecasting period

Year Industries Services Agriculture, Forestry & Fishing 2016 26.3% 56.6% 7.8% 2020 26.8% 56.5% 7.5% 2025 27.1% 56.3% 7.3% 2030 27.5% 56.2% 7.0% 2035 28.0% 56.1% 6.6%

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LONG TERM ECONOMETRIC FORECAST

Electricity Demand Forecast 2021-2042 from Econometric Approach

5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 Demand (GWh) Year Electricity Demand (GWh)

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COMBINATION OF MEDIUM TERM TIME TREND AND LONG TERM ECONOMETRIC RESULTS

5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 Demand (GWh) Year Time Trend Modelling (GWh) Econometric Modelling (GWh)

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COMBINATION OF MEDIUM TERM TIME TREND AND LONG TERM ECONOMETRIC RESULTS

5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 Demand (GWh) Year Time Trend Modelling (GWh) Econometric Modelling (GWh)

Identified a gap and analyzed the external effects for long term demand forecast

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BASE LOAD FORECAST

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Base Load Forecast = Time Trend Forecast + (Econometric Forecast + External Effects)

External effects on long term demand forecast were analyzed and considered.

10000 15000 20000 25000 30000 35000 40000 45000 50000 55000 Generation (GWh) Year Base Load Forecast Distribution Divions Forecast

14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 2017 2018 2019 2020 2021 Generation (GWh) Year All Distribution Divisions (GWh) Time Trend Forecast (GWh)

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ANALYSIS OF CHANGES IN DEMAND PROFILE

Present Daily Demand Profile Shape

  • Overall country demand profile with night peak dominant
  • Colombo city (Capital of Sri Lanka) demand profile with day peak dominant

500 1000 1500 2000 2500 3000 0:30 1:30 2:30 3:30 4:30 5:30 6:30 7:30 8:30 9:30 10:30 11:30 12:30 13:30 14:30 15:30 16:30 17:30 18:30 19:30 20:30 21:30 22:30 23:30 Demand (MW) Time Peak Day (25-04-2016) Colombo City (25-07-2016)

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ANALYSIS OF CHANGES IN DEMAND PROFILE

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Provincial Analysis – Night Peak, Day Peak & Off Peak

Western Province Southern Province

200 400 600 800 1000 1200 JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL 2011 2012 2013 2014 2015 2016 Demand (MW) Year Night Peak Day Peak Off Peak 50 100 150 200 250 JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL 2011 2012 2013 2014 2015 2016 Demand (MW) Year Night Peak Day Peak Off Peak

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ANALYSIS OF CHANGES IN DEMAND PROFILE

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North-Central/North-Western/Central Provinces Sabaragamuwa/Uva/Eastern Provinces

100 200 300 400 500 600 700 JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL 2011 2012 2013 2014 2015 2016 Demand (MW) Year Night Peak Day Peak Off Peak 50 100 150 200 250 300 350 400 450 JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL 2011 2012 2013 2014 2015 2016 Demand (MW) Year Night Peak Day Peak Off Peak 10 20 30 40 50 60 70 80 90 100 JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL OCT JAN APR JUL 2011 2012 2013 2014 2015 2016 Demand (MW) Year Night Peak Day Peak Off Peak

Northern Province

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ANALYSIS OF CHANGES IN DEMAND PROFILE

31 500 1000 1500 2000 2500 JAN MAY SEP JAN MAY SEP JAN MAY SEP JAN MAY SEP JAN MAY SEP JAN MAY 2011 2012 2013 2014 2015 2016 Demand (MW) Year Night Peak Day Peak Off Peak 200 400 600 800 1000 1200 1400 JAN MAY SEP JAN MAY SEP JAN MAY SEP JAN MAY SEP JAN MAY SEP JAN MAY 2011 2012 2013 2014 2015 2016 Demand (MW) Year Night Peak Day Peak Off Peak

With Western Province Without Western Province

Overall Country Representation – Night Peak, Day Peak & Off Peak

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ANALYSIS OF CHANGES IN DEMAND PROFILE

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Future Projected Daily Load Profile

▪ Change in the load profile shape (Night peak → Day Peak) assumed in year 2030.

▪ Higher energy demand in day time

0% 20% 40% 60% 80% 100% 120% 0:30 1:30 2:30 3:30 4:30 5:30 6:30 7:30 8:30 9:30 10:30 11:30 12:30 13:30 14:30 15:30 16:30 17:30 18:30 19:30 20:30 21:30 22:30 23:30 Precentage (%) Time Normal Day with Night Peak Day Peak=0.9*Night Peak Day Peak =Night Peak Day Peak =1.05*Night Peak Day Peak=1.1*Night Peak Day Peak=1.15*Night Peak

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ANALYSIS OF CHANGES IN DEMAND PROFILE

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System Load Factor Forecast for Peak Determination

  • Increasing trend of system load factor and 72.5% maximum by 2030
  • Further improvements can be achieved with DSM measures

50 55 60 65 70 75 Load Factor (%) Year Past LF Future LF

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BASE DEMAND FORECAST 2018-2042

34 Year Demand Net Loss* Net Generation Peak Demand (GWh) (%) (GWh) (MW)

2017 13656 9.92 15160 2585 2018 14588 9.88 16188 2738 2019 15583 9.84 17285 2903 2020 16646 9.81 18456 3077 2021 17478 9.77 19370 3208 2022 18353 9.73 20331 3346 2023 19273 9.69 21342 3491 2024 20242 9.65 22404 3643 2025 21260 9.61 23522 3804 2026 22332 9.58 24697 3972 2027 23459 9.54 25933 4149 2028 24639 9.50 27225 4335 2029 25867 9.46 28570 4527 2030** 27164 9.42 29990 4726 2031 28388 9.38 31328 4939 2032 29637 9.35 32692 5157 2033 30926 9.31 34099 5381 2034 32251 9.27 35546 5612 2035 33642 9.23 37063 5854 2036 35090 9.19 38642 6107 2037 36613 9.15 40302 6372 2038 38165 9.12 41992 6642 2039 39733 9.08 43699 6915 2040 41324 9.04 45431 7193 2041 42967 9.02 47227 7481 2042 44700 9.00 49121 7784

5 Year Average Growth (2018-2022) 5.9% 5.9% 5.1% 10 Year Average Growth (2018-2027) 5.4% 5.4% 4.7% 20 Year Average Growth (2018-2037) 5.0% 4.9% 4.5% 25 Year Average Growth (2018-2042) 4.8% 4.7% 4.4%

* Net losses include losses at the Transmission & Distribution levels and any non-technical losses, Generation (Including auxiliary consumption) losses are

  • excluded. This forecast will vary depend on the hydro thermal generation mix of the future.

** It is expected that day peak would surpass the night peak from this year onwards

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THANK YOU

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