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Application of Big Data in the Energy Service Industry (Energy Efficiency & DSM)/Alexander Komakech-Akena

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Presented during Uganda Open Data/Open Science National Dialogue 25-26 April 2018.

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Application of Big Data in the Energy Service Industry (Energy Efficiency & DSM)/Alexander Komakech-Akena

  1. 1. Application of Big Data in the Energy Service Industry, (Energy Efficiency & DSM) Alexander Komakech-Akena, CEA. Eng. Kampala 26.04.2018 National Dialogue on Mainstreaming Open Data Access and Use in Uganda 25 -26th April 2018
  2. 2. Overview 1. This Big Picture • Features of Bigdata • Countries w. Pioneering Research 2. Narrowing the Picture • Case I: Application in RE industry • Case II: Application in DSM and EMS AOT Consulting (Uganda)A. Komakech-Akena This session focus on mainly application of large data repositories in the energy industry – particularly DSM and EM Open data ??? The question is always; “IS THERE GUARANTEED SECURITY” of all swath of data collected?
  3. 3. What is Bigdata? • “Volume” refers to the large data capacity, which is beyond the processing capacity of conventional database software tool. • “Variety” includes the variety of data types and data sources. • “Velocity” has several meanings, including high growth rate of data, fast data transfer, high data storage speed and processing speed, high real-time demands, etc. • “Value” means that the data has high value but the value density is low. • “Energy” refers to that the value of electric power big data is increasing in using and refining process, which can provide guidance for the energy saving and loss reduction of electric power industry. AOT Consulting (Uganda)A. Komakech-Akena
  4. 4. BigData AOT Consulting (Uganda)A. Komakech-Akena Time Weat her kV area gend er kg volu me Tariff kWh kW Hz BiG DATA Volume Value Variety Velocity Characteristics of Bigdata (4”Vs”)
  5. 5. Big Data Sources Electric power big data has many resources and mainly comes from the following information collection and management systems: – Supervisory Control And Data Acquisition (SCADA) system, – Energy Management System (EMS), – Wide area measurement system (WAMS), – Operation management system (OMS), Automatic Dispatching System, – Distribution management system (DMS), – Electricity Consumption Information collection system, – Advanced Metering Infrastructure (AMI), tele-meter reading (TMR), – power quality monitoring system, marketing business system, customer service system, electricity trading platform, – wind power and photovoltaic power prediction system, – Production management system (PMS), management information system, – enterprise resource planning (ERP), – Geographic Information System (GIS), – Weather Forecast System (WFS) AOT Consulting (Uganda)A. Komakech-Akena
  6. 6. 6 A. Komakech-Akena 0.75 0.8 0.85 0.9 0.95 1 1.05 0 20000 40000 60000 80000 100000 120000 140000 160000 P.F VA/W Duration (1min interval) PT+ (W Load) (1 min) QT (var) (1 min) ST (VA) (1 min) PFT+ (Load) (1 min) Example: Load Profile
  7. 7. 7 A. Komakech-Akena 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100 110 120 130 140 150 kVA 1:10:00 PM 11/22/2017 5:05:00 PM 11/23/2017 5 h/Div 1:03:55:00 (d:h:min:s) Start of day ShoulderOff-peakPeak Domestic & GH Lighting GH All except lighting in GH Load Profile – Flower Farm
  8. 8. Big Data AOT Consulting (Uganda)A. Komakech-Akena Longlist of potential large data in the electricity (200+) Only the few studied Generation Prediction (Wind and Solar PV) Low voltage detection Risk assessment and Early warning of distribution Smart grids and meter Energy monitoring systems Specified on next page
  9. 9. Case of EE& DSM (Assessment) AOT Consulting (Uganda)A. Komakech-Akena Observe and measure Analyse Historical with present Identification of influentional Opportunities • What energy sources input? • Where is energy being used? • How much is being consumed? • What are climatic conditions exist? (e.g. RH, temperature) • What is the cost (UGX) of a kWh, litre, m3, kg?
  10. 10. CASE I: Coffee Processing Factory
  11. 11. Base year (2016) – Energy Expenditure 11 A. Komakech-Akena HFO 12% Electricity 86% Diesel 2% Elec. 533.24 MUGX HFO 72.3 MUGX Diesel 14.5 MUGX MUGX is million Uganda Shillings HFO 32% Electricity 64% Diesel 4% Energy Source (Used) Total Energy: 4,484 GJ pa Observations
  12. 12. 12 A. Komakech-Akena kVA, demand is lower than energy consumed (kWh) 2016 – Electrical Energy Consumption Observations
  13. 13. 13 A. Komakech-Akena 0.0000 0.0050 0.0100 0.0150 0.0200 0.0250 0.0300 0.0350 0.0400 0.0450 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec SPECIFIC CONSUMPTION 2017 – Electrical Energy Consumption Best: 0.0207 kWh/kg SEU is combines direct and indirect energy in coffee processing Application
  14. 14. 14 A. Komakech-Akena - 1,000,000.00 2,000,000.00 3,000,000.00 4,000,000.00 5,000,000.00 6,000,000.00 - 20,000.00 40,000.00 60,000.00 80,000.00 100,000.00 120,000.00 1 2 3 4 5 6 7 8 9 10 11 12 1 3 2 4 5 1 Electricalenergy KilogramsofCoffeebeans Inefficiency: - Could be idle running of the machines?? - Loads that are seldom used coming on??? Specific Energy Consumption
  15. 15. 15 A. Komakech-Akena 0.00 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100 110 120 130 140 150 160 170 180 kVA 12:13:00.000 PM 7/4/2017 12:22:00.000 PM 7/5/2017 4 h/Div 1:00:09:00 (d:h:min:s) Typical Load Profile
  16. 16. 16 A. Komakech-Akena 0 20 40 60 80 100 120 140 160 Compressors Hulling section Dryer Ware house lighting Production Floor lights and fans Bulk loader Office Admin Grading, destoning, colour sorting Security lighting Measured Energy Consumption kW kVA Measurements (Results)
  17. 17. 17 A. Komakech-Akena y = 0.0166x + 19885 R² = 0.92488 - 20,000.00 40,000.00 60,000.00 80,000.00 100,000.00 120,000.00 140,000.00 - 1,000,000.00 2,000,000.00 3,000,000.00 4,000,000.00 5,000,000.00 6,000,000.00 7,000,000.00 Energy(kWh) Produc on (Kg of Coffee) Coef. Reg: 92.5%, Base energy: 19,885 kWh SEU: 0.0166 kWh/kg Simple Regression Analysis Energy Required (Y) = 0.0166 (Quantity produced) + Base energy
  18. 18. 18 A. Komakech-Akena y = 0.0166x + 19885 R² = 0.92488 - 20,000.00 40,000.00 60,000.00 80,000.00 100,000.00 120,000.00 140,000.00 - 1,000,000.00 2,000,000.00 3,000,000.00 4,000,000.00 5,000,000.00 6,000,000.00 7,000,000.00 Energy(kWh) Produc on (Kg of Coffee) Coef. Reg: 92.5%, Base energy: 19,885 kWh SEU: 0.0166 kWh/kg Simple Regression Analysis Very strong relationship
  19. 19. 19 A. Komakech-Akena EMO- Energy Monitoring 1000kVA, 415V 11kV Transformer CB CB CB CB CB CB Loading/Pre-cleaning Grading,De- stoning,sorting Dryers Administration Compressors Hulling Energy metering point Circuit Breaker KEY Savings realised for energy monitoring systems installation = 10% Advantage: Ability to control and change consumption real time.
  20. 20. Demand Response AOT Consulting (Uganda)A. Komakech-Akena Actions taken to shift demand Source: USAID DSM Program in Tanzania
  21. 21. 21 A. Komakech-Akena Conclusion 1. Benefits of using bigdata increases efficiency and lowers O&M cost 2. Its application is till at a toddler stage in Uganda and East Africa 3. Demand Response – saving on demand= CO2 emission saving, LCOE generally cheaper
  22. 22. Asante!

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