Stand-Alone vs. Grid Extension for Electrification in Kenya

1. Stand-Alone vs. Grid Extension for Electrification in Kenya Marianne Zeyringer University of Natural Resources and Life Sciences, Vienna (BOKU) & European Commission Joint Research Centre- Institute for Energy Ulrich Morawetz, University of Natural Resources and Life Sciences, Vienna (BOKU) ShonaliPachauri, International Institute for Applied Systems Analysis (IIASA) Erwin Schmid, University of Natural Resources and Life Sciences, Vienna (BOKU) Johannes Schmidt, University of Natural Resources and Life Sciences, Vienna (BOKU)

2. Objectives Background Methodology Results Conclusion Outlook Outline • Objectives • Background • Methodology • Results • Conclusion • Outlook

3. Objectives Background Methodology Results Conclusion Outlook Objectives • Develop a model which helps to decide between off- grid and grid technologies for electrification combing supply and demand side analysis • Apply the model to Kenya and derive policy conclusions

4. Objectives Background Methodology Results Conclusion Outlook Important Numbers on Kenya • GDP/ capita: USD 738 (World Bank, 2010) • Population: 36 millions (estimates for 2010 – Kenyan Bureau of Statistics ) • Population density: 68/km2 • Kenya’s current electricity supply to grid (Ministry of Energy, 2005) • Hydropower: 75% • Thermal: 14% • Geothermal: 11% • Population without electricity in SSA (IEA, 2002) • Total: 77% • Urban: 49% • Rural: 92% • Population without electricity in Kenya (Kenyan Bureau of Statistics, 2003) • Total: 86% • Urban: 49% • Rural: 96%

5. Objectives Background Method Results Conclusion Outlook The rate of Electrification deviates strongly from the Targets

6. Objectives Background Methodology Results Conclusion Outlook Overview of the Methodology 1) Analysis of current patterns of consumption 2) Demand Side Regression Model 3) Supply Side Least Cost Optimization Model

7. Objectives Background Methodology Results Conclusion Outlook 1) Analysis of current patterns of consumption • Kenyan Integrated Household Budget Survey 2005/2006: • 13,340 households • Focus in the analysis on electricity used for lighting • Division in rural and urban expenditure quintiles

8. Objectives Background Methodology Results Conclusion Outlook 2) Demand Side Regression Model- I • Aim: forecast latent demand (demand if there was no constraint on the access) • Sample: connected households (demand ≥0 kWhs) and households living within 100 metres of a connected household (demand= 0kWhs) reduction from 13,340 to 3,650 households • Data is censored from the left side use of a Tobit/ Censored Regression Model

9. Objectives Background Methodology Results Conclusion Outlook 2)Demand Side Regression Model- II ED=b0 + b1ex + b2exw+ b3edu + b4nr+ b5val + b6si + b7dy + u ED= electricity Demand in kWh edu= number of years of education of household head nr= number of cattle ex= expenditures on durables val= value of the house exw= expenditures on water si= size of the land dy= district dummies

10. Objectives Background Methodology Results Conclusion Outlook 3) Supply Side Least Cost Optimization Model- I • Least cost optimization model: - 3 options: 1. grid extension 2. stand-alone diesel generators 3. stand- alone photovoltaic systems • Find cost optimal solution for every grid cell (2000km2)

11. Objectives Background Methodology Results Conclusion Outlook 3) Supply Side Least Cost Optimization Model- II • Satisfy the demand in every grid cell: - Aggregation of population in every grid cell - Latent demand per capita from the demand model

12. Objectives Background Methodology Results Conclusion Outlook 3) Supply Side Least Cost Optimization Model- III Indata solar irradiation: hourly values from 1985 to 2004 with a spatial resolution of 30 km2

13. Objectives Background Methodology Results Conclusion Outlook 3) Supply Side Least Cost Optimization Model- iv Scenarios • Baseline scenario: input parameters as they are in 2005 • High demand scenario: increase in income and demand as predicted by the Kenyan government for 2020 • High electricity generation (grid) scenario: increase in electricity generation costs  predicted by the Kenyan Ministry of Energy for 2012-2015

14. Objectives Background Method Results Conclusion Outlook 1) Main Source of Lighting

15. Objectives Background MethodlogyResults Conclusion Outlook 1) Average Electricity Consumption Of Connected Households

16. Objectives Background Method Results Conclusion Outlook 2) Demand Side Regression Model Households consuming electricity: actual vs. latent demand

17. Objectives Background Method Results Conclusion Outlook 3) Baseline Scenario Baseline Scenario Population Density

18. Objectives Background Methodology Results Conclusion Outlook 3)High Demand Scenario Baseline Scenario High Demand Scenario

19. Objectives Background Methodology Results Conclusion Outlook 3) High Electricity Generation Costs Scenario Baseline Scenario High Electricity Generation costs scenario

20. Objectives Background Methodology Results Conclusion Outlook Conclusions • Gap in actual and latent demand • Adapt electrification to the features of a region • Importance of grid infrastructure planning • Importance of diversification of grid electricity generation • Effect of changes in input parameters is lower than expected

21. Objectives Background Methodology Results Conclusion Outlook Outlook • Include commerce and industries • Include limits of grid transportation and generation capacities • Include geographic features