1. PORTFOLIO RISK ANALYSIS BASED GENERATION EXPANSION PLANNING Presenter: Nguyen Xuan Phuc Asian Institute of Technology School of Environment, Resources and Development Energy Field of Study M ay 17 th, 2010 Thesis competition

2. Content Result and discussion Review of methodology Literature review Conclusion objectives Introduction

3. I. Introduction Generation expansion planning: analyzing, evaluating when and what new plants must be added A proper generation expansion planning of a nation is very important Risk analysis based electricity generation planning is one of new approaches for GEP

4. I. Introduction In Viet Nam, thermal power plants account for 58% of total capacity. Price of fuels usually fluctuate Capital cost of Renewables is uncertain Global warming is one of the biggest concerns of the world

5. I. Introduction Source: EIA, 7/2009

6. I. Introduction Source: EIA, 7/2009

7. II. Review of Objectives The effect of CO2 trading on efficient curve Technology and fuel mix in Viet Nam during 2013-2030 Business as usual Risk analysis based model CO2 trading based model Developing efficient curve Risk & CO2 trading model

8. I. Review of Objectives

9. III. Literature review  Huanga and Wu (2007) introduce a model that integrates portfolio theory into a vintage electricity planning frame-work. In this model, risk of generating cost is introduced for volatile fuel prices and uncertainty of technological change and capital cost reduction  Zon and Fuss (2006) use clay-clay-vintage-portfolio model that starts from the notion that investment in electricity production equipment is irreversible to accept a higher degree of fuel price risk by doing so. The model considers different variances and covariances in the respective prices and rates of technical progress.  Krey and Zweifel (2005) use financial portfolio theory to investigate energy mixes of Switzerland. The efficient frontier is constructed estimating time- varying variances and covariances in energy prices using GARCH models  Awerbuch and Berger (2003) apply mean-variance portfolio optimization techniques in the style of Markowitz (1952) to the energy market of the European Union.

10. IV. Review of methodology 4.1 Business as usual case 4.2 Portfolio risk analysis based model 4.3 CO2 trading based model 4.4 Risk and CO2 trading based model

11. 4.1 BAU case Objective function: Minimize PVTC Subject to: (Demand constraint) (Reliability constraint) (Resource availability) (Plant availability) (Annual Energy)

12. 4.1 BAU case Fuel cost Variable O&M Fixed O&M Capital cost

13. 4.2 Risk analysis based model Objective function: Minimize RW( PVTC) Subject to: Power demand constraint Plant availability constraint Annual energy constraint Reliability constraint Resource availability constraint

14. 4.2 Risk analysis based model RW(PVTC) = PVTC + *Var(PVTC) Where: PVTC: Present value of total generation cost :Risk-aversion parameter ( is positive) Var(PVTC): Variance of generating cost. Var(PVTC) PVTC 11 22 33 Efficient curve Indifferent curve

15. 4.2 Risk analysis based model Where:

16. 4.3 CO2 trading based model Objective function: Minimize Net PVTC Subject to: Power demand constraint Plant availability constraint Annual energy constraint Reliability constraint Resource availability constraint

17. 4.3 CO2 trading based model net(PVTC) = PVTC – Revenue from CO2 trading Revenue from CO2 trading = Eo(t) is the CO2 emission in year t in the BAU case E(t) is the CO2 emission in year t when CO2 trading is considered Cp is CO2 market price Df(t) discounted rate in year t

18. 4.4 Risk & CO2 trading based model Objective function: Minimize RW(PVTC) – Revenue from CO2 trading Subject to: Power demand constraint Plant availability constraint Annual energy constraint Reliability constraint Resource availability constraint

19. V. Result and discustion 5.1 Business as usual case 5.2 Portfolio risk analysis 5.3 Effect of CO2 trading 5.4 Combination of risk and CO2 trading

20. 5.1 Business as usual case  Total cumulative capacity: 131,794 MW  Total cumulative generation: 6,189 TWh  PVTC: $79.5 billion  Total cumulative CO2 emission: 4.42 billion ton

21. 5.2 Portfolio risk analysis Efficient curve Cumulative capacity mix by 2030 at selected risk- aversion factors Cumulative generation mix during 2013-2030 at selected risk-aversion factors

22. 5.2 Risk analysis based model Efficient curve lamda1510508090100 Change in PVTC5%10%15%18%19% Change in risk-45%-61%-66%-67%

23. 5.2 Risk analysis based model Total cumulative installed capacity by 2030 - Total installed capacity increases with the increase in risk-aversion factor - Gas is substituted by hydro and coal - At lamda = 50, solar becomes attractive

24. 5.2 Risk analysis based model Total cumulative generation during 2013-2030 - Total cumulative generation during 2013-2030 is - Gas and coal is substituted by hydro and nuclear - At lamda = 10, generation from oil and gas is inconsiderable

25. 5.3 Effect of CO2 trading Effect of CO2 trading on total cumulative installed capacity by 2030 at selected CO2 prices Effect of CO2 trading on total cumulative generation during 2013-2030 at selected CO2 price Effect of CO2 trading on annual CO2 emission during 2013-2030 Effect of CO2 trading on PVTC

26. 5.3 Effect of CO2 trading Total cumulative installed capacity by 2030 - Total installed capacity is almost the same at different prices - Coal is substituted by hydro when CO2 market price increases - Solar is not attractive

27. 5.3 Effect of CO2 trading Total cumulative generation during 2013-2030 - Total cumulative generation during 2013-2030 is 6189 TWh - Generation from gas and coal is substituted by generation from hydro

28. 5.3 Effect of CO2 trading Total cumualtive CO2 emission Item CO 2 price, $/tonCO 2 01020304050 Cumulative CO 2 emission during 2013-2030 (10 9 ton CO2) 4.423.873.773.7 CO 2 emission reduction (%) -121516

29. 5.3 Effect of CO2 trading Total cost at selected CO2 prices - Total investment and O&M cost increase with the increase in CO2 pirce - Revenue from CO2 trading increases - Net PVTC decreases

30. 5.4 Combination of risk and CO2 trading - Changing in efficient curve - Changing in hydro - Changing in coal - Changing in oil and gas - Changing in renewables

31. 5.4 Combination of risk and CO2 trading Changing in efficient curve - If CO2 price increases, the slope of corresponding curve will increase - At risk aversion factor >= 50, all the curves coincide - 2 curves corresponding to cp=40 and cp=50 are the same

32. 5.4 Combination of risk and CO2 trading Changing in efficient curve

33. VI. Conclusion - During 2013 – 2030: nuclear becomes an important source. - Hydro, nuclear, wind and solar reduces the volatility of fuel cost and CO2 emission - By considering CO2 trading and fuel risk, the share of clean technologies in technology mix will increase. - CO2 trading results to the reduce in net PVTC

34. Thank you!