Electricity Generation in Fiji
Crawford School of Economics and Government
Electricity Generation in Fiji Assessing the Impact of Renewable Technologies on Costs and Financial Risk
Matthew Dornan and Frank Jotzo Resource Management in Asia-Pacific Program Crawford School of Economics and Government
Contact: [email protected]
AARES, February 2011
Contents • • • • •
Context Research Purpose Modelling Method and Results Findings Refining the Model
Context – Impact of Oil Price Volatility on Cost of Electricity in the Region 250
200 US$/barrel F$/barrel
150
100
50
01/09
07/08
01/08
07/07
01/07
07/06
01/06
07/05
01/05
07/04
01/04
07/03
01/03
07/02
01/02
0
Context: Response of the Fiji Electricity Authority (FEA) • FEA: target of 90% of power from renewables by 2011 • Primary goal: – Lower costs and reduced vulnerability
Figure 3.2 Grid-Based Electricity Generation in Fiji, 2009 1% 2%
Hydro-based generation Oil-based generation Biomass and Bagasse Wind and Solar pow er
39%
900
58%
800 700
Total GWh
600 500
Hydro-based generation Oil-based generation
400
Wind-based generation Total generation (MWh)
300 200
– Environmental concerns are of secondary importance • Reflected in language of National Energy Policy: “provision of adequate, secure and
100 0 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
cost-effective energy supplies”
Research Purpose • Gap in knowledge – Despite FEA investments, there have been few attempts to assess or quantify the impact of renewables on financial risk in Fiji‟s electricity sector
• Purpose of research: – Apply a method for simultaneously assessing the potential contribution of renewable technologies to the „security‟ and cost of electricity supply in Fiji
Method: Stand-alone Costs of Electricity in Fiji Generation Costs (FJc/kWh) 0
20
40
60
80
100
Hydro-power Oil-power Bagasse
Biomass Wind-power Solar-power Geothermal
Existing Costs Future Costs
Limitations of Stand-alone Least Cost Analysis • Does not incorporate risk • Does not address the variability of output from renewable sources or required backup capacity • Capacity factors are assumed
• Increasingly, portfolio analysis is used to value electricity sector investments • Awerbuch and Berger (2003) • Application of portfolio theory to Europe + United Kingdom, United States, Mexico
Method: Features of the Model Incorporating risk • Model improves on least-cost analysis by estimating financial risk for each technology, using: – STDev of historical cost of oil-based generation – IEA data on cost variation associated with renewables
• The model measures the impact of each technology on the risk profile of the portfolio
Method: Features of the Model • Incorporating risk – capital cost, O&M, fuel Financial Risk by Technology STDev of past costs 0.00 Oil - New Oil - Existing Hydro - New Hydro - Existing Bagasse - New Biomass - New Biomass - Existing Wind - New Wind - Existing Solar - New Geothermal - New
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
Method: Features of the Model This model goes beyond standard portfolio theory by addressing the issue of intermittency and backup capacity requirements: – Calculating output of renewables capacity endogenously in the model
These are used to calculate: – Required oil-based backup capacity for a given renewable capacity – Actual oil-based generation
Method: FEA Scenario • Model also sets limits to production from renewables based on resource availability • Investment in renewables is compared to FEA 2025 scenario, where: – Annual output forecast to reach 1435 GWh – The FEA reference scenario forecasts: • 52.3 MW additional hydro-based generation capacity • 25.8 MW bagasse capacity • 20 MW additional biomass capacity
Cost and Risk Implications of Portfolios of Electricity Generation Technologies, 2025
Cost: Expected levelised average cost (FJD/kWh)
0.325 Existing Renewable Capacity
0.320
FEA plus Wind, Solar FEA with no bagasse
0.315 FEA Scenario
0.310 FEA plus Hydro, Geothermal
0.305 0.300 0.295 0.290 0.285
FEA plus Bagasse, Geothermal
FEA plus double Biomass and Geothermal FEA plus Hydro, Bagasse and Geothermal
0.280 0.025
0.035
0.045
0.055
Existing plus Bagasse, Biomass, Geothermal (not Hydro)
0.065
0.075
Risk: Standard deviation of expected levelised average cost
0.085
0.095
Cost and Risk Implications of Changes in 2025 Electricity Production 0.325
FEA Scenario with Higher Demand (1722 GWh per year)
0.320
Existing Renewable Capacity
Cost: Expected levelised average cost (FJD/kWh)
0.315 FEA Scenario (1435 GWh per year)
0.310 0.305
FEA Scenario with Lower Demand (1148 GWh per year)
0.300 0.295 FEA plus double 0.290 Biomass and Geothermal FEA plus Hydro, 0.285 Bagasse and Geothermal 0.280 0.025 0.035 0.045
0.055
0.065
0.075
0.085
Risk: Standard deviation of expected levelised average cost
0.095
Findings (1) Modelling results indicate that: • Investment in low cost renewables modestly lowers average generation costs, and significantly reduces financial risk – There are significant energy security benefits to investments in renewables beyond those predicted by the FEA, as well as moderate cost reductions – Low cost renewables in Fiji include: • Biomass, Bagasse, Geothermal, Hydro
Findings (2) • Investment in high cost renewables such as wind and solar-power reduces financial risk but increases average generation costs
• The scenario where there is no production from bagasse has higher expected generation costs and involves more financial risk
Findings (3) • The worst case scenario is one where there is no further investment in renewables • Limiting electricity demand (through say, energy efficiency measures) has a similar, but less significant impact on expected generation costs and financial risk compared to investment in low cost renewables
Refining the Model • Sensitivity analysis: – Both 5% and 10% discount rates (consistent with IEA)
• Incorporation of energy efficiency measures – Data availability an issue
Thank You
Electricity Generation in Fiji Assessing the Impact of Renewable Technologies on Costs and Financial Risk
Matthew Dornan and Frank Jotzo Resource Management in Asia-Pacific Program Crawford School of Economics and Government
Contact: [email protected]
AARES, February 2011
Contents • • • • •
Context Research Purpose Modelling Method and Results Findings Refining the Model
Context – Impact of Oil Price Volatility on Cost of Electricity in the Region 250
200 US$/barrel F$/barrel
150
100
50
01/09
07/08
01/08
07/07
01/07
07/06
01/06
07/05
01/05
07/04
01/04
07/03
01/03
07/02
01/02
0
Context: Response of the Fiji Electricity Authority (FEA) • FEA: target of 90% of power from renewables by 2011 • Primary goal: – Lower costs and reduced vulnerability
Figure 3.2 Grid-Based Electricity Generation in Fiji, 2009 1% 2%
Hydro-based generation Oil-based generation Biomass and Bagasse Wind and Solar pow er
39%
900
58%
800 700
Total GWh
600 500
Hydro-based generation Oil-based generation
400
Wind-based generation Total generation (MWh)
300 200
– Environmental concerns are of secondary importance • Reflected in language of National Energy Policy: “provision of adequate, secure and
100 0 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
cost-effective energy supplies”
Research Purpose • Gap in knowledge – Despite FEA investments, there have been few attempts to assess or quantify the impact of renewables on financial risk in Fiji‟s electricity sector
• Purpose of research: – Apply a method for simultaneously assessing the potential contribution of renewable technologies to the „security‟ and cost of electricity supply in Fiji
Method: Stand-alone Costs of Electricity in Fiji Generation Costs (FJc/kWh) 0
20
40
60
80
100
Hydro-power Oil-power Bagasse
Biomass Wind-power Solar-power Geothermal
Existing Costs Future Costs
Limitations of Stand-alone Least Cost Analysis • Does not incorporate risk • Does not address the variability of output from renewable sources or required backup capacity • Capacity factors are assumed
• Increasingly, portfolio analysis is used to value electricity sector investments • Awerbuch and Berger (2003) • Application of portfolio theory to Europe + United Kingdom, United States, Mexico
Method: Features of the Model Incorporating risk • Model improves on least-cost analysis by estimating financial risk for each technology, using: – STDev of historical cost of oil-based generation – IEA data on cost variation associated with renewables
• The model measures the impact of each technology on the risk profile of the portfolio
Method: Features of the Model • Incorporating risk – capital cost, O&M, fuel Financial Risk by Technology STDev of past costs 0.00 Oil - New Oil - Existing Hydro - New Hydro - Existing Bagasse - New Biomass - New Biomass - Existing Wind - New Wind - Existing Solar - New Geothermal - New
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
Method: Features of the Model This model goes beyond standard portfolio theory by addressing the issue of intermittency and backup capacity requirements: – Calculating output of renewables capacity endogenously in the model
These are used to calculate: – Required oil-based backup capacity for a given renewable capacity – Actual oil-based generation
Method: FEA Scenario • Model also sets limits to production from renewables based on resource availability • Investment in renewables is compared to FEA 2025 scenario, where: – Annual output forecast to reach 1435 GWh – The FEA reference scenario forecasts: • 52.3 MW additional hydro-based generation capacity • 25.8 MW bagasse capacity • 20 MW additional biomass capacity
Cost and Risk Implications of Portfolios of Electricity Generation Technologies, 2025
Cost: Expected levelised average cost (FJD/kWh)
0.325 Existing Renewable Capacity
0.320
FEA plus Wind, Solar FEA with no bagasse
0.315 FEA Scenario
0.310 FEA plus Hydro, Geothermal
0.305 0.300 0.295 0.290 0.285
FEA plus Bagasse, Geothermal
FEA plus double Biomass and Geothermal FEA plus Hydro, Bagasse and Geothermal
0.280 0.025
0.035
0.045
0.055
Existing plus Bagasse, Biomass, Geothermal (not Hydro)
0.065
0.075
Risk: Standard deviation of expected levelised average cost
0.085
0.095
Cost and Risk Implications of Changes in 2025 Electricity Production 0.325
FEA Scenario with Higher Demand (1722 GWh per year)
0.320
Existing Renewable Capacity
Cost: Expected levelised average cost (FJD/kWh)
0.315 FEA Scenario (1435 GWh per year)
0.310 0.305
FEA Scenario with Lower Demand (1148 GWh per year)
0.300 0.295 FEA plus double 0.290 Biomass and Geothermal FEA plus Hydro, 0.285 Bagasse and Geothermal 0.280 0.025 0.035 0.045
0.055
0.065
0.075
0.085
Risk: Standard deviation of expected levelised average cost
0.095
Findings (1) Modelling results indicate that: • Investment in low cost renewables modestly lowers average generation costs, and significantly reduces financial risk – There are significant energy security benefits to investments in renewables beyond those predicted by the FEA, as well as moderate cost reductions – Low cost renewables in Fiji include: • Biomass, Bagasse, Geothermal, Hydro
Findings (2) • Investment in high cost renewables such as wind and solar-power reduces financial risk but increases average generation costs
• The scenario where there is no production from bagasse has higher expected generation costs and involves more financial risk
Findings (3) • The worst case scenario is one where there is no further investment in renewables • Limiting electricity demand (through say, energy efficiency measures) has a similar, but less significant impact on expected generation costs and financial risk compared to investment in low cost renewables
Refining the Model • Sensitivity analysis: – Both 5% and 10% discount rates (consistent with IEA)
• Incorporation of energy efficiency measures – Data availability an issue
Thank You
