1. LCOE as a policy tool to design RES-E support schemes Tourkolias C
LCOE as a policy tool to design RES-E support schemes Tourkolias C., Vougiouklakis Y., Papandreou V., Tigas K., Nakos C., Theofilidi M.
6th International Scientific Conference on "Energy and Climate Change”
Athens, 10/10/2013
Christos Tourkolias
Energy expert
Division for Energy Policy and Planning

2. Problem Significant variations of RES-E production cost 10/10/2013
Source: Ministry of Energy, Environment and Climate Change (2012)

3. Aim of the paper
Development of a methodological framework for the effective:
evaluation of the existing RES-E support mechanisms
design of the future RES-E support mechanisms
Examination of the potential fluctuations of RES-E production cost due to the uncertainties of the input parameters.
Indicative implementation of the proposed methodology for a typical wind and photovoltaic plant.
Potential utilization of the methodology for the rest types of RES.
6th International Scientific Conference on "Energy and Climate Change” /10/2013

4. Methodological approach
Identification of the input parameters
Technological
Cost
Macro-economic
Financial
Implementation of the methodological framework
LCOE
Monte Carlo
Evaluation and exploitation of the obtained results
Assessment of existing and design of new efficient RES-E support mechanisms
6th International Scientific Conference on "Energy and Climate Change” /10/2013

5. LCOE
The Levelized Cost of Electricity (LCOE) refers to the overall costs for the generation of electricity on the basis of net power supplied to the grid.
CC: Capital cost minus any investment tax credit or grant
OM: Annual operational and maintenance cost
DR: Discount rate
RV: Residual value
DEG: Degradation rate
N: Lifetime
DP: Depreciation
IT : Interest payment
LN: Loan payment
TR: Taxation rate
6th International Scientific Conference on "Energy and Climate Change” /10/2013

6. Monte Carlo simulation
Monte Carlo simulation evaluates iteratively the specified output using sets of random numbers for the examined input parameters.
Steps for the implementation of Monte Carlo analysis
Step I: Identification of input and output parameters.
Step II: Generation of a set of random values for all input parameters from a probability distribution for a specified number of iterations.
Step III: Assessment of the obtained results of output parameters.
Step IV: Reiteration of the procedure utilizing different assumptions regarding the input parameters.
Step V: Analysis of the results with the demonstration of appropriate histograms and summary statistics like mean or median value, variance etc.
6th International Scientific Conference on "Energy and Climate Change” /10/2013

7. Study
7 different scenarios were evaluated for three different time slides, namely 2013, 2015 and 2020.
Input parameters (expressed with triangular distributions):
Capital cost
Operational & Maintenance cost
Capacity factor
Discount rate
Loan share
Interest rate
All parameters
Output parameters:
LCOE

8. Assumptions Wind 2013 2015 2020 min mode max Capital cost (€/kW) 1,100
1,200
1,400
1,070
1,250
990
1,040
Discount rate (%)
8.0%
9.0%
11.0%
7.0%
10.0%
Loan (%)
30.0%
60.0%
70.0%
80.0%
Interest rate (%)
9.5%
6.0%
5.0%
Capacity factor (%)
21.0%
26.0%
35.0%
21%
O&M (%)
3.0%
3.4%
4.0%
2.0%
3.2% PV
2013
2015
2020
min
mode
max
Capital cost (€/kW)
1,000
1,250
1,400
950
1,100
1,184
865
1,075
Discount rate (%)
8.0%
9.0%
11.0%
7.0%
10.0%
Loan (%)
0.0%
40.0%
70.0%
50.0%
30.0%
60.0%
80.0%
Interest rate (%)
6.0%
5.0%
Capacity factor (%)
16.0%
17.0%
18.7%
16.2%
18.0%
19.8%
17.1%
19.0%
20.9%
O&M (%)
1.5%
2.5%
3.0%
2.0%
6th International Scientific Conference on "Energy and Climate Change” /10/2013

9. Results Wind - Variation
6th International Scientific Conference on "Energy and Climate Change” /10/2013

10. Results Wind - Percentiles 2013
6th International Scientific Conference on "Energy and Climate Change” /10/2013

11. Results Wind - Percentiles 2015
6th International Scientific Conference on "Energy and Climate Change” /10/2013

12. Results Wind - Percentiles 2020
6th International Scientific Conference on "Energy and Climate Change” /10/2013

13. Results PV- Variation
6th International Scientific Conference on "Energy and Climate Change” /10/2013

14. Results PV- Percentiles 2013
6th International Scientific Conference on "Energy and Climate Change” /10/2013

15. Results PV- Percentiles 2015
6th International Scientific Conference on "Energy and Climate Change” /10/2013

16. Results PV- Percentiles 2020
6th International Scientific Conference on "Energy and Climate Change” /10/2013

17. Conclusions
Significant decrease of LCOE until 2020 for both wind and photovoltaic energy.
Capacity factor for the case of wind energy and capital cost for photovoltaic energy are considered as the most uncertain parameters.
The continuous monitoring of LCOE with the simultaneous implementation of Monte Carlo analysis constitute as priority for the effective development of RES market.
Implementation of the proposed methodology for the rest types of RES.
Additional uncertainty techniques can be integrated into the methodology such as Fuzzy sets, ROV method etc.
6th International Scientific Conference on "Energy and Climate Change” /10/2013

18. Thank you for your attention!
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