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 email: ctourkolias@cres.gr

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

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” 10/10/2013

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” 10/10/2013

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” 10/10/2013

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” 10/10/2013

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

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” 10/10/2013

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

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

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

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

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

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

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

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

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” 10/10/2013

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