180 likes | 306 Views
LCOE as a policy tool to design RES-E support schemes Tourkolias C., Vougiouklakis Y., Papandreou V., Tigas K., Nakos C., Theofilidi M. Christos Tourkolias Energy expert Division for Energy Policy and Planning email: ctourkolias@cres.gr. Problem.
E N D
LCOE as a policy tool to design RES-E support schemesTourkolias C., Vougiouklakis Y., Papandreou V., Tigas K., Nakos C., Theofilidi M. Christos Tourkolias Energy expert Division for Energy Policy and Planning email: ctourkolias@cres.gr 6th International Scientific Conference on "Energy and Climate Change” Athens, 10/10/2013
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 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 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
Thank you for your attention! 19thkm Marathonos Avenue, 19009 Pikermi, Greece Τ:+30 2106603300, F: +30 2106603301-2 www.cres.gr, cres@cres.gr