Power capacity expansion planning considering endogenous technology cost learning (vol 204, pg 831, 2017)

We present an power systems optimisation model for national-scale power supply capacity expansion considering endogenous technology cost reduction (ESO-XEL). The mixed-integer linear program minimises total system cost while complying with operational constraints, carbon emission targets, and ancillary service requirements. A data clustering technique and the relaxation of integer scheduling constraints is evaluated and applied to decrease the model solution time. Two cost learning curves for the different power technologies are derived: one assuming local learning effects, the other accounting for global knowledge spill-over. A piece-wise linear formulation allows the integration of the exponential learning curves into the ESO-XEL model. The model is applied to the UK power system in the time frame of 2015 to 2050. The consideration of cost learning effects moves optimal investment timings to earlier planning years and influences the competitiveness of technologies. In addition, the maximum capacity build rate parameter influences the share of power generation significantly; the possibility of rapid capacity build-up is more important for total system cost reduction by 2050 than accounting for technology cost reduction.

[1]  S. Messner,et al.  Endogenized technological learning in an energy systems model , 1997 .

[2]  R. Solow.  TECHNICAL CHANGE AND THE AGGREGATE PRODUCTION FUNCTION , 1957 .

[3]  Leo Schrattenholzer,et al.  Endogenous Technological Change in Energy Systems Models: Synthesis of Experience with ERIS, MARKAL, and MESSAGE , 1998 .

[4]  Claudio Gentile,et al.  Tight MIP formulations of the power-based unit commitment problem , 2015, OR Spectr..

[5]  N. M. Dowell,et al.  On the potential for BECCS efficiency improvement through heat recovery from both post-combustion and oxy-combustion facilities. , 2016, Faraday discussions.

[6]  Leonardo Barreto,et al.  The Extended Energy-Systems ERIS Model: An Overview , 2004 .

[7]  John P. Weyant,et al.  Issues in modeling induced technological change in energy, environmental, and climate policy , 1999 .

[8]  M. Lieberman.  The Learning Curve and Pricing in the Chemical Processing Industries , 1984 .

[9]  Iain Staffell,et al.  Electricity in Europe: exiting fossil fuels? , 2016 .

[10]  Edward S. Rubin,et al.  Estimating Future Costs of CO2 Capture Systems Using Historical Experience Curves , 2006 .

[11]  A Walker,et al.  Counting the cost : the economic and social costs of electricity shortfalls in the UK , 2022 .

[12]  T. P. Wright,et al.  Factors affecting the cost of airplanes , 1936 .

[13]  Socrates Kypreos,et al.  Incorporating different endogenous learning formulations in MERGE , 2003 .

[14]  S. Kahouli-Brahmi.  Technological learning in energy–environment–economy modelling: A survey , 2008 .

[15]  Qixin Chen,et al.  Electricity markets evolution with the changing generation mix: An empirical analysis based on China 2050 High Renewable Energy Penetration Roadmap ☆ , 2017 .

[16]  Quang Kha Tran.  Importance sampling for stochastic programming , 2016 .

[17]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[18]  Mort Webster.  Incorporating Operational Flexibility into Electric Generation Planning , 2013 .

[19]  Edward S. Rubin,et al.  A review of uncertainties in technology experience curves , 2012 .

[20]  R. Loulou,et al.  Documentation for the TIMES Model PART I April 2005 , 2005 .

[21]  Richard G. Newell,et al.  Modeling endogenous technological change for climate policy analysis , 2008 .

[22]  Lena Neij,et al.  Cost development of future technologies for power generation--A study based on experience curves and complementary bottom-up assessments , 2008 .

[23]  F. Ferioli,et al.  Use and limitations of learning curves for energy technology policy: A component-learning hypothesis , 2009 .

[24]  Michael Grubb,et al.  The Transition to Endogenous Technical Change in Climate-Economy Models: A Technical Overview to the Innovation Modeling Comparison Project , 2006 .

[25]  Mohammad Shahidehpour,et al.  Co-optimization of electricity transmission and generation resources for planning and policy analysis: review of concepts and modeling approaches , 2016 .

[26]  Samer Takriti,et al.  Incorporating Fuel Constraints and Electricity Spot Prices into the Stochastic Unit Commitment Problem , 2000, Oper. Res..

[27]  P. Graham,et al.  A global and local endogenous experience curve model for projecting future uptake and cost of electricity generation technologies , 2013 .

[28]  Iain Staffell,et al.  Divide and Conquer? ${k}$-Means Clustering of Demand Data Allows Rapid and Accurate Simulations of the British Electricity System , 2014, IEEE Transactions on Engineering Management.

[29]  Arnulf Grubler,et al.  The costs of the French nuclear scale-up: A case of negative learning by doing , 2010 .

[30]  Edward S. Rubin,et al.  Effects of technological learning on future cost and performance of power plants with CO2 capture , 2009 .

[31]  Alban Kitous,et al.  Mitigation strategies and energy technology learning: an assessment with the POLES model , 2015 .

[32]  Eric Williams,et al.  Residual learning rates in lead-acid batteries: Effects on emerging technologies , 2015 .

[33]  A. G. Kagiannas,et al.  Power generation planning: a survey from monopoly to competition , 2004 .

[34]  P. Jaramillo,et al.  A review of learning rates for electricity supply technologies , 2015 .

[35]  Gregory F. Nemet,et al.  Beyond the learning curve: factors influencing cost reductions in photovoltaics , 2006 .

[36]  S. Pfenninger,et al.  Using bias-corrected reanalysis to simulate current and future wind power output , 2016 .

[37]  Antonio Soria,et al.  Technical change dynamics: evidence from the emerging renewable energy technologies , 2001 .

[38]  N. Nakicenovic,et al.  Modeling Technological Change: Implications for the Global Environment , 1999 .

[39]  Edward S. Rubin,et al.  The potential of renewables versus natural gas with CO2 capture and storage for power generation under CO2 constraints , 2015 .

[40]  Tirso Leonardo Barreto Gómez,et al.  Technological Learning in Energy Optimisation Models and Deployment of Emerging Technologies , 2001 .

[41]  Socrates Kypreos,et al.  A Merge Model with Endogenous Technological Change and the Cost of Carbon Stabilization , 2005 .

[42]  Iain Staffell,et al.  A systems approach to quantifying the value of power generation and energy storage technologies in future electricity networks , 2017, Comput. Chem. Eng..

[43]  Henrik Madsen,et al.  Integrating Renewables in Electricity Markets: Operational Problems , 2013 .

[44]  Ari Rabl,et al.  Prospects for PV: a learning curve analysis , 2003 .

[45]  Edward S. Rubin,et al.  Use of experience curves to estimate the future cost of power plants with CO2 capture , 2007 .

[46]  Iain Staffell,et al.  Levelised Value of Electricity - A Systemic Approach to Technology Valuation , 2016 .

[47]  Leo Schrattenholzer,et al.  Learning rates for energy technologies , 2001 .

[48]  Wim Turkenburg,et al.  Global experience curves for wind farms , 2005 .

[49]  Thomas S. Brinsmead,et al.  Modelling the Future Grid Forum scenarios , 2013 .

[50]  Chris Marnay,et al.  Learning and cost reductions for generating technologies in the national energy modeling system (NEMS) , 2004 .

[51]  Andrea Ramírez,et al.  Future technological and economic performance of IGCC and FT production facilities with and without CO2 capture: Combining component based learning curve and bottom-up analysis , 2013 .

[52]  K. Arrow.  The Economic Implications of Learning by Doing , 1962 .

[53]  Paul L. Joskow,et al.  The Effects of Technological Change, Experience, and Environmental Regulation on the Construction Cost of Coal-Burning Generating Units , 1985 .

[54]  Pandelis N. Biskas,et al.  Generation Expansion Planning by MILP considering mid-term scheduling decisions , 2012 .

[55]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[56]  S. Pfenninger,et al.  Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data , 2016 .

[57]  Rick Tidball,et al.  Cost and Performance Assumptions for Modeling Electricity Generation Technologies , 2010 .

[58]  Michael Grubb,et al.  Induced Technological Change: Exploring its Implications for the Economics of Atmospheric Stabilization: Synthesis Report from the innovation Modeling Comparison Project , 2006 .

[59]  Martin B. Zimmerman,et al.  Learning Effects and the Commercialization of new Energy Technologies: The Case of Nuclear Power , 2015 .

[60]  Efstratios N. Pistikopoulos,et al.  A spatial multi-period long-term energy planning model: A case study of the Greek power system , 2014 .

[61]  Robert Gross,et al.  Presenting the future: An assessment of future cost estimation methodologies in the electricity sector , 2013 .

[62]  Wim Turkenburg,et al.  Cost Reduction Prospects for Offshore Wind Farms , 2004 .

[63]  Dennis Anderson,et al.  Induced Technical Change in Energy and Environmental Modeling: Analytic Approaches and Policy Implications , 2002 .

[64]  Iain Staffell,et al.  The cost of domestic fuel cell micro-CHP systems , 2013 .

[65]  Keith Norris,et al.  The Effects of Technological Change , 2018 .

[66]  E. L. Michelson,et al.  Electric transmission and distribution , 1954 .

[67]  Frederic H. Murphy,et al.  Generation Capacity Expansion in Imperfectly Competitive Restructured Electricity Markets , 2005, Oper. Res..

[68]  Michal Wierzbowski,et al.  MILP model for long-term energy mix planning with consideration of power system reserves , 2016 .