A non-autonomous optimal control model of renewable energy production under the aspect of fluctuating supply and learning by doing

Given the constantly raising world-wide energy demand and the accompanying increase in greenhouse gas emissions that pushes the progression of climate change, the possibly most important task in future is to find a carbon-low energy supply that finds the right balance between sustainability and energy security. For renewable energy generation, however, especially the second aspect turns out to be difficult as the supply of renewable sources underlies strong volatility. Further on, investment costs for new technologies are so high that competitiveness with conventional energy forms is hard to achieve. To address this issue, we analyze in this paper a non-autonomous optimal control model considering the optimal composition of a portfolio that consists of fossil and renewable energy and which is used to cover the energy demand of a small country. While fossil energy is assumed to be constantly available, the supply of the renewable resource fluctuates seasonally. We further on include learning effects for the renewable energy technology, which will underline the importance of considering the whole life span of such a technology for long-term energy planning decisions.

[1]  Tobias N. Rasmussen CO2 abatement policy with learning-by-doing in renewable energy , 2001 .

[2]  C. Harmon,et al.  Experience Curves of Photovoltaic Technology , 2000 .

[3]  Michel Moreaux,et al.  Resource Use under Climate Stabilization: Can Nuclear Power Provide Clean Energy? , 2012 .

[4]  Espagne Etienne,et al.  The Environment and Directed Technical Change: Comment , 2011 .

[5]  Michel Moreaux,et al.  "Twin Peaks" in Energy Prices: A Hotelling Model with Pollution and Learning , 2008 .

[6]  H. B. Keller,et al.  Boundary Value Problems on Semi-Infinite Intervals and Their Numerical Solution , 1980 .

[7]  Linda Argote,et al.  Organizational Learning Curves: A Method for Investigating Intra-Plant Transfer of Knowledge Acquired Through Learning by Doing , 1991 .

[8]  J. Hale,et al.  Dynamics and Bifurcations , 1991 .

[9]  Patrik Söderholm,et al.  Modeling technical change in energy system analysis: analyzing the introduction of learning-by-doing in bottom-up energy models , 2006 .

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

[11]  Michel Moreaux,et al.  Would Hotelling Kill the Electric Car , 2011 .

[12]  W. Palz Solar Radiation Data , 1982 .

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

[14]  Tatiana Kiseleva,et al.  Structural analysis of complex ecological economic optimal control problems , 2011 .

[15]  Peter M. Kort,et al.  Anticipation effects of technological progress on capital accumulation: a vintage capital approach , 2006, J. Econ. Theory.

[16]  D. Grass,et al.  Numerical computation of the optimal vector field: Exemplified by a fishery model , 2012, Journal of economic dynamics & control.

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

[18]  C. L. Benkard Learning and Forgetting: the Dynamics of Aircraft Production , 1999 .

[19]  Rong-Gang Cong An optimization model for renewable energy generation and its application in China: A perspective of maximum utilization , 2013 .

[20]  Fanny Henriet,et al.  Carbon Price and Optimal Extraction of a Polluting Fossil Fuel with Restricted Carbon Capture , 2011 .

[21]  Florian Wagener,et al.  Bifurcations of optimal vector fields in the shallow lake model , 2010 .

[22]  L. Argote,et al.  Learning Curves in Manufacturing , 1990, Science.

[23]  Till Requate,et al.  Subsidies for renewable energies in the presence of learning effects and market power , 2012 .

[24]  Gustav Feichtinger,et al.  Optimale Kontrolle ökonomischer Prozesse : Anwendungen des Maximumprinzips in den Wirtschaftswissenschaften , 1986 .

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

[26]  Arnulf Grubler,et al.  Technological change and the timing of mitigation measures , 1998 .

[27]  Stephen Wiggins,et al.  Existence and Computation of Hyperbolic Trajectories of Aperiodically Time Dependent Vector Fields and their Approximations , 2003, Int. J. Bifurc. Chaos.

[28]  Eduard Reithmeier Periodic Solutions of Nonlinear Dynamical Systems: Numerical Computation, Stability, Bifurcation, and Transition to Chaos , 1991 .

[29]  L. Argote,et al.  The persistence and transfer of learning in industrial settings , 1990 .

[30]  Michel Moreaux,et al.  A Hotelling Model with a Ceiling on the Stock of Pollution , 2004 .

[31]  Bob van der Zwaan,et al.  Gross world product and consumption in a global warming model with endogenous technological change , 2003 .

[32]  Nitin Goel,et al.  Solar Radiation Data , 2007 .

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

[34]  Sandip Deshmukh,et al.  Modeling of hybrid renewable energy systems , 2008 .

[35]  J. C. Vink,et al.  Twin Peaks , 2000 .

[36]  P. Hartley,et al.  Innovation, Renewable Energy, and Macroeconomic Growth , 2010 .

[37]  J. Caulkins,et al.  Optimal Control of Nonlinear Processes: With Applications in Drugs, Corruption, and Terror , 2008 .

[38]  Rajesh Kumar Nema,et al.  A current and future state of art development of hybrid energy system using wind and PV-solar: A review , 2009 .

[39]  L. Schrattenholzer,et al.  Endogenous technological change in climate change modelling , 1999 .