An agent-based spatial simulation to evaluate the promotion of electricity from agricultural biogas plants in Germany

In this paper we investigate how changes in the support scheme may affect electricity generation from agricultural Combined Heat and Power (CHP) biogas plants in Germany. An agent-based simulation model for investment decision-making is coupled with GIS data. The spatial-temporal diffusion model accounts for the limited availability of substrate resources, alternative plant sizes and different heat use combinations. For illustration, we apply the model to the German federal states of North Rhine-Westphalia and Bavaria, for which we estimate an additional economical capacity potential of 409MWel. Overall, we conclude that current feed-in payments per unit of electric power provided are probably not too far off the optimum level, if one considers the maximum diffusion of CHP units possible. However, the current feed-in system may overtly favor small generating units, thereby failing to incentivize coordination among farmers for joint resource utilization in larger and more efficient plants. In addition, optimization of the biogas conversion process and feedstock use would also be highly beneficial.

[1]  M. Kaltschmitt,et al.  Energie aus Biomasse: Grundlagen, Techniken und Verfahren , 2001 .

[2]  G. Silverberg,et al.  A percolation model of eco-innovation diffusion: the relationship between diffusion, learning economies and subsidies , 2009 .

[3]  Elmar Kiesling,et al.  Agent-based simulation of innovation diffusion: a review , 2011, Central European Journal of Operations Research.

[4]  S. Ward,et al.  Prospects for expanded utilization of biogas in Germany , 2010 .

[5]  Andreas König.  Cost efficient utilisation of biomass in the German energy system in the context of energy and environmental policies , 2011 .

[6]  Jürgen Wittlinger.  Erneuerbare-Energien-Gesetz (EEG) , 2012 .

[7]  Benjamin K. Sovacool,et al.  The importance of comprehensiveness in renewable electricity and energy-efficiency policy , 2009 .

[8]  Stefan Krusche,et al.  Rationelle Energienutzung im Gartenbau , 2002 .

[9]  H. Randy Gimblett,et al.  Integrating geographic information systems and agent-based modeling techniques for simulating social and ecological processes , 2001 .

[10]  William H. Kaye-Blake,et al.  Multi-agent simulation models in agriculture: a review of their construction and uses , 2010 .

[11]  Kuratorium fur Technik und Bauwesen in der Landwirtschaft Darmstadt.  Cooperation in livestock farming. A discussion from the KTBL. , 1980 .

[12]  Olivier Barreteau,et al.  Multi-agent systems and role games : collective learning processes for ecosystem management , 2002 .

[13]  Thomas Berger,et al.  Land use decisions in developing countries and their representation in multi-agent systems , 2006 .

[14]  Marcus Janssen,et al.  The battle of perspectives: a multi-agent model with adaptive responses to climate change , 1998 .

[15]  Michael Batty,et al.  Multi-agent simulation: new approaches to exploring space-time dynamics in GIS , 1999 .

[16]  C. Vlek,et al.  Behaviour in commons dilemmas: Homo economicus and Homo psychologicus in an ecological-economic model , 2000 .

[17]  M. Frondel,et al.  Economic impacts from the promotion of renewable energy technologies: The German experience , 2010 .

[18]  Alfons Balmann,et al.  Adjustment costs of agri-environmental policy switchings: an agent-based analysis of the German region Hohenlohe. , 2002 .

[19]  François Bousquet,et al.  Multi-agent simulations and ecosystem management: a review , 2004 .

[20]  Marko P. Hekkert,et al.  Explaining the success of emerging technologies by innovation system functioning: the case of biomass digestion in Germany , 2008, Technol. Anal. Strateg. Manag..

[21]  Andrew Reeson,et al.  Agent‐based modeling in ecological economics , 2010, Annals of the New York Academy of Sciences.

[22]  Mario Ragwitz,et al.  A historical review of promotion strategies for electricity from renewable energy sources in EU countries , 2011 .

[23]  François Bousquet,et al.  Multi-agent simulations to explore rules for rural credit in a highland farming community of Northern Thailand , 2008, Ecological Economics.

[24]  Matthew Leach,et al.  Progress in renewable energy. , 2003, Environment international.

[25]  Peter H. Janssen,et al.  Exploring domestic micro-cogeneration in the Netherlands: An agent-based demand model for technology diffusion , 2010 .

[26]  C. Castaldi,et al.  Strategies for the Diffusion of Innovations on Social Networks , 2005 .

[27]  R. Calantone,et al.  The effects of market network heterogeneity on innovation diffusion: An agent-based modeling approach , 2010 .

[28]  Y. Gagnon,et al.  An analysis of feed-in tariff remuneration models: Implications for renewable energy investment , 2010 .

[29]  Steven M. Manson,et al.  Agent-based modeling and genetic programming for modeling land change in the Southern Yucatán Peninsular Region of Mexico , 2005 .

[30]  Jorge J. Gómez-Sanz,et al.  Validation and Verification of Multi-agent Systems , 2002 .

[31]  Reinhard Madlener,et al.  Spatial diffusion of biogas technology in Switzerland: a GIS-based multi-agent simulation approach. , 2009 .

[32]  Wu Jie,et al.  A multi-agent solution to energy management in hybrid renewable energy generation system , 2011 .

[33]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[34]  Christian Schmid,et al.  Diffusion der Biogastechnologie in der Schweiz: eine GIS-basierte Multiagenten-Simulation , 2008 .

[35]  E. Rogers.  Diffusion of Innovations , 1962 .

[36]  Thillainathan Logenthiran,et al.  Multi-agent system for energy resource scheduling of integrated microgrids in a distributed system , 2011 .

[37]  Christian Reise,et al.  Which factors influence the expansion of bioenergy? An empirical study of the investment behaviours of German farmers , 2012 .

[38]  D. Franks,et al.  Simulating the diffusion of organic farming practices in two New EU Member States , 2009 .

[39]  K. Happe,et al.  Research, part of a Special Feature on Empirical based agent-based modeling Agent-based Analysis of Agricultural Policies: an Illustration of the Agricultural Policy Simulator AgriPoliS, its Adaptation and Behavior , 2006 .

[40]  Alfons Balmann,et al.  Does structure matter? The impact of switching the agricultural policy regime on farm structures , 2008 .

[41]  Nina Schwarz,et al.  Agent-based modeling of the diffusion of environmental innovations — An empirical approach , 2009 .

[42]  W. Short,et al.  A manual for the economic evaluation of energy efficiency and renewable energy technologies , 1995 .

[43]  Johnathan Mun,et al.  Real Options Analysis , 2003 .

[44]  Thomas Berger,et al.  Agent-based spatial models applied to agriculture: A simulation tool , 2001 .

[45]  Wander Jager,et al.  Stimulating diffusion of green products , 2002 .

[46]  Tao Zhang,et al.  Evaluating Government's Policies on Promoting Smart Metering Diffusion in Retail Electricity Markets via Agent-Based Simulation* , 2011, Journal of Product Innovation Management.

[47]  Carlo Alberto Magni,et al.  Average Internal Rate of Return and Investment Decisions: A New Perspective , 2010 .

[48]  Eduardo S. Schwartz,et al.  Investment Under Uncertainty. , 1994 .

[49]  Malte Schwoon,et al.  Simulating the adoption of fuel cell vehicles , 2006 .

[50]  Thomas Fent,et al.  Agent-Based Computational Modelling: Applications in Demography, Social, Economic and Environmental Sciences (Contributions to Economics) , 2006 .

[51]  Thomas Berger,et al.  Multi-agent simulation for the targeting of development policies in less-favored areas , 2006 .

[52]  Wander Jager,et al.  Faculteit Der Economische Wetenschappen En Econometric Serie Research Memoranda Fashions, Habits and Changing Preferences: Simulations of Psychological Factors Affecting Market Dynamics Fashions, Habits and Changing Preferences: Simulation of Psychological Factors Affecting Market Dynamics , 2022 .

[53]  Thomas Berger,et al.  An agent-based simulation model of human-environment interactions in agricultural systems , 2011, Environ. Model. Softw..

[54]  Patricia Thornley,et al.  The effectiveness of policy instruments in promoting bioenergy , 2008 .

[55]  Michael Osborne.  A Resolution to the NPV - IRR Debate? , 2010 .

[56]  Marco Janssen,et al.  Simulating Market Dynamics: Interactions between Consumer Psychology and Social Networks , 2003, Artificial Life.

[57]  M. Binswanger.  Technological progress and sustainable development: what about the rebound effect? , 2001 .

[58]  A. Balmann.  Farm-Based Modelling of Regional Structural Change: A Cellular Automata Approach , 1997 .

[59]  Bertha Maya Sopha,et al.  Exploring policy options for a transition to sustainable heating system diffusion using an agent-based simulation , 2011 .

[60]  K. Happe.  Agricultural policies and farm structures: agent-based modelling and application to EU-policy reform , 2004 .

[61]  Reinhard Madlener,et al.  Sustainability-guided promotion of renewable electricity generation , 2005 .