Preferences for micro-cogeneration in Germany: Policy implications for grid expansion from a discrete choice experiment

Applied Energy

Volumn 206, Issue , 2017, Pages 612-622

  Rommel, Kai ^a   Sagebiel, Julian ^b  

^a TU DORTMUND UNIVERSITY   (Germany)

^b Institute for Ecological Economy Research (I W)   (Germany)

Author keywords

Environmental benefits; Load shifting; Power to heat; Renewable energies; Stated preferences; Unobserved preference heterogeneity

Indexed keywords

CARBON DIOXIDE; COMMERCE; ECONOMICS; INVESTMENTS; PUBLIC POLICY;

ENVIRONMENTAL BENEFITS; LOAD SHIFTING; PREFERENCE HETEROGENEITY; RENEWABLE ENERGIES; STATED PREFERENCES;

COGENERATION PLANTS;

COGENERATION; COST ANALYSIS; DISCRETE CHOICE ANALYSIS; ELECTRICITY; ENERGY MARKET; ENERGY POLICY; ENERGY RESOURCE; EXPERIMENTAL DESIGN; INSTRUMENTATION; RENEWABLE RESOURCE; WILLINGNESS TO PAY;

GERMANY;

EID: 85028616395     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2017.08.216     Document Type: Article

References (34)

1. IEA. World energy outlook 2015. Special report: energy and climate change - executive summary. Paris (France): International Energy Agency; 2015.
2. REN21. Renewables 2015. Global status report: distributed renewable energy for energy access. Paris (France): REN21 Secretatiat; 2016.
3. ECOFYS. Impacts of restricted transmission grid expansion in a 2030 perspective in Germany. Final report. 2013.
4. Barker, G., Blacklock, P., Bugler, M., Fotheringham, N., Gibson, M., Marijewycz, M., et al. The role of micro CHP in a smart energy world. 2013, Ecuity Consulting LLP, Solihull.
5. Guy R, Sykes B. Micro CHP (combined heat and power) accelerator - final report. Carbon Trust; 2011.
6. Feuerriegel, S., Neumann, D., Integration scenarios of demand response into electricity markets: load shifting, financial savings and policy implications. Energy Policy 96 (2016), 231–240, 10.1016/j.enpol.2016.05.050.
7. Pellegrino, S., Lanzini, A., Leone, P., Techno-economic and policy requirements for the market-entry of the fuel cell micro-CHP system in the residential sector. Applied Energy 143 (2015), 370–382, 10.1016/j.apenergy.2015.01.007.
8. German Federal Office for Economic Affairs and Export Control. Report 2015/2016. Eschborn; 2016.
9. Jung, N., Moula, M.E., Fang, T., Hamdy, M., Lahdelma, R., Social acceptance of renewable energy technologies for buildings in the Helsinki Metropolitan Area of Finland. Renewable Energy 99 (2016), 813–824, 10.1016/j.renene.2016.07.006.
10. Sauter, R., Watson, J., Strategies for the deployment of micro-generation: Implications for social acceptance. Energy Policy 35 (2007), 2770–2779, 10.1016/j.enpol.2006.12.006.
11. ASUE - Arbeitsgemeinschaft für sparsamen und umweltfreundlichen Energieverbrauch e.V. BHKW-Kenndaten 2014/15; 2014.
12. Rosato, A., Sibilio, S., Ciampi, G., Energy, environmental and economic dynamic performance assessment of different micro-cogeneration systems in a residential application. Appl Therm Eng 59 (2013), 599–617, 10.1016/j.applthermaleng.2013.06.022.
13. Johnson, G., Beausoleil-Morrison, I., Wills, A., Micro-cogeneration versus conventional technologies: considering model uncertainties in assessing the energy benefits. Appl Therm Eng 114 (2017), 1457–1467, 10.1016/j.applthermaleng.2016.11.128.
14. Schüwer, D., Krüger, C., Merten, F., Nebel, A., The potential of grid-orientated distributed cogeneration on the minutes reserve market and how changing the operating mode impacts on CO2 emissions. Energy 110 (2016), 23–33, 10.1016/j.energy.2016.02.108.
15. Pehnt, M., Environmental impacts of distributed energy systems—the case of micro cogeneration. Environ Sci Policy 11 (2008), 25–37, 10.1016/j.envsci.2007.07.001.
16. Icha, P., Kuhs, G., Entwicklung der spezifischen Kohlendioxid-Emissionen des deutschen Strommix in den Jahren 1990 bis 2015. 2016, Umweltbundesamt, Dessau.
17. Hoffmann, G., Schingnitz, D., Schnapke, A., Bilitewski, B., Reduction of CO2-emissions by using biomass in combustion and digestion plants. Waste Manage 30 (2010), 893–901, 10.1016/j.wasman.2009.12.001.
18. Alberini A, Banfi S. Energy efficiency investments in the home: Swiss homeowners and expectations about future energy prices. Energy J 2014;34.
19. Ruokamo, E., Household preferences of hybrid home heating systems – a choice experiment application. Energy Policy 95 (2016), 224–237, 10.1016/j.enpol.2016.04.017.
20. Michelsen, C.C., Madlener, R., Homeowners’ preferences for adopting innovative residential heating systems: a discrete choice analysis for Germany. Energy Economics 34 (2012), 1271–1283, 10.1016/j.eneco.2012.06.009.
21. Mahapatra, K., Gustavsson, L., Adoption of innovative heating systems—needs and attitudes of Swedish homeowners. Energy Efficiency 3 (2010), 1–18, 10.1007/s12053-009-9057-7.
22. Decker, T., Menrad, K., House owners’ perceptions and factors influencing their choice of specific heating systems in Germany. Energy Policy 85 (2015), 150–161, 10.1016/j.enpol.2015.06.004.
23. Achtnicht, M., Do environmental benefits matter? Evidence from a choice experiment among house owners in Germany. Ecol Econ 70 (2011), 2191–2200, 10.1016/j.ecolecon.2011.06.026.
24. Claudy, M.C., Michelsen, C., O'Driscoll, A., The diffusion of microgeneration technologies – assessing the influence of perceived product characteristics on home owners’ willingness to pay. Energy Policy 39 (2011), 1459–1469, 10.1016/j.enpol.2010.12.018.
25. McFadden D. Conditional logit analysis of qualitative choice behavior. In: Zarembka P, editor. Frontiers in econometrics. Academic Press; 1974. p. 105–42.
26. Train KE. Discrete choice methods with simulation. Repr. Cambridge: Cambridge Univ. Press; 2008.
27. Bliemer, M.C.J., Rose, J.M., Confidence intervals of willingness-to-pay for random coefficient logit models. Transp Res Part B: Methodological 58 (2013), 199–214, 10.1016/j.trb.2013.09.010.
28. Sagebiel, J., Glenk, K., Meyerhoff, J., Spatially explicit demand for afforestation. For Policy Econ 78 (2017), 190–199, 10.1016/j.forpol.2017.01.021.
29. ChoiceMetrics. NGene 1.1.1 user manual & reference guide; 2012.
30. Rose JM, Bliemer MC. Stated preference experimental design strategies. Handbook of transport modelling. Oxford: Elsevier; 2008. p. 151–80.
31. Scarpa, R., Ferrini, S., Willis, K., Performance of error component models for status-quo effects in choice experiments. Scarpa, R., Alberini, A., (eds.) Applications of simulation methods in environmental and resource economics, 2005, Springer, Netherlands, 247–273.
32. Greene, W., Econometric analysis. 6th ed., 2008, Prentice Hall, New Jersey.
33. Johnston, R.J., Boyle, K.J., Adamowicz, W., (Vic), Bennett, J., Brouwer, R., Cameron, T.A., et al. Contemporary guidance for stated preference studies. J Assoc Environ Resour Econ 4 (2017), 319–405, 10.1086/691697.
34. Leuven E, Sianesi B. PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing; 2003. http://ideas.repec.org/c/boc/bocode/s432001.html. This version 4.0.11.