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Abstract The price effect of the rising share of renewable electricity, which is called ‘merit-order-effect’, leads to noticeable changes in the German power industry and debates about the electricity market design. This paper estimates the merit-order-effect induced by variable renewable energy in the German-Austrian electricity sector with a multivariate regression model. The research focus lies on the impact of the estimated effects on the marketability of variable renewable electricity generation. The results show a systematic decline of the average market revenues for wind and photovoltaic plants in the period from January 2011 to December 2013. Current market data shows a continuation of this trend into 2016. According to the German long term goals for the use of renewables, wind and solar power will play a crucial role in the future electricity generation mix. If investments in these technologies will be profitable without any regulatory remuneration mechanisms in addition to the market revenues, depends on the further cost degression and the development of the merit-order-effect.

Abstract The price effect of the rising share of renewable electricity, which is called ‘merit-order-effect’, leads to noticeable changes in the German power industry and debates about the electricity market design. This paper estimates the merit-order-effect induced by variable renewable energy in the German-Austrian electricity sector with a multivariate regression model. The research focus lies on the impact of the estimated effects on the marketability of variable renewable electricity generation. The results show a systematic decline of the average market revenues for wind and photovoltaic plants in the period from January 2011 to December 2013. Current market data shows a continuation of this trend into 2016. According to the German long term goals for the use of renewables, wind and solar power will play a crucial role in the future electricity generation mix. If investments in these technologies will be profitable without any regulatory remuneration mechanisms in addition to the market revenues, depends on the further cost degression and the development of the merit-order-effect.

Abstract Social acceptance of innovative technologies is a key element of an effective transition towards more sustainable energy economies. However, innovative technologies like genetic modification also tend to spark controversy and backlash. So far, efforts to inform the public about any risks and benefits of novel technologies not only have struggled to foster acceptance but also neglect the interdependent foundations of consumer decision-making. Through a controlled experiment with German consumers (N = 322), we examine whether consumer support and rejection of genetic modification in bioenergy crops is influenced by the statements and actions of actors throughout the supply chain. In specific, we show that the decision of energy companies to sell and support GM bioenergy positively impacts consumer decisions to support. To ensure that decision outcomes were specifically impacted by the expressions of corporate actors, we controlled for the content and valence of information by random assignment to one of three treatments in which participants received positive, negative, or balanced (risks and benefits) information. We find that negative messaging diminished support and increased rejection relative to the other treatments. Lastly, the statements and actions of corporate actors also exerted an indirect influence on consumer decisions through their interactions with social trust and labels, e.g. greater support by farmers had a positive influence only for those who are more generally trustworthy. Given these results, we anticipate more attention to the importance of actors such as farmers and energy companies for the social acceptance of novel technologies in the energy sphere.

Abstract Social acceptance of innovative technologies is a key element of an effective transition towards more sustainable energy economies. However, innovative technologies like genetic modification also tend to spark controversy and backlash. So far, efforts to inform the public about any risks and benefits of novel technologies not only have struggled to foster acceptance but also neglect the interdependent foundations of consumer decision-making. Through a controlled experiment with German consumers (N = 322), we examine whether consumer support and rejection of genetic modification in bioenergy crops is influenced by the statements and actions of actors throughout the supply chain. In specific, we show that the decision of energy companies to sell and support GM bioenergy positively impacts consumer decisions to support. To ensure that decision outcomes were specifically impacted by the expressions of corporate actors, we controlled for the content and valence of information by random assignment to one of three treatments in which participants received positive, negative, or balanced (risks and benefits) information. We find that negative messaging diminished support and increased rejection relative to the other treatments. Lastly, the statements and actions of corporate actors also exerted an indirect influence on consumer decisions through their interactions with social trust and labels, e.g. greater support by farmers had a positive influence only for those who are more generally trustworthy. Given these results, we anticipate more attention to the importance of actors such as farmers and energy companies for the social acceptance of novel technologies in the energy sphere.

Abstract Emissions and efficiency of a pellet boiler (40 kW) at nominal load were compared with emissions and efficiency at reduced load, while fired with six biomass pellets. The pellets include reed canary grass ( Phalaris arundinacea ), pectin waste from citrus shells ( Citrus reticulata ), sunflower husk ( Helianthus annuus ), peat, wheat straw ( Triticum aestivum ) and wood pellets. The measurements of emissions comprised of carbon monoxide (CO), nitrogen oxides (NO x ), sulphur oxides (SO x ) and flue dust mass concentrations (using DIN plus and isokinetic sampling techniques). Emissions varied as a function of operational loads, for each type of pellets. The CO emissions were insignificant with reed canary grass (RCG), citrus pectin waste (CPW) and straw pellets at nominal load, however, at reduced load same pellets emitted 1.9, 4.0 and 7.4 times higher CO than wood pellets, respectively. Peat pellets emitted maximum CO at nominal load (4221.1 mgNm −3 , 12.6 times higher than wood pellets) however; at reduced load CO emission was insignificant. The highest NO x emissions were reported with CPW, which were 3.4 and 4.6 times higher than wood pellets at nominal load and reduced load, respectively. Dust emissions were highest with sunflower husk and lowest with RCG pellets, at both operational modes. The best performance was reported with wood pellets, followed by RCG and pectin pellets, however, wood pellets combustion emitted 1.7 and 2.0 times higher dust DIN plus than RCG at nominal and reduced loads, respectively. Not only fuel specific combustion optimization but also operational load specific optimization is essential for efficient use of agro-pellets in this type of boilers.

Abstract Emissions and efficiency of a pellet boiler (40 kW) at nominal load were compared with emissions and efficiency at reduced load, while fired with six biomass pellets. The pellets include reed canary grass ( Phalaris arundinacea ), pectin waste from citrus shells ( Citrus reticulata ), sunflower husk ( Helianthus annuus ), peat, wheat straw ( Triticum aestivum ) and wood pellets. The measurements of emissions comprised of carbon monoxide (CO), nitrogen oxides (NO x ), sulphur oxides (SO x ) and flue dust mass concentrations (using DIN plus and isokinetic sampling techniques). Emissions varied as a function of operational loads, for each type of pellets. The CO emissions were insignificant with reed canary grass (RCG), citrus pectin waste (CPW) and straw pellets at nominal load, however, at reduced load same pellets emitted 1.9, 4.0 and 7.4 times higher CO than wood pellets, respectively. Peat pellets emitted maximum CO at nominal load (4221.1 mgNm −3 , 12.6 times higher than wood pellets) however; at reduced load CO emission was insignificant. The highest NO x emissions were reported with CPW, which were 3.4 and 4.6 times higher than wood pellets at nominal load and reduced load, respectively. Dust emissions were highest with sunflower husk and lowest with RCG pellets, at both operational modes. The best performance was reported with wood pellets, followed by RCG and pectin pellets, however, wood pellets combustion emitted 1.7 and 2.0 times higher dust DIN plus than RCG at nominal and reduced loads, respectively. Not only fuel specific combustion optimization but also operational load specific optimization is essential for efficient use of agro-pellets in this type of boilers.

Cogeneration of heat and electricity is an important pillar of energy and climate policy. To plan the production and distribution system of combined heat and power (CHP) systems for residential heating, suitable methods for decision support are needed. For a comprehensive feasibility analysis, the integration of the location and capacity planning of the power plants, the choice of customers, and the network planning of the heating network into one optimization model are necessary. Thus, we develop an optimization model for electricity generation and heat supply. This mixed integer linear program (MILP) is based on graph theory for network flow problems. We apply the network location model for the optimization of district heating systems in the City of Osorno in Chile, which exhibits the “checkerboard layout” typically found in many South American cities. The network location model can support the strategic planning of investments in renewable energy projects because it permits the analysis of changing energy prices, calculation of break-even prices for heat and electricity, and estimation of greenhouse gas emission savings.

Cogeneration of heat and electricity is an important pillar of energy and climate policy. To plan the production and distribution system of combined heat and power (CHP) systems for residential heating, suitable methods for decision support are needed. For a comprehensive feasibility analysis, the integration of the location and capacity planning of the power plants, the choice of customers, and the network planning of the heating network into one optimization model are necessary. Thus, we develop an optimization model for electricity generation and heat supply. This mixed integer linear program (MILP) is based on graph theory for network flow problems. We apply the network location model for the optimization of district heating systems in the City of Osorno in Chile, which exhibits the “checkerboard layout” typically found in many South American cities. The network location model can support the strategic planning of investments in renewable energy projects because it permits the analysis of changing energy prices, calculation of break-even prices for heat and electricity, and estimation of greenhouse gas emission savings.