Future competitive bioenergy technologies in the German heat sector: Findings from an economic optimization approach
Matthias Jordan, Volker Lenz, Markus Millinger, Katja Oehmichen, Daniela Thrän
aa r X i v : . [ ec on . GN ] A ug Future competitive bioenergy technologies in the German heat sector: Findings froman economic optimization approach
Matthias Jordan a, ∗ , Volker Lenz b , Markus Millinger a , Katja Oehmichen b , Daniela Thrän a,b a Helmholtz Centre for Environmental Research - UFZ, Permoserstraße 15, 04318 Leipzig, Germany b DBFZ Deutsches Biomasseforschungszentrum gGmbH, Torgauer Strasse 116, 04347 Leipzig, Germany
Abstract
Meeting the defined greenhouse gas (GHG) reduction targets in Germany is only possible by switching to renewabletechnologies in the energy sector. A major share of that reduction needs to be covered by the heat sector, which accountsfor ∼ of the energy based emissions in Germany. Biomass is the renewable key player in the heterogeneous heatsector today. Its properties such as weather independency, simple storage and flexible utilization open up a wide fieldof applications for biomass. However, in a future heat sector fulfilling GHG reduction targets and energy sectors beingincreasingly connected: which bioenergy technology concepts are competitive options against other renewable heatingsystems? In this paper, the cost optimal allocation of the limited German biomass potential is investigated under long-term scenarios using a mathematical optimization approach. The model results show that bioenergy can be a competitiveoption in the future. Especially the use of biomass from residues can be highly competitive in hybrid combined heat andpower (CHP) pellet combustion plants in the private household sector. However, towards 2050, wood based biomass usein high temperature industry applications is found to be the most cost efficient way to reduce heat based emissions by95% in 2050. Keywords: h eat sector, bioenergy, renewable energy, optimization, hybrid heat technologies
1. Introduction
Global climate change, depleting energy resources andenergy security are issues affecting all countries. In Ger-many ambitious emission reduction and efficiency improve-ment targets are defined by the government [13]. GHGemissions are to be reduced by − until 2050 com-pared to 1990 by improving efficiency and switching to re-newable technologies in the energy sector. A major shareof that reduction needs to be covered by the heat sector,which accounts for ∼ of the energy based emissions[45] and 54% of the final energy demand [11] in Germanytoday.The German heat sector is characterized by its hetero-geneity due to different demand profiles, applications andinfrastructures. Heat consumption takes place in millionsof residential buildings (which accounts for 43% of the fi-nal heat demand), trade and commerce buildings (17%), aswell as in many different fields of the industry (40%) [11],mainly the steel and chemical industries in high tempera-ture applications. Within these sectors, different temporaldemands occur, ranging from seasonal to daily fluctuatingneeds. In addition to this complex demand structure, 8%of heat is not produced at the location of demand, but dis-tributed via district heating grids [11]. To reduce green- ∗ Corresponding author
Email address: [email protected] (Matthias Jordan) house gas emissions in the heat sector both the demandand supply sides need to be addressed.Heat demand in buildings needs to be decreased by in-creasing the refurbishment rate. Additionally, the heattransition needs different renewable technological solutionsthat fit this complex market structure, combining renew-able power and biomass energy sources.In 2017, biomass was the largest renewable energy con-tributor in Germany (54%), particularly in the heat sec-tor where 87% of the renewable energy was covered bybiomass. Solid biomass was contributing the highest shareof renewable heat with 68% [1]. However, alternative re-newable heat options take up more market shares, the re-source biomass is limited and a great share of the Germanyearly usable potential is already exploited [8]. On theother hand, bioenergy has clear advantages compared toother renewable fluctuating energy sources in the heat sec-tor: weather independency, the possibility of simple stor-age and flexible utilization. These properties open up awide field of application for biomass within the differentsub-sectors of the heat sector. But in which sub-sectors isbiomass competitive against other renewable applications,while fulfilling the GHG reduction targets?Several studies are available on the development of theGerman energy transition in general [30–33], focusing onthe power sector and examining energy from biomass onlyroughly. Thrän et al. [41] investigated the allocation ofbiomass in different German energy sectors. The results
Preprint submitted to Energy August 29, 2019 how that wood based biomass in the transport and powersector is only competitive under special circumstances, ex-pecting to have more competitive applications in the heatsector, which was not modelled in the mentioned study. Tothe authors’ knowledge, there is no study modelling thecomplex structure of the complete heat sector in detail,while including hybrid heating technologies and represen-tative bioenergy technology concepts, also in combinationwith other renewable technologies. Additionally, reviewsfocussing on model-based analysis in the heat sector, donot identify any studies combining the above mentionedresearch intentions [7, 24].In this paper, the cost optimal allocation of biomassbetween different heat sub-sectors is investigated in theframe of long-term energy scenarios. The following re-search question is assessed:- Which bioenergy technology concepts are competitiveoptions in a future, climate target fulfilling heat sector andhow does their potential role differ in different heat sub-sectors?
2. Materials and method
In this study, the heat sector was divided into severalsub-sectors, with different properties in terms of demandprofiles and infrastructures. Representative bioenergy-,fossil- and other renewable (hybrid-)heat-technology con-cepts were defined for each sub-sector and the technolog-ical competition was optimized in the system within theframework of the German climate protection plan [10, 13]in two scenarios. A consistent scenario framework wasset up and detailed biomass feedstock data were defined,leading to a set of five biomass types, which can be pro-cessed into 20 biomass products. With additionally threefossil products, they can be applied to 47 different technol-ogy concepts. Within the model these technology conceptswere in competition on 19 different sub-sectors to identifythe optimal allocation of biomass in the heat sector.
A mathematical optimization approach was chosen tomodel the heat sector. The approach of the model fol-lows BENOPT (BioENergyOPTimisation model), whichhas been applied on the transport and power sector [27–29]. As a programming environment GAMS [16] is usedin combination with MATLAB [40]. GAMS is an alge-braic modelling language for mathematical optimization.In Matlab the input data is imported from Microsoft Ex-cel [25], edited and automatically sent to GAMS, wherethe minimum costs are calculated. The results from theoptimizer are exported back to Matlab, where they areevaluated and graphically prepared.The model in this paper is fully deterministic and usesperfect foresight. The technology choice is optimizedwithin the competition. It is a linear model, using theCplex solver. The spatial boundary is Germany as a whole. The objective function is minimizing the total system costsover all technologies i , all sub-sectors s and the completetimespan t =2015...2050 (1). The total system costs are thesum of the technology specific marginal costs mc , multi-plied with the amount of heat produced π , and the invest-ment costs ic , discounted with the annuity method (dis-count rate q ) [18], multiplied with the number of heatingsystems installed n cap . In the model each (hybrid-)heat-technology concept is separated into different modules j ,assigned with different lifetimes ˆ t and individual invest-ment costs. Objective function min X t,i,s,b mc t,i,s,b · π t,i,s,b + X t,i,j,s ic t,i,j,s · n capt,i,j,s · q (1 + q ) ˆ t j (1 + q ) ˆ t j − (1) subject to δ t,s = X i,b π t,i,s,b , ∀ ( t, i, s, b ) ∈ ( T, I, S, B ) (2) φ Rest + Λ
Landt · Y t,b ≥ X i,s,b ˙ m t,i,s,b , ∀ ( t, i, s, b ) ∈ ( T, I, S, B bio ) (3) ε maxt ≥ X i,s,b α i,s · ( ε relt,i,s · π t,i,s + ε feedt,i,s,b · ˙ m t,i,s,b ) , ∀ ( t, i, s, b ) ∈ ( T, I, S, B ) (4) π t,i,s,b = ˙ m t,i,s,b · η t,i,s , ∀ ( t, i, s, b ) ∈ ( T, I, S, B ) (5) n capt =2015 ,i,j,s = n initiali,j,s , ∀ ( t, i, j, s ) ∈ ( T, I, J, S ) (6) n capt +1 ,i,j,s = n capt,i,j,s + n extt +1 ,i,j,s − n dect +1 ,i,j,s , ∀ ( t, i, j, s ) ∈ ( T, I, J, S ) (7) n dect,i,j,s = n initialdect,i,j,s + n extdect,i,j,s , ∀ ( t, i, j, s ) ∈ ( T, I, J, S ) (8) n extdect +ˆ t j ,i,j,s = n extt,i,j,s , ∀ ( t, i, j, s ) ∈ ( T, I, J, S ) (9)Marginal costs include feedstock costs (fossil orbiomass), costs for power demand, maintenance and aCO -certificate price. The sum of these costs has a dy-namic development, which depends on the time point, usedtechnology, sub-sector and if applicable the consumed feed-stock product b . Generated power in a combined heat andpower (CHP) system is included as a credit within thevariable costs. For details on how the credit is calculatedsee section 2.5.2he main model restrictions are as follows: First, theheat demand δ in each sub-sector needs to be fulfilled.Therefore the sum of the produced heat within one sub-sector equals the heat demand within a sub-sector in eachyear. Second, the yearly consumed biomass ˙ m withinthe system must not be higher as the sum of the lim-ited biomass potential from residues φ res and the limitedland use potential Λ Land multiplied with the correspond-ing yield Y of the energy crop. More details on the biomasspotential and possible biomass pathways are explained insection 2.4 and 2.6. Third, the yearly maximal allowedamount of GHG emissions ε max , representing the federalclimate targets in Germany, must be greater or equal tothe sum of the technology-based ε rel and feedstock-based ε feed emissions (4). The relationship between the pro-duced heat and the utilised feedstock product is given inequation (5) and determined by the conversion efficiency η of each technology. Equation (6) to (9) explain therelationship between the number of heating systems in-stalled ( n cap ) at time point t , the number of heating sys-tems newly invested in ( n ext ) and the number of heatingsystems decommissioned ( n dec ). The status quo of all in-stalled heating systems in 2015 serves as a starting point( n initial ). This portfolio is linearly decommissioned overthe corresponding lifetime of each technology ( n initialdec ).Heating systems newly installed in the model ( n ext ) aredecommissioned after they have reached their lifetime, de-fined by the variable n extdec . Premature decommissioningof heating systems is only allowed for fossil technologiesand limited to 1%/a. As a restriction for energy crops,every type may maximally double its land use per year. Heat utilisation differs from power utilisation, which issupplied through one uniform grid with a unique frequencyand different voltage levels which can be transformed upand down. For heat supply, beside local heating grids, dif-fering in temperature, pressure and extension, numeroussingle object solutions exist, with temperatures rangingfrom 1.000 °C for industrial processes down to low tem-perature heating with about 40 °C [44]. Additionally, theamount of heat required differs, with a corresponding ca-pacity variation for heat generators. Furthermore, heatingsystems based on solid fuels (biomass, coal or waste) varyin terms of operation efficiency and emissions depending onthe load [19]. Differing patterns for peak demand, yearlydemand variations, temperature requirements and the re-lation between base load (e.g. hot water supply) and thevarying proportion of the heat demand (e.g. space heating)require specially adapted technology concepts. Thus, heatdemand can be divided into a whole series of sub-sectorsin which different heating concepts have to be applied.In reality, each heating object is individually examinedand a decision on the best case is taken by the owner or anordered decision maker according to an individual set ofdecision parameters and the knowledge of the involved ac-tors. For an artificial model, a fixed set of decision param- eters is required as well as a simplification of the decisioncases (see section 2.1). Therefore, similar demand caseswere aggregated to one sub-sector with mean values and acertain set up of suitable technology options. Special caseswith low heat demands were included in the most suitablesub-sector.The main difference in the heat supply depends onthe required temperature level, which is basically distin-guished between industrial applications (60 °C to morethan 1.000 °C) and building heat demand (usually lessthan 95 °C). Considering comparable renewable heatingconcepts, industrial heat supply was separated into foursub-sectors by different temperature levels [20]:< 200 °C, 200 - 500 °C, 500 - 1.500 °C and one sub-sectorfor special coal demand (fossil or bio-coal) in industrialapplications for steel production.In addition to industrial applications, more than ofthe total heat demand in Germany is used for space heat-ing and hot water supply at a temperature level below95 °C [44]. When supplying individual objects of differ-ent sizes with fossil systems, no major technological dif-ference is required. A heat supply by bioenergy, how-ever, requires the use of different technological solutionsdepending on the size of the boiler. From smaller appli-cations in single family houses using stoves or wood logboilers, through pellet boilers in multi-family houses up towood chip boilers in e.g. schools or hospitals, a variety oftechnological solutions and combinations are possible [19].Additionally, CHP-technologies based on solid biomass fu-els are favourable options for cases with a high base loaddemand, such as in indoor swimming pools. Consideringthese aspects, the private household and trade and com-merce sector was structured into 14 sub-sectors accordingto the peak demand, the relation of hot water demandto total heat demand and the required temperature levels[23]. The future development of the heat demand in eachsub-sector is based on the external results of the model’B-STar’ [21]. As a stocks exchange model, it representsthe building stock in Germany and models the future re-furbishment in different scenarios.Centralized heating supply was summarized in one sub-sector, determined by the resolution of the data basis.In total, 19 sub-sectors were defined and described (seeLenz and Jordan [23] and Table 2 in appendix B). The av-erage thermal peak load demand and the annual final heatdemand until 2050 serve as input data for the optimizationmodel and the design of the different technology conceptsin each sub-sector.
In order to determine the future use of biomass in theheat sector, the market competition has to be depictedin the optimization model. Consequently, different fossiland renewable technological systems were selected for thecompetition in each sub-sector. Beside single technologysolutions, also hybrid systems were included. Hybrid sys-tems are combining different types of fuels, leading to a3ariety of possible technical solutions. For the final selec-tion of the defined heating concepts, the following aspectswere taken into account:• The status quo of the national biomass feedstock mixand all installed heating systems in 2015 were consid-ered.• As the research is focused on biomass, at least onebioenergy heat concept as well as one bioenergy CHPconcept, based on solid fuels, is integrated in eachsub-sector.• Solar thermal was integrated as an established tech-nology on the market.• One heat pump concept per low temperature sub-sector was defined, as this technology offers the po-tential to fulfil the complete heat demand for appli-cations lower than 200 °C in a renewable way.• In order to ensure a net renewable power supply forheat pumps, a heat pump concept is always designedin combination with a PV system, which produces themajor share of the electricity demand over the year.As the most competitive fossil references a gas boileror gas boiler in combination with a solar thermal systemas well as a gas fuel cell plus solar thermal system weredefined in the most cases. Oil-fired boilers were not in-cluded in the modelling as they are more costly and emitmore CO equivalents than gas-fired boilers. Every gas-fired concept can either obtain natural gas or biomethane,which is fed into the gas network. Different single bioen-ergy solutions were described according to the amount ofheat and the thermal peak demand. Additionally, bioen-ergy hybrid or multibrid systems including a heat-pump,solar thermal or PV were selected according to the heatdemand parameters of the sub-sector. Future technical im-provements were considered through yearly increase ratesof thermal efficiency, electrical efficiency and a decrease ininvestment costs [23]. For gasification systems, a changefrom combustion engines to fuel cells is considered withinthe next two decades.Table 2 and 3 in appendix B show which concepts areconsidered in which sub-sectors. As there are some basicdifferences in the concepts between heating in buildingsand industrial/ district heating provisions, these two sec-tors are shown in separate tables. However, the allocationof biomass over the sub-sectors is treated equally.In total, 42 technical concepts where described. Thecomplete technical and economic data for each technologyconcept per sub-sector can be found in a published dataset [23]. The calculated infrastructure emission factors ofthe single technology components as well as the feedstockspecific emission factors are attached in table 5 and 6 ofappendix B. According to the above described technology concepts,four main feedstocks are considered in this model to gen-erate heat or combined heat and power. Biomass fromresidues and energy crops is used for all bioenergy tech-nologies. The basis for all other renewable heat technolo-gies is the usage of electricity and for the most competitivefossil technologies gas and coal have been chosen as a ref-erence. The heat production from plastic waste has beenset as a constant to the amount of generation in 2015. De-tails on fossil and power based energy prices are shown inFig. 3.The technical potential for biomass residues are shownuntil 2050 based on Brosowski et al. [8], shown in Fig.1. Additionally, crops for energetic and material use arecultivated on 2.4 Mio ha of land in Germany today [6].In this study, the maximum permitted land use is reducedlinearly to 2.0 Mio ha in 2050, which is at the lower limit ofidentified values from currently available long-term energyscenario studies [30–33]. On this land area, ten types ofenergy crops are cultivated for heat and CHP applicationstoday [5]. In table 7 of appendix B the applied yields andthe status quo of land use for these crops in the year 2015are attached. B i o m a ss ( P J ) Manure Straw Residual wood Log woodcase (a) case (b)2015 2020 2030 2040 205000.511.52
Land u s e ( M i o ha ) land use for heating in case (a)land use for heating in case (b) Figure 1: Technical biomass potential from residues in Germany [8](top). Available pre-allocated biomass potential and available landarea in case (a) and (b) shown by the coloured lines. The model isfree to pick from any category of residues and is free to cultivate anyof the defined energy crops, as long as the defined upper scenariolimit is not violated.
Different prices arise for the defined feedstocks. A com-mon method to estimate future prices of energy crops is toadd the per hectare profit of a benchmark crop to the perhectare production costs of the energy crops [47]. In Ger-many, the most common crop is wheat [34], which holds forthe benchmark crop in this study. Based on the price in-4
015 2020 2030 2040 2050510203040506070 Wood chips (residues)Log woodStrawManureCorn silageSugar beetPoplar wood chipsMiscanthus chipsSilphieAgricultural grassSorghumGrasslandGrainGrain Silage
Figure 2: Cost developments of the biomass feedstocks for a yearlywheat price increase of 3% (solid lines) and 5% (dotted lines). crease of wheat in the last decades [48], two biomass pricedevelopment scenarios are modelled in this study with ayearly increase of wheat by 3% and 5%. For a detaileddescription of the applied method in this paper the readeris referred to Millinger and Thrän [26]. Prices for biomassproducts from residues in 2015 are according current prices[4, 15, 39]. For the future development, the yearly increaserate of wheat in the corresponding scenario is also appliedto biomass residues. Fig. 2 shows the resulting price de-velopment of the considered biomass feedstocks. Appliedsurcharges for extra processing steps, such as pelletisingetc. can be found in table 8 of appendix B.Biomass from residues and energy crops can be con-verted into several secondary energy carriers. In this study,20 biomass products and three fossil products have beendefined. Table 4 in appendix B shows which products canbe used in which technologies. All fermentable feedstocksare processed into biomethane, which is fed into the gassupply network. Since multiple options per technology arepossible, a differentiation between feedstock specific andtechnology specific emissions has to be made. Table 5 and6 in appendix B give an overview of the technology andfeedstock specific emission factors and the corresponding allocation factors applied.
The heat sector is strongly linked to the power sector,especially when CHP and power to heat options are mod-elled. To generate conclusive results for the heat sector,a linkage to the power sector is inevitable. In order toachieve this linkage, a scenario framework was set up. Cer-tain input parameters, such as the electricity price, theelectricity-mix specific emission factor and the CO cer-tificate price, which are highly influential for the marketdevelopment of the heat sector, do also rely strongly on thedevelopment of the power sector. These parameters andpredicted fossil feedstock price developments are adoptedfrom the ’KS95’ scenario of the study of Repenning et al.[32]. Governmental subsidies, such as e.g. the EEG are notconsidered in this study. The only market steering instru-ment is the CO price, which is applied on the completeheat sector. As a result, the linkage of the heat sector tothe power sector in relation to power prices, feed-in tar-iffs, own electricity consumption and emission allocationis shown in Table 1.Repenning et al. [32] projects the future development ofpower and gas prices for the energy only markets. Therequired end consumer prices for our investigations arecalculated consumption-dependent according to the mon-itoring report of the federal network agency for the modelstarting year 2015 [12]. The future price developments areprojected combining both sources [12, 32], see Fig 3. In this study, a scenario of 95% GHG emission reduc-tion compared to 1990 is analysed. The focus of the in-vestigation lies on the development of biomass in the heatsector, but still considering the interactions to other en-ergy sectors by setting a scenario framework, derived fromthe ’KS95’ scenario from the study of Repenning et al.[32]. From currently available long term energy scenariosin Germany [30–33], Repenning et al. [32] is the only onemodelling a transformation path towards a 95% reductionscenario and also reaching this target in 2050. However,within the study of Repenning et al. [32] biomass is de-picted in a rough level of detail and only a minor share ofthe available biomass potential is distributed to the heatsector in the ’KS95’ scenario. In this paper, a broader
Table 1: Model linkage of the heat sector to the power sector in terms of power consumed for heating and power use of CHP / PV technologies.The emissions from grid-based electricity are allocated to the heat sector in accordance to the power mix specific emission factor [32].
Power Price Credit Heat sector emissionsexternal demand
Final consumer price 0 Emissions from grid power mix internally used for heating internally used for non heating fed into the grid f i na l po w e r p r i c e ( C en t/ k W h ) HouseholdTrade & commerceIndustry2015 2020 2030 2040 205051015202530 f i na l ga s p r i c e ( C en t/ k W h ) Household < 20 GJHousehold 20 - 200 GJHousehold > 200 GJTrade & commerceIndustry
Figure 3: End consumer power (top) and gas (bottom) prices. Owncalculations based on Repenning et al. [32] and Bundesnetzagenturand Bundeskartellamt [12]. range of biomass potential is pre-allocated to the heat sec-tor. Szarka et al. [38] reviews the role of bioenergy in long-term energy scenarios. The allocation of biomass to theheat sector in 2050 varies strongly between the reviewedstudies, ranging from ∼ − of the overall potential.Hence, two extreme scenarios are investigated in this pa-per, where one time a major share of the biomass potential(case a) and the other time a minor share of the biomasspotential (case b) is pre-allocated for heating applications,for details see Fig. 1. Consequently, the biomass potentialfor heat applications is fixed for each year and scenario,but the model is free to pick from any category of residuesand is free to cultivate any of the defined energy crops, aslong as the defined upper scenario limit is not violated. Inboth scenarios, the actual status quo of national biomassuse in 2015 serves as a starting point. Biomass imports are not allowed in order to avoid a shift of negative environ-mental effects abroad. For all scenarios, it is assumed thatEurope and especially the neighbouring countries of Ger-many follow similar, ambitious climate targets and that norelocation of industries or imports arise. Carbon captureand storage (CCS) is not considered in this study.Within the model a discount rate is considered for theinvestment costs. According to the recommendations ofSteinbach [35], considering the methodology to derive so-cial discount rates as well as discount rates used in anal-ysed energy scenarios, the applied value in this model isset to 4%.
3. Results
In the following paragraph, a transformation path to-wards a 95% emission reduction in 2050 in the heat sectoris shown. Modelling results are shown for cases (a) and(b) from 2015 to 2050. The market share of all technologytypes is shown in Fig. 4. As expected, the major marketshare shifts from natural gas technologies in 2015 to powerbased heat pumps in 2050. The share of bioenergy in theyear 2050 is at 29.0 % in scenario (a) and 5.7 % in scenario(b). In both cases, the complete pre-allocated biomass po-tential is used up from the year 2035 onwards. The largestbiomass shares are holding wood chip and pellet technolo-gies. Additionally, in case (a), log wood technologies holda constant market share of ∼ .A more detailed illustration shows which biomass prod-ucts are used for heating or CHP technologies, see Fig. 5.In 2015, one third of the utilised biomass was in the formof biogas, mostly based on corn silage. Without federalsubsidies, as it is the case in this model, biogas productionis not competitive and market shares decrease rapidly inboth scenarios. A constant use of log wood over time isfound in case (a), however, log wood technologies are theleast cost competitive wood based bioenergy technologies,as their market share decreases rapidly with decreasingbiomass potential in case (b) from 2030 onwards. In 2015residual wood was mainly used for wood chip technologies. N e t ene r g y gene r a t i on ( P J ) a Bio-CokeWood chipWood pelletLog woodElectric heatingHeat pumpSolar thermalBiomethaneNatural gasCoal (incl. coke) b Figure 4: Model resulting development of the technology market shares for the complete heat sector in case (a) and (b) in a yearly resolution.
015 2020 2030 2040 2050020040060080010001200 B i o m a ss ( P J ) a Grain SilageGrainGrasslandSorghumAgricultural grassSilphieMiscanthus pelletsMiscanthus chipsPoplar pelletsPoplar briquettesPoplar wood chipsSugar beetCorn silageManureStrawLog woodPellets (residues)Briquettes (residues)Wood chips (residues) b Figure 5: Model resulting consumption of biomass products in case (a) and (b) in a yearly resolution.
The model results show, that in a 95 % emission reductionscenario the use of residual wood is most competitive overthe next three decades in the form of pellets. However,in the last years until 2050, the use of residual wood inthe form of wood chips is the favourable option to fulfilclimate targets in a cost optimal way.The available land area for energy crops is cultivatedwith Miscanthus and processed to chips beginning afterthe decreasing cultivation of biogas feedstocks, see Fig. 5.Due to low feedstock costs and high yields, Miscanthus isa competitive option in such a scenario. Notable is the useof Miscanthus in form of chips in contrast to the use ofresidual wood in form of pellets.Fig. 6 shows in which specific sub-sectors and technol-ogy concepts the biomass potential is distributed. In sixsub-sectors, biomass technologies are competitive optionsin both scenarios. Five of these sub-sectors belong to theprivate household sector, in which pellet CHP and tor-refied pellet CHP technologies in combination with a heatpump and a photovoltaic system are most competitive overthe next three decades. However, between 2040 and 2050,with emission targets to be fulfilled and increasing powerprices, a shift of biomass use towards high temperatureindustry applications is carried out. Consequently, pel-let technologies are replaced by heat pumps or log woodtechnologies after their lifetime expansion.The market share of log wood technologies is stronglydependent on the available biomass potential, as it is theleast competitive wood based option. In case (a), with ahigh available potential, market shares are constant. Logwood achieves a share of ∼ in the 7,5 kW single familyhouses sector, where the log wood stove is combined witha heat pump and photovoltaic system, while in case (b)this technology holds only a minor market share.To sum it up: in the trade and commerce sub-sectorsnone of the defined bioenergy technologies are a compet-itive option. Pellet-CHP and log wood technologies arefavourable options in the private household sector, butonly in combination with a heat pump and PV-system.Towards 2050, the use of residual wood is more cost effi- cient in high temperature heat applications.
4. Discussion
In this paper, the future role of biomass in a sustainableheat sector is investigated. First of all, the results showthat a substantial emission reduction of 95% compared to1990 is possible in the German heat sector. A reductionof 98%, as it is the case in other studies using ’backup ca-pacities’ [21, 32], was not possible. Second, bioenergy is acompetitive option within the defined scenario framework,which confirms the hypothesis from Thrän et al. [41, 42, 43]expecting to have more competitive applications for woodbased biomass in the heat sector compared to the trans-port and electricity sector. Third, it is identified whichbiomass products are most competitive in which technol-ogy systems and on which sub-sectors of the heat sector.According to the model results, in the next three decadesuntil 2040-2045 biomass is identified to be most competi-tive in the private household sector, which is in line withKoch et al. [21] and Repenning et al. [32]. The mostfavourable options are decentralised hybrid CHP combus-tion applications using residual wood as feedstock. Espe-cially the combination of a (torrefied-) wood pellet gasifierCHP with a heat pump and a PV-system is a favourableoption. This is a unique finding in energy systems mod-elling. One reason for this finding is that in available stud-ies on the German energy transition, bioenergy is onlyconsidered as single technology option and not analysedin hybrid heat systems [30–33, 38]. Additionally, this find-ing shows that the future power price development has astrong impact on the competitiveness of heating systems.Fig. 7 shows the merit order of the prime costs for themost competitive biomass options and their correspond-ing competitors in selected sub-sectors for 2015, 2035 and2050. With increasing power prices in 2035 and 2050 (seeFig. 3), hybrid heat technology systems develop to bethe cheapest options of all. Despite these findings, hybridsystems seem to offer the highest degree of self-sufficiencyand therefore being more resilient to any kind of feedstock7 e t ene r g y gene r a t i on ( P J ) Industry > 500 °C
Direct biomass firingWood chip gasifierDirect biomethane firingElectric arc furnaceDirect coal firingDirect gas firing
80 kW - Apart. Build. 45 - 180
Wood pellet gasifier CHP+HP+PVWood pellet gasifier CHPPellet boiler+STHeat pump+PVGas fuel cell+Gas cond. boiler+STGas cond. boiler + STGas cond. boiler
20 kW - MFH 45-180, Mixed use 90
Torrefied wood pellet gasifier CHPWood pellet gasifier CHP+HP+PVLog wood gasification boiler+STPellet boilerHeat pump+PVGas fuel cell+STGas cond. boiler + STGas cond. boiler
Tor. wood pellet gasifier CHP+HP+PVLog wood gasification boiler+STBuffer integrated pellet burner+STPellet boilerHeat pump+PV+Log wood stoveHeat pump+PVGas fuel cell+STGas cond. boiler + STGas cond. boiler+Wood log stove+STGas cond. boiler
Tor. wood pellet gasifier CHP+HP+PVLog wood gasification boiler+STBuffer integrated pellet burner+STPellet boilerHeat pump+PV+Log wood stoveHeat pump+PVGas fuel cell+STGas cond. boiler + STGas cond. boiler+Wood log stove+STGas cond. boiler a Tor. wood pellet gasifier CHP+HP+PVLog wood gasification boiler+STBuffer integrated pellet burner+STPellet boilerHeat pump+PV+Log wood stoveHeat pump+PVGas fuel cell+STGas cond. boiler + STGas cond. boiler+Wood log stove+STGas cond. boiler b Figure 6: Model resulting development of the technology shares in selected heat sub-sectors in case (a) and (b). The sub-sectors in whichbiomass technologies are most competitive are illustrated (6 out of 19). SFH = Single Family Houses; MFH = Multi Family Houses; ST =Solar thermal; PV = Photovoltaic; HP = Heat Pump; CHP = Combined Heat and Power a s c ond . bo il e r + S T H ea t pu m p + P V H ea t pu m p + P V + Log w ood s t o v e G a s c ond . bo il e r + S T H ea t pu m p + P V T o r . w ood pe ll e t ga s i f i e r C H P + H P + P V G a s c ond . bo il e r + S T Log w ood ga s i f i c a t i on bo il e r + S T H ea t pu m p + P V + Log w ood s t o v e T o r . w ood pe ll e t ga s i f i e r C H P + H P + P V D i r e c t ga s f i r i ng D i r e c t b i o m a ss f i r i ng E l e c t r i c a r c f u r na c e G a s c ond . bo il e r + S T H ea t pu m p + P V H ea t pu m p + P V + Log w ood s t o v e G a s c ond . bo il e r + S T H ea t pu m p + P V T o r . w ood pe ll e t ga s i f i e r C H P + H P + P V G a s c ond . bo il e r + S T Log w ood ga s i f i c a t i on bo il e r + S T H ea t pu m p + P V + Log w ood s t o v e T o r . w ood pe ll e t ga s i f i e r C H P + H P + P V D i r e c t ga s f i r i ng D i r e c t b i o m a ss f i r i ng E l e c t r i c a r c f u r na c e G a s c ond . bo il e r + S T H ea t pu m p + P V H ea t pu m p + P V + Log w ood s t o v e G a s c ond . bo il e r + S T H ea t pu m p + P V T o r . w ood pe ll e t ga s i f i e r C H P + H P + P V G a s c ond . bo il e r + S T Log w ood ga s i f i c a t i on bo il e r + S T H ea t pu m p + P V + Log w ood s t o v e T o r . w ood pe ll e t ga s i f i e r C H P + H P + P V D i r e c t ga s f i r i ng D i r e c t b i o m a ss f i r i ng E l e c t r i c a r c f u r na c e Figure 7: Merit order of the most competitive biomass technologies and their corresponding competitors in selected sub-sectors for the years2015, 2035 and 2050. Selected sub-sectors are from the private household sector 7.5 kW, 10.5 kW, 14.9 kW and Industry > 500 °C. ST =Solar thermal; PV = Photovoltaic; HP = Heat Pump; CHP = Combined Heat and Power price developments than the competing heating systems.Hence, we conclude that the synergies from hybrid heattechnology systems and their GHG mitigation potentialare highly underestimated and that such systems can sub-stantially contribute to the success of the energy transitionin Germany.In the long term, in a 95% reduction scenario, bioenergyis most competitive in high temperature industrial applica-tions in the form of wood chips. From 2040-2045 onwards,biomass use shifts almost entirely from the household sec-tor to high temperature industry applications. This shiftaway from decentralised private households is in line withKoch et al. [21]. The use of wood based biomass for in-dustry applications towards 2050 confirms the projectionsof several studies ([2, 9, 17, 32, 38]). Derived from theresults, see Fig. 6, we conclude that with emission targetsto be fulfilled in 2050 the sub-sector "‘Industry > 500 °C"’requires a major share of renewable technologies. Possiblerenewable options are heating from biomass or the use ofelectric arc furnaces. Prime costs of the electric arc are in-creasing strongly in 2050 compared to biomass heating orheat pumps, see Fig. 7. In the private household sector,the heat pump is an additional option, being more effi-cient and more cost effective than the electric arcs. Con-sequently, biomass use shifts to high temperature industryapplications, avoiding the use of electric arcs. However,the benefits granted to industry, apart from the generallylower power prices (see Fig 3), are not depicted in thismodel, making the electric arc a possibly cheaper option.On the other hand, the use of electric arcs requires signifi-cantly more renewable electricity capacity than the use ofheat pumps, which, in contrast, also make use of ambientheat.In the trade and commerce sector, as well as in districtheating, biomass is not a favourable option. For district heating, biogas plants exist today as a result of federalsubsidies in the last decades. Without this support, bio-gas shares are dropping rapidly in case (a) and (b), whichis in line with findings from other studies in literature pro-jecting the use of fermentable residues in the transportsector instead of the heat sector, [21, 32, 33, 41].From the results it is also found that available land forenergy crops is cultivated with Miscanthus. Again, this isa unique finding in the modelling of the heat sector. Whilethe cultivation of Miscanthus is an endogenous model re-sult in this study, the above mentioned scenario analysisfrom literature set the type of energy crops as an inputparameter. In addition, it is notable from our results,that Miscanthus is almost exclusively used as chips in in-dustry applications. One explanation is that in privatehouseholds additional costs for a separator are required ifMiscanthus is used in pellet technologies. However, highyields and low production costs lead to a monopoly posi-tion among energy crops. So why does Miscanthus playonly a minor role in agriculture today? Witzel and Fin-ger [47] identify several major barriers, e.g. a lack of es-tablished markets, high establishment costs as well as un-certainties, arising to a large extent from the necessarylong term commitment. These factors are not representedin our optimization model and must be considered sepa-rately. Nevertheless, to generate an indicator, a model runexcluding perennial crops was performed, resulting in theuse of biomethane from maize silage in high temperatureindustry applications in the long term.
Limitations:
Modelling of the heat sector, as it is per-formed here, depends on several research studies serving asinput data. Research insights may change, e.g. the poten-tial of wood based residues was recently corrected down-wards [3]. Do the results and conclusions change, when the9re-allocated biomass potential is changed? How wouldthe results change if the share of the projected districtheating network would be higher or if biomass allocationis optimized across all energy sectors? The scenario designwith a higher and lower amount of biomass pre-allocatedto the heat sector is supposed to represent such shifts ofbiomass use, but such an approach is limited. However,the outlined results in this study show the same tendencyin both scenarios, indicating that these factors might haveonly a minor impact.Of course, modelling has its limits, so does this model.The private household sector is depicted in a high levelof detail, which was not possible for the industry and dis-trict heating sector, due to the limited available data basis.Further research in this direction is highly recommendedfrom the authors’ view.As mentioned before, the power market is not modelledwithin this study. Therefore a new approach was estab-lished for linking the power and heat sector, see section 2.5.By setting a scenario framework it is not necessary to havea high temporal resolution, having the advantage of a shortmodel run time leading to the possibility to represent theheat sector and their technology concepts in more detail.To increase the annual resolution to a monthly one seemsworthwhile to investigate, since the heat demand, PV yieldetc. varies seasonally. However, our model results fit wellinto the results of the long-term energy scenarios in liter-ature studies [21, 30–33, 38].When future long-term modelling is done, uncertaintiesin the input parameters apply and have an effect on themodel outcome. Using the applied model, with its shortmodel run time compared to established energy scenariomodels, opens up the opportunity to apply a comprehen-sive sensitivity analysis. In future research we will imple-ment all input parameters, having an uncertainty, into asensitivity analysis and determine the effect of each param-eter and all its interactions with all other parameters onthe model outcome. A detailed description of the methodand results goes beyond the scope of this article.
5. Conclusions
In this paper, a 95% reduction scenario is investigatedwith two extreme cases of available biomass potential. Inboth scenarios, the same trends develop, once in an at-tenuated and once in a stronger manner. It is found thatemission targets in the heat sector can be fulfilled in bothcases and bioenergy is found to be a future competitiveoption for heat applications. Especially hybrid heat tech-nology systems were found to be extremely favourable.More specifically, the most cost efficient options for thenext decades until 2040 were found to be in the privatehousehold sector in form of a hybrid CHP (torrefied-) pel-let combustion plant in combination with a heat pumpand a PV-system. A key driver for the competitiveness ofthese systems is the future development of power prices. In times of sector coupling, the advantages of such sys-tems and their potential for emission reduction should notbe underestimated and should be taken into account whendesigning policies. However, in the long term, wood basedbiomass use is found to shift almost entirely from the pri-vate household sector to high temperature applications inthe industry. With increasing power prices, the use ofwood chips from residues and energy crops in high tem-perature industry applications is found to be the most costefficient way to reduce the heat based emissions by 95% in2050.Another finding from this study is, that available landfor energy crops is almost entirely cultivated with Mis-canthus. Despite several major barriers, arising to a largeextent from the long term commitment, this finding shouldbe discussed when designing policies.
6. Acknowledgements
Thank you to Öko-Institut e.V. for sharing the heat de-mand data calculated with B-STar (Building Stock Trans-formation Model), which have been used in this study forthe defined household, trade and commerce and districtheating markets [21].This work was funded by the Helmholtz Association ofGerman Research Centers and supported by HelmholtzImpulse and Networking Fund through Helmholtz Inter-disciplinary Graduate School for Environmental Research(HIGRADE). Declarations of interest: none.
7. Appendix A. Supplementary data
Supplementary data related to this article can be foundat http://dx.doi.org/10.17632/v2c93n28rj.1
8. Appendix B. able 2: Applied heating concepts per sub-sector for private households, trade and commerce. Each row represents a technology concept, eachcolumn represents a sub-sector. Per sub-sector the required technology capacity and the specific heat demand of the buildings in kWh/m²aare described. SFH = Single Family House; MFH = Multi Family House; FT = Full Time; PT = Part Time; ST = Solar Thermal; HP =Heat Pump; CHP = Combined Heat and Power; 1: additional peak load heat supply of 25% of total heat demand from gas condensing boiler;2: additional peak load heat supply of 20% of total heat demand from gas condensing boiler . k W - S F H k W h / m ² a 5 k W - S F H , M F H , M i x e du s e .
30 7 . k W - S F H
90 10 . k W - S F H , M F H - , M i x e du s e . -
45 14 . k W - S F H , A p a r t . B u il d .
30 20 k W - M F H - , M i x e du s e
90 80 k W - A p a r t . B u il d . -
180 45 k W - A p a r t . B u il d .
45 27 k W - M i x e du s e & t r a d e -
180 31 k W - F T A cc o mm o d a t i o n s i n ce k W - F T A cc o mm o d a t i o nun t il k W - P T A cc o mm o d a t i o n / s p o r t / c u l t u r e k W - P T A cc o mm o d a t i o n / s p o r t / c u l t u r e / t r a d e k W - Sp o r t / c u l t u r e Electric direct heating + ST ✕ Gas condensing boiler ✕ ✕ X ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ Gas condensing boiler + ST ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕
Gas boiler + Log wood stove ✕ ✕ ✕
Gas fuel cell + ST ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ Heat pump + PV ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕
Heat pump + PV + ST ✕ ✕ ✕ ✕
Heat pump + PV + log wood stove ✕ ✕ ✕
Heat pump + PV + Pellet boiler ✕ ✕ ✕ ✕
Buffer int. pellet burner + ST ✕ ✕ ✕ ✕ ✕
Pellet boiler ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕
Pellet boiler + ST ✕ ✕ ✕ ✕ ✕ ✕ ✕
Log wood stove + ST ✕ Log wood gasification boiler + ST ✕ ✕ ✕ ✕ ✕ ✕
Wood chip boiler + ST ✕ Torrefied wood pellet gasifier CHP ✕ ✕ ✕
Torr. wood pellet g. CHP + HP + PV ✕ ✕ ✕ ✕
Wood pellet gasifier CHP ✕ ✕
Wood pellet gasifier CHP + HP + PV ✕ ✕ ✕ ✕ ✕ ✕
Wood pellet gasifier CHP + ST + PV ✕ ✕ ✕ able 3: Applied heating concepts per sub-sector in industry and district heating. ST = Solar Thermal; CHP = Combined Heat and Power;HT = High Temperature I ndu s t r y < ° C I ndu s t r y - ° C I ndu s t r y - . ° C Sp ec i a l c oa l d e m a nd D i s t r i c t h e a t i n g Gas condensing boiler ✕ ✕
Gas fuel cell ✕ HT heat pump + ST (5%) ✕ Wood chip boiler X ✕ Wood chip gasifier CHP ✕ ✕
Heat pump + ST (5%) + Wood chip boiler (40%) ✕ Gas turbine CHP ✕ Biomethane gas turbine CHP ✕ Wood chip gasifier with gas turbine CHP ✕ Direct gas firing ✕ Direct coal firing ✕ Electric arc furnace ✕ Direct biomethane firing ✕ Wood chip gasifier with direct gas firing ✕ Direct biomass firing ✕ Coke ✕ Bio-coke ✕ Coal CHP plant ✕ Gas and steam turbine CHP ✕ Coal CHP plant with 5% wood chips ✕ HT heat pump + ST + Methane CHP boiler ✕ Waste CHP plant + Wood chip boiler ✕ able 4: Defined application possibilities of the feedstocks in the technologies. CHP = Combined Heat and Power G a s c o nd e n s i n g b o il e r / f u e l ce ll / p l a n t L og w oo d s t o v e L og w oo d ga s i fi c a t i o nb o il e r W oo dp e ll e t b o il e r /ga s i fi e r W oo dp e ll e t C H P T o rr e fi e d w oo dp e ll e t C H P W oo d c h i pb o il e r H a r d c oa l C H P / c oa l c o k e W oo d c h i p - h a r d c oa l C H P B i o m e t h a n e a pp li c a t i o n s W a s t e C H P p l a n t W oo d c h i p ga s i fi e r C H P G a s t u r b i n e / d i r ec t h e a t i n g W oo d ga s i fi e r ga s t u r b i n e C oa l d i r ec t h e a t i n g B i o m a ss d i r ec t h e a t i n g B i o - C o k e Wood chips (residues) ✕ ✕ ✕ ✕ ✕ ✕ ✕
Briquettes (residues) ✕ Pellets (residues) ✕ ✕ ✕ ✕
Log wood ✕ ✕
Straw ✕ ✕
Manure ✕ ✕
Corn silage ✕ ✕
Sugar beet ✕ ✕
Poplar wood chips ✕ ✕ ✕ ✕ ✕ ✕
Poplar briquettes ✕ Poplar pellets ✕ ✕ ✕
Miscanthus chips ✕ ✕ ✕
Miscanthus briquettesMiscanthus pellets ✕ ✕
Silphie ✕ ✕
Agricultural grass ✕ ✕
Sorghum ✕ ✕
Grassland ✕ ✕
Grain ✕ ✕
Grain Silage ✕ ✕
Natural gas ✕ ✕
Coal ✕ ✕ ✕
Plastic waste ✕ able 5: Applied emission factors caused by infrastructure expensesin 2015 [36, 37, 46] and the calculated allocation factor according thefinnish method. The allocation factor is also applied to the deployedfeedstock. Infrastructure emissions are linearly reduced by 80% until2050. CHP = Combined Heat and Power; PH = Private Household I n f r a s t r u c t u r ee m i ss i o n s i n g C O − e q / M J o u t A ll o c a t i o n f a c t o r Electric direct heating 0.75Gas condensing boiler 0.25Solar thermal 6.89Gas fuel cell 125kWe 5.53 0.30Heat pump 1.87Wood pellet boiler 1.72Log wood gasification boiler 0.55Torrefied wood pellet gasifier 0.55Buffer integrated pellet burner 0.55Wood pellet gasifier CHP 1.93 0.59Gas condensing boiler (Industry) 0.03Wood chip boiler (PH) 0.22Wood chip boiler (Industry) 1.60Wood chip gasifier CHP (Ind. low Temp.) 0.14 0.29Gas Fuel cell (Industry) 5.53 0.46High temperature heat pump 1.94Wood chip gasifier CHP (District heating) 1.27 0.45Gas turbine CHP 0.11 0.13Biomethane gas turbine CHP 0.11 0.13Wood chip gasifier CHP (Ind. high Temp.) 0.30 0.13Direct Gas firing 0.03Direct Coal firing 0.03Electric arc furnace 0.08Direct biomethane firing 0.03Wood chip gasifier with direct gas firing 0.03Direct biomass firing 0.03Coal CHP plant 0.11 0.13Gas and steam turbine CHP 0.13 0.34Coal CHP plant with 5% wood chips 0.11 0.13Methane CHP boiler 1.14 0.38Waste CHP plant 0.11 0.64Photovoltaic system (gCO -eq/kWel) 78.99 Table 6: Applied feedstock emission factors [36, 37, 46]. Emissionsbased on power consumed from the grid are calculated according thescenario depended, power mix specific emission factor [32]. In rela-tion to biomass emissions: Including the effects on carbon storage invegetation and soil, biomass can only be considered CO neutral if itwould rot quickly without energy use (residual and waste materials),or if land and vegetation are managed in such a way that they ab-sorb more CO than they would without bioenergy use (taking intoaccount indirect land use effects). One example is the establishmentof short rotation plantations on pasture land [14]. F ee d s t o c k e m i ss i o n s i n g C O − e q / M J i n Wood chips (residues) 1.36Briquettes (residues) 7.94Pellets (residues) 7.94Log wood 4.47Straw 3.93Manure 0.00Corn silage 7.35Sugar beet 7.20Poplar wood chips 3.83Poplar briquettes 8.25Poplar pellets 8.25Miscanthus chips 4.10Miscanthus briquettes 8.53Miscanthus pellets 8.53Silphie 5.27Agricultural grass 14.83Sorghum 16.11Grassland 15.41Grain 4.78Grain Silage 12.07Natural gas 59.60Coal 108.00Plastic waste 59.75Coal coke 123.00Bio-coke 27.7815 able 7: Yield of the defined energy crops [22] and their corresponding land use in 2015 for heat or combined heat and power applications[6]. SRC = Short Rotation Coppice
Yield(GJ/ha) Land use (ha)2015Corn silage
177 872 000
Sugar beet
150 15 600
Grain
91 151 000
Grain Silage
138 123 000
Agr. grass
137 20 150
Grassland
90 157 849
Silphie
126 400
Sorghum
152 0 (est.)
SRC
137 6 630
Miscanthus
273 4 500
Table 8: Applied surcharges in the model based on own calculations.
Surcharge ( e /GJ)Pellets compared to wood chips Pellet torrefication + 14 %
Briquettes compared to wood chips Separator for torrefied poplar pellets in pellet technologies
Separator for miscanthus pellets in pellet technologies
Separator for poplar briquettes in log wood technologies
Separator for straw in wood chip technologies
Separator for poplar wood chips in wood chip gasification technologies
Separator for miscanthus chips in wood chip technologies
Transport fee for wood based feedstocks per delivery e eferencesReferences [1] Übersicht zur entwicklung der energiebedingten emissionen undbrennstoffeinsätze in deutschland 1990-2016.[2] Sektorkopplung – optionen für die nächste phase der en-ergiewende.[3] Dbfz - data repository: Ressourcendatenbank, 2019. URL http://webapp.dbfz.de/resources .[4] agrarheute. Heu und strohpreise, 2018. URL .[5] A. Becker, D. Peter, and D. Kemnitz. Anbau und ver-wendung nachwachsender rohstoffe in deutschland. URL https://fnr.de/fileadmin/fnr/pdf/mediathek/22004416.pdf .[6] Raik Becker and Daniela Thrän. Optimal siting of wind farmsin wind energy dominated power systems. Energies , 11(4):978,2018. ISSN 1996-1073. doi: 10.3390/en11040978.[7] Andreas Bloess, Wolf-Peter Schill, and Alexander Zerrahn.Power-to-heat for renewable energy integration: A review oftechnologies, modeling approaches, and flexibility potentials.
Applied Energy , 212:1611–1626, 2018. doi: 10.1016/j.apenergy.2017.12.073.[8] André Brosowski, Philipp Adler, Georgia Erdmann,Walter Stinner, Daniela Thrän, and Udo Mantau.
Biomassepotenziale von Rest- und Abfallstoffen: Sta-tus Quo in Deutschland , volume 36 of
Schriftenreihenachwachsende Rohstoffe . Fachagentur NachwachsendeRohstoffe e.V. (FNR), Gülzow-Prüzen, 2015. URL .[9] Thomas Bründinger, Julian Elizalde König, Oliver Frank, Diet-mar Gründig, and Christoph Jugel. dena-leitstudie integrierteenergiewende: Impulse für die gestaltung des energiesystemsbis 2050 teil a: Ergebnisbericht und handlungsempfehlungen(dena) teil b: Gutachterbericht (ewi energy research & scenar-ios ggmbh).[10] Bundesministerium für Umwelt, Naturschutz, Bau und Reak-torsicherheit. Klimaschutzplan 2050 - klimaschutzpolitischegrundsätze und ziele der bundesregierung: Kurzfassung. URL .[11] Bundesministerium für Wirtschaft und En-ergie. Energiedaten: Gesamtausgabe. URL .[12] Bundesnetzagentur and Bundeskartellamt.Monitoringbericht 2016, 2017. URL .[13] Bundesregierung. Energiekonzept für eine umweltschonende,zuverlässige und bezahlbare energieversorgung. 2010. URL .[14] Selina Byfield. Impulspapier-trialog-bioenergie_23.02.2018.2018.[15] C.A.R.M.E.N. e.V. C.a.r.m.e.n. e.v. - preisindizes, 2018. URL .[16] GAMS Development Corp. Gams, 2019. URL .[17] Philipp Gerbert, Patrick Herhold, Jens Burchardt, StefanSchönberger, Florian Rechenmacher, Almut Kirchner, AndreasKemmler, and Marco Wünsch. Klimapfade für deutschland.[18] Klaus Heuck, Klaus-Dieter Dettmann, and Detlef Schulz.
Elektrische Energieversorgung: Erzeugung, Übertragung undVerteilung elektrischer Energie für Studium und Praxis . Vieweg+ Teubner, 8 edition, 2010.[19] Martin Kaltschmitt, Hans Hartmann, and Hermann Hof-bauer, editors.
Energie aus Biomasse: Grundlagen, Tech-niken und Verfahren . Springer Vieweg, Berlin and Heidelberg,3., aktualisierte und erweiterte auflage edition, 2016. ISBN9783662474389.[20] Andreas Kemmler, Samual Straßburg, Friedrich Seefeldt, Na-talia Anders, Clemens Rohde, Tobias Fleiter, Ali Aydemir,Heinrich Kleeberger, Lukas Hardi, and Bernd Geiger. Daten-basis zur bewertung von energieeffizienzmaßnahmen in derzeitreihe 2005 – 2014. [21] Matthias Koch, Klaus Hennenberg, Katja Hünecke,Markus Haller, and Tilman Hesse. Rolle der bioen-ergie im strom- und wärmemarkt bis 2050 unter ein-beziehung des zukünftigen gebäudebestandes. URL .[22] Kuratorium für Technik und Bauwesen in der Landwirtschafte.V., editor.
Energiepflanzen: Daten für die Planung des En-ergiepflanzenanbaus . 2. auflage edition, 2012.[23] Volker Lenz and Matthias Jordan. Technical and economic dataof renewable heat supply systems for different heat sub-sectors.,2019.[24] Erik Merkel, Russell McKenna, Daniel Fehrenbach, and WolfFichtner. A model-based assessment of climate and energytargets for the german residential heat system.
Journal ofCleaner Production , 142:3151–3173, 2017. ISSN 09596526. doi:10.1016/j.jclepro.2016.10.153.[25] Microsoft. Mircrosoft excel, 2019. URL https://products.office.com/de-de/excel .[26] M. Millinger and D. Thrän. Biomass price developments inhibitbiofuel investments and research in germany: The crucial futurerole of high yields.
Journal of Cleaner Production , 172:1654–1663, 2016. ISSN 09596526. doi: 10.1016/j.jclepro.2016.11.175.[27] M. Millinger, K. Meisel, and D. Thrän. Greenhouse gas abate-ment optimal deployment of biofuels from crops in germany.
Transportation Research Part D: Transport and Environment ,69:265–275, 2019. ISSN 13619209. doi: 10.1016/j.trd.2019.02.005.[28] Markus Millinger.
Systems assessment of biofuels. Mod-elling of future cost and greenhouse gas abatement com-petitiveness between biofuels for transport on the caseof Germany.
Leipzig, 2018. ISBN 1860-0387. URL http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa2-332464 .[29] Markus Millinger. Bioenergyoptimisation model, 2019.[30] Joachim Nitsch, Thomas Pregger, Tobias Naegler, DominikHeide, Diego Luca de Tena, Franz Trieb, Yvonne Scholz,Kristina Nienhaus, Norman Gerhardt, Michael Sterner, andTobias Trost. Langfristszenarien und strategien für den ausbauder erneuerbaren energien in deutschland bei berücksichtigungder entwicklung in europa und global: Schlussbericht. URL .[31] Benjamin Pfluger, Bernd Tersteegen, Bernd Franke, ChristianeBernath, Tobias Bossmann, Gerda Deac, Rainer Elsland, TobiasFleiter, André Kühn, Mario Ragwitz, Matthias Rehfeldt, JanSteinbach, Andreas Cronenberg, Alexander Ladermann, Chris-tian Linke, Christoph Maurer, Sebastian Willemsen, BenediktKauertz, Martin Pehnt, Nils Rettenmaier, Michael Hartner,Lukas Kranzl, Wolfgang Schade, Giacomo Catenazzi, MartinJakob, and Ulrich Reiter. Modul 10.a: Reduktion der treibhaus-gasemissionen deutschlands um 95 % bis 2050 grundsätzlicheüberlegungen zu optionen und hemmnissen: Langfristszenar-ien für die transformation des energiesystems in deutschland -studie im auftrag des bundesministeriums für wirtschaft undenergie.[32] Julia Repenning, Lukas Emele, Ruth Blanck, Hannes Böttcher,Günter Dehoust, Hannah Förster, Benjamin Greiner, RalphHarthan, Klaus Hennenberg, Hauke Hermann, Wolfram Jörß,Charlotte Loreck, Sylvia Ludig, Felix Matthes, MargaretheScheffler, Katja Schumacher, Kirsten Wiegmann, Carina Zell-Ziegler, Sibylle Braungardt, Wolfgang Eichhammer, RainerElsland, Tobais Fleiter, Johannes Hartwig, Judit Kockat,Ben Pfluger, Wolfgang Schade, Barbara Schlomann, FrankSensfuß, and Hans-Joachim Ziesing. Klimaschutzszenario2050: 2. endbericht -studie im auftrag des bundesministeri-ums für umwelt, naturschutz, bau und reaktorsicherheit. URL .[33] Michael Schlesinger, Dietmar Lindenberger, and Chris-tian Lutz. Entwicklung der energiemärkte - energieref-erenzprognose: Projekt nr. 57/12 studie im auftrag desbundesministeriums für wirtschaft und technologie. URL .[34] Statistisches Bundesamt. Land- und forstwirtschaft, fischerei: andwirtschaftliche bodennutzung - anbau auf dem ackerland.[35] Jan Steinbach. Modellbasierte Untersuchung von Politikinstru-menten zur Förderung erneuerbarer Energien und Energieef-fizienz im Gebäudebereich . Dissertation, Fraunhofer-Institut fürSystem- und Innovationsforschung and Fraunhofer IRB-Verlag,2015.[36] Swiss centre for life cycle inventories. Ecoinvent 2.2 for umberto,2010.[37] Swiss centre for life cycle inventories. Ecoinvent 3.3 for umberto,2016.[38] Nora Szarka, Marcus Eichhorn, Ronny Kittler, Alberto Bezama,and Daniela Thrän. Interpreting long-term energy scenarios andthe role of bioenergy in germany.
Renewable and SustainableEnergy Reviews , 68:1222–1233, 2017. ISSN 13640321. doi: 10.1016/j.rser.2016.02.016.[39] TFZ. Entwicklung der brennstoffpreise, 2018. URL .[40] Inc. The MathWorks. Matlab, 2019. URL https://de.mathworks.com/products/matlab.html .[41] Daniela Thrän, Oliver Arendt, Jens Ponitka, Julian Braun,Markus Millinger, Verena Wolf, Martin Banse, Rüdiger Schal-dach, Jan Schüngel, Sven Gärtner, Nils Rettenmaier, Katja Hü-necke, Klaus Hennenberg, Bernhard Wern, Frank Baur, UweFritsche, and Hans-Werner Gress. Meilensteine 2030: Ele-mente und meilensteine für die entwicklung einer tragfähigenund nachhaltigen bioenergiestrategie.[42] Daniela Thrän, Rüdiger Schaldach, Markus Millinger, VerenaWolf, Oliver Arendt, Jens Ponitka, Sven Gärtner, Nils Ret-tenmaier, Klaus Hennenberg, and Jan Schüngel. The mile-stones modeling framework: An integrated analysis of nationalbioenergy strategies and their global environmental impacts.
Environmental Modelling & Software , 86:14–29, 2016. ISSN13648152. doi: 10.1016/j.envsoft.2016.09.005.[43] Daniela Thrän, Oliver Arendt, Martin Banse, Julian Braun,Uwe Fritsche, Sven Gärtner, Klaus J. Hennenberg, Katja Hün-neke, Markus Millinger, Jens Ponitka, Nils Rettenmaier, Rüdi-ger Schaldach, Jan Schüngel, Bernhard Wern, and Verena Wolf.Strategy elements for a sustainable bioenergy policy based onscenarios and systems modeling: Germany as example.
Chem-ical Engineering & Technology , 40(2):211–226, 2017. ISSN09307516. doi: 10.1002/ceat.201600259.[44] Umweltbundesamt. Energieverbrauch fürfossile und erneuerbare wärme. URL .[45] Umweltbundesamt. Erneuerbare energien in zahlen, 2017. URL .[46] Umweltbundesamt. Probas - prozessorientierte ba-sisdaten für umweltmanagementsysteme, 2019. URL .[47] Carl-Philipp Witzel and Robert Finger. Economic evaluation ofmiscanthus production – a review.
Renewable and SustainableEnergy Reviews , 53:681–696, 2016. ISSN 13640321. doi: 10.1016/j.rser.2015.08.063.[48] World Bank. Global economic monitor (gem) commodities:Wheat, hrw, 2019. URL databank.worldbank.org ..