Biomass District Heating Systems Based on Agriculture Residues

applied sciences Article Biomass District Heating Systems Based on Agriculture Residues Víctor M. Soltero 1,*ID , Ricardo Chacartegui 2, Carlos Ortiz 3ID , Jesús Lizana 4 and Gonzalo Quirosa 1 1Department of Design Engineering, University of Seville, 41011 Seville, Spain; gonzaloquir[email protected] 2Department of Energy Engineering, University of Seville, 41092 Seville, Spain; ricar[email protected] 3Department of Electronic and Electromagnetism, University of Seville, 41012 Seville, Spain; [email protected] 4Department of Building Construction, University of Seville, 41012 Seville, Spain; [email protected] *Correspondence: [email protected]; Tel.: +34-630-877-180 Received: 5 February 2018; Accepted: 19 March 2018; Published: 21 March 2018   Featured Application: Biomass District Heating Systems in Rural Areas. Abstract: This paper presents a methodology for analyzing the regional potential for developing biomass district heating systems combining forestry biomass and agriculture residues as fuel. As a case study , this methodology is applied to the continental region of Spain. With this analysis the potential for the implementation of biomass district heating systems based on the use of agriculture residues is applied to 501 rural municipalities in Spain. The renewable forestry biomass and agriculture residues resources availability is analyzed and the biomass required for heating is assessed. The results of applying the methodology show the interest of the combination of biomass sources in a relevant number of municipalities with estimated Internal Rate of Return (IRR) values above 10% and for the analyzed region an IRR mean value of 4.3%. Keywords: district heating; biomass; rural areas; agriculture residues biomass 1. Introduction One of the key factors to control global warming is to improve the energy efficiency at residential areas, one of the large energy consumption sectors. Buildings are responsible of above 40% of the total energy demand, mainly covered by fossil fuels, with the associated greenhouse gases emissions. Even though technology evolution produces more efficient energy systems, the global energy consumption is continuously increasing [ 1 ]. The improvement in the quality of energy distribution is one of the great challenges facing today’s society for future decades [2]. On this goal, the replacement of existing low efficiency heating systems by high efficiency district heating and cooling (DHC) facilities based on renewable sources is of the highest interest [ 3 ]. Their implementation can generate significant fuel and cost savings, reducing significantly greenhouse gases emissions [ 4 , 5 ]. On the other hand, despite their high interest, it is necessary the development of adequate regulatory and financial frameworks to support the large scale implementation of advanced district heating networks. It should also be noted that the development of new DH systems involves the displacement of existing heating technologies and it implies infrastructures development in public areas [6]. In Europe, the Energy Efficiency Directive (EDD-2012/27/EU) [ 7 ] establishes a common framework on measures to promote energy efficiency in the European Union. According to EED-2012/27, DHC cogeneration systems deployment is a promising pathway for reducing primary energy consumption. Appl. Sci. 2018,8, 476; doi:10.3390/app8040476 www.mdpi.com/journal/applsci Appl. Sci. 2018,8, 476 2 of 17 However, DHC technologies are largely untapped. One of the key points addressed in EDD-2012/27/EU is that the potential for the implementation of district heating networks should be evaluated. DHC systems will have quite different design and performance characteristics depending on their location and local resources availability. There will be different requirements to fulfil from social, legal and economic points of view depending on location that will affect to their viability and sustainability. On this sense a first general classification for DHC systems can be done in terms of the implantation area: urban, rural and industrial. Each of these types usually has common characteristics and specific requirements for viable and sustainable District Heating and Cooling systems development. Usually they have different ranges of population density, heat demand density, space availability, infrastructure development constrains, gas natural supply, local regulations development, air quality constrains, etc. Therefore, different methodologies are required in order to an adequate evaluation of their potential implantation [8]. On this purpose, previous research works have studied the potential for DHC [ 8 , 9 ]. In [ 8 ], a methodology is established to evaluate the potential of a large scale implementation of Combined Heat & Power District Heating (CHP-DH) systems in urban areas with natural gas network availability. The application to the continental region of Spain results in a quite efficient of CHP-DH, simultaneous thermal and electrical generation system. In [ 9 ], a procedure is established and applied to evaluate the massive implementation of DH networks in rural areas. For this case, and focused on rural areas where there is no natural gas network for continuous supply of natural gas, biomass district heating systems (BioDH) are evaluated. The use of biomass as fuel in these areas implies the use of a resource fully integrated in their environment and with capacity for reducing greenhouse gases emissions and with potential to improve the economy of the region [10]. Depending on the location, an usual situation is that rural municipalities have not all the forest biomass resources required by the BioDH systems in their own territory [ 9 ]. So, supply from neighboring municipalities is required to achieve the required biomass supply for the development of BioDH. It involves challenges in the management of the forestry biomass resource [6]. Following this research line, in this article is evaluated an option for BioDH systems in rural areas: the combined use of existing agricultural biomass residues as complementary to the forest biomass resources in the municipality. Agricultural residues have been obviated in previous studies but they present an interesting potential as fuel [ 11 , 12 ]. The combination of both biomass sources may improve the economic profitability of BioDH systems by using as much as possible the local biomass resources available in the area where the network is installed. In this paper is established a methodology for the assessment of the potential of a massive implementation of BioDH systems in a region. It takes into account the simultaneous evaluation of the forest and agricultural biomass resources in a given area. As it is oriented for the evaluation at regional scale, it will be necessary to establish a relatively fast and efficient procedure to quantify the biomass resource available in a given area, as well as to determine the costs involved in the exploitation of the different types of agricultural biomass residues. Once evaluated the available quantity of renewable biomass resource, biomass forestry and agriculture residues, and their associated costs, it is possible to evaluate the use of agricultural biomass as fuel in BioDH systems and to evaluate the associated operational expenditures. The methodology is applied to a case study to evaluate the potential of BioDH based on the addition to renewable forestry biomass agriculture residues. The case study is carried out in the continental area of Spain, the same area in which application cases are developed in bibliography [ 8 , 9 ], in order to allow the comparison of results with previous studies to advance in the definition of the framework for the application of district heating systems at this region. Before approaching the methodology, the biomass resource management model is developed in Section 2. It identifies the agents involved and the relationships among them, within the exploitation model of agricultural and forestry biomass resources. Viability and sustainability along the operation of BioDH systems with agricultural and forestry biomass depends to a large extent on the adequate identification, coordination and correct functional relationship between all the agents. Appl. Sci. 2018,8, 476 3 of 17 2. Forest and Agricultural Biomass Model Management for Universal District Heating The management of biomass resources for biomass district heating system involves different stakeholders and sectors. In the case of forest biomass, from private or public dominium, the management from the forest to the final use in the biomass district heating system involves many different agents that interact in the process. In Figure 1, [ 6 ] is graphically presented how the forest biomass resource is managed in combination with the agriculture residue and the relationships between the involved stakeholders for their exploitation and use in biomass universal district heating systems. Appl. Sci. 2018, 8, x FOR PEER REVIEW 3 of 17 2. Forest and Agricultural Biomass Model Management for Universal District Heating The management of biomass resources for biomass district heating system involves different stakeholders and sectors. In the case of forest biomass, from private or public dominium, the management from the forest to the final use in the biomass district heating system involves many different agents that interact in the process. In Figure 1, [6] is graphically presented how the forest biomass resource is managed in combination with the agriculture residue and the relationships between the involved stakeholders for their exploitation and use in biomass universal district heating systems. (a) (b) Figure 1. (a) Universal Network Heating model; (b) Integrated management between different universal heating networks of the forestry resource, extrapolated from [6]. In Figure 1 can be appreciated the complexity of the system with multiple interactions, decision agents and regulations to apply, already developed or required to be developed. If, in addition, the mountain, and the forest resource, belongs to different municipalities the management complexity is increased. All these are factors that increase the uncertainties regarding the viability and sustainability of BioDH and affects to investments. In this sense, the consideration of local agriculture residues in the biomass district heating fuel management chain, as proposed in this work, could reduce some of these management issues increasing the local biomass resource availability. At the same time it could reduce the global system transport carbon footprint, and depending on the specific cases, with the increase of the biomass fuel resource, to reduce costs and fuel availability uncertainties. 3. Methodology In this section is presented a methodology for evaluating the potential for forest/agriculture residues BioDH systems in a region. It evaluates the factors that affect to the viability of these systems though a top-down/bottom-up methodology integrating the renewable forest biomass energy source and complemented with agriculture residues. This global methodology is summarized in Figure 2. The methodology analyzes the critical factors that directly affect to the economic viability of BioDH systems and their sustainability. These factors and the stages where they are evaluated within the methodology are: availability of the biomass resource (stage 5); thermal energy demand (stage 2); environmental factors (stage 3); socio-economic factors, such as energy poverty, housing occupation or the age of buildings (stage 4). Figure 1. ( a ) Universal Network Heating model; ( b ) Integrated management between different universal heating networks of the forestry resource, extrapolated from [6]. In Figure 1can be appreciated the complexity of the system with multiple interactions, decision agents and regulations to apply, already developed or required to be developed. If, in addition, the mountain , and the forest resource, belongs to different municipalities the management complexity is increased. All these are factors that increase the uncertainties regarding the viability and sustainability of BioDH and affects to investments. In this sense, the consideration of local agriculture residues in the biomass district heating fuel management chain, as proposed in this work, could reduce some of these management issues increasing the local biomass resource availability. At the same time it could reduce the global system transport carbon footprint, and depending on the specific cases, with the increase of the biomass fuel resource, to reduce costs and fuel availability uncertainties. 3. Methodology In this section is presented a methodology for evaluating the potential for forest/agriculture residues BioDH systems in a region. It evaluates the factors that affect to the viability of these systems though a top-down/bottom-up methodology integrating the renewable forest biomass energy source and complemented with agriculture residues. This global methodology is summarized in Figure 2. The methodology analyzes the critical factors that directly affect to the economic viability of BioDH systems and their sustainability. These factors and the stages where they are evaluated within the methodology are: availability of the biomass resource (stage 5); thermal energy demand (stage 2); environmental factors (stage 3); socio-economic factors, such as energy poverty, housing occupation or the age of buildings (stage 4). Appl. Sci. 2018,8, 476 4 of 17 Appl. Sci. 2018, 8, x FOR PEER REVIEW 4 of 17 Figure 2. Methodology for evaluation of potential of BioDH System. Step 1. Regional characterization and selection area of study, The region under study is defined through the energy characterization and available resources. In a first approach three main types can be identified for district heating analyses: (i) industrial areas, where the heat source comes from residual processes and cogeneration; (ii) urban areas, with high population density, scarcity of available spaces and, usually, natural gas network. In them the logistic associated to the use of biomass at certain scale can be complex; and (iii) rural areas where usually population density is low or moderate and biomass resource is available with a relatively easier management for logistic and supply. Step 2. Thermal System assessment. Preliminary evaluation It comprises the Sanitary Hot Water (SHW) and heating demands of the set of building to be connected to the heating network. The former can be estimated from adjusted estimation of the consumption rates, usually defined in local regulations. The heating demand can be evaluated through the evaluation of energy losses through buildings envelope and the contribution of internal and external heat gains [8]. Once estimated the thermal demand, the linear heat density (LHD) can be used as first approximation to the systems viability, based on a mean length of the network defined as function of the buildings density. The BioDH systems are designed for supplying the whole thermal demand, and no auxiliary backup systems are considered for these urban heating networks. They are considered to be reliable systems and, once implanted, with lower fuel costs than those with fossil fuels. Although in some biomass district heating systems gas or diesel boilers are used for peak demand, this study has opted for full decarbonization of the heating system by using only local renewable resources, even though in some cases it may penalize the investment costs of the boilers. Step 3. Assessment of viability factors Rural BioDH systems require specific evaluation of the factors that directly influence their viability: • Biomass resources: Cost and availability must be analyzed. This can be done by means of GIS tools or statistical data. • Environmental factors for thermal plant location: this includes characteristics of available and feasible spaces for thermal station plant implementation, available area, affections, contaminants and particles dispersion and safety distances. • Socioeconomic factors. These should include analyses of energy poverty and occupancy of dwellings. Step 4. Municipality assessment After the classification of potential BioDH locations in previous steps, this step goes into the detail of the specific BioDH at the selected locations. Heating demand is the main factor for the viability of the systems. It goes up to 90% of the total energy consumption in the DH. An accurate demand estimation allows an accurate prediction of incomes and system profitability. It can be done Total heating demand can be calculated as a function of internal (occupation, lighting and domestic Figure 2. Methodology for evaluation of potential of BioDH System. Step 1. Regional characterization and selection area of study The region under study is defined through the energy characterization and available resources. In a first approach three main types can be identified for district heating analyses: (i) industrial areas, where the heat source comes from residual processes and cogeneration; (ii) urban areas, with high population density, scarcity of available spaces and, usually, natural gas network. In them the logistic associated to the use of biomass at certain scale can be complex; and (iii) rural areas where usually population density is low or moderate and biomass resource is available with a relatively easier management for logistic and supply. Step 2. Thermal System assessment. Preliminary evaluation It comprises the Sanitary Hot Water (SHW) and heating demands of the set of building to be connected to the heating network. The former can be estimated from adjusted estimation of the consumption rates, usually defined in local regulations. The heating demand can be evaluated through the evaluation of energy losses through buildings envelope and the contribution of internal and external heat gains [8]. Once estimated the thermal demand, the linear heat density (LHD) can be used as first approximation to the systems viability, based on a mean length of the network defined as function of the buildings density. The BioDH systems are designed for supplying the whole thermal demand, and no auxiliary backup systems are considered for these urban heating networks. They are considered to be reliable systems and, once implanted, with lower fuel costs than those with fossil fuels. Although in some biomass district heating systems gas or diesel boilers are used for peak demand, this study has opted for full decarbonization of the heating system by using only local renewable resources, even though in some cases it may penalize the investment costs of the boilers. Step 3. Assessment of viability factors Rural BioDH systems require specific evaluation of the factors that directly influence their viability: • Biomass resources: Cost and availability must be analyzed. This can be done by means of GIS tools or statistical data. • Environmental factors for thermal plant location: this includes characteristics of available and feasible spaces for thermal station plant implementation, available area, affections, contaminants and particles dispersion and safety distances. • Socioeconomic factors. These should include analyses of energy poverty and occupancy of dwellings. Step 4. Municipality assessment After the classification of potential BioDH locations in previous steps, this step goes into the detail of the specific BioDH at the selected locations. Heating demand is the main factor for the viability of the systems. It goes up to 90% of the total energy consumption in the DH. An accurate demand estimation allows an accurate prediction of incomes and system profitability. It can be done Total heating demand can be calculated as a function of internal (occupation, lighting and domestic appliances) and external (solar radiation) gains, ventilation loads and heat transmission by hourly integration of the heating power. Appl. Sci. 2018,8, 476 5 of 17 Besides it must be adjusted by additional factors as: (i) availability and costs of the biomass resource, that can be evaluated using GIS sources [ 13 , 14 ]; (ii) environmental factors for the location of the thermal plant; and (iii) socioeconomic factors, they include the ponderation of the energy poverty and houses occupation and use on the real demand [ 15 , 16 ]. They must be analyzed for each one of the BioDH under study. In the specific methodology for BioDH in rural areas the following correction factors of the demand are included: (i) Occupancy of dwellings: for permanent residences an intermittence factor for the theoretical demand of 0.78 [ 8 ] is considered, whereas for partially occupied dwellings (weekends and holidays periods), the value is set to 0.4. (ii) Energy poverty: a correction factor for energy poverty is only applied to permanent dwellings. This information can be obtained directly through local surveys or indirectly through statistical data. For the application case surveys were used. (iii) Age of the buildings: theoretical demand must be corrected in function of the age of buildings, with a factor that corrects the time passed since their construction or the last refurbishment. These data can be estimated through statistical data of the municipalities. Finally, the future demand evolution must be taken into account correcting the estimated degree-days taking into account the effect of estimated temperature increase. Network length affects directly to the investment and expected profitability of DH [ 17 ]. Besides, operational expenditures are affected of network length due to the effect of distribution losses. BioDH length can be evaluated through Equation (1): LP=0.02·SDH +0.4·SDH0.5 (1) And the specific network length Lspec for each building can be calculated as follows [18]: Lspec =1207.36 ρbuilding −0.5894 (2) Both can be adjusted from the evaluation of existing DH networks. Step 5. Biomass residues evaluation - 5.1 Biomass from forestry residues Characteristics of the available biomass can be evaluated using existing databases and/or GIS tools, where available. They include main biomass characteristics (composition, LHV, humidity) and extraction and manipulation costs. Forest biomass availability is calculated as a function of type Qmi and supply cost Pmi . If local biomass is not able to cover the demand, it must be taken from the excess biomass in neighbouring areas, with quantities Qexti and supply cost Pexti [ € /ton]. The mean forest biomass cost per tonne for each BioDH is given by Equation (3): PB=∑Pmi·Qmi+∑Pexti·Qexti ∑Qmi+∑Qexti (3) - 5.2 Agricultural residues biomass A procedure able to quantify and economically evaluate the available agricultural biomass in an area under study. To do this, it is recommended, where available, the use of GIS tools to determine available resources and then to use different experimental expressions to evaluate the costs of the different agricultural biomass. GIS tools can also provide economic data but they are usually less accurate than those provided by the experimental formulas adjusted to the specific cases (Figure 3). Appl. Sci. 2018,8, 476 6 of 17 Appl. Sci. 2018, 8, x FOR PEER REVIEW 6 of 17 Figure 3. Agricultural Residues Biomass. - 5.3 Extraction, collection and conditioning costs of agricultural biomass from Bionline [19] For the application case of the methodology shown in this paper, to quantify the amount of agricultural biomass available, the Bionline web tool [19] (Figure 4) (with application for countries in South Europe) has been used. Other tools and programs with similar information are available for other regions of Europe and different regions of the World. Where no available the same procedure could be applied directly from agriculture databases. They provide data related to the extraction, collection, conditioning and transport of the biomass. In the present article, the evaluation of costs related to cereal straw, corn borer, olive groves, fruit trees and vineyards is mainly based on the work by Esteban et al. [20]. For the application of the Bionline GIS tool the following typologies of agricultural biomass are classified in: - Woody residues: generated from pruning of olive groves, fruit trees and vineyards. - Herbaceous residues: mainly cereal straws and corn boletus. The extraction, collection and conditioning costs used for each type within methodology are presented in the following subsections: Figure 3. Agricultural Residues Biomass. - 5.3 Extraction, collection and conditioning costs of agricultural biomass from Bionline [19] For the application case of the methodologyshownin this paper, toquantify the amount of agricultural biomass available, the Bionline web tool [ 19 ] (Figure 4) (with application for countries in South Europe) has been used. Other tools and programs with similar information are available for other regions of Europe and different regions of the World. Where no available the same procedure could be applied directly from agriculture databases. They provide data related to the extraction, collection, conditioning and transport of the biomass. In the present article, the evaluation of costs related to cereal straw, corn borer, olive groves, fruit trees and vineyards is mainly based on the work by Esteban et al. [20]. For the application of the Bionline GIS tool the following typologies of agricultural biomass are classified in: - Woody residues: generated from pruning of olive groves, fruit trees and vineyards. - Herbaceous residues: mainly cereal straws and corn boletus. The extraction, collection and conditioning costs used for each type within methodology are presented in the following subsections: Appl. Sci. 2018, 8, x FOR PEER REVIEW 6 of 17 Figure 3. Agricultural Residues Biomass. - 5.3 Extraction, collection and conditioning costs of agricultural biomass from Bionline [19] For the application case of the methodology shown in this paper, to quantify the amount of agricultural biomass available, the Bionline web tool [19] (Figure 4) (with application for countries in South Europe) has been used. Other tools and programs with similar information are available for other regions of Europe and different regions of the World. Where no available the same procedure could be applied directly from agriculture databases. They provide data related to the extraction, collection, conditioning and transport of the biomass. In the present article, the evaluation of costs related to cereal straw, corn borer, olive groves, fruit trees and vineyards is mainly based on the work by Esteban et al. [20]. For the application of the Bionline GIS tool the following typologies of agricultural biomass are classified in: - Woody residues: generated from pruning of olive groves, fruit trees and vineyards. - Herbaceous residues: mainly cereal straws and corn boletus. The extraction, collection and conditioning costs used for each type within methodology are presented in the following subsections: Figure 4. Cont. Appl. Sci. 2018,8, 476 7 of 17 Appl. Sci. 2018, 8, x FOR PEER REVIEW 7 of 17 Figure 4. GIS evaluation of available resource of residues biomass (Bionline. IDAE [19]). - 5.3.1 Straws of cereal and corn canister For the case of cereal straws and corn the costs of collection, extraction and conditioning are presented in Table 1. They include the transport costs required to perform these tasks. It requires the evaluation of the surface biomass density [20]. Table 1. Basic costs of harvesting and forwarding annual crops in Spain [20]. Crop Baling in Rank (€/t) (x, Surface Density in t/ha) Baling out of Rank (€/t) Forwarding (Max 500 m) and Piling (€/t) Winter cereals −4.2752x + 37.685 3.89 (x > 7.94 t/ha) 3.00 - (2.64 > x < 7.94) 26.97 (x < 2.64 t/ha) - Oil crops, Maize, Cotton −3.2064x + 38.264 2.92 (x > 8 t/ha) 3.00 - (2.67 > x < 8) 20.22 (x < 2.67 t/ha) - - 5.3.2 Olive groves, fruit trees and vineyards In this case, the costs of extraction, collection and conditioning can be directly visualized independently of the surface biomass density in the area. The costs are divided into different sub sections, finally obtaining the total costs. The transport costs required to perform these tasks are also taken into account. Data are grouped in Table 2: Table 2. Basic costs of harvesting and forwarding woody crops in Spain [20]. Woody Crop Alignment Machine (€/t) Crushing (€/t) Forwarding (Max 15 km) (€/t) Total (€/t) Orchard 6.00 15.03 9.02 30.05 Olive 6.00 24.00 7.20 37.20 Vineyard 6.00 24.00 7.20 37.20 These data are applicable to Spain. To extrapolate the results to other European regions different to South of Europe, correction factors based on the economic indicators of the region should be used. For countries in central and northern Europe, the results obtained in Sweden should be used as a reference. The extrapolation of the correction factors to be used in several countries are presented in Table 3: Figure 4. GIS evaluation of available resource of residues biomass (Bionline. IDAE [19]). - 5.3.1 Straws of cereal and corn canister For the case of cereal straws and corn the costs of collection, extraction and conditioning are presented in Table 1. They include the transport costs required to perform these tasks. It requires the evaluation of the surface biomass density [20]. Table 1. Basic costs of harvesting and forwarding annual crops in Spain [20]. Crop Baling in Rank (€/t) (x, Surface Density in t/ha) Baling out of Rank (€/t) Forwarding (Max 500 m) and Piling (€/t) Winter cereals −4.2752x + 37.685 3.89 (x > 7.94 t/ha) 3.00 - (2.64 > x < 7.94) 26.97 (x < 2.64 t/ha) - Oil crops, Maize, Cotton −3.2064x + 38.264 2.92 (x > 8 t/ha) 3.00 - (2.67 > x < 8) 20.22 (x < 2.67 t/ha) - - 5.3.2 Olive groves, fruit trees and vineyards In this case, the costs of extraction, collection andconditioning canbe directly visualized independently of the surface biomass density in the area. The costs are divided into different sub sections, finally obtaining the total costs. The transport costs required to perform these tasks are also taken into account. Data are grouped in Table 2: Table 2. Basic costs of harvesting and forwarding woody crops in Spain [20]. Woody Crop Alignment Machine (€/t) Crushing (€/t) Forwarding (Max 15 km) (€/t) Total (€/t) Orchard 6.00 15.03 9.02 30.05 Olive 6.00 24.00 7.20 37.20 Vineyard 6.00 24.00 7.20 37.20 These data are applicable to Spain. To extrapolate the results to other European regions different to South of Europe, correction factors based on the economic indicators of the region should be used. For countries in central and northern Europe, the results obtained in Sweden should be used as a reference. The extrapolation of the correction factors to be used in several countries are presented in Table 3: Appl. Sci. 2018,8, 476 8 of 17 Table 3. Correction factors used for the extrapolation of the basic costs calculated for Spain and Sweden (data extracted from [20]). Spain as Base Correction Factor Sweden as Base Correction Factor Spain 1.000 Sweden 1.000 France 1.121 Austria 0.985 Italy 1.169 Denmark 1.124 Greece 0.851 Finland 0.979 Portugal 0.820 Germany 1.057 - - Norway 1.245 - - Poland 0.514 - 5.3.3 Straw In the case of using straw as fuel, the process of obtaining the biomass has a different impact in the cost and in the relative effect over the total fuel cost. The transport costs required to perform these tasks are taken into account. It is also important to note that in the case of straw, there are different final geometries of packaging the biomass, and it affects to the final price in each case as it affects to the transport and management. Three different tables (Tables 4–6) [ 21 ] are presented with the differential costs for different final geometries of fuel compilation. Table 4. Calculation of differential costs of energy use with straw prepared in small square bales [ 21 ]. Costs Category (Operation) Costs (€/t) Costs Structure (%) Bailing 1.90 17.27 Materials 1.90 17.27 Loading 1.20 10.91 Transportation 3.00 27.27 Manipulation 1.20 10.91 Storage 1.80 16.36 Total costs 11.00 100.00 Table 5. Calculation of differential cost of energy use with straw collected in the form of cylinder-shaped bales is used [21]. Costs Category (Operation) Costs (€/t) Costs Structure (%) Bailing 2.30 26.44 Materials 0.50 5.75 Loading 3.10 35.63 Transportation 0.60 6.90 Manipulation 0.80 9.20 Storage 1.40 16.09 Total costs 8.70 100.00 Table 6. Differential cots of briquetting per ton of briquettes produced [21]. Costs Category (Operation) Costs (€/t) Costs Structure (%) Bailing 8.70 21.48 Materials 21.70 53.58 Manipulation 3.60 8.89 Storage 6.50 16.05 Total costs 40.50 100.00 This procedure can be extrapolated for the evaluation of the costs of other of agriculture biomasses. Step 6. Cost and benefit evaluation At the BioDH system level, viability is evaluated through a Cost-Benefit analysis [ 8 ]. It involves the analysis of: Appl. Sci. 2018,8, 476 9 of 17 (i) CAPEX. It includes all the required investments to start up each of the subsystems: biomass thermal power station ( CAPEXTP ), distribution network ( CAPEXN ) and energy transfer stations (CAPEXETS). CAPEXBioDH =CAPEXTP +CAPEXN+CAPEXETS (4) Thermal plant investment for BioDH systems is in the range of 855 € /kW to 1453 € /kW of the total plant capacity [ 22 , 23 ]. For a power range of 8 MW to 50 MW, higher than those expressed in [ 22 , 23 ], reduces the capital investment to a much lower range, 200 € /kW to 250 € /kW, due to economies of scale. The cost of the distribution network must be evaluated according to its length and specific diameter. The specific pipe diameter for each BioDH network can be calculated using [24]: da=0.0486·ln(D/LP)+0.0007 (5) where d a is the pipe diameter (m), and LP the network pipe length (m), together with D, forms D/L P the linear heat density (GJ/m). According to [24], network investment can be evaluated as follows: CAPEXN=(C1+C2·da)·LP= (C1+C3·P0.5 th )·LP(6) The use of plastic pipes in heating networks has reduce the costs at levels of C 1 = 120 € /m and C3= 750 €/m·W0.5 evaluated from real installation within the scope of this research. (ii) Reference Baseline definition. The baseline ( Lbase ) in a BioDH project, which replaces heating and ACS generation from individual or collective boilers in buildings, is the unit cost of the useful energy, plus the depreciation and maintenance costs of the individual boiler (Equation (7)) [8]. Lbase >PricekWh > CBioDh f ixed +Ccentral a+Cnetwork a+Cnetwork E+Ccentral f uel Esold (7) where CBioDh f ixed is the network fixed cost ( € ), Ccentral a and Cnetwork a are the central and network amortisation costs respectively, Cnetwork E is the network energy cost, Ccentral f uel is the fuel cost and Esold is the total energy sold (kWh). The reference baseline cost for the current heating system is based on the use of individual or collective diesel boilers, with a cost of 45 € /MWh. If considering also maintenance costs and amortization of the individual boilers are added and combined with a seasonal average yield of 70%, the reference baseline cost is 85 €/MWh. (iii) Incomes. Incomes are calculated as follows: The price should be clearly below than the baseline price to make it attractive to the consumer avoiding to introduce additional costs to the end user. In=D·PricekWh (8) where Inare the incomes (€) and Dthe energy sold (kWh). To promote the subscription and to give a clear benefit to the end user the sale price for the case study includes a discount on the current cost of heating of 20%, resulting in a selling price of 68 €/MWh. (iv) OPEX. Operational Expenditures (OPEX) in the cost-benefit analysis are given by biomass cost and conveying energy consumption cost, which depends linearly on the heating demand. They must be included together with the fixed operation costs. OPEX =Ccentral f uel +Cnetwork E+CBioDH f ixed (9) Appl. Sci. 2018,8, 476 16 of 17 Therefore, for the region selected, as long as there is not a situation in which a greater amount of biomass resources are available or one in which the reuse of agricultural biomass is for some reason of a considerably lower cost, from the direct analysis of mean values it could be concluded that BioDH systems based on the combination of both resources do not provide clear benefits compared to only forest biomass. In other regions, with different patterns of agriculture production and renewable forestry biomass resources, different results would arise with the application of methodology. However, the methodology and analysis has identified a relevant number of municipalities where the integration results in values of IRR above 10% operating only with local biomass resource. These values for an infrastructure investment can be high enough for BioDH deployment in these municipalities. In addition, in most of the villages under analysis there is an increase of the profitability with both fuel sources. If BioDH systems were implanted in them the combination of both biomass fuel sources would extend the availability of biomass resource of the municipalities for future scenarios. Besides with an increase of profitability, small but positive, there are additional positive impacts in the local economy. New economic activity and jobs can be created both in the agriculture and in the collection and exploitation of the biomass [27]. Any case, special attention must be paid to additional issues associate to the use of biomass multifuel. On one hand, different fuels should be stored in isolated places of other types of fuel, which would mean an increase in storage costs [ 28 ] with an increase of the complexity in the management of the storage and fuel preparation. On the other hand, special attention must be paid to the boiler selection and biomass fuel mixes preparation for an adequate performance as with several types of biomass there will be different combustion characteristics and fuel geometries (chips, pellets, etc.) [ 29 ]. These aspects must be taken into account when including agricultural biomass in BioDH systems. 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