# Biomass District Heating Systems Based on Agriculture Residues
## 3 Methodology
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. Figure 2: Methodology for evaluation of potential of BioDH System. 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:  
1. 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. 2. 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. 3. 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):  
\[L_{P}=0.02\cdot S_{DH}+0.4\cdot{S_{DH}}^{0.5} \tag{1}\]  
And the specific network length \(L_{spec}\) for each building can be calculated as follows [18]:  
\[L_{spec}=1207.36\;\rho_{building}^{-0.5894} \tag{2}\]  
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 \(Q_{m_{i}}\) and supply cost \(P_{m_{i}}\). If local biomass is not able to cover the demand, it must be taken from the excess biomass in neighbouring areas, with quantities \(Q_{ext_{i}}\) and supply cost \(P_{ext_{i}}\) [\(\epsilon\)/ton]. The mean forest biomass cost per tonne for each BioDH is given by Equation (3):  
\[\overline{P}_{B}=\frac{\sum P_{m_{i}}\cdot Q_{m_{i}}+\sum P_{ext_{i}}\cdot Q_ {ext_{i}}}{\sum Q_{m_{i}}+\sum Q_{ext_{i}}} \tag{3}\]  
- 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). 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.