Development of Indicators for the Energy Assessment of Biomass Integration into Electrical Grids in Colombia

Abstract

The increasing need for flexible and decentralized electricity systems in Colombia has renewed interest in biomass as a complementary renewable energy source beyond conventional large-scale applications. Rather than focusing on specific conversion technologies, this study develops an indicator-based framework aimed at qualifying the energetic suitability of diverse biomass resources for integration into electrical microgrids and distributed generation schemes. The research follows a documentary and comparative methodological design structured around sequential analytical stages, including the systematization of biomass resources, their physicochemical and energetic characterization based on reported data, conceptual analysis of the biomass-to-electricity pathway, and the formulation of quantitative energy indicators. These indicators are subsequently transformed into qualitative categories through a discretization procedure that enables relative comparison across resource types. Agricultural residues, livestock by-products, urban pruning waste, and residues from dedicated energy crops were considered within a unified analytical framework. The resulting indicator set captures resource availability, energy content, and conversion-relevant attributes, allowing biomass alternatives to be assessed in a consistent and comparable manner without relying on site-specific technological assumptions. By translating quantitative parameters into qualitative energy profiles, the proposed approach supports early-stage planning and decision-making for decentralized power systems. The framework provides a systematic basis for identifying biomass resources with favorable energetic characteristics and contributes to the broader discussion on sustainable and diversified electricity generation in Colombia.

1. Introduction

Population growth and industrial development have intensified the search for sustainable energy sources that reduce environmental impact. In this context, biomass stands out as a renewable resource with significant potential, particularly in countries such as Colombia, where agricultural and livestock activities generate large volumes of usable organic waste [1].

Although Colombia’s electricity generation matrix is predominantly clean dominated by hydropower (65.3%) it still relies considerably on thermal generation (30%), while non-conventional renewable sources such as solar, wind, and cogeneration remain marginal [2] (see Figure 1). This scenario highlights the need to diversify energy sources and increase system resilience, especially in decentralized generation schemes.

Biomass can play a strategic role in this transition, particularly in the context of electrical microgrids, which enable local generation and energy management either in grid-connected or isolated operation modes [3]. Despite its availability and renewable nature, the practical implementation of biomass-based generation faces technical, economic, and decision-making challenges, partly due to the lack of systematic evaluation tools that allow comparing different biomass resources and their energy applications.

In response to this gap, this study focuses on identifying qualitative indicators for the energy assessment of biomass resources available in Colombia, aimed at supporting their integration into microgrid supply projects. The work begins with the identification and basic characterization of biomass resources with energy potential, considering their physical, chemical, and energetic properties and their current use in the Colombian context. Based on this analysis, common attributes are extracted to construct qualitative indicators that facilitate the evaluation of alternative biomass uses. These indicators are then used to define criteria that support informed decision-making for the efficient integration of biomass into distributed energy systems.

This article contributes to the field of distributed generation by:

1.

Identifying and characterizing the main biomass resources available in Colombia, emphasizing their potential for electricity generation in microgrids.

2.

Establishing qualitative indicators that support the evaluation of these resources and their conversion alternatives.

3.

Providing analytical criteria to guide the efficient integration of biomass into the country’s energy transition.

2. Methodology

The research was developed under a descriptive–analytical approach, supported by a methodological design of a documentary and comparative nature, aimed at building a framework of qualitative indicators for the energy assessment of biomass in the context of electrical networks in Colombia. Figure 2 summarizes the methodological workflow adopted in this study, showing the sequential phases followed from information collection to the qualitative discretization of energy indicators.

The methodology was structured into five sequential and logically articulated phases:

1.

Collection and classification of information on biomass in Colombia

In the first phase, a search and systematization of secondary information were carried out, drawing from the scientific literature, technical reports, and institutional documents related to biomass in Colombia. Based on this review, the main biomass resources relevant to the national energy sector were identified and selected. The resources were organized into two categories—residual biomass (agricultural, livestock, and municipal solid waste) and energy crops, as illustrated in Figure 3—which provided the initial analytical framework.

2.

Characterization of biomass resources

Subsequently, a comprehensive characterization of the identified resources was carried out, using three levels of analysis:

• Proximate analysis: determination of moisture, ash, volatile matter, and fixed carbon.
• Ultimate analysis: quantification of the main constituent elements (C, H, O, N, S).
• Energy analysis: estimation of the calorific value as a central parameter for energy assessment.

This characterization provided a homogeneous and comparable basis for the subsequent development of the indicators.

3.

Analysis of the utilization pathway

In the third phase, the biomass utilization pathway was addressed, understood as the process that leads from resource availability to its conversion into electrical energy. The analysis included logistical aspects (collection, transport, and storage), technological aspects (pre-treatment and thermochemical or biological conversion), and energy aspects (efficiency and process losses). This step was used to identify critical factors and potential bottlenecks in the transformation chain.

4.

Definition and construction of energy indicators

Based on the characterization of the resources and the analysis of the utilization pathway, the definition of energy indicators was carried out. These indicators were derived from key factors associated with both the physicochemical properties of biomass and the conversion processes and end use in electrical networks. The construction of the indicators was based on criteria of technical relevance, comparability, and practical applicability, so that they reflected the differential conditions of each resource and its energy potential.

5.

Discretization and qualification of indicators

Since the indicators initially obtained were quantitative in nature, a qualitative discretization process was implemented to facilitate their interpretation and applicability in energy planning scenarios. For this purpose, each indicator was evaluated for the different identified biomass resources. The resulting values were organized and grouped by indicator, from which classification ranges were established as part of the discretization procedure. Finally, ordinal categories with three levels (high, medium, and low) were defined, assigned according to the relative performance of each resource with respect to the evaluated parameters.

The data processing and analysis were carried out using Microsoft Excel as part of the methodological implementation, applying descriptive and comparative statistical techniques. These included the calculation of averages, ranges, and correlation coefficients, as well as normalization and discretization procedures that allowed the classification of resources into ordinal qualitative categories (high, medium, and low). This ensured the comparability and internal consistency of the information used to construct the energy indicators.

3. Identification of Energy Resources Derived from Biomass in Colombia

3.1. Residual Energy Resources Derived from Biomass in Colombia

3.1.1. Residual Energy Resources from the Agricultural Sector

Agricultural activities represent one of the main sources of residual biomass in Colombia. Due to the country’s large territorial extension and the heterogeneity of its productive systems, the identification of representative agricultural residues requires the application of selection criteria that allow focusing the analysis on the most relevant crops.

In this study, agricultural residual biomass was identified using secondary information from the Atlas of the Energy Potential of Residual Biomass in Colombia [4]. The identification process considered factors such as cultivated area and the geographical distribution of crops across departments. Based on these criteria, a reduced set of representative permanent and temporary crops was selected to support the subsequent energy assessment.

3.1.2. Residual Energy Resources from the Livestock Sector

Livestock activity refers to the raising of animals for food security, commercialization, and economic utilization. In Colombia, this activity is considered a subsector of agriculture and includes different forms of production, among which cattle ranching, poultry farming, and swine farming stand out. Other activities such as sheep and goat farming, aquaculture, and small-scale animal production also exist, although they are not currently developed on a large scale [5].

Livestock residual biomass is mainly derived from the manure generated during animal production processes [4]. Similar to the approach adopted for agricultural residues, the identification of livestock biomass resources focused on representative sources with significant relevance at the national level.

Based on their contribution to Colombia’s agricultural gross domestic product, cattle ranching, poultry farming, and swine farming were selected as the main livestock sectors for analysis [6]. These sectors constitute the primary sources of livestock-based residual biomass considered in this study and form the basis for the subsequent quantitative and spatial analysis of manure generation.

3.1.3. Residual Energy Resources from the Urban Sector

Waste is defined as “materials generated in production, transformation, and consumption activities that have not attained any economic value in the context in which they were generated” [7]. Within this classification, organic waste generated in urban centers is considered part of residual and/or industrial biomass [8], and the organic fraction of municipal solid waste (MSW) is classified as biomass [9].

The main forms of urban residual biomass include the biodegradable organic fraction of plant origin, pruning residues, municipal landfill waste suitable for biogas production, and agricultural, forestry, and industrial residues located near urban centers [8,9,10]. Additionally, urban wastewater contains a high organic load whose treatment is necessary and may represent an indirect biomass-related resource [7].

In Colombia, there is no comprehensive quantification of urban biomass resources. However, partial estimates have been developed for specific components, such as organic waste generated in marketplaces and wholesale centers, pruning residues from parks and green areas, and wastewater treatment volumes in major cities [8]. These estimates provide the basis for identifying representative urban biomass resources considered in this study.

3.2. Distribution of Energy Crops in Colombia

Energy crops, also known as agroenergy crops, are those primarily intended for energy production from biomass. They are classified into three types: agricultural, forestry, and aquatic [11]. These crops include both species specifically cultivated for energy purposes as well as residues and surpluses from harvests and certain industrial crops, which can be used for the production of biofuels, bioelectricity, and biogas [11,12]. In Colombia, the area allocated to energy crops is still limited and is concentrated exclusively in agricultural crops, mainly sugarcane and oil palm, which are used for the production of ethanol and biodiesel, respectively [11,13]. Sugarcane, an agro-industrial crop whose main purpose is sugar production, has in recent years been allocated 19% of the total cultivated area in the country dedicated to bioethanol production, thereby consolidating its role as an energy crop in Colombia [13].

In this way,

Table 1. Summary of identified biomass resources in Colombia.

Category	Resource-Producing Activity	Resource
Residual resources	Agro-industrial activity	Banana crop residues
		Coffee crop residues
		Sugarcane crop residues
		Panela cane crop residues (sugarcane for panela residues)
		Oil palm crop residues
		Plantain crop residues
		Rice crop residues
		Maize crop residues (corn crop residues)
	Livestock activity	Cow dung
		Swine manure
		Poultry manure
	Pruning of parks and urban green areas	Urban pruning residues
Resources from energy crops	Oil palm cultivation	Palm kernel used for biofuel production

Source: Taken from [4].

shows the consolidation of the identified energy resources, which will serve as the basis for the physicochemical description and energy assessment presented in Section 4.

4. Characterization of the Identified Energy Resources

The description and characterization of biomass are essential to identify the properties of resources that contribute to their energy assessment. Building on the consolidated inventory presented in Table 1 (see Section 3), various aspects are considered, including the characterization of the physical and chemical properties of the residues to be used [14]. Such characterization can be carried out at different levels, the most common being elemental analysis, analysis of organic compounds, proximate analysis, and determination of calorific value [15].

Given that the focus of utilizing the resources identified in Section 3 is of an energy nature, it is essential to perform a physicochemical characterization of their residues. The inventory summarized in Table 1 guides the selection of representative cases for detailed analysis. It should be noted that the characterization and description of the resources were based exclusively on information available in the literature. No experimental tests were conducted on any of the resources, and priority was given to characteristics such as calorific value, moisture content, and quantity, in addition to those provided by elemental and proximate analyses.

Accordingly, for each resource previously presented, a consistent descriptive structure was applied. It begins with a general overview, identifying the main types of residues and their current or potential energy applications, followed by a physicochemical characterization including proximate, energetic, and elemental analyses. These data are summarized in

Table 2. Values of proximate, energetic, and elemental analysis of banana residues.

Type of Analysis	Parameter	Rachis	Pseudostem	Rejected Banana
Proximate analysis	Moisture content (%)	94.54	93.62	83.75
	Volatile matter (%)	86.98	66.09	73.61
	Ash (%)	23.80	12.99	5.38
	Fixed carbon (%)	17.23	20.51	21.00
Energetic analysis	Higher heating value (MJ/kg)	7862.64	8836.40	10,819.83
	CI (combustibility index)	-	-	-
Elemental analysis	C (%)	32.38	36.45	38.55
	H (%)	4.71	5.55	6.14
	N (%)	1.13	0.59	0.78
	O (%)	37.70	44.31	49.13
	S (%)	0.38	0.07	0.02

Source: Taken from [4].

. The same structure was followed for all thirteen identified resources; however, only selected examples are shown here due to spatial limitations.

4.1. General Description of Banana Crop Residues

The types of residues obtained from bananas can be classified according to the parts of the plant, and their energy potential is closely related to the bunch from which they originate. Banana residues include pseudostem (stalk), leaves, and flowers, commonly used for composting and biofuel production; rachis, typically employed in biogas generation; rejected bananas, used to produce biogas and ethanol; and peels, which are also used for biogas and ethanol production. Among these, the stalk, leaves, and their mixture are the residues with the highest energy potential [16].

The utilization of these residues can occur through the direct combustion of biomass for thermal and electrical energy generation, or through the production of biofuels such as bioethanol, biodiesel, and biogas [16].

4.1.1. CAR

The crop agricultural residues (CAR) of bananas include several materials that, although often treated as waste, have significant energy potential. The banana leaf is one of the most relevant residues in this regard, especially when derived from bunches with suitable characteristics. This residue is commonly used for composting and biofuel production due to its favorable physical and chemical properties. After drying, it exhibits low moisture content, facilitating its use in direct combustion processes, and presents an adequate calorific value. Its density is medium compared with other banana residues. Chemically, the leaf contains a high percentage of volatile compounds, which favor thermal degradation, and a considerable amount of fixed carbon, although its ash content is relatively high. In terms of elemental composition, the leaf shows notable proportions of carbon, oxygen, and hydrogen, while nitrogen and sulfur appear in smaller quantities [16,17,18].

The pseudostem (also known as stalk) of the banana is another residue of great interest for composting and energy generation. Together with the leaf and their mixture, the pseudostem is considered one of the residues with the highest energy potential [16]. This material has a lower moisture content than the leaf, making it more suitable for thermochemical processes. Its calorific value is also higher, and its density is greater. The pseudostem contains a high proportion of volatile compounds, a moderate amount of fixed carbon, and a low ash content. Its elemental composition is similar to that of the leaf, although with slightly lower carbon and oxygen levels [16,17].

Banana flowers are considered a useful residue, mainly for composting and biofuel production. However, unlike other banana residues, no detailed physicochemical or energy data are available for the flowers, making their technical characterization difficult [16].

4.1.2. IAR

Within the industrial agricultural residues (IAR) of bananas, other materials with high energy utilization potential can also be found. Among them is the rachis—the central axis of the bunch—which is notable for its high organic load and is commonly used in biogas production [16]. Banana peels, in turn, are another usable residue, mainly employed in the production of biogas and ethanol [16]. Additionally, rejected bananas—those that do not meet commercial standards—are also used for energy purposes, primarily for the production of biogas and ethanol [16].

4.1.3. Physicochemical Analysis of Banana Crop and Processing Residues

Table 2 presents the physicochemical characterization of banana residues, considering their proximate, energetic, and elemental analyses. It should be noted that the combustion index was not included, as it was not found in the consulted literature.

5. Indicators and Evaluation Criteria

To define indicators for the energy evaluation of different biomass resources, it is essential to understand biomass as a high-potential source for supplying electrical systems, whose utilization depends on transformation processes that vary according to the resource (Section 4). In this section, we identify factors and mathematical expressions that characterize each resource in energy terms and enable comparison for integration into electrical systems. We also propose a biomass utilization pathway that encompasses the stages from the resource to the generation of usable electrical energy, where these factors and expressions serve as evaluation indicators. Finally, based on the available information on biomass resources in Colombia, ranges were established to support the proposed evaluation criteria.

5.1. Biomass as a Source for Powering Electrical Systems

Amid the energy transition and global decarbonization, electrical systems seek to integrate diverse energy sources—through models such as microgrids—with solar, wind, and fossil fuels being the most common [19,20]. Although biomass is not frequently used in these systems, there are emerging experiences in Colombia, such as the wood-waste gasification plant in Necoclí and a pilot plant using plant residues at the Bogotá Botanical Garden [21]. The design of a microgrid must consider social, economic, environmental, and technical factors; in the case of biomass, source selection depends on origin and the most suitable conversion technology, as noted in Section 4. Both Castrillón [20] and Bucheli [21] emphasize that resource availability is a central criterion; in addition, laboratory characterization is required to estimate energy potential and define the appropriate technology. Thus, integrating biomass as a power source for electrical systems requires a comprehensive assessment of availability, physicochemical properties, and technological processes to determine its real and sustainable contribution to electricity generation.

5.2. General Pathway of Biomass Utilization

To identify key factors for the energy evaluation of biomass resources in supplying electrical systems, the stages in the utilization process are outlined below, along with a brief description of each.

5.2.1. Identification of Energy Resources and Integration into the Supply Chain

Biomass can be transformed into electrical energy through different processes and infrastructures depending on its final use [22]. Its sources include crop and agro-industrial residues, energy crops, livestock residues [22], and homogeneous municipal solid waste. Once identified, these resources enter the supply chain (collection, transport, and storage), where storage marks the transition toward conversion into a biofuel [23].

5.2.2. Conversion of the Resource into Biofuel

Stored resources are transferred to processing plants, where the technological choice depends on moisture level: dry biomass (<50%) is typically converted through thermochemical processes, while wet biomass (>75%) is treated with biological processes, such as wet or dry digestion [24,25]. In Colombia, agricultural, forestry, and energy-crop biomass is mainly processed using thermochemical methods, while livestock biomass is treated through biochemical processes, especially anaerobic digestion [4]. The resulting biofuel depends on both the resource and the applied process.

5.2.3. Conversion of Biofuel into Electrical Energy

The final stage of biomass utilization consists of transforming the biofuel into heat or electricity through combustion [24]. In Colombia, biogas—derived from agricultural, urban, and livestock residues—and solid biofuels from IAR and CAR are the main sources for electricity generation [26].

The entire process of biomass resource utilization described above is shown in Figure 4.

6. Energy in Biomass

The process of converting biomass into electrical energy exhibits an energy behavior similar to that of other conversion systems: it begins with input energy contained in the resource, undergoes transformation processes in which losses occur, and finally delivers useful or final energy. This approach makes it possible to analyze overall system efficiency and to establish comparisons among different biomass resources.

6.1. Energy Pathway of Biomass

To define indicators that guide the selection of biomass as an energy source, it is necessary to analyze the energy flow through the system—from the initial resource to electricity generation. This analysis seeks to identify mathematical expressions that characterize the energy performance of the resource and allow comparative bases with other types of biomass.

Figure 4 shows the general utilization process and the different energy states of the resource. The transition from one state to another requires a transformation and, in general terms, the energy passes through three successive processes. As stated by [27], these transformations are key stages, since they determine both efficiency and the level of usable energy, as they represent unavoidable losses.

Figure 5 graphically shows the energy flow, whose components can be grouped into two categories: energy states (input, output, and resulting energy) and transformation processes (the different conversions). This scheme reflects the most complex scenario, in which a solid or liquid biofuel is obtained before generating energy. In contrast, if the resource is utilized through direct combustion or incineration, the biofuel production stage does not need to be considered, since the resource itself acts as fuel [24].

6.2. Mathematical Expressions for the Energy Characterization of the Biomass Energy Flow

Once the components of the flow have been identified, it is essential to establish mathematical expressions that allow the quantification of input energy and transformation losses. These expressions are the most relevant, as they directly influence the final electrical energy. Subsequent energy states (biofuel, mechanical, or thermal energy) are not considered, since they depend directly on the input energy and do not provide additional information about system efficiency [27].

6.2.1. Energy Present in the Input Energy Resource

According to the Biomass Atlas [4] and the methodology of Tauro [27], the energy of the resource is expressed in terms of energy potential (EP), which mainly depends on calorific value and available quantity. The EP can be calculated in a similar manner for resources within the same biomass category, provided that equivalent utilization methods (thermochemical or biological) are applied.

• Agricultural residues. EP is determined by Equation (1), considering five key variables: cultivated area, yield, residue mass, dry fraction, and lower heating value (LHV).

  $$E{P}_{BRA}=\alpha ·A·R_C·M_{rg}·Y_{rs}·LHV$$

  (1)
  Here, A corresponds to cultivated hectares; $R_C$ to yield; $M_{rg}$ to residue mass per unit of product; $Y_{rs}$ to dry fraction (related to moisture content); and $LHV$ to the effectively releasable energy of the fuel [4,24].
• Livestock residues. In biological processes such as anaerobic digestion, EP is calculated using Equation (2), which depends on the number of animals, dry matter, volatile solids, biogas production, and the LHV of methane.

  $$E{P}_{BRP}=NA·DM·VS·Bo·LH{V}_{CH4}$$

  (2)
  Unlike agricultural residues, the LHV is constant because it corresponds to methane, the base gas of the biogas obtained [4,25].
• Pruning MSW. These residues share characteristics with forest biomass, which justifies their thermochemical utilization. EP depends on three variables: residue mass, dry fraction, and LHV, as expressed in Equation (3).

  $$E{P}_{BRSOP}=M_r·Y_{rs}·LHV$$

  (3)
• Dedicated crops. In Colombia, energy crops are mainly oriented toward the production of liquid biofuels (biodiesel and fuel alcohol) for transportation [12,26]. However, their residues can be utilized similarly to agricultural residues, applying thermochemical processes and the same EP formulation [4,28].

6.2.2. Energy in Transformation Processes

Transformation processes involve significant technical complexity, with multiple subprocesses and technologies. However, their overall effect can be summarized by efficiency, which expresses the proportion of input energy converted into useful energy:

$${\eta }_p={\displaystyle \frac{E_s}{E_e}}\times 100$$

(4)

where $E_s$ is the output energy and $E_e$ is the input energy. This parameter is key to characterizing system performance and quantifying the inherent losses of each transformation process [27,29].

7. Comparative Indicators and Energy Evaluation

7.1. Comparative Indicators and Energy Evaluation for the Agricultural Sector

In the agricultural sector, four indicators are proposed, all derived from Equation (1). These allow the measurement of both resource availability and energy quality:

• Resource availability: quantifies the net amount generated over a period (generally one year). This is a key factor, as the selection of biomass as an energy source depends primarily on its availability.
• Dry fraction: expresses the usable portion of the resource, which is essential in thermochemical processes where efficiency improves with higher dry content [4,24,25].
• Lower heating value (LHV): indicates the effectively usable energy of a resource, excluding the unrecoverable heat from water vapor.
• Energy potential (EP): integrates all the above variables, making it the most comprehensive indicator for comparing different resources, even those of diverse origins.

7.2. Comparative Indicators and Energy Evaluation for the Livestock Sector

Based on Equation (2), three indicators are defined for this sector, focused on availability and the potential for biogas conversion:

• Total resource availability: measures the annual amount of resource generated, sufficient to estimate its contribution without the need for adjustment by dry fraction.
• Biogas production capacity: evaluates the amount of biogas generated through anaerobic digestion, dependent on volatile solids content and the methane production factor.
• Energy potential: analogous to the agricultural sector, it represents the total energy contained in the available livestock resource.

7.3. Comparative Indicators and Energy Evaluation for Pruning Municipal Solid Waste (MSW)

Based on Equation (3), two indicators are established to assess the usefulness of pruning MSW as an energy resource:

• Total resource availability: quantifies the amount of usable residue, taking into account the dry fraction.
• Energy potential: measures the total energy contained in the resource, following the same principle applied to the agricultural and livestock sectors.

Taken together, these indicators are quantitative in nature and allow the objective measurement and comparison of the availability and potential of different biomass resources. Details can be found in

Table 3. Quantitative energy indicators by type of resource.

Quantitative Indicators by Sectors
Type of Measure		Indicator	Mathematical Expression or Symbolic Representation	Description	Type of Resource
	Availability	Resource availability	$M.D=A·R_C·M_{rg}$	Indicates the amount of resource available in a defined period of time, usually one year.
	Energy capacity of the resource	Dry fraction	$Y_{rs}={\displaystyle \frac{1-CH}{100}}$	Indicates the proportion of the energy resource that can be considered “useful” as it is free of moisture.	Agricultural and dedicated crops
	Energy capacity of the resource	Lower heating value	$PCI$	Indicates the ideal amount of heat that a resource can release.
	Energy capacity of the resource	Energy potential	$P{E}_{BRA}=\alpha ·A·R_C·M_{rg}·Y_{rs}·PCI$	Indicates the total energy contained in an energy resource.
	Availability	Total resource availability	$M.D=NA·MS$	Indicates the amount of resource available in a defined period of time, usually one year.
	Energy capacity of the resource	Biogas production capacity	$C.P.B=SV·Bo$	Indicates the capacity of the resource to produce biogas through the process of anaerobic digestion.	Livestock
	Energy capacity of the resource	Energy potential	$P{E}_{BRP}=NA·MS·SV·Bo·PC{I}_{CH4}$	Indicates the total energy contained in an energy resource.
	Availability	Total resource availability	$M.D=M_r·F_{rs}$	Indicates the amount of resource available in a defined period of time, usually one year.	Urban pruning solid waste
	Energy capacity of the resource	Energy potential	$P{E}_{BRSOP}=M_r·F_{rs}·PCI$	Indicates the total energy contained in an energy resource.

Legend: First-level indicator; Second-level indicator.

, organized by type of measure, indicator name, mathematical formulation, description, and sector of application.

Additionally, each indicator is hierarchically classified using a color code: identifies first-level (primary) indicators, and identifies second-level (secondary) indicators.

The classification criterion is based on the indicator’s scope of comparison and degree of aggregation. First-level indicators are those that enable direct and cross-sectoral comparisons among biomass resources, regardless of their origin, because they express intrinsic energetic attributes such as calorific value, energy potential, or available quantity. Second-level indicators, on the other hand, represent complementary or derived parameters that describe contextual or process-related aspects—such as conversion efficiency, logistic feasibility, or regional availability—which are useful for intra-sectoral analysis and characterization.

As shown in Table 3, the indicators are grouped according to the origin of the resource and, for that reason, the same indicator name may adopt slightly different definitions across sectors to better reflect their physical or operational conditions.

The only first-level indicator identified in this study is the energy potential (EP), since it provides an objective parameter for comparing resources from any sector. Meanwhile, the second-level indicators allow a more detailed description of resource behavior within each sector and serve as the basis for constructing more precise energy profiles at the regional scale.

It is important to emphasize that the proposed indicators are quantitative in nature. Although they provide a solid basis for evaluation, they do not by themselves fulfill the central objective of this study, which is to establish qualitative indicators. To achieve this, the quantitative indicators are transformed into qualitative equivalents through the definition of specific evaluation criteria and range categories, as developed in Section 8, in accordance with the fourth objective of the study.

8. Evaluation Criteria

As noted in Section 7, the purpose of defining evaluation criteria is to provide a qualitative representationto quantitative indicators, thus constituting the foundation of this section.

Definition of Evaluation Criteria for a Qualitative Representation of Indicators Based on Ranges

For the development of the criteria, the residues identified in Section 3, together with the physicochemical properties described in Section 4, were considered. Based on the expressions in Table 3, the values of each indicator were calculated for the different sectors of origin of the residues.

The initial analysis was applied to the agricultural sector, whose results are presented in

Table 4. Application of the established indicators to the identified agricultural sector energy resources.

Crop	Residues	Resource Availability (Tons/Year)	Lower Heating Value (KJ/Kg)	Dry Fraction	Energy Potential (TJ/Year)
Permanent oil palm	Nut shell	189,075.00	17,340.04	0.80	2627.44
	Fiber	546,381.00	18,584.22	0.67	6778.85
	Rachis	924,618.00	17,484.69	0.41	6607.31
Permanent panela cane	Top and green leaves	5,680,790.00	16,018.10	0.32	28,663.57
	Bagasse	3,832,640.00	19,374.24	0.57	42,035.47
Permanent sugarcane	Top and leaves	8,525,718.00	16,018.10	0.31	41,707.20
	Bagasse	7,008,873.00	19,374.24	0.57	76,735.83
Permanent coffee	Pulp	2,008,192.00	18,517.50	0.19	7206.78
	Husk	193,460.00	19,264.57	0.90	3338.58
	Stems	2,849,596.00	19,062.23	0.71	38,561.52
Permanent banana	Rachis	1,878,194.00	7862.64	0.05	806.31
	Pseudostem	9,390,968.00	8836.40	0.06	5294.27
Permanent banana	Rejected banana	281,729.00	10,819.83	0.16	495.34
Temporary rice	Straw	5,789,669.00	13,537.42	0.26	20,699.41
	Husk	492,738.00	15,666.22	0.92	7136.53
	Stubble	1,278,642.00	14,912.28	0.66	12,573.09
	Cob	369,629.00	14,739.63	0.71	3845.88
	Husk (capacho)	288,858.00	16,589.50	0.91	4383.73

(annexes). This table includes six columns: source crop, type of residue, and, for each case, the values of availability, lower heating value, dry fraction, and energy potential. It is important to note that banana crop residues were not considered in this analysis, due to the lack of physicochemical information in the literature that would allow the application of the necessary expressions for calculating the indicators.

Based on this information, the values of each indicator were organized into deciles, which allowed the establishment of ten consecutive ranges: from the minimum value to the first decile (R1), from the first to the second decile (R2), and so on up to the maximum (R10).

Table 5. Evaluation criteria based on ranges for energy resources in the agricultural sector.

Availability				Rating	Lower Heating Value				Rating
Range	Lower Value	Upper Value			Range	Lower Value	Upper Value
R10	7,463,926.50	9,390,968.00		High	R10	19,297.47	19,374.24		High
R9	5,746,117.40	7,463,926.50			R9	18,871.03	19,297.47
R8	3,734,335.60	5,746,117.40			R8	18,414.22	18,871.03
R7	2,176,472.80	3,734,335.60		Medium	R7	17,368.97	18,414.22		Medium
R6	1,578,418.00	2,176,472.80			R6	16,303.80	17,368.97
R5	848,970.60	1,578,418.00			R5	15,947.72	16,303.80
R4	498,102.30	848,970.60		Low	R4	14,987.67	15,947.72		Low
R3	321,166.40	498,102.30			R3	14,018.30	14,987.67
R2	255,248.30	321,166.40			R2	10,224.80	14,018.30
R1	189,075.00	255,248.30			R1	7862.64	10,224.80
Dry Fraction				Rating	Energy Potential				Rating
Range	Lower Value	Upper Value			Range	Lower Value	Upper Value
R10	0.90	0.92		High	R10	41,805.68	76,735.83		High
R9	0.76	0.90			R9	34,602.34	41,805.68
R8	0.70	0.76			R8	19,886.78	34,602.34
R7	0.66	0.70		Medium	R7	8280.04	19,886.78		Medium
R6	0.57	0.66			R6	6957.69	8280.04
R5	0.39	0.57			R5	6344.71	6957.69
R4	0.31	0.39		Low	R4	4474.78	6344.71		Low
R3	0.22	0.31			R3	3541.50	4474.78
R2	0.13	0.22			R2	2081.10	3541.50
R1	0.05	0.13			R1	495.34	2081.10

summarizes these intervals and their respective qualitative rating (Low, Medium, or High). To assign these categories, a mean value within the 0.4 to 0.6 percentiles was taken as reference, considering that, under a normal distribution, this region concentrates the highest density of observations. Based on this, the extreme ratings (“High” and “Low”) were defined in relation to the position of the values with respect to the center of the distribution.

The same methodology was applied to the livestock and urban pruning sectors. First, the indicators of each sector were calculated, and the results are presented in

Table 6. Application of the established indicators to the identified energy resources of the livestock sector.

Activity	Subsector	Total Availability of the Resource (Tons/Year)	Biogas Production Capacity (m3/kg DM)	Energy Potential (TJ/Year)
Poultry	Layers	1,911,835.00	3.77062 $\times {10}^{-4}$	25,879.57
	Fattening	1,534,512.00	5.99672 $\times {10}^{-5}$	3303.53
Swine	Suckling piglet	59,246.00	4.44678 $\times {10}^{-5}$	94.58
	Weaners	580,857.00	4.40148 $\times {10}^{-5}$	917.83
	Growers	1,176,390.00	4.26012 $\times {10}^{-5}$	1799.15
	Breeding stock	73,906.00	3.82553 $\times {10}^{-5}$	101.50
	Lactating sow	774,476.00	4.31939 $\times {10}^{-5}$	1200.95
	Pregnant sow	138,236.00	3.91785 $\times {10}^{-5}$	194.43
Cattle	Calves < 12 months	6,275,870.00	2.36894 $\times {10}^{-5}$	5337.32
	Between 12 and 24 months	17,753,799.00	2.35631 $\times {10}^{-5}$	15,018.20
	Between 24 and 36 months	30,140,247.00	2.37681 $\times {10}^{-5}$	25,717.94
	>36 months	44,998,692.00	2.36360 $\times {10}^{-5}$	38,182.87

and

Table 7. Application of the established indicators to the identified energy resources of urban pruning.

City	Total Availability of the Resource [Tons/Year]	Energy Potential [TJ/Year]
Bogotá	7892.00	53.95
Medellín	7156.00	25.89
Cali	2232.00	14.76
Barranquilla	1988.00	26.10
Bucaramanga	5037.00	23.52
Cartagena	7922.00	103.97
Cúcuta	4212.00	19.66
Ibagué	3685.00	25.19
Manizales	3832.00	13.86
Montería	855.00	11.23

. Subsequently,

Table 8. Evaluation criteria based on ranges for energy resources in the livestock sector.

Total Availability of the Resource				Score	Rating	Biogas Production Capacity				Score	Rating
Range	Lower Value	Upper Value				Range	Lower Value	Upper Value
R10	28,901,602.20	44,998,692.00		10	High	R10	5.84172 $\times {10}^{-5}$	3.77062 $\times {10}^{-4}$		10	High
R9	15,458,213.20	28,901,602.20		9		R9	4.43772 $\times {10}^{-5}$	5.84172 $\times {10}^{-5}$		9
R8	4,966,659.50	15,458,213.20		8		R8	4.37685 $\times {10}^{-5}$	4.43772 $\times {10}^{-5}$		8
R7	1,760,905.80	4,966,659.50		7	Medium	R7	4.29568 $\times {10}^{-5}$	4.37685 $\times {10}^{-5}$		7	Medium
R6	1,355,451.00	1,760,905.80		6		R6	4.08898 $\times {10}^{-5}$	4.29568 $\times {10}^{-5}$		6
R5	935,241.60	1,355,451.00		5		R5	3.86246 $\times {10}^{-5}$	4.08898 $\times {10}^{-5}$		5
R4	638,942.70	935,241.60		4	Low	R4	2.81143 $\times {10}^{-5}$	3.86246 $\times {10}^{-5}$		4	Low
R3	226,760.20	638,942.70		3		R3	2.37052 $\times {10}^{-5}$	2.81143 $\times {10}^{-5}$		3
R2	80,339.00	226,760.20		2		R2	2.36414 $\times {10}^{-5}$	2.37052 $\times {10}^{-5}$		2
R1	59,246.00	80,339.00		1		R1	2.35631 $\times {10}^{-5}$	2.36414 $\times {10}^{-5}$		1
		Energy Potential				Score		Rating
		Range	Lower Value		Upper Value
		R10	25,863.41		38,182.87	10		High
		R9	23,577.99		25,863.41	9
		R8	12,113.94		23,577.99	8
		R7	4523.80		12,113.94	7		Medium
		R6	2551.34		4523.80	6
		R5	1440.23		2551.34	5
		R4	1002.77		1440.23	4		Low
		R3	339.11		1002.77	3
		R2	110.79		339.11	2
		R1	94.58		110.79	1

and

Table 9. Evaluation criteria based on ranges for energy resources in the urban pruning sector.

Total Availability of the Resource				Score	Rating	Energy Potential				Score	Rating
Range	Lower Value	Upper Value				Range	Lower Value	Upper Value
R10	7895.00	7922.00		10	High	R10	58.95	103.97		10	High
R9	7303.20	7895.00		9		R9	31.67	58.95		9
R8	5672.70	7303.20		8		R8	25.95	31.67		8
R7	4542.00	5672.70		7	Medium	R7	25.47	25.95		7	Medium
R6	4022.00	4542.00		6		R6	24.36	25.47		6
R5	3773.20	4022.00		5		R5	21.98	24.36		5
R4	3249.10	3773.20		4	Low	R4	18.19	21.98		4	Low
R3	2183.20	3249.10		3		R3	14.58	18.19		3
R2	1874.70	2183.20		2		R2	13.60	14.58		2
R1	855.00	1874.70		1		R1	11.23	13.60		1

were constructed, showing the qualitative representation of the quantitative values, organized into ranges for each indicator and sector.

Table 8 presents slight differences in design compared to Table 5 and Table 9. The reason for these differences lies in the difficulty of the design; however, the essence and meaning remain exactly the same.

Once the ranges and criteria were defined,

Table 10. Qualitative energy indicators for agricultural, urban, and dedicated-crop resources.

Qualitative Indicators by Sector
Type of Measure		Indicator	Rating			Description	Type of Resource
			Low	Medium	High
	Availability	Resource availability	[189,075.00–848,970.60] tons/year	(848,970.60–3,734,335.60] tons/year	(3,734,335.60–9,390,968.00] tons/year	Indicates the amount of resource available in a defined time period, typically one year. The unit is tons per year (tons/year).	Agricultural and dedicated crops
	Energy capacity of the resource	Dry fraction	[0.05–0.39]	(0.39–0.70]	(0.70–0.92]	Indicates the proportion of the energy resource that can be considered “useful” by being free of moisture. Its value is dimensionless, as it is a percentage.
	Energy capacity of the resource	Lower heating value	[7862.64–15,947.72] kJ/kg	(15,947.72–18,414.22] kJ/kg	(18,414.22–19,374.24] kJ/kg	Indicates the ideal amount of heat that a resource can release. The unit is kilojoules per kilogram of mass (kJ/kg).
	Energy capacity of the resource	Energy potential	[495.34–6344.71] TJ/year	(6344.71–19,886.78] TJ/year	(19,886.78–76,735.83] TJ/year	Indicates the total energy contained in an energy resource. The unit is terajoules per year (TJ/year).
	Availability	Total resource availability	[59,246.00–935,241.60] tons/year	(935,241.60–4,966,659.50] tons/year	(4,966,659.50–44,998,692.00] tons/year	Indicates the amount of resource available in a defined time period, typically one year. The unit is tons per year (tons/year).	Livestock
	Energy capacity of the resource	Biogas production capacity	[2.35631 $\times {10}^{-5}$–3.86246 $\times {10}^{-5}$] m3/kg DM	(3.86246 $\times {10}^{-5}$–4.37685 $\times {10}^{-5}$] m3/kg DM	(4.37685 $\times {10}^{-5}$–3.77062 $\times {10}^{-4}$] m3/kg DM	Indicates the resource’s capacity to produce biogas after anaerobic digestion. The unit is cubic meters per kilogram of dry matter (m3/kg DM).
	Energy capacity of the resource	Energy potential	[94.58–1440.23] TJ/year	(1440.23–12,113.94] TJ/year	(12,113.94–38,182.87] TJ/year	Indicates the total energy contained in an energy resource. The unit is terajoules per year (TJ/year).
	Availability	Total resource availability	[855.00–3773.20] tons/year	(3773.20–5672.70] tons/year	(5672.70–7922.00] tons/year	Indicates the amount of resource available in a defined time period, typically one year. The unit is tons per year (tons/year).	Urban pruning solid waste
	Energy capacity of the resource	Energy potential	[11.23–21.98] TJ/year	(21.98–25.95] TJ/year	(25.95–103.97] TJ/year	Indicates the total energy contained in an energy resource. The unit is terajoules per year (TJ/year).

Legend: First-level indicator, Second-level indicator. High, Medium, Low.

was prepared, analogous to Table 3, but replacing the mathematical expressions with the corresponding qualitative ratingaccording to the range in which each indicator is located.

The purpose of this approach is to enable an energy comparison among different biomass resources intended for microgrid supply. In this sense, first-order indicators allow comparisons only among resources of the same sector, while second-order indicators can be applied transversally, regardless of the resource’s origin.

Finally,

Table 11. Energy profiles according to indicator results for the agricultural sector.

Availability	LHV	Dry Fraction	Energy Profile
			Low quantity, low energy per kg, and high moisture: not suitable for direct combustion.
			Low quantity, low energy per kg, and medium moisture.
			Low quantity, low energy per kg, and very dry: can be blended with more energetic resources.
			Low quantity, medium energy per kg, and high moisture.
			Low quantity, medium energy per kg, and medium moisture.
			Low quantity, medium energy per kg, and very dry.
			Low quantity, high energy per kg, and high moisture: enables greater energy release; requires pre-drying.
			Low quantity, high energy per kg, and medium moisture: enables greater energy release.
			Low quantity, high energy per kg, and very dry: ideal combustion; high energy release; useful as an energy additive in blends.
			Moderate quantity, low energy per kg, and high moisture.
			Moderate quantity, low energy per kg, and medium moisture.
			Moderate quantity, low energy per kg, and very dry: can be blended with more energetic resources.
			Moderate quantity, medium energy per kg, and high moisture.
			Moderate quantity, medium energy per kg, and medium moisture.
			Moderate quantity, medium energy per kg, and very dry.
			Moderate quantity, high energy per kg, and high moisture: enables greater energy release; requires pre-drying.
			Moderate quantity, high energy per kg, and medium moisture: enables greater energy release.
			Moderate quantity, high energy per kg, and very dry: ideal combustion; high energy release.
			High quantity, low energy per kg, and high moisture: requires pre-drying.
			High quantity, low energy per kg, and medium moisture.
			High quantity, low energy per kg, and very dry.
			High quantity, medium energy per kg, and high moisture: requires pre-drying.
			High quantity, medium energy per kg, and medium moisture: suitable as a primary energy source.
			High quantity, medium energy per kg, and very dry: suitable as a primary energy source.
			High quantity, high energy per kg, and high moisture: enables greater energy release; requires pre-drying.
			High quantity, high energy per kg, and medium moisture: enables greater energy release; suitable as a primary energy source.
			High quantity, high energy per kg, and very dry: ideal combustion; high energy release; suitable as a primary energy source.

Legend: High, Medium, Low.

and

Table 12. Energy profiles according to indicator results of the livestock sector.

Total Availability	Biogas Production Capacity	Energy Profile
		Low amount and low biogas capacity: not very suitable for biogas production.
		Low amount and intermediate biogas capacity.
		Low amount and high biogas capacity: useful to complement other substrates.
		Moderate amount and low biogas capacity.
		Moderate amount and intermediate biogas capacity.
		Moderate amount and high biogas capacity.
		Large amount and low biogas capacity.
		Large amount and intermediate biogas capacity.
		Large amount and high biogas capacity: suitable as a main source of biogas.

Legend: High, Medium, Low.

present examples of possible combinations of qualitative indicators for the agricultural and livestock sectors, respectively. Each combination is associated with a practical interpretation called an energy profile, which guides the reading of the results. An equivalent table was not prepared for the urban pruning sector, since it only has one first-order and one second-order indicator, making the construction of combined profiles unnecessary.

9. Results and Discussion

9.1. Agricultural Residual Biomass Resources

Quantifying crop residues in Colombia reveals a strong dependence on cultivated area and spatial distribution. Figure 6 illustrates the relationship between planted area and the amount of residues generated, based on data from [4]. Although a positive correlation can be observed, the dispersion of the data indicates that planted area alone does not fully explain residue generation, as some crops produce higher residue volumes despite having smaller cultivated areas.

Based on the applied selection criteria, eight representative crops were identified, including both permanent and temporary crops, as summarized in

Table 13. Representative crops generating residual biomass in Colombia.

Type of Crop	Representative Crops
Permanent crops	Banana, Coffee, Sugarcane, Panela cane, Oil palm, Plantain
Temporary crops	Rice, Corn

Source: Adapted from [4].

. These crops concentrate a significant share of agricultural residue generation in Colombia, which justifies their use as representative resources for the subsequent energy evaluation.

The departmental distribution of cultivated area and residue generation for the selected crops is presented in

Table 14. Departmental distribution of planting and residues of selected crops.

Department	Oil Palm		Corn		Panela Cane		Sugar Cane		Coffee		Banana		Rice		Plantain
	[ha]	[t/Year]	[ha]	[t/Year]	[ha]	[t/Year]	[ha]	[t/Year]	[ha]	[t/Year]	[ha]	[t/Year]	[ha]	[t/Year]	[ha]	[t/Year]
Amazonas	-	-	-	-	-	-	-	-	-	-	-	-	28	97	670	12,350
Antioquia	354	8417	50,079	128,612	40,378	1,140,744	-	-	114,180	890,946	34,430	7,261,105	21,635	139,931	64,683	3,614,374
Arauca	-	-	16,765	40,517	1301	27,946	-	-	-	-	-	-	3646	36,185	9399	452,966
Atlántico	-	-	11,584	28,091			-	-	-	-	-	-	-	-	142	10,670
Bolívar	6760	30,365	78,093	218,782	1150	47,885	-	-	619	2849	-	-	33,374	319,388	5457	200,256
Boyacá	-	-	15,181	35,050	14,219	917,461	-	-	10,679	62,048	509	18,099	-	-	4775	212,003
Caldas	-	-	3017	12,483	16,945	475,442	2625	194,903	78,493	530,181	1616	67,828	-	-	18,493	1,123,660
Caquetá	385	1205	7410	9632	3442	114,817	-	-	4044	17,685	-	-	1268	3924	12,920	498,805
Casanare	15,652	52,822	3224	7720	-	-	-	-	1930	6593	813	28,552	51,189	699,743	2414	196,591
Cauca	-	-	7540	11,608	17,426	591,969	34,486	2,317,443	53,996	321,263	473	14,576	1446	13,648	130,249	309,267
Cesar	33,830	210,240	52,455	146,078	2325	95,889	1734	64,889	23,542	78,486	78	4797	24,780	369,485	2990	127,299
Chocó	3234	13,084	8445	19,766	1793	185,919	-	-	295	1122	-	-	11,946	54,991	17,151	628,882
Cundinamarca	3189	26,102	27,765	88,124	51,436	1,591,493	-	-	25,675	151,923	5511	239,923	1666	26,872	9781	458,377
Córdoba	154	499	70,741	301,429	175	4095	-	-	-	-	-	-	32,404	265,858	28,738	1,696,882
Guainía	-	-	179	289	-	-	-	-	-	-	-	-	-	-	110	5074
Guaviare	-	-	46	98	-	-	-	-	-	-	-	-	676	1397	3352	130,644
Huila	-	-	29,749	101,608	14,518	883,646	-	-	91,039	687,709	1840	63,484	30,258	545,797	25,832	717,004
La Guajira	395	1403	13,279	27,761	85	4270	-	-	3380	10,360	340	31,980	2750	34,547	1861	85,384
Magdalena	30,167	198,161	29,618	46,797	320	10,048	-	-	16,331	68,088	12,322	2,787,204	2563	32,655	2227	92,287
Meta	80,097	418,757	22,565	110,473	7132	7314	-	-	3683	17,276	11	135	65,456	901,466	16,302	1,505,206
Nariño	32,000	292,378	18,140	42,839	16,334	795,744	-	-	25,926	166,226	2487	83,459	799	1200	21,051	878,626
Norte de Santander	5123	24,040	9468	18,038	10,196	273,518	969	61,895	28,385	85,620	1139	50,584	20,642	306,342	12,477	453,310
Putumayo	-	-	16,994	28,921	742	3244	-	-	40	244	75,634	11,550,891	1010	3173	6392	259,220
Quindío	-	-	1532	3871	2354	25,435	-	-	39,687	341,189	919	111,063	-	-	34,988	2,034,801
Risaralda	-	-	2459	13,101	3953	154,306	2719	227,841	48,644	425,830	1975	172,920	-	-	20,536	1,197,306
Santander	49,006	352,636	24,645	88,004	19,792	1,135,870	-	-	20,675	112,878	1269	95,795	470	7925	8545	360,975
Sucre	1500	8030	19,348	55,209	284	11,231	-	-	-	-	2798	116,405	41,505	470,776	1289	46,992
Tolima	-	-	24,569	117,654	14,577	637,523	-	-	100,052	737,511	6202	399,849	99,880	1,945,928	30,365	1,690,187
Valle del Cauca	-	-	31,586	223,472	6216	221,234	168,033	12,667,620	72,563	340,509	3	399	5970	102,079	15,650	904,074
Vaupés	-	-	7510	15,135	-	-	-	-	-	-	-	-	45	6	1626	9668
Vichada	-	-	610	807	157	4324	-	-	-	-	-	-	22	77	265	6704

Source: Adapted from [4].

. The results show a highly heterogeneous spatial distribution of agricultural residues across the country, with a strong concentration in specific departments.

Analysis of Table 14 confirms that cultivated area is a relevant factor in residue availability; however, it also reveals cases where crops with smaller planted areas generate larger amounts of residues. This finding highlights the influence of additional agronomic and processing-related factors beyond cultivated area alone.

Based on these results, the map shown in Figure 7 was constructed, illustrating the crop with the highest residue production in each department and emphasizing the spatial concentration of agricultural biomass resources.

9.2. Livestock Residual Biomass Resources

Based on the identification of representative livestock sectors described in Section 3, the distribution and magnitude of livestock residual biomass in Colombia were analyzed using data from the Atlas of the Energy Potential of Residual Biomass in Colombia [4].

Table 15. Main animal species generating biomass in Colombia.

Livestock Sector	Representative Animals and Purposes
Poultry sector	Birds for egg and meat production
Cattle sector	Cattle for milk, meat, and dual-purpose production
Swine sector	Pigs from technified and non-technified farms

shows that livestock activity in Colombia is dominated by cattle ranching and poultry farming, which together account for the largest share of organic residues. This dominance has a direct influence on the overall energy potential of livestock-derived biomass and supports the prioritization of these sectors in the indicator-based assessment.

To further analyze the spatial distribution of livestock residual biomass,

Table 16. Main livestock activity and activity with the highest manure production in each department.

Department	Poultry Sector		Cattle Sector		Swine Sector
	[Heads/Year]	[Tons/Year]	[Heads/Year]	[Tons/Year]	[Heads/Year]	[Tons/Year]
Antioquia	8,533,850	261,210	2,656,856	11,906,012	1,072,601	715,695
Atlántico	4,137,270	119,535	254,169	1,118,254	44,232	25,515
Bolívar	1,352,800	36,678	892,013	3,989,728	36,021	19,185
Boyacá	2,282,662	66,239	786,628	3,180,966	124,200	93,277
Caldas	1,011,950	36,724	390,345	1,710,981	71,593	54,438
Caquetá	-	-	1,204,803	5,300,448	74,354	56,955
Cauca	2,029,100	63,000	250,824	4,111,160	80,423	65,987
Cesar	144,870	3985	1,593,664	7,054,798	41,148	32,256
Córdoba	1,390,150	38,009	2,218,079	9,118,032	303,007	279,800
Cundinamarca	32,312,272	945,412	1,109,119	4,824,628	553,566	424,768
Chocó	-	-	127,280	569,674	-	-
Huila	1,600,077	51,441	478,965	2,130,289	26,782	20,675
La Guajira	18,700	483	293,667	1,289,815	108,716	98,102
Magdalena	223,000	8546	1,419,319	6,271,265	30,543	25,491
Meta	-	-	1,495,820	6,944,267	104,753	81,822
Nariño	1,493,025	38,365	314,696	1,424,644	185,989	110,438
Norte de Santander	2,467,880	83,413	391,935	1,757,031	46,225	36,527
Quindío	3,577,380	100,191	88,984	389,074	44,763	31,779
Risaralda	2,779,970	76,498	110,004	472,020	95,178	73,174
Santander	27,606,680	817,654	1,509,193	6,723,189	90,258	71,475
Sucre	246,700	7256	890,813	3,489,049	87,030	73,438
Tolima	3,553,365	120,023	694,013	3,086,989	94,827	69,893
Valle del Cauca	18,058,422	570,494	538,201	2,331,423	372,010	251,382
Arauca	-	-	683,000	3,152,804	40,496	50,856
Casanare	-	-	1,620,700	7,697,000	40,027	30,792
Putumayo	46,550	1189	134,208	599,960	10,738	9439
San Andrés	-	-	1163	4297	-	-

Source: Adapted from [4].

was constructed, allowing a parallel comparison of livestock population and manure production across departments. The results indicate that cattle manure represents the predominant livestock residue in most departments, regardless of the dominant livestock activity in terms of animal population.

The predominance of cattle manure explains the spatial patterns observed in the regional distribution of livestock biomass resources. Using the information presented in Table 16, the map shown in Figure 8 was constructed to visualize the predominant livestock activity by department. Poultry and cattle sectors are the most widespread across regions, while the swine sector does not predominate in any department.

9.3. Urban Residual Biomass Resources

Based on the identification of urban residual biomass resources described in Section 3, available estimates were analyzed to assess their distribution and relevance for energy applications.

Table 17. Estimation of municipal solid waste in Colombia.

Department	Pruning	Marketplaces and Collection Centers
	[Tons/Year]	[Tons/Year]
Bogotá	36,912	7892
Medellín	15,754	7156
Cali	19,451	2232
Barranquilla	9770	1988
Bucaramanga	9812	5037
Cartagena	1365	7922
Cúcuta	4869	4212
Ibagué	8531	3685
Pereira	1793	0
Villavicencio	1817	0
Manizales	3676	3832
Montería	6455	855

Source: Adapted from [4].

presents estimated values of organic municipal solid waste generated in marketplaces and wholesale centers, as well as pruning residues from parks and green areas in major Colombian cities.

The results indicate that urban biomass resources, particularly pruning residues and organic waste from marketplaces, are mainly concentrated in large metropolitan areas. Bogotá, Medellín, Cali, and Barranquilla present the highest estimated values for both types of waste. This spatial concentration suggests that urban residual biomass represents a localized but relevant opportunity for decentralized electricity generation, especially in cities with high population density and consolidated waste management systems.

In summary, the residual biomass resources identified across agricultural, livestock, and urban sectors are consolidated below for clarity and subsequent characterization (see Section 4).

10. Conclusions

This study proposes a practical and replicable qualitative evaluation framework for assessing biomass resources as energy sources for decentralized electricity generation. By integrating five key indicators—availability, dry fraction, lower heating value, energy potential, and biogas production capacity—into a structured classification scheme, the work provides a decision-support tool that enables the comparative assessment of heterogeneous biomass resources across agricultural, livestock, and urban sectors.

The application of this framework to the Colombian context demonstrates that agro-industrial residues such as sugarcane bagasse, coffee husks, and oil palm residues exhibit the highest energetic potential for electricity generation, while livestock manure and urban pruning residues, despite their lower calorific values, remain strategically relevant due to their abundance and suitability for biogas-based systems. These results highlight the technical feasibility of combining diverse biomass sources within microgrid configurations to enhance local energy resilience and resource utilization.

From a methodological perspective, the main contribution of this work lies in the formulation of a qualitative evaluation algorithm that transforms dispersed quantitative data into actionable planning criteria. This approach reduces complexity in early-stage project assessment and supports informed decision-making in microgrid design and energy planning processes.

Nevertheless, the study has certain limitations. The analysis relies on aggregated and secondary data, which may not capture local variability in biomass availability or seasonal fluctuations. Additionally, economic, environmental, and logistical factors—such as collection costs, transport distances, and emissions—were not explicitly incorporated into the indicator set.

Future research should extend the proposed framework by integrating techno-economic and environmental indicators, validating the methodology with real microgrid case studies, and exploring dynamic or spatially resolved data to improve accuracy at the local level. These extensions would further strengthen the applicability of the proposed approach as a comprehensive planning tool for sustainable and decentralized energy systems.