Introduction

At present, fossil fuels are still the major source of the world’s electricity production [1]. In 2017, the total volume of the world’s electricity generation was 25,551.3 TWh, of which 65% came from fossil fuels (oil, natural gas, and coal) [1]. As a result, greenhouse gas emissions from the power sector remain a global environmental concern. In addition, the International Energy Agency (IEA) estimated that in the next 25 years, the demand for electricity will increase continuously with the highest proportion of this demand being the consumption of final energy [2]. Thus, renewable energy-based power generation has been adopted by various countries as an essential national energy policy. As of 2018, 162 countries have national renewable energy targets for electricity generation [3]. Moreover, a number of countries have set targets of 100% renewable electricity generation by 2050, such as Denmark, Honduras, Malaysia, Portugal, and the Philippines [3]. Biomass from plants, such as crop residues, forest residues, and agro-industrial residues, is an important renewable energy source in most Asian countries such as Thailand. As bioenergy is a CO2-neutral energy source, biomass residues are widely used for generating heat and power [4].

In 2018, Thailand’s electricity was mainly generated from fossil fuels, especially natural gas (60.5%). Therefore, the electricity sector contributes the largest percentage (38.5%) of the country’s total amount of CO2 emissions (238,026 kt) [5]. To reduce the impact on the environment, Thailand has adopted the Alternative Energy Development Plan (AEDP2015), which has set the target of generating up to 20% of the country’s total electrical energy demand from renewable energy by 2036. To this end, specific targets have been set for building the country’s renewable power capacity by types of energy source, including solar (6000 MW), biomass (5570 MW), hydro (3282.4 MW), wind (3002 MW), biogas from wastewater, solid waste, and energy crop (1280 MW) and municipal and industrial waste (550 MW) [6]. In addition, the new version of the National Power Development Plan 2018 (PDP2018) includes plans to increase the renewable power generation and efficiency of the national power system, which could reduce the CO2 emissions from the power sector to 103,845 thousand tons year−1 by 2037 or equivalent to 0.283 kgCO2 kWh−1 [7]. Since Thailand is an agricultural-based economy, there is an abundance of biomass residues providing potential feedstock for heat and power production. In 2017, the electricity generated from renewable sources was 29,019.8 GWh (44.2% of the target for 2036), of which 54.8% was generated from biomass [8]. Currently, biomass residues from agro-industries such as bagasse have mostly been used for producing power and heat supplied for their processes with any excess power then supplied to the national grid. However, biomass from agricultural crop residues has still not been widely used. Therefore, this study focuses on the use of crop residues as a renewable source for increasing biomass power generation.

There are a number of studies providing assessments of the potential of various types of biomass residues in Thailand. For example, from a study conducted in 2012 on the potential of biomass residues from nine types of crops in 51 provinces, the Department of Alternative Energy Development and Efficiency (DEDE) estimated the total potential of rice, sugar cane, corn, cassava, oil palm, para rubber, soybean, mung bean, and peanut to be 8083 ktoe [9]. In another study, Prasertsan and Sajjakulnukit [10] analyzed the potential of main biomass resources as well as discussing the opportunities and barriers to promoting the utilization of biomass energy. Barz and Delivand [11] explored the potential of different agricultural residues for power generation and provided an overview of the conversion technologies used for these biomass residues. From this, it can be seen that previous research on biomass residues in Thailand has mostly focused on agro-industry residues or some crop-residue types. However, there are still a number of crop residues that have not been used or studied. To promote the use of crop residue for power generation, a study of the geographical potential of biomass and the viability of using crop residues for electricity generation should be carried out for supporting investment decisions and informing government planning. In addition, a precise assessment of biomass potential, which includes spatial distribution and seasonal variation of crop residue availability, should be considered to support the achievement of the appropriate utilization. For instance, Sagani et al. [12] conducted a systematic assessment of the technical, economic, and environmental contexts for using tree pruning residues to generate electricity in Greece. Meanwhile, Nie et al. [13] estimated and mapped the spatial distribution of unused agricultural residues and technical bioenergy potential at a 1-km resolution in China. Also, in the China-based study, Yang et al. [14] evaluated the spatial distribution and seasonal variation of the crop residues for biofuel production in some regions of the country.

Since most crop residues are left scattered across the field, the Geographical Information System (GIS) is an appropriate tool that is widely used for assessing the energy production potential of biomass residues [15,16,17], analyzing the power generation potential and optimal biomass collection and transport network [18], analyzing the feasibility of the energy utilization of biomass [19], assessing the technical and economic potential of energy production from biomass [20, 21], identifying appropriate areas/locations for biomass energy production [22, 23], assessing the sustainable and economic potential of power generation from biomass residues [24], assessing the agricultural land availability for wood crops [25], and assessing the logistics of forestry biomass residue utilization for bioenergy [26]. In addition, GIS has been applied for creating a spatial model to quantify the biomass potential [27], while it has also been combined with simulation/optimization models to create a decision system for the biofuel supply chain [28].

The previous studies mentioned above analyzed the seasonal variation of crop residues and their spatial distribution, which are the significant characteristics of the exact availability and location of biomass as well as the levelized costs of electricity (LCOE) of grid-connected power generation. Thailand is an agricultural-based economy, and a number of its crop residues are still unused. Thus, the objectives of this research were to (a) assess the utilization potential of crop residues for grid-connected power generation of which the effects of seasonal variation and spatial distribution were included in order to gain more detail of their availability and (b) estimate the levelized cost of generated electricity based on the results of (a).

Materials and Methods

In assessing the utilization potential of biomass residues for grid-connected power generation in Thailand, this study was performed in four steps, as shown in Fig. 1a. The process began with the selection of the crop types to be studied in each region, with the minimum requirement being the inclusion of all the main crops accounting for at least 80% of the total agricultural area in their regions. This was followed by evaluating the seasonal and spatial distribution of the available biomass and its energy potential. The third step involved assessing the utilization potential of biomass residues for grid-connected power generation before estimating their unit electricity cost CO2 abatement cost as the final step. Two scenarios were considered in assessing the potential of biomass residues and power generation: (i) base case (BC) and (ii) the case of improvements in the collection and transportation of biomass residues (ICT). ArcGIS was used as the tool for mapping and assessing the spatial potential of the crop residues and their grid-connected power generation. The input data and assessment approaches for each step are explained in detail below.

Fig. 1
figure 1

Overview methodology for assessing grid-connected power generation potential from crop residues in Thailand: a assessment procedure and b GIS-based method for mapping the spatial energy potential of crop residues

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Study Area

Thailand is a country in Southeast Asia located between the latitudes of 5° 37′ and 20° 27′ N and the longitudes of 97° 22′ and 105° 37′ E. It covers a total area of 51,312 thousand hectares (kha) [29], of which about 54% is used for agricultural purposes. The country is divided into five regions according to their geographic position in the country, namely northern (6651 kha), northeastern (11,469 kha), eastern (2156 kha), central (3412 kha), and southern (4201 kha). The agricultural land is mainly used for paddy fields (44%) followed by other field crops (23%), perennial trees (21%), fruit trees (6%), and others (6%) [30].

Selection of Crop Types

Various crops are grown in the different regions of Thailand, depending on the main factors of local climate, soil properties, and water availability. In this study, the total crop residue potential was estimated for at least 80% of the total agricultural area in each region of Thailand. The crop types were selected in descending order, starting from the most widely grown until up to 80% of the total agricultural land in each region had been included. Digital maps of the selected crops at a scale of 1:50000 were obtained from Thailand’s Land Development Department, and these were then merged for mapping the plantation areas of the selected crop types by region (Fig. 1b).

Estimation of Biomass Residue Potential

After mapping the selected cropland, the types and amount of available crop residues were identified. The crop residues used in this study were obtained from what was left in the field after the trees had been harvested and pruned. The amount of crop harvesting residue from each agricultural area varied based on crop production, the residue production ratio, and its moisture content [31], while the amount of crop pruning residue depended on the amount of biomass pruning that took place and its moisture content. The potential availability of spatial biomass residues was estimated by the following equation:

$$ \mathrm{BR}\left(\mathrm{j}\right)=\sum \limits_{i=1}^n\mathrm{AA}\left(i,\mathrm{j}\right)\times \mathrm{BR}\mathrm{Y}(i)\times \mathrm{RAF}(i) $$
(1)

where BR(j) is the biomass residue potential of the region j (t year−1); AA(i, j) is the agricultural areas of the crop i in region j (ha); BRY(i) is the biomass residue yield of the crop i (t year−1 ha−1); and RAF(i) is the residue availability factor of the crop i. The crop harvesting residue yield was determined from the average residue production ratio and the amount of crop production per plantation area. The yield of crop pruning residue was estimated from the total annual amount of residue per plantation area. As previously mentioned, most of the crop residues are not usually utilized. The crop residue potential depends on the amount of crop residue collected from the field [32], which was considered 75% based on its collecting possibility for the BC scenario and 85% for the ICT scenario. The biomass distribution mapping was conducted using the data of crop residue yields. The selected crop maps of each region are illustrated in Fig. 1b.

Estimation of the Energy Potential of Biomass Residues

The energy potential of the different crop residues depends on their biomass amount and heating value. A low heating value is usually used for estimating the energy potential [31, 33, 34] of biomass residues, which can be calculated by the equation below:

$$ \mathrm{EB}\left(\mathrm{j}\right)=\sum \limits_{i=1}^n\mathrm{AA}\left(i,\mathrm{j}\right)\times \mathrm{BRY}(i)\times \mathrm{RAF}(i)\times \mathrm{LHV}(i) $$
(2)

where EB(j) is the energy potential from biomass residues in region j (MJ year−1), and LHV(i) is a lower heating value of the residues from crop i (MJ t−1). Next, maps of the spatial energy potential were created based on the calculated results from Eq. (2), as shown in Fig. 1b. Data on the lower heating values collected from previous studies, the biomass database of the Department of Alternative Energy Development and Efficiency, and a field survey were used in the estimation of energy potential. Then the biomass distribution maps and the lower heating values of the crop residues were used to create energy distribution maps for the regions.

The availability of crop residues varied depending on the harvesting and pruning period. For example, rice is usually harvested from October to December for the main rice crop and February to May for the second rice crop. Thus, although it was usually excluded from the previous studies, the harvesting and pruning period is a significant factor in the planning of biomass feedstock availability for biomass power plants. In this study, the seasonal biomass potential of each crop residue is presented in terms of monthly energy potential, which was estimated by dividing the annual energy potential of each crop residue from Eq. (2) by its number of harvesting or pruning months based on the assumption of equal monthly potential. The data on the harvesting and pruning periods of the selected crops in Thailand were obtained from the Office of Agricultural Economics [35].

Assessing the Grid-Connected Electricity Generation Potential

As previously mentioned, Thailand has set the target in its AEDP2015 of increasing the electricity generated from renewable energy [6] in order to reduce the current CO2 emissions from the power sector, which currently generates most of its electricity from fossil fuel [5]. For this reason, an assessment of the potential of crop residues as a renewable energy source for grid-connected electricity generation had been conducted. The most common process for converting chemical energy stored in crop residues into heat is direct combustion [11, 36]. Among the several technologies available for generating electricity from biomass in Thailand, the biomass-fired boiler with steam turbine and generator is widely used [37]. In addition, the capacity of most new biomass power plants is less than 10 MW, which is classed as a very small power producer (VSPP), since the procedure for receiving permission to start operating a VSPP power plant is less complicated. Thus, in this study, the technology in the conversion of biomass residues to electricity was considered to be a biomass-fired boiler with a steam turbine generator and a power plant capacity of less than 10 MW. The power and net electricity generation potential were calculated from the following equation:

$$ \mathrm{PGP}\left(\mathrm{j}\right)=\sum \limits_{i=1}^n\frac{\mathrm{AA}\left(i,\mathrm{j}\right)\times \mathrm{BRY}(i)\times \mathrm{CF}(i)\times \mathrm{LHV}(i)\times \eta \times \left(1-\mathrm{SC}/100\right)}{T(i)} $$
(3)

where PGP(j) is the net power generation potential of biomass residues in the region j (MW); CF(i) is the collection factor of the crop residue i as feedstock for power generation; SC is the amount of self-consumption by the power plant (%); T is the power plant operating time in the region j (s year−1); and η is the overall plant efficiency. It should be noted that the value of LHV used in Eq. (3) varied in line with the biomass moisture content, and therefore affects the power plant efficiency (η). As clearly shown in the work of Striugas et al. [38], a change in the biomass moisture content from 37 to 50% w.b. caused boiler efficiency to decrease from 90.3 to 87%. Thus, in using Eq.(3), the efficiency value should correspond with the LHV of the biomass at a specific moisture content, and for mixed biomass feed, the weighted average of both the moisture content and its corresponding LHV was proposed. From previous studies, the annual total of operation hours for biomass power assessment was in the range of 6000–8472 h year−1 [33, 34, 39, 40] depending on the availability of biomass residues, while the overall efficiency of a power plant with a capacity of 10 MW was assumed to be 23% [33, 34]. In this study, the gross power plant efficiency was assumed to be 23% with the self-consumption of the power plant at 10% [41]. Therefore, the net power plant efficiency was approximately 20.7%, which was a conservative value compared with the net conversion efficiency range from 20 to 40% of the biomass-fired power plants [39]. Since various crop types are planted in the agricultural areas of the five different regions of Thailand depending on the local crop growing conditions such as soil nutrients and weather conditions, the plant operating hours per year were based on the seasonal biomass residue availability in each region. The collection factor for the biomass residue feedstock, accounting for collection and transportation losses, was assumed to be 50% for the BC scenario and 75% for the ICT scenario.

The grid-connected power generation potential was assessed based on the main parameters, namely the location of the electrical transmission system and the crop residue collection area. With a capacity of less than 10 MW, the power plants included in this study have to be connected through a 115-kV transmission system according to the Regulations of the Power Network System Interconnection Code (2016) [42]. Several previous studies considered the size of the crop-residue collection area in terms of the biomass-collecting radius, such as 25, 50, 75, and 100 km [43] depending on the power plant size, the spatial density of the available biomass, and transportation costs. In this study, the crop residue collection area was considered to be within a radius of 50 km from each 115 kV substation. In the energy distribution maps, a boundary was created around the 50-km buffer of the 115-kV substations based on the methodology shown in Fig. 1b, which used three types of digital map data, namely the electrical substation and transmission maps obtained from Electricity Generating Authority of Thailand, and the energy distribution map obtained from the above section.

Estimation of Levelized Cost of Electricity

The LCOE is an economic aspect that is widely used for assessing the performance of biomass power plants [44, 45] and comparing the emerging renewable energy technologies [45]. The LCOE can be calculated from the present value of the total costs (investment costs, biomass costs, operating and maintenance costs) over the lifetime of the power plant divided by the present value of the total amount of electricity generated, as expressed in Eq. (4).

$$ \mathrm{LCOE}=\frac{\sum \limits_{t=0}^n\frac{I_t+{M}_t+{F}_t}{{\left(1+r\right)}^t}}{\sum \limits_{t=0}^n\frac{E_t}{{\left(1+r\right)}^t}} $$
(4)

where It is the total investment costs in year t (USD); Mt is the operation and maintenance costs in year t (USD); Ft is the biomass fuel expenditure in year t (USD); Et is the net electricity production in year t (MWh); r is the discounted rate; and n is the project lifetime (year). The fuel costs are composed of the biomass residue costs and their transportation costs, which can be expressed as follows:

$$ {F}_t=\mathrm{BC}+\mathrm{TC} $$
(5)

where Ft is the fuel costs in each collection area (USD); BC is the biomass residue costs (USD); and TC is the transportation costs (USD). The biomass residue costs can be determined by multiplying the amount of biomass feedstocks by their prices, as shown in Eq. (6). Transportation is an important cost of biomass residues [33, 40, 44] due to its low energy density and wide spatial distribution. According to the survey data, the transportation costs by truck (maximum load of 15 tons) were charged based on the freight distance, which had a flat rate of US$1.918 t−1 for a freight distance within 25 km and an additional charge of US$0.317 t km−1 for any freight distance over 25 km. Equation (7) presents the estimated transportation costs (TC) as a function of freight distance (L(km)).

$$ \mathrm{BC}=\sum \limits_{i=1}^n{\mathrm{BRF}}_i\times {\mathrm{BRP}}_i $$
(6)

where BRFi is the amount of biomass feedstock i for power generation (t year−1); and BRPi is the price of biomass feedstock i (USD t−1).

$$ \mathrm{TC}=\sum \limits_{i=1}^n{\mathrm{BRF}}_i\left[1.918+\left(0.317\times L\right)\right] $$
(7)

The biomass feedstocks were collected from within a 50-km radius of a 115-kV substation. According to the spatial distribution of crop residues, the costs for each collection area vary depending on the crop residue type and freight distance. To cover all ranges of biomass costs, the estimation of the LCOE was conducted based on three values of fuel costs, namely the maximum, minimum, and average costs of the biomass residues in each region. As the power plant size used for assessing electricity generation potential in this study was less than 10 MW, the LCOE was estimated for three power plant sizes, namely 5, 7, and 9 MW [41], for which the investment and operating expenses are presented in Table 1. The investment and operating cost of power plants and cost of grid-connected system were estimated from the database of the power plant in Thailand and Electricity Generating Authority of Thailand, respectively. The annual net electricity production was estimated based on the technical assumption of the power plants. All other assumptions were based on several previous studies: (a) a project lifetime of 20 years [33, 39, 44]; (b) a 10% discounted rate [33]; and (c) maintenance and replacement costs at 3.5% of the power plant and grid-connection system investment costs and 8% of the tractor and shear shredder investment costs [44, 45]. The escalation rate of 3% per annum was applied to biomass residue costs, operating costs, and labor costs.

Table 1 Investment and operating expenses of the biomass power plants
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Estimation of Marginal Abatement Cost

Since power generation from biomass residues is one approach to reducing greenhouse gas emissions (GHG) from the power sector, the cost per unit of GHG reduction, which is called the marginal abatement cost (MAC), was also estimated. The MAC can be calculated from the net present value (NPV) divided by the total GHG emissions abated over the lifetime of the power plant [46] as expressed in Eq. (8). Since a positive NPV means a revenue greater than the cost of a project, calculating the MAC using Eq. (8) requires the multiplication of the NPV by a negative one (−1) [46]. A negative MAC means this project has net economic benefits over its lifetime. Conversely, a positive value shows the project incurs a cost in reducing emissions.

$$ \mathrm{MAC}=\frac{\left(-1\right)\mathrm{NPV}}{\mathrm{TER}} $$
(8)

where MAC is the marginal abatement cost (USD tCO2−1); NPV is the difference between the present values of the total revenues and total cost over the lifetime of the power plant (USD); and TER is the total GHG emission reduction over the lifetime of the power plant (tCO2). The investment and operating expenses of the three power plant sizes (5, 7, and 9 MW) are expressed in Table 1. The revenue was obtained from selling electricity to the national grid. The feed-in tariff of a VSPP biomass power plant with an installed capacity larger than 3 MW comprises two main parts: (i) fixed rate (US$0.072 kWh−1) [47] and (ii) variable rate (US$0.057 kWh−1 in 2019 [48]). The variable rate is continuously increasing based on the core inflation rate, which fluctuated in the range of 0.5–0.74% during 2016–2020, as reported in the previous literature [49]. The forecasted values of the variable rate, however, are not available. Thus in this study, it was assumed to be 0.63% from 2021 onward. The other economic parameters were addressed in the previous section. Since the electricity generated from biomass residues will be used to displace grid electricity, the volume of GHG emission avoided as a result was estimated using a national grid emission factor (0.5664 tCO2 MWh−1) from the Thailand Grid Emission Factor for GHG Reduction Project/Activity (2017) announced by the Approval and Monitoring Office, Thailand Greenhouse Gas Management Organization (Public Organization) [50].

Results and Discussion

Selected Crop Types and Plantation Areas for Potential Assessment

According to the data on the crop plantation areas in Thailand, 17 types of crops were selected for the assessment of their biomass residues. These seventeen crop types were composed of four field crops (rice, sugarcane, cassava, and maize), two oil crops (oil palm and coconut), eight fruit tree crops (durian, lychee, longan, mango, mangosteen, orange, rambutan, and wollongong), and three perennial tree crops (coffee, para rubber, and tamarind). The combined plantations of these crops covered a total area of 25,504 kha. This accounted for approximately 91% of the total agricultural area of Thailand, of which approximately 42% was in the northeastern region, 22% in the northern region, 16% in the southern region, 12% in the central region, and 8% in the eastern region. The studied plantation areas accounted for more than 80% of each region’s agricultural area. Table 2 presents a list of all the studied crops and the size of their plantation areas in each region. Among the studied crops, the combined plantation area for rice was the largest at approximately 45.71% of the total agricultural area, followed by para rubber (22.73%), sugarcane (8.88%), cassava (8.30%), maize (6.21%), oil palm (3.27%), coconut (1.15%), longan (1.07%), mango (1.05%), and the other fruits and perennial trees (1.65%). Rice, para rubber, and oil palm were found to be grown in all regions, with the northeastern region having the largest rice plantation areas, while the southern region had the largest para rubber and oil palm plantation areas. In the northern region, there were 12 studied crops, of which rice and maize covered 47 and 22% of the northern plantation areas, respectively. In the northeastern region, there were nine studied crops, of which rice occupied approximately 64% of the total plantation areas. In the eastern region, there were 14 studied crops with approximately 49% of its plantation areas occupied by para rubber, and only 22% assigned to rice fields. There were eight studied crops in the central region, with rice fields accounting for 44% of the total area, and para rubber plantations covering 23%. For the southern region (seven studied crops), para rubber and oil palm were the two major crops, covering 67 and 18% of the southern plantation areas, respectively.

Table 2 Plantation areas of the 17 crops in five regions of Thailand
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Spatial Distribution of Available Crop Residue Yields

The biomass residues of the studied crop types can be classified into three main groups based on their physical characteristics, as follows: (i) rice straws, sugarcane leaves, maize haulms, and cassava leaves; (ii) coconut and oil palm fronds; and (iii) the branches of coffee, durian, lychee, longan, mango, mangosteen, orange, rambutan, tamarind, wollongong, and para rubber trees. The biomass residues from groups (i) and (ii) were obtained during their harvesting periods, while those in category (iii) were obtained from pruning after product harvesting, except for para rubber. Para rubber branches were obtained during the clear-cutting stage of the mature rubber plantations, which had a rotation period of 25–30 years [10], due to the decrease in latex yields. As informed by an official of the Office of the Rubber Replanting Aid Fund, approximately 1.3% of the total para rubber plantation area is clear cut annually for replanting. Table 3 shows an overview of the characteristics (residue types, moisture content, low heating value, and crop residue yield), energy intensity, and cost of the biomass residues. The amount of crop residue yield was estimated using the data of the biomass residue ratio and the annual amount of crop product. The biomass residue yield varied significantly from 0.14 to 15.72 t year−1 ha−1, while the as-received moisture level ranged between 9.23 and 59.41% w.b. Lychee branches produced the highest yield of 15.72 t year−1 ha−1 followed by sugarcane leaves of 12.16 t year−1 ha−1, while para rubber branches had the lowest residue yield of 0.142 t year−1 ha−1. The rice straw yield of 1.96 t ha−1 was relatively low when compared with previous research in which it ranged from 2.06 to 3.71 t ha−1 at the same moisture content of 10% w.b. [34]. Since the energy intensity of crop residues is an indicator of the energy potential of the residues per plantation area, the energy intensity of all studied biomass residues was also calculated in this study, as presented in Table 3. The value of energy intensity ranged from 0.022 to 4.456 toe year−1 ha−1, depending on the crop residue yields and the low heating values. Sugarcane leaves and lychee branches had the highest energy intensity of 4.456 and 4.119 toe year−1 ha−1, respectively, followed by the fronds of oil palm trees (1.957 toe year−1 ha−1) and coconut trees (1.931 toe year−1 ha−1), which had an energy intensity of approximately less than half that of lychee branches. Para rubber had the lowest energy intensity of 0.022 toe year−1 ha−1 since its residue yield was the lowest. Rice had the highest plantation area, but its energy intensity was only 0.572 toe year−1 ha−1. From this study’s collected data, sugarcane leaves are interesting as a biomass fuel since they have high energy intensity with low moisture content and unit cost.

Table 3 Selected crop residue characteristics and unit cost
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The spatial distribution map of the crop residue availability of each region, as presented in Fig. 2 was assessed based on the availability factor of 75% and the moisture content (as-received basis). The spatial distribution of the available biomass residues depended on their plantation areas and residue yields, which had values in the range of 0.11 to 11.79 t year−1 ha−1 in the northern region; 0.11 to 5.58 t year−1 ha−1 in the southern region; and 0.11 to 9.12 t year−1 ha−1 in the northeastern, central, and eastern regions. As can be seen from the distribution maps, the biomass residue distribution in the northern region (Fig. 2a), which was mainly obtained from sugarcane leaves, rice straws, cassava leaves, and maize haulm, had high density in the lower part of the region. In the northeastern region (Fig. 2b), the rice straws were distributed throughout the region, while the sugarcane and cassava leaves were mostly available in the middle and southwestern parts of the region, respectively. The eastern region (Fig. 2c) had the highest diversity of crops when compared with the other regions. The distribution of the sugarcane leaves and rice straws were observed to be in the middle to upper part and upper part of the region, respectively. Even though para rubber had the largest plantation areas, the amount of residue it generated per plantation area had the lowest value. For the central region (Fig. 2d), rice straws and sugarcane leaves were mostly distributed in the upper to the middle parts of the region. In the southern region (Fig. 2e), para rubber and oil palm had the largest plantation areas, which were distributed almost throughout the region. Since the as-received moisture contents of the collected crop residue data had various values, the total crop residue potential of all regions was estimated using Eq. (1) with LHV at 10% moisture content (w.b.) of crop residues. The total potential of all regions in the BC scenario was 63.36 Mt year−1, of which approximately 48% (30.67 Mt year−1) was in the northeastern region, 25% (16.04 Mt year−1) in the northern region, 14% (8.98 Mt year−1) in the central region, 7% (4.64 Mt year−1) in the southern region, and 5% (3.04 Mt year−1) in the eastern region. Based on the ICT scenario, the increase of total potential was estimated to be 13.3% when compared with the BC scenario, as presented in Table 4. The results of spatial distribution of biomass residues in this study and the previous work in China [13] showed that the spatial distribution of biomass residues in each region was a significant difference based on biomass residue types.

Fig. 2
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a–e Spatial distribution of the crop residue availability per plantation area in Thailand

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Table 4 Potential of biomass residues, power generation, and GHG emission reduction in five regions of Thailand
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Energy Potential and Seasonal Distribution of Biomass Residues

The estimation of the energy potential of the various biomass residues was conducted in terms of annual energy potential by region, crop residue type, intensity and seasonal variation, and the estimated results of each region, which depended on the amount of crop residue yield, crop plantation area, and residue heating values. The estimations are summarized in Table 4. According to the BC and ICT scenarios, the crop residues in all regions had a combined total energy potential of 16,946 and 19,207 ktoe year−1, equivalent to 43 and 49%, respectively, of the total renewable energy consumption target for 2036 as set in the ADEP2015 [6]. The energy potential of the crop residues in the five regions can be ordered based on their percentage share of the total energy potential as follows: northeastern (45%), northern (26%), central (15%), southern (9%), and eastern (5%). In addition, the energy potential shares by crop residue types were also estimated, as presented in Fig. 3a–e. Sugarcane leaves and rice straws contributed major shares of the overall energy potential in the northern (68%), northeastern (90%), eastern (62%), and central (81%) regions, while the oil palm fronds (72%) accounted for the major share in the southern region. Even though the para rubber tree plantations cover the largest agricultural area of the southern region, their residue yield is relatively low. Hence para rubber trees contributed only 3% of the total energy potential in the region. When considering the share of overall energy potential by residue types, sugarcane leaves and rice straws contributed major portions of 45 and 30%, respectively, followed by oil palm fronds (7%), maize haulms (5%), cassava leaves (5%), and the other 12 residues combined (8%). Table 4 summarizes the average available energy intensity of all crop residues by region, ranging from 0.375 to 0.814 toe year−1 ha−1 with the country’s average value being 0.62 toe year−1 ha−1. The central region had the highest intensity value of 0.814 toe year−1 ha−1 followed by the northern (0.799 toe year−1 ha−1), northeastern (0.710 toe year−1 ha−1), eastern (0.406 toe year−1 ha−1), and southern (0.375 toe year−1 ha−1) regions. As the sugarcane leaves had very high energy intensity, the central region, which had a 58% share of energy potential from sugarcane leaves, had the highest average energy intensity. The spatial energy intensity of the biomass residues in all regions was mapped, as shown in Fig. 4a. For most of the studied areas, the energy distribution of the available biomass residues was within the range of 0.608 to 0.764 toe year−1 ha−1.

Fig. 3
figure 3figure 3

a–e The share of energy potential by crop residue type in Thailand

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Fig. 4
figure 4

Spatial distribution of energy intensity from crop residues in Thailand based on a the overall studied areas and b the studied area within a 50-km collection radius around the 115-kV substations

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The seasonal availability of biomass residues depends on the crop harvesting and pruning periods, which then affects the availability of the biomass residue feedstock for power generation planning. Thus, the patterns of the biomass residue availability over a year were identified as monthly energy potential using the data from the crop harvesting and pruning calendar for 2018 obtained from the Agricultural Economic Information 2018 [35]. Table 5 presents the duration of the available studied crop residues over a year based on the seasonal classification by the Meteorological Department of Thailand [51]. Most of the studied crops were harvested yearly or pruned in certain periods, except oil palm, coconut, and para rubber, which were harvested and pruned throughout the year. Therefore, the oil palm, coconut, and para rubber plantations provided residues all year round. Figure 5 presents the monthly energy potential distribution of the studied biomass residues in each region based on the BC scenario. Only the southern region (Fig. 5e) had uniform monthly energy distribution within a narrow range from 113 to 139 ktoe due to its energy potential mainly coming from oil palm and coconut fronds. As presented in Fig. 5a–d, the energy potential of the other four regions, namely the northern, northeastern, eastern and central regions, was in monthly ranges of 47 to 708, 4 to 1549, 16 to 154, and 24 to 520 ktoe month−1, respectively. The wide range of different values between the minimum and maximum energy potential and their similar distribution patterns resulted from the seasonal availability of sugarcane leaves and rice straws, which were the main contributors toward the energy potential. Even though sugarcane leaves had the highest energy potential, there were only available for 4 months (December to March). For rice straws, which had the second-highest energy potential, they were only available within two periods, namely October to December for the main rice crop, and February to May for the second rice crop. In addition, the harvesting period of sugarcane overlapped with half of the harvesting period of the rice crop; therefore, the highest energy potential was observed during December, February, and March. The low monthly energy potential in the northern region was found during June to August with an average of just 70 ktoe month−1, while the northeastern, eastern, and central regions were found to have longer periods of low-energy potential lasting 4 months (June to September) with averages of 16, 20, and 18 ktoe month−1, respectively. The low monthly energy potential of the crop residues in these four regions was only 1 to 13% of their highest values. The durations of the periods when the monthly energy potential was higher than the low period were 75% of a year in the northern region and 65% of a year in the northeastern, eastern, and central regions. For the ICT scenario, the energy potential was increased by 13%, with the same distribution pattern. These findings were used for identifying the optimum operating hours per year for generating power from crop residues in each region. Most of the crop residues were available in some periods of the year. Thus, using mixed biomass fuel could reduce the limitations of biomass feedstock availability and also help to control the heat rate requirement from the combustion of biomass with different moisture content [38]. Comparison between the results of seasonal availability of biomass residues in this study and the previous work in North and Northeast China [14] showed that both regions of China had a higher seasonal variation than our study in the amount of crop residues and no available crop residues during January to March in the case of China. The high seasonal variation of biomass feedstock will affect the size of power plant and its capacity factor which needs to be carefully selected and designed in order to obtain the optimal output of electricity.

Table 5 Seasonal availability of the 17 studied crop residues in Thailand
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Fig. 5
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a–e Monthly distribution of energy potential from the crop residues based on the BC scenario in Thailand

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Potential of Grid-Connected Electricity Generation from Biomass Residues

To increase the electricity generation from renewable energy, biomass residues were proposed as a fuel for generating electricity supplied to the national grid. As previously mentioned, the annual operating time of power plants in each region was identified from the patterns and the high energy potential distribution period of biomass residue availability, as shown in Fig. 5. It was found that these periods were 75% of the year for the northern region and 65% for the northeastern, eastern, and central regions. The operating hours per year of the power plants were, therefore, assumed to be 6750 and 5840 h year−1, respectively. For the southern region, even though it has relatively uniform monthly energy distribution from biomass residues throughout the year, the plant availability factor of 75% (6750 h year−1) was used to take into account a period of shutting down for maintenance. The other parameters applied for assessing the potential of grid-connected electricity generation from biomass residues were the locations of 115 kV electrical transmission systems and the collection areas of biomass residues within a 50-km radius from the electrical substations. Figure 4b shows the boundary map of the overall collection areas within a 50-km radius around each of the 115-kV substations, which was mapped using the electrical substations the 115-kV electrical transmission lines and the spatial energy intensity of the studied crop residues (Fig. 4a). Based on a biomass residue collection factor of 50%, the total energy potential of the power generation from the five regions was 11,299 ktoe year−1. Next, the potential of grid-connected electricity generation from biomass residues was assessed by region using Eq. (3) based on the biomass power system of direct combustion with a steam turbine generator. The results of the estimated total power and electricity generation of the five regions (as shown in Table 4) were 5019 MW and 30,491 GWh year−1 for the BC scenario and 7529 MW and 45,738 GWh year−1 for the ICT scenario, respectively. For the BC scenario, approximately 71% of the total power generation potential was in the northeastern (2355 MW, 13,755 GWh year−1) and northern (1206 MW, 7925 GWh year−1) regions, while the central, southern, and eastern regions had estimated power potential of 785 MW (4586 GWh year−1), 414 MW (2710 GWh year−1), and 259 MW (1515 GWh year−1), respectively. According to Thailand’s PDP2018, the electricity demand forecast for 2037 is 367,458 GWh and 53,997 MW [7]. Therefore, the estimated electricity generation potential of biomass residues represents 8.3% of the electricity demand forecast in PDP2018. Considering that the potential electricity generated from biomass residues will be used to displace grid electricity, the volume of GHG emissions avoided as a result was preliminarily estimated using a national grid emission factor (0.5664 tCO2 MWh−1) from the Thailand Grid Emission Factor for GHG Reduction Project/Activity (2017) announced by the Approval and Monitoring Office, Thailand Greenhouse Gas Management Organization (Public Organization) [50]. The result for the volume of GHG emissions avoided was found to be 17.3 MtCO2 year−1. For ICT scenario, the power generation and avoided GHG emission was about 1.5 times higher than the base case. In our study, the power potential estimation was based on the weighted average biomass within the specific area, as mentioned earlier. However, in the normal practice of a small biomass power plant operation, the biomass feed is stored for a certain period for air-drying or sun-drying to reduce its moisture in order to get the optimum feed moisture. Note that using the dryer to remove feed moisture requires that consideration be given to the trade-off between the energy use and the energy gain in the boiler.

Levelized Cost of Grid-Connected Electricity Generation from Biomass Residues

To ensure the possibility of biomass residue utilization as a viable fuel for generating power to be supplied to the grid, it is necessary to evaluate the levelized unit cost of electricity. The LCOE for biomass power generation capacities of 5, 7, and 9 MW were evaluated in each region under the BC scenario and three scenarios (maximum, average and minimum values) of biomass fuel costs in terms of USD per GJ of biomass energy (including transport costs) in a collection area of 50 km around each 115 kV substation. The cost of biomass fuel in each collection area was calculated using Eqs. (5)–(7) based on the data given in Table 1 and the spatial distribution of crop residues in Fig. 4a. Then, the maximum, minimum, and average values of the biomass fuel costs per unit of energy in the collection areas of each region were identified. In addition, the amounts of energy from all crop residue types in these collection areas were also presented in terms of total energy and percent contribution by each residue type, as summarized in Table 6. The biomass unit cost for all the collection areas ranged between US$0.00261 and 0.00302 TJ−1. It can be observed that in each region, a collection area that had a high total amount of energy from crop residues had a low biomass unit cost. Based on the three biomass cost scenarios as mentioned above, the LCOE for the 5-, 7-, and 9-MW power plants were estimated (as shown in Fig. 6) in the ranges of US$77.72 to 87.66 MWh−1, US$71.42 to 80.54 MWh−1, and US$67.69 to 76.36 MWh−1, respectively, which were lower than the current feed-in tariff. According to the economies of scale, the 9-MW power plant had the lowest unit cost, followed by the 7- and 5-MW plants in descending order. As observed in Fig. 6a–c, the LCOEs in the northern (US$67.69 to 79.60 MWh−1) and southern (US$67.78 to 79.69 MWh−1) regions had almost equal values and were also the lowest since they had low biomass fuel costs and also long operating hours per year. These regions were followed by the central (US$71.20 to 84.27 MWh−1), northeastern (US$72.78 to 85.72 MWh−1), and eastern (US$74.96 to 87.66 MWh−1) regions. The LCOEs of the eastern region were the highest due to it having the highest biomass costs and lowest annual operating hours in its power plants. Even though the central region had the lowest biomass costs, it also had the lowest operating hours, resulting in its LCOE not being the lowest. The cost distribution of the country’s average LCOE for the three power plant capacities (5, 7, and 9 MW) was based on the average biomass costs (US0.00275 GJ−1) and operating hours (6132 h year−1) of the five regions. Approximately 55.2 to 63.9% of the LCOE was from biomass costs, while the investment, maintenance, and operating costs accounted for 19.0 to 19.5, 12.3 to 19.1, and 4.4 to 6.8%, respectively. For most of the biomass power plants, the fuel costs represented the major portion of LCOE, concurring with the findings of previous research in the related literature [52]. The estimated LCOEs can be used as a reference value by the government to identify the policy instrument to be used for promoting investment in grid-connected power generation from crop residues. According to the amount of GHG reduction, the MACs of the power plants with three different capacities were estimated based on the BC and the average fuel cost scenario in each region. The results showed that the estimated MAC in all regions had a negative value ranging from – US$89.36 to − 115.83 tCO2−1. The MACs in the northern region (− US$89.36 to − 94.22 tCO2−1) and southern region (− US$90.63 to − 94.25 tCO2−1) had almost equal values. The MACs in the eastern, central, and northern region were in the range of – US$92.33 to − 95.71 tCO2−1, − US$95.63 to − 98.57 tCO2−1, and − US$93.97 to − 115.83 tCO2−1, respectively. The negative value of the MACs showed that this is an attractive approach to reducing the GHG emissions from power generation. In addition, using the biomass residues for power generation can create new jobs and also income opportunities [12].

Table 6 The cost and characteristics of biomass residues in the collection area (within 50 km around 115 kV) representing the minimum, average, and maximum unit cost of each region based on the BC scenario
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Fig. 6
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The LCOEs for biomass power generation capacity of a 5, b 7, and c 9 MW power plants located in five regions based on the BC scenario

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Conclusions

In this study, the assessment of the potential of crop residues for grid-connected power generation in Thailand included the effects of seasonal variation and spatial distribution. The 17 studied crop residues, as listed in Table 3, covered a combined agricultural area of 25,504 kha, representing approximately 91% of the total agricultural area of the country.

Sugarcane leaves and rice straw contributed to the major portion of total energy potential, at 45 and 30%, respectively. However, their seasonal availability was relatively short and also included some overlapping periods, which was the main cause of variation in the seasonal distribution of the residues in the northern, northeastern, eastern, and central regions. Meanwhile, the southern region was interesting since its main residue was from oil palm fronds, which had a relatively uniform energy distribution pattern throughout the year.

The spatial distribution and seasonal variation of crop residues were specifically characterized by region, with these factors used in assessing the potential of crop residues for grid-connected power generation. Based on a 50% of collection factor (BC scenario), the total energy potential of the available biomass residues was 11,299 ktoe year−1, which can generate 5019 MW (30,491 GWh year−1) of power, representing 8.3% of the forecasted electricity demand in 2037 according to Thailand’s PDP2018. As a result, 17.3 MtCO2 year−1 of GHG emissions could be avoided. The estimated LCOEs for power plant capacities of 5 to 9 MW were in the range of US$77.72 to 87.66 MWh−1, which were lower than the current feed-in tariff rate. According to the amount of GHG emission reduction, the estimated MAC was in the range of − US$89.36 to − 115.83 tCO2−1. The negative value of the MAC showed that this is an attractive approach to reducing GHG emissions from power generation. With an improvement in the collection factor of up to 75%, the power generation and avoided GHG emissions were about 1.5 times higher than the base case. These findings will be useful for the government in adopting a future policy for the zoning, management, and promotion of biomass residue utilization.