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A Geographical Information System (GIS) based methodology for determination of potential biomasses and sites for biogas plants in southern Finland
Applied Energy 113 (2014) 1–10
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A Geographical Information System (GIS) based methodology for determination of potential biomasses and sites for biogas plants in southern Finland J. Höhn, E. Lehtonen, S. Rasi ⇑, J. Rintala MTT Agrifood Research Finland, 31600 Jokioinen, Finland
h i g h l i g h t s The biomethane potential in southern Finland is over 3 TWh. Agricultural biomass accounts >90% of the biomethane potential in study regions. The GIS method can be used for detailed biogas plant planning. The GIS provides tools for e.g. land locations, cost and emission calculations.
a r t i c l e
i n f o
Article history: Received 28 March 2013 Received in revised form 20 June 2013 Accepted 2 July 2013
Keywords: Biomass potential Biomethane Biogas Regional assessment GIS
a b s t r a c t Objective: The objective of this study was to analyse the spatial distribution and amount of potential biomass feedstock for biomethane production and optimal locations, sizes and number of biogas plants in southern Finland in the area of three regional waste management companies. Methods: A Geographical Information System (GIS) based methodology, which also included biomass transport optimisation considering the existing road network and spatially varied biomass sources, was used. Kernel Density (KD) maps were calculated to pinpoint areas with high biomass concentration. Results: The results show that the total amount of biomass corresponds to 2.8 TWh of energy of which agro materials account for more than 90%. It was found that a total of 49 biogas plants could be built in three case regions with feedstock available within maximum transportation radius of 10 or 40 km. With maximum of 10 km biomass transportation distance, the production capacity of the planned plants ranges from 2.1 to 8.4 MW. If transportation distance was increased to 40 km, the plant capacities could also increase from 2.3 to 16.8 MW. Conclusions: As demonstrated in this study, the studied GIS methodology can be used for identification of the most suitable locations for biogas plants by providing the tools for e.g. transportation routes and distances. Practice implications: The methodology can further be used in environmental impact assessment as well as in cost analysis. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction Biomethane is increasingly considered a feasible biofuel with its use being stepped up in many European countries. The EU has a target that 10% of its transport fuel should be renewable by 2020 [1]. In addition to its use as a traffic fuel, methane gas can also be used in heat and power production, which is a more traditional application. Biomethane is one of the most sustainable biofuels available today [2]. The EU directive 2009/28/EY [1] stipulates that energy from biofuels and bioliquids should contribute to a
⇑ Corresponding author. Tel.: +358 295317655. E-mail address: saija.rasi@mtt.fi (S. Rasi). 0306-2619/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.apenergy.2013.07.005
reduction of at least 35% of greenhouse gas emissions in order to be taken into account. From 2017 onwards, the emissions savings should be increased to 50%. According to Adelt et al. biogas produced from energy crops, fulfils this criteria [3]. The main components of biogas are methane and carbon dioxide. Biogas is produced by micro-organisms from biodegradable organic material under anaerobic conditions. Materials include those such as biowaste, sludge, manure, agro residues and energy crops. Biogas technology has been used for waste treatment because the technology can employ waste materials and the treated materials can in many cases be used as fertilisers, thus enabling the recycling of nutrients. There are several different techniques available for biogas upgrading to biomethane [4] and the number of gaseous fuel distribution networks is increasing in many countries.
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Different types of biomasses are available for biogas production depending on e.g. population and the economic structure of a region. For example in highly populated areas the amount of waste materials is great while in agricultural areas field biomass and livestock manure are available. The environmental and political demand for more efficient use of renewable energy and biomass is increasing the need to utilise different types of biomasses. Biomethane potential analyses in different areas have been studied using various types of agro biomasses and waste materials [5–9] in order to find more sustainable feedstock for renewable energy production. When energy crops are considered as a raw material for biogas production, it is important to consider also the sustainability of whole agricultural production chain. The crop production should not be based on maximum yields from a single crop but rather on maximising the methane yield from the whole system through use of sustainable and environmentally friendly crop rotation [6,10]. Various measures are needed to ensure the efficient introduction of biomethane, as is the case with any other new technology in society. Systematic analysis and planning of regional biomass resources and of technological implementations as well as the evaluation of impacts provide an option of developing sustainable and economical biomass and energy production and utilisation at the regional level. Due to the fact that biomass to energy schemes are highly geographically dependent [11] since biomass supply and biogas demand is often distributed dispersedly, finding suitable locations for biogas plants by minimizing transportation distances and associated CO2 emission is a key issue for sustainable biogas production. Because of the spatially varied locations of different biomass sources the assessment of biomass potential for biogas production and siting biogas plants in optimal locations includes the use and handling of a wide range of geographical data. Geographical Information Systems (GIS) have been considered as an appropriate platform for spatial related issues and have been studied for assessing the potential biomasses for biogas production and for site location analysis [12–16]. However, these studies considered just a limited selection of different biomass types and/or used aggregated spatial information data in order to capture the spatial variation of biomass sources. The main aim of this study was to develop methods in GIS environment in order to determine different potential biomasses for biomethane production in southern Finland taking a wide range of available biomasses including biowaste, sludge, livestock manure, energy crops and agricultural residues into consideration. Resource locations were captured at a very fine scale (i.e. farm and field level for agricultural sources, street addresses for biowaste and sludge sources) to allow information for more detailed further planning. Furthermore, this study aimed to determine suitable biogas plant locations and to allocate optimally biomass sources to candidate biogas power plants through transportation distance minimization using a road network. The specific objectives of this study were to develop: (i) An approach to evaluate, assess and map the regional potential for biogas production generated from biowaste, sludge, livestock manure, energy crops and agricultural residues. (ii) A method to find suitable biogas plant locations considering the spatial distribution of biomass sources and biogas usage end points. (iii) A method to allocate optimally biomass sources to biogas plants with transportation distance minimization and to compute plants’ specific production capacities. This approach was finally applied to a case study including three regions in southern Finland (Turku, Salo and Kymenlaakso)
in order to promote more sustainable and balanced planning of renewable energies both at a municipal and an inter-municipal level. 2. Materials and methods This study comprises the assessment of regional biomass potential for biogas production, site suitability analysis of candidate biogas plants and determination of plants production capacity, spatially optimized biomass collection areas and transportation distances (Fig. 1). All spatial related tasks were performed using ArcGIS version 10 software and its associated extensions such as the Spatial Analyst and Network Analyst. 2.1. Case study The methodology as described in the following sections is applied for three case regions in southern Finland including Turku, Salo and Kymenlaakso. Southern Finland, which includes both rural agricultural areas and built-up urban areas, is of interest because it is the most highly populated area in the country. Also, the use of methane/natural gas in southern Finland is more common than in other parts of Finland because of the existence of the natural gas pipeline which is only in the south (Fig. 2, Table 1). 2.2. Regional biomass and biomethane potential The feedstock considered for biomethane production were: biowastes, sludges, agricultural residues and energy crops (specifically grass silage). The quantity and location of different feedstock in 2009 were extracted using statistics and studies and by interviewing major industrial waste producers (Table 2). The term biowaste means food waste and green waste generated in households, at public sector sites (e.g. schools, hospitals) and at private sector sites (e.g. restaurants, hotels). The amounts for the public and private sector sites have been estimated due to a lack of detailed statistical data availability. The amount of sludge at municipal and industrial waste water treatment plants (WWTPs) was obtained from the plant operators. Agricultural biomass (i.e. agro biomass) consists of manure, straw, agro residues (like greenhouse waste and sugar beet waste) and grass silage that is cultivated for energy production on available fields. Energy crops were assumed to be cultivated on crop production farms which in case regions covered about 200,000 ha of agricultural land. Crop production farms were selected because it was assumed that grass is cultivated for biomethane production in rotation with cereal cultivation. For the purpose of estimating the amount of manure produced by cattle, pork, poultry, horses, sheep and goats, the livestock were categorised by sex, age, and production type (see Tables A1 and A2). The feedstock potentials were categorised and the resulting methane potential was calculated. Total solids (TS) and the ratio of volatile solids (VS) and TS were estimated (Table 3). The theoretical biomethane potential was calculated for each region in the study area using the biomethane potential from literature of each feedstock material. The actual methane potential is less than the theoretical potential due to incomplete anaerobic conversion of the feedstock in the biogas plant, which efficiency is however dependent e.g. on the scale and operation of the plant. 2.3. Mapping biomass and associated biomethane supply potential In order to visualize the spatial distribution of biomass sources and their associated potential for biomethane production each biomass source was stored in a geo-referenced database with attributes indicating the type of biomass, annual generated amount and
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Fig. 1. Description of general methodology and case study.
Fig. 2. The study regions in southern Finland.
Table 1 Description of the study regions.
Number of municipalities Population 2009 Area, km2 Agricultural field area 2009, ha Share of field area of total area, %
Turku region
Salo region
Kymenlaakso region
14 325,600 4000 98,400 25
4 75,600 3 200 84,000 26
8 185,500 5 900 95,300 16
biomethane potential. Based on this database Kernel Density (KD) maps were calculated to pinpoint areas with high biomass concen-
tration. KD maps deliver density values based on the feedstock quantities within a defined search radius. In this study a search
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Table 2 The calculation principles for available biomasses. Assumption of biomass production/method of calculation Biowastes
Sludges Slurries/manures (see also Tables A1 and A2)
Silage and crop residues
Households: 58 kg/a per cap separate collection rate of 65% Schools: 23 kg/person/a Universities and vocational schools: 19 kg/person/a Daycares: 50 kg/person/a Hospitals: 110 kg/person/a Other nursing homes: 260 kg/person/a Restaurants and hotels: 1030 kg/person/a Retail: 2100 kg/person/a
Industries: total amounts from major biowaste producers from food and forest industries Municipal and industrial waste water treatment plants Number of cattle: 44,300 Number of pork: 134,400 Number of horses, sheep and goats: 14,400 Number of poultry: 1.6 million Amount of excrement produced by animals during the pasturing period was deducted Available field: crop production farms. Crop rotation of grass was assumed with cereal in sequence of two seasons of grass and three seasons of cereals. Straw of cereal: 3 t/ha Straw of rapeseed: 2 t/ha Sugar beet top: 7.5 t/ha Potato waste: 5 t/ha Vegetable top: 7 t/ha Greenhouse waste: 35 t/ha Silage: 21.4 t/ha
[17–19] [20], interviews [20], interviews [20], interviews [20], interviews [20], interviews [20], interviews PETRA-waste benchmarking systema Interviews The plant owners [21] [21] [21] [21] [22] [23,21,24]
[25] [25] [25] [25] [25] [25] [26]
a
Refs.
Waste data bank in which companies and organizations in the Helsinki region can voluntary report their yearly amounts ant treatment of different waste fractions.
Table 3 Characteristics of materials used in biomethane potential calculations [16,36].
Biowaste from households, industry, private and public services and ferries Bakery waste Milk waste (whey) Fat waste Slaughter waste Vegetable waste Manure of horses Manure from slaughterhouse Solid manure of cattle Liquid manure of cattle Solid manure of pig Liquid manure of pig Solid manure of poultry Sludge from municipality waste water treatment and food industry Sludge from paper and pulp mill, biol. Sludge from paper and pulp mill, primary Straw of cereals Vegetable top Grass silage Straw of rapeseed
radius of 3 km was used taking into account the high availability of biomass sources. 2.4. Candidate plant site selection In order to locate optimal sites for biogas plants two different approaches were applied while taking into consideration the regional distinctions for biogas production. In regions where a natural gas grid is available (Kymenlaakso) that can serve as potential gas storage and distribution system, the main priority during site selection process was to identify areas with high availability of biomass source close to the grid with the help of the Kernel density maps. In absence of a natural gas grid end use points of biomethane are concentrated mainly at city centres (Turku and Salo regions). Therefore one has to consider whether it’s more practicable to produce the biomethane in areas with high raw
CH4 (m3/tVS)
CH4 (m3/tww)
TS (%)
VS/TS (%)
400 400 420 800 600 400 250 250 200 200 300 300 300 300 100 300 230 300 350 250
97 97 18 288 216 97 48 18 23 10 58 10 81 42 14 42 178 28 104 207
27 66 6 40 40 27 32 10 19 6 24 4 38 20 20 20 85 11 35 90
90 90 70 90 90 90 60 70 60 80 80 85 71 70 70 70 91 85 85 92
material supply and transport the biomethane to the consumer market or to produce biomethane close to the demand point and transport the raw material to the production site. Due to the fact that a high proportion of available raw material originates from rural areas where digestate utilisation also takes place, it was assumed that it is more feasible to produce biomethane in areas with high raw material availability. Therefore, site selection in areas without a natural gas grid was mainly driven by the availability of biomass feedstock. Also here, the Kernel density map was used in order to identify areas with high availability of biomass. 2.5. Determination of plants’ collection areas, production capacity and biomass transportation distances In the second step collection areas for each initial candidate site were derived using the Service Area application in ArcGIS
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Network Analyst. This application computes an area that encompasses all accessible streets that lie within a specified maximal transportation distance and assigns each biomass source point to the closest candidate facility in order to take the competition for biomass resources between biogas plants into account. According to Dagnal et al. and Palm [27,28] maximum transportation distances for raw materials vary from ten to 40 km. In the present study the lower and upper end was used to study the impact of varying transportation distance on collection area and associated biogas production potential. Thereafter, for each initial candidate site biomass amounts which were available within the collection area were determined and plant-specific theoretical biomethaneproduction capacities were calculated assuming an annual running time of 8000 h (T) according to following formula:
Ptheo ¼ Eavail =T
ð1Þ
where Ptheo is the theoretical installed power (MW); Eavail is energy of the feedstock available in collection area (MWh) (Table 3); T is the annual running time of plant (h/yr). Feedstock energy (Eavail) was calculated using the coefficients provided in Table 3 and assuming that 1 m3 CH4 equals to 10 kWh Finally, distances and vehicle-specific transportation amounts/ driven kilometres in total between the biomass sources and the
potential site, were calculated. Transportation distances were derived using the OD Cost Matrix application in ArcGIS Network Analyst which uses the Dijkstra’s algorithm in order to determine the shortest path from a starting point to a destination location [29]. 2.6. Variation of the number of biogas plants The initial solution of number and location of biogas plants considers multiple sites, assuming that at each identified suitable location a biogas plant is built that competes with neighboring plants for biomass sources. Biomass collection areas for each plant differ as a function of the presence of competitors. In order to study how the number of plants influence plant specific collection areas and associated availability of biomass sources, collection areas were derived with varying number of plants. For this purpose the number of plants was continuously reduced ending with one centralized plant serving the entire regions. For each reduction step plant specific collection areas and associated biomass availability and biomethane production potential was calculated using the Location–Allocation model. This tool solves the p-median problem by choosing facilities such that the total sum of weighted distances allocated to a facility is minimized [30]. In this study the p-median model was applied in order to
Table 4 Amount of different biomass (tTS/a) in the study regions. Biomass
Turku region
Municipal biowaste Industrial biowaste Municipal WWTP sludge Industrial WWTP sludge Manure Grass silage Straw Agricultural waste and side productsa Total
Salo region
Kymenlaakso region
tTS/yr
tww/yr
GWh (% of total)
tTS/yr
tww/yr
GWh (% of total)
tTS/yr
tww/yr
GWh (% of total)
6200 2000 12,000 0 54,300 201,414 119,200 6800 402,000
23,100 5400 60,000 0 327,400 584,630 140,223 47,563 1188,300
22 (2) 9 (1) 25 (2) 0 (0) 103 (10) 601 (58) 250 (24) 22 (2) 1032
1400 1300 1500 50 26,600 176,515 104,200 8000 320,000
5000 4000 7400 170 200,100 520,000 122,580 66,380 925,500
5 (1) 7 (1) 3 (0.4) 0.1 (0) 46 (6) 525 (63) 220 (27) 23 (3) 827
3400 5900 6000 37,600 38,100 197,256 117,140 91 405,000
12,600 9500 30,100 125,200 316,700 570,000 137,800 780 1202,700
12 (1) 22 (2) 13 (1) 49 (5) 55 (6) 588 (60) 245 (25) 0.2 (0) 984
TS = total solids; ww = wet weight. a Vegetables, greenhouse waste, potato waste, sugar beet.
±
T17
Turku region T1
Mynämäki T19
T22 T11
Kymenlaakso region
T18 T14
T12
K14 K1
T5
Aura T13 T15 T6 T2 T8 T4 T21 T7 T16 Marttila S4 Raisio Lieto T10 S3 Naantali T9 S1 T3 S5 Paimio Turku Kaarina Halikko Salo S7 T20 Sauvo S8 S2 S11 Parainen
S12
Salo region
Perniö S10 S9
K2
S6
S13
K4
K11 K9
Valkeala K3 Kouvola K7 K8
K13
Anjalankoski K5
K12
Loviisa
Candidate Site
Hamina K6
K10
Kotka
Biogas potential
40 Kilometers
w
30
Lo
20
H
0 5 10
ig
h
Natural gas grid
Fig. 3. Biomethane potential intensity and potential biogas plant locations in the study regions (T = Turku region, S = Salo region, K = Kymenlaakso region).
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determine p facilities in a predefined set with n candidate facilities (n > p) so that total weighted transport distances between each biomass source and facility is minimized. Transport distances were weighted by the amount of vehicle loads which are needed to transport the annual quantity of biomass to the facility. In this study it was assumed that sludge and liquid manure could be transported in 32 m3 tankers and solid manure in 20 t trailers. Biowaste could be carried in 12 t collection vehicles, silage in 9 t trailers and straw and other agricultural side products in 18 t trailers. With this notation, the p-median model can be formulated as follows:
Min
XX j
hi dij Y ij
ð2Þ
i
s:t:
X Y ij ¼ 1 8i 2 I
ð3Þ
j
Y ij X j 6 0 8i 2 I;
8j 2 J
ð4Þ
X Xj ¼ p
ð5Þ
j
X j 2 f0; 1g 8j 2 J Y ij 2 f0; 1g 8j 2 I;
ð6Þ
8j 2 J
ð7Þ
where I is the set of demand nodes and J the set of candidate facilities; i = index of demand nodes and j = index of candidate facilities;
Table 5 Descriptions of biogas plant candidates in the Turku (T), Salo (S) and Kymenlaakso (K) regions. Plant ID
T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 T16 T17 T18 T19 T20 T21 T22
Plant size (MW) 7.0 4.5 7.4 7.1 5.7 3.8 3.1 6.7 6.2 2.6 5.9 4.2 3.2 3.2 4.8 2.7 2.1 2.7 3.4 2.8 5.8 2.8
Feedstock (1000 tTS/yr) 22 14 23 22 18 12 9 21 19 7 19 13 10 10 15 8.5 7 8 11 9 18 9
Feedstock composition (% of TS)
Total transportation distances (km)
Average transportation distance (km)
Tons (FM/km)
28 35 10 31 31 33 36 30 33 28 26 29 31 35 34 31 21 32 28 35 33 29
34,600 15,000 18,000 29,000 19,000 16,100 9300 26,500 23,700 7400 21,200 12,800 11,000 8400 13,600 8800 4900 8000 14,200 12,500 21,700 12,700
6.2 4.8 3.9 5.4 5.0 5.4 4.4 5.7 5.8 4.7 5.2 4.6 4.6 3.8 4.0 5.0 3.5 4.1 5.4 6.3 5.5 5.4
2.1 2.6 5.1 2.2 2.3 2.7 2.5 2.4 1.9 2.5 2.7 2.8 3.0 3.2 3.3 2.3 3.8 3.3 2.7 1.7 2.2 2.7
Biowaste
Sludge
Manure
Silage
Straw
0 0 20 1 0 0 1 1 1 23 0 0 0 0 0 0 0 0 0 2 0 0
0 0 52 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
19 6 2 12 16 10 1 20 10 3 31 22 16 8 9 19 39 13 24 3 11 19
47 58 16 51 51 55 61 49 55 46 43 48 52 58 57 51 36 54 46 58 55 48
TOTAL TURKU S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13
97.7 6.7 6.3 4.3 8.4 8.1 5.4 6.6 6.5 4.7 5.0 4.5 4.2 4.0
305 19 19 14 26 25 17 20 20 14 15 14 13 12
7 0 0 0 0 0 3 1 0 0 0 0 0
0 0 0 0 0 0 7 0 0 0 0 0 0
10 10 19 6 8 16 4 5 2 13 9 4 2
50 50 50 57 55 52 52 56 59 47 56 58 61
30 30 30 35 33 31 31 34 35 28 34 35 37
26,200 32,300 16,400 34,200 36,400 22,600 29,600 30,500 23,800 24,600 19,300 21,600 15,200
5.5 5.9 5.5 5.7 6.0 5.9 6.3 6.1 7.0 5.4 5.9 7.1 5.8
2.3 2.1 2.3 2.0 1.9 2.2 2.0 1.9 1.5 2.2 2.2 1.6 1.8
TOTAL SALO K1 K2 K3 K4 K5 K6 K7 K8 K9 K10 K11 K12 K13 K14
74.6 2.6 6.0 3.8 7.4 7.0 5.9 3.6 4.8 4.8 2.5 6.4 3.3 7.4 3.8
230 8 18 20 23 27 22 11 15 15 8 20 11 23 12
0 0 8 0 0 24 9 1 0 0 0 0 0 0
0 0 59 0 55 70 18 0 0 0 0 0 0 0
9 2 2 9 2 0 2 13 9 17 11 13 8 15
57 61 19 57 26 4 44 54 56 51 55 54 58 53
34 36 11 34 16 2 27 32 34 30 33 33 35 32
8000 22,500 12,000 32,400 19,900 13,000 9900 18,700 20,600 12,400 28,000 12,500 30,100 17,800
4.4 5.6 4.2 6.2 4.6 4.5 4.5 5.4 6.1 6.7 5.8 5.2 5.7 6.3
2.9 1.9 4.8 2.0 4.1 5.4 3.0 2.4 2.1 1.9 2.5 2.5 2.2 2.1
TOTAL KYMENLAAKSO
69.2
234
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3.1. Biomass and biomethane resources The amount of potential biomass (biowaste, wastewater sludge, manure, crop residues and energy crops) in the three regions was estimated to be 3.3 million tons wet weight (tww)/a (1.1 million tTS/a) which corresponds theoretically to 2.8 TWh energy (Table 4). The highest biomethane potential in the study regions is in agro materials, although in cities biowastes and sludges are good resources for bioenergy. Agro materials account for 94, 98, and 91% of the total theoretical biomethane potential in Turku, Salo and Kymenlaakso regions, respectively (Table 4). Different types of manures account for 6 to 10% of the total theoretical biomethane potential in study regions. In the Turku region, manure from poultry, pork and cattle accounts for 43, 30 and 20% of energy potential of manure in the region while in Kymenlaakso 73% of the energy content from manure comes from cattle manure. In Salo the manure from cattle, pork and poultry account for 39, 34 and 18%, respectively, of the theoretical manure energy potential in the region. About 56, 15 and 96 GWh of biomethane energy, respectively, could be produced from municipal and industrial biowaste and sludges in the Turku, Salo and Kymenlaakso regions (Table 4). In the Kymenlaakso region industrial sludges accounts for 80% of the total theoretical biomethane energy potential of sludges (5% of total biomethane potential), which is mainly from the forest industry. The grass silage accounts for more than half of the total theoretical biomethane potential in all study areas. A quarter of the total biomethane potential comes from cereal straws (Table 4). The grass silage and straw are not yet commonly used raw materials in biomethane production but interest in these materials is increasing [31]. In Germany there are over 7000 biogas plants using agro biomasses (mainly manure and maize) as raw materials using some 700,000 ha energy maize with almost 3 TW of installed electric power output [32]. The concepts for sustainable large scale production of biomethane from grass silage is still under development but as grass silage’s energy potential is high the possibilities for utilising grass silage or other boreal crops should be encouraged[33]. 3.2. Location of biogas plants, collection area and transportation distances Kernel density maps were made to point out the regional patterns of biomass distribution (Fig. 3). Hot spots of high biomethane density are clearly visible and are mainly concentrated around the city centres which have high availability of municipal and industrial biowaste and sludge from wastewater treatment plants. Such areas are concentrated around the city centre of Turku, Kouvola, Anjalankoski, and Kotka. In such areas the annual availability of
(a) 1 800 Total transportation distance (1000 km)
3. Results and discussion
biomass can reach up to 20,000 t/km2 with an associated biomethane potential of roughly 820,000 Nm3 (e.g. Turku city centre). In rural areas local hot spots of biomass are identified by high availability of agricultural resources. In Turku and Salo region the greatest portion of such biomass is generated in rural areas and also the south-western part of Kymenlaakso has significant biomass potential. Based on the density maps (Fig. 3) initial suitable candidate sites for biomethane production were derived according to the criteria described in Section 2.4. In total 22 sites for the Turku region and 13 for the Salo region were initially considered as potential production sites. They are all situated in areas with high biomethane density (Fig. 3) whereby two of them are furthermore situated close to the city centre of Turku. In the Kymenlaakso region, initially seven candidate sites were chosen which are all located directly nearby or in close proximity to the natural gas grid and which are characterized by high biomass availability. Nevertheless, since a significant portion of crop production is concentrated in the south-western area of Kymenlaakso region with a high amount of agro residues available this part of the region was also taken into consideration resulting in six additional candidate sites. The production capacity, the used feedstock and the transportation distances of the selected initial biogas plant candidate sites was calculated for maximum transport distances of 10 km (Table 5) and 40 km (data not shown in tables). It can be seen that, when applying a maximum transportation distance of 10 km, 76%, 72% and 56% of the available biomasses could be collected and transported to the selected sites in Turku, Salo and Kymenlaakso regions, respectively. If a maximum transportation distance of 40 km was applied, almost 100% (Turku: 96.5%, Salo: 99%, Kymenlaakso 99.5%) of the available biomasses could be utilised (data not shown). Only a small portion of the available biomass
1 600 1 400 1 200 1 000 800 600 400 200 0
1
4
7
10
13
16
19
22
Number of plants
(b) Average biogas production capacity (MW)
dij = distance between demand node i and candidate facility j; h = weight associated with demand node i; Xj = 1 if the facility is located at candidate point j, and 0 otherwise; Yij = 0 if demand node i is assigned to a candidate facility j, and 0 otherwise. The objective function (2) minimizes the demand-weighted total distance. The first constraint (3) stipulates that each node is assigned, the second constraint (4) limits assignments to open or selected sites. Constraint (5) states that p facilities are to be located. Finally, constraints (6) and (7) are integrality constraints. This type of assessment could be useful for specific regional planning processes where the objective could be to identify most suitable locations for a defined number of intended biogas plants to be built.
120 100 80 60 40 20 0
1
4
7
10
13
16
19
22
Number of plants Fig. 4. Variation of plant capacity and accumulated feedstock transportation distance with different amount of plants for the Turku region.
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(a)
(b)
(c)
(d)
Chosen Candidate
0 5 10
20
30
Kilometers 40
Lines
Fig. 5. Origin–destination connectivity in Turku region with (a) 22 plants, (b) 15 plants, (c) 8 plants and (d) 1 plant.
resources is further than 40 km away from a potential site and is therefore not allocated to the plants. With maximum of 10 km biomass transportation distance, the production capacity of the planned plants ranges from 2.1 to 7.4 MW, from 4 to 8.4 MW and from 2.5 to 7.4 MW in Turku, Salo and Kymenlaakso regions, respectively (Table 5). If transportation distance was increase to 40 km, the plant capacities could also increase from 2.3 to 10.3 MW, from 4.9 to 13.4 MW and from 3.8 to 16.8 MW in Turku, Salo and Kymenlaakso regions, respectively (data not shown). The highest biomass potential available per transportation kilometre is found for plant T3 in Turku (5.1 t/km) and plant K6 (5.4 t/km) in Kymenlaakso (Table 5). These plants are located in the Turku and Kotka city area and use mainly biowaste and sludge as primary biomethane production feedstock. These plants are located nearby biomass resources as the average transportation distances (3.9 km for T3 and 4.5 km for K6) indicate. Additionally they are situated close to the city centres and therefore biofuel demand market which pronounces the suitability of these locations. In general, transportation distances over which the feedstock has to be transported, are higher for agricultural resources as they are more dispersedly distributed. The highest average transportation distances were found for plants S9 and S12 in the Salo region (from 7.0 to 7.1 km) which use mainly field biomass as primary biomethane production feedstock.
In the case when biogas is used for electricity and heat production, the best biogas plant locations are in areas of high population density and therefore high energy demand [12]. In this study some of the potential biogas plants were located near to city centres and from an energy demand point of view these could be the best locations in the beginning phase of biomethane production. Overall, when the use of renewable energy increases, other biomasses in the regions are needed to fulfill this demand. According to Pöschl et al. the transportation of manure (TS 8%) results in a negative energy input–output ratio if transportation distances are over 20 km because of the low energy content of manure [34]. Even with a distance of 5 km the energy input–output ratio is over 60% meaning that a biogas plant treating manures should be located near the farms. For other feedstocks the transportation distances are not that crucial as with manure as with them the energy content is higher. This is especially true in areas where gas can be transported via pipelines. By reducing the transportation of feedstocks the total energy input output ratio can be minimized. 3.3. The effect of plant number on biomass collection areas and production capacity The results presented in the previous chapter are based on a multiple biogas plant scenario with the objective to allocate the biomass sources to a facility in that way, that the total weighted
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transportation distances are minimized. Due to the fact, that in specific regional integrated planning the objective could be to find the most suitable location for a pre-defined set of biogas plants, this study provides an approach to identify preference sites for scenarios with varying number of biogas plants. For this purpose, the number of plants under consideration was continuously reduced ending with one centralized plant serving the entire region using the Location-Allocation model. Fig. 5 shows the optimal utilization of available biomass resources for the Turku region for a 22 plants scenario (a), 15 plants scenario (b), 8 plants (c) and a single plant scenario (d). With 22 plants, as provided by the initial solution, the average plant capacity would be 4.4 MW with max. 10 km transportation distance (5.7 MW with 40 km distance, data not shown) and the accumulated transportation distance would be 344,000 km (Fig. 4) (700,000 km with max. 40 km distance, data not shown). With decreasing number of plants, the production capacity per plant naturally increases since there are less competitors, but also transportation distances increase first at a slower rate and then more rapidly. A scenario which would include eight plants would result in an average production capacity of 15.4 MW with an accumulated transportation distance of some 980,000 km/ yr. In a single plant scenario, the average transportation distance would be 25 km resulting in an overall transport distance of 1900,000 km annually with an overall production capacity of 108 MW.
3.4. Assessing feasibility of the method for biogas plant location analyses In this study GIS based methods were developed in order to analyse suitable biogas plant location considering the spatial variation in the distribution of biomass sources and end-use points and applied to a case study for three regions in southern Finland. This approach consists of a detailed and comprehensive evaluation of available biomass sources including location information at a fine scale (i.e. farm and field level for agricultural sources and street addresses for biowaste from the private and public sector and sludge sources). Together with the usage of a real road network this approach provides detailed and accurate information about potential biogas plants characteristics including potential biomethane production capacities and expected transportation distances distinguishing this study from previous work conducted in that field which has been mainly based on aggregated spatial information data. [e.g. 12,13]. Due to the modular framework of the proposed methodology (see Fig. 1) the method can be in general adapted to other case areas and can be adjusted to specific regional planning. However, as mentioned above, this study employs spatial data at a very fine scale which might not be available for a specific selected case area. Furthermore, data collection was for some biomass types time consuming including primary data acquisition through interviews (e.g. biowaste). Furthermore, this study does not consider potential environmental and social constraints when identifying suitable initial locations for biogas plants. In fact, the methodology aims to determine suitable sites for biogas plants as a function of biomass availability and demand locations in order to render biomethane production sites in advance by minimizing transportation distances. Potential negative impacts on social and ecological systems can be taken into account by defining exclusion criteria which restrict site development for energy production purposes [35] and through the use of Multi Criteria Evaluation (MCE) in order to derive a parcel or grid based suitability ranking taking social and environmental constraints such as proximity to settlement areas, distance to waterbodies and parks and recreational areas into consideration [17]. Results of the spatial allocation method can be further used in cost analysis and the proposed
approach can also be part of improving the waste management practices at the municipal level [12]. 4. Conclusions In this study a regional GIS based methodology was developed to analyze suitable locations and number of biogas plants and their capacities based on resource location and transportation distances in three study areas. In total the theoretical biomethane potential was found to be 2.8 TWh in study regions. Most of the biomass potential is located in rural areas as agro feedstocks accounts for more than 90% of the total biomethane potential in the Turku, Salo and Kymenlaakso regions. From agro biomass, grass silage account for almost 50%. In all 49 biogas plants with capacities of between 2 and 8 MW could be introduced according to the feedstock available within a maximum transportation radius of 10 km. Few biogas plants would be located close to city centres due to the high availability of preferential biomass resources (e.g. biowaste) and demand allocation resulting in low transportation distances and transportation costs. The studied GIS base methodology uses the existing road network and the ArcGIS Network Analyst extension in the Site Suitability Analysis, which improves the identification of most suitable locations of biogas plants as it calculates real road transport routes. Accurate transportation distance information can provide valuable information at the planning stage and can be used to calculate CO2 emissions and associated transportation and investment costs accurately. Acknowledgements This study was funded by EU Central Baltic Interreg IV A Programme and Regional Council of Southwest Finland and the project partners. The authors would like to thank HSY Helsinki Region Environmental Services Authority, Turun seudun jätehuolto Oy, Kymen Vesi Oy, Kymenlaakson Jäte Oy, Rouskis Oy, Liikelaitos Salon Vesi and the Finnish Biogas Association. A special thank you to Esa Aro-Heinilä, Hannu Ojanen, Erja Heino, Saana Ahonen, Sanna Marttinen and Maarit Hellstedt for their contribution to the project. Appendix A. See Tables A1 and A2.
Table A1 Excrement amount per animal and year [26]. Livestock
Solid and liquid manure (t/animal/yr)
Livestock
Solid and liquid manure t/ animal/yr
Dairy cows
24–25
1
Suckler cows Heifers
17 10–11
Bulls
13–14
Calves male (<12 months) Calves female (<12 months) Sows + piglets <11 weeks Gilts Boars Fattening pigs 50–110 kg
8
Pigs 20– 50 kg Horses Sheeps and goats Laying hens Broilers Broiler hens Cockerels Chickens Turkeys
0.08
6 5–7 1–2 1–2 1–2
13 1 0.05 0.03
0.09 0.03 0.09
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Table A2 Pasturing period, proportion of pastured animals and proportion of excreted manure on pasture for different types of livestock [22]. Livestock
Pasturing period (d)
Pastured animals (%)
Manure excreted on pasture (%)
Dairy cows Suckler cows Heifers Bulls Calves <12 month Pigs Horses Sheep Goats Poultry
125 140 140 0 100 0 140 130 130 0
90 95 90 0 25 0 95 90 90 0
26 36 35 0 7 0 36 32 32 0
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[16] Sliz-Szkliniarz B, Vogt J. A GIS-based approach for evaluating the potential of biogas production from livestock manure and crops at a regional scale: a case study for the Kujawsko-Pomorskie Voivodeship. Renew Sust Energy Rev 2012;16:752–63. [17] Roström H, Uggeldahl P. MO9 Kotitalouksien ja vähittäiskaupan jätteiden koostumuksen muutos Turussa 1987-2002. Finnisd Environmental Administration publications 9/2003, [In Finnish].. [18] Uggeldahl P. Personal communication, 2010. [19] Ministry of environment. Towards a recycling society. National waste plan until 2016, 2008, [in Finnish]. . [20] Statistics Finland; 2009. [21] Finnish food safety authority evira. Sheep and goat register. 2011. . [24] Tike. Information centre of the ministry of agriculture and forest, 2010, [In finnish]. . [25] Kahiluoto H, Kuisma M. Elintarvikeketjun jätteen ja sivuvirrat energiaksi ja lannoitteiksi, 2010, [In finnish]. . [26] Tike. Information centre of the ministry of agriculture and forest, 2008, [In finnish]. [27] Dagnall S, Hill J, Pegg D. Resource mapping and analysis of farm livestock manures–assessing the opportunities for biomass-to-energy schemes. Bioresour Technol. 2000;71:225–34. [28] Palm R. The economic potential for production of upgraded biogas used as vehicle fuel in Sweden. Technical report no FRT 2010:03. Chalmers University of Technology: Göteborg, Sweden; 2010, p. 36. [29] Dijkstra EW. A note on two problems in connexion with graphs. Numer Math 1959;1:269–71. [30] Hakimi S. Optimum location of switching centers and the absolute centers and medians of a graph. Opera Res 1964;12:450–9. [31] IEA Bioenergy task 37. Energy from biogas and landfill gas. . [32] Linke B. Country report, Germany. IEA Bioenergy task 37. [33] Murphy JD, Power NM. An argument for using biomethane generated from grass as a biofuel in Ireland. Biomass Bioenergy 2009;33:504–12. [34] Pöschl M, Ward S, Owende P. Evaluation of energy efficiency of various biogas production and utilization pathways. Appl Energy 2010;87:3305–21. [35] Kahiluoto H, Kuisma M. Energy and fertilisers from agrifood wastes (elintarvikeketjun jätteet ja sivuvirrat energiaksi ja lannoitteiksi). MTT Kasvu reports 12, 2010, [in Finnish]. . [36] Lehtomäki A. Biogas production from energy crops and crop residues. Doctoral thesis. Jyväskylä studies in biological and environmental Science. 163. University of Jyväskylä: Finland; 2006.
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Applied Energy journal homepage: www.elsevier.com/locate/apenergy
A Geographical Information System (GIS) based methodology for determination of potential biomasses and sites for biogas plants in southern Finland J. Höhn, E. Lehtonen, S. Rasi ⇑, J. Rintala MTT Agrifood Research Finland, 31600 Jokioinen, Finland
h i g h l i g h t s The biomethane potential in southern Finland is over 3 TWh. Agricultural biomass accounts >90% of the biomethane potential in study regions. The GIS method can be used for detailed biogas plant planning. The GIS provides tools for e.g. land locations, cost and emission calculations.
a r t i c l e
i n f o
Article history: Received 28 March 2013 Received in revised form 20 June 2013 Accepted 2 July 2013
Keywords: Biomass potential Biomethane Biogas Regional assessment GIS
a b s t r a c t Objective: The objective of this study was to analyse the spatial distribution and amount of potential biomass feedstock for biomethane production and optimal locations, sizes and number of biogas plants in southern Finland in the area of three regional waste management companies. Methods: A Geographical Information System (GIS) based methodology, which also included biomass transport optimisation considering the existing road network and spatially varied biomass sources, was used. Kernel Density (KD) maps were calculated to pinpoint areas with high biomass concentration. Results: The results show that the total amount of biomass corresponds to 2.8 TWh of energy of which agro materials account for more than 90%. It was found that a total of 49 biogas plants could be built in three case regions with feedstock available within maximum transportation radius of 10 or 40 km. With maximum of 10 km biomass transportation distance, the production capacity of the planned plants ranges from 2.1 to 8.4 MW. If transportation distance was increased to 40 km, the plant capacities could also increase from 2.3 to 16.8 MW. Conclusions: As demonstrated in this study, the studied GIS methodology can be used for identification of the most suitable locations for biogas plants by providing the tools for e.g. transportation routes and distances. Practice implications: The methodology can further be used in environmental impact assessment as well as in cost analysis. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction Biomethane is increasingly considered a feasible biofuel with its use being stepped up in many European countries. The EU has a target that 10% of its transport fuel should be renewable by 2020 [1]. In addition to its use as a traffic fuel, methane gas can also be used in heat and power production, which is a more traditional application. Biomethane is one of the most sustainable biofuels available today [2]. The EU directive 2009/28/EY [1] stipulates that energy from biofuels and bioliquids should contribute to a
⇑ Corresponding author. Tel.: +358 295317655. E-mail address: saija.rasi@mtt.fi (S. Rasi). 0306-2619/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.apenergy.2013.07.005
reduction of at least 35% of greenhouse gas emissions in order to be taken into account. From 2017 onwards, the emissions savings should be increased to 50%. According to Adelt et al. biogas produced from energy crops, fulfils this criteria [3]. The main components of biogas are methane and carbon dioxide. Biogas is produced by micro-organisms from biodegradable organic material under anaerobic conditions. Materials include those such as biowaste, sludge, manure, agro residues and energy crops. Biogas technology has been used for waste treatment because the technology can employ waste materials and the treated materials can in many cases be used as fertilisers, thus enabling the recycling of nutrients. There are several different techniques available for biogas upgrading to biomethane [4] and the number of gaseous fuel distribution networks is increasing in many countries.
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Different types of biomasses are available for biogas production depending on e.g. population and the economic structure of a region. For example in highly populated areas the amount of waste materials is great while in agricultural areas field biomass and livestock manure are available. The environmental and political demand for more efficient use of renewable energy and biomass is increasing the need to utilise different types of biomasses. Biomethane potential analyses in different areas have been studied using various types of agro biomasses and waste materials [5–9] in order to find more sustainable feedstock for renewable energy production. When energy crops are considered as a raw material for biogas production, it is important to consider also the sustainability of whole agricultural production chain. The crop production should not be based on maximum yields from a single crop but rather on maximising the methane yield from the whole system through use of sustainable and environmentally friendly crop rotation [6,10]. Various measures are needed to ensure the efficient introduction of biomethane, as is the case with any other new technology in society. Systematic analysis and planning of regional biomass resources and of technological implementations as well as the evaluation of impacts provide an option of developing sustainable and economical biomass and energy production and utilisation at the regional level. Due to the fact that biomass to energy schemes are highly geographically dependent [11] since biomass supply and biogas demand is often distributed dispersedly, finding suitable locations for biogas plants by minimizing transportation distances and associated CO2 emission is a key issue for sustainable biogas production. Because of the spatially varied locations of different biomass sources the assessment of biomass potential for biogas production and siting biogas plants in optimal locations includes the use and handling of a wide range of geographical data. Geographical Information Systems (GIS) have been considered as an appropriate platform for spatial related issues and have been studied for assessing the potential biomasses for biogas production and for site location analysis [12–16]. However, these studies considered just a limited selection of different biomass types and/or used aggregated spatial information data in order to capture the spatial variation of biomass sources. The main aim of this study was to develop methods in GIS environment in order to determine different potential biomasses for biomethane production in southern Finland taking a wide range of available biomasses including biowaste, sludge, livestock manure, energy crops and agricultural residues into consideration. Resource locations were captured at a very fine scale (i.e. farm and field level for agricultural sources, street addresses for biowaste and sludge sources) to allow information for more detailed further planning. Furthermore, this study aimed to determine suitable biogas plant locations and to allocate optimally biomass sources to candidate biogas power plants through transportation distance minimization using a road network. The specific objectives of this study were to develop: (i) An approach to evaluate, assess and map the regional potential for biogas production generated from biowaste, sludge, livestock manure, energy crops and agricultural residues. (ii) A method to find suitable biogas plant locations considering the spatial distribution of biomass sources and biogas usage end points. (iii) A method to allocate optimally biomass sources to biogas plants with transportation distance minimization and to compute plants’ specific production capacities. This approach was finally applied to a case study including three regions in southern Finland (Turku, Salo and Kymenlaakso)
in order to promote more sustainable and balanced planning of renewable energies both at a municipal and an inter-municipal level. 2. Materials and methods This study comprises the assessment of regional biomass potential for biogas production, site suitability analysis of candidate biogas plants and determination of plants production capacity, spatially optimized biomass collection areas and transportation distances (Fig. 1). All spatial related tasks were performed using ArcGIS version 10 software and its associated extensions such as the Spatial Analyst and Network Analyst. 2.1. Case study The methodology as described in the following sections is applied for three case regions in southern Finland including Turku, Salo and Kymenlaakso. Southern Finland, which includes both rural agricultural areas and built-up urban areas, is of interest because it is the most highly populated area in the country. Also, the use of methane/natural gas in southern Finland is more common than in other parts of Finland because of the existence of the natural gas pipeline which is only in the south (Fig. 2, Table 1). 2.2. Regional biomass and biomethane potential The feedstock considered for biomethane production were: biowastes, sludges, agricultural residues and energy crops (specifically grass silage). The quantity and location of different feedstock in 2009 were extracted using statistics and studies and by interviewing major industrial waste producers (Table 2). The term biowaste means food waste and green waste generated in households, at public sector sites (e.g. schools, hospitals) and at private sector sites (e.g. restaurants, hotels). The amounts for the public and private sector sites have been estimated due to a lack of detailed statistical data availability. The amount of sludge at municipal and industrial waste water treatment plants (WWTPs) was obtained from the plant operators. Agricultural biomass (i.e. agro biomass) consists of manure, straw, agro residues (like greenhouse waste and sugar beet waste) and grass silage that is cultivated for energy production on available fields. Energy crops were assumed to be cultivated on crop production farms which in case regions covered about 200,000 ha of agricultural land. Crop production farms were selected because it was assumed that grass is cultivated for biomethane production in rotation with cereal cultivation. For the purpose of estimating the amount of manure produced by cattle, pork, poultry, horses, sheep and goats, the livestock were categorised by sex, age, and production type (see Tables A1 and A2). The feedstock potentials were categorised and the resulting methane potential was calculated. Total solids (TS) and the ratio of volatile solids (VS) and TS were estimated (Table 3). The theoretical biomethane potential was calculated for each region in the study area using the biomethane potential from literature of each feedstock material. The actual methane potential is less than the theoretical potential due to incomplete anaerobic conversion of the feedstock in the biogas plant, which efficiency is however dependent e.g. on the scale and operation of the plant. 2.3. Mapping biomass and associated biomethane supply potential In order to visualize the spatial distribution of biomass sources and their associated potential for biomethane production each biomass source was stored in a geo-referenced database with attributes indicating the type of biomass, annual generated amount and
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Fig. 1. Description of general methodology and case study.
Fig. 2. The study regions in southern Finland.
Table 1 Description of the study regions.
Number of municipalities Population 2009 Area, km2 Agricultural field area 2009, ha Share of field area of total area, %
Turku region
Salo region
Kymenlaakso region
14 325,600 4000 98,400 25
4 75,600 3 200 84,000 26
8 185,500 5 900 95,300 16
biomethane potential. Based on this database Kernel Density (KD) maps were calculated to pinpoint areas with high biomass concen-
tration. KD maps deliver density values based on the feedstock quantities within a defined search radius. In this study a search
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Table 2 The calculation principles for available biomasses. Assumption of biomass production/method of calculation Biowastes
Sludges Slurries/manures (see also Tables A1 and A2)
Silage and crop residues
Households: 58 kg/a per cap separate collection rate of 65% Schools: 23 kg/person/a Universities and vocational schools: 19 kg/person/a Daycares: 50 kg/person/a Hospitals: 110 kg/person/a Other nursing homes: 260 kg/person/a Restaurants and hotels: 1030 kg/person/a Retail: 2100 kg/person/a
Industries: total amounts from major biowaste producers from food and forest industries Municipal and industrial waste water treatment plants Number of cattle: 44,300 Number of pork: 134,400 Number of horses, sheep and goats: 14,400 Number of poultry: 1.6 million Amount of excrement produced by animals during the pasturing period was deducted Available field: crop production farms. Crop rotation of grass was assumed with cereal in sequence of two seasons of grass and three seasons of cereals. Straw of cereal: 3 t/ha Straw of rapeseed: 2 t/ha Sugar beet top: 7.5 t/ha Potato waste: 5 t/ha Vegetable top: 7 t/ha Greenhouse waste: 35 t/ha Silage: 21.4 t/ha
[17–19] [20], interviews [20], interviews [20], interviews [20], interviews [20], interviews [20], interviews PETRA-waste benchmarking systema Interviews The plant owners [21] [21] [21] [21] [22] [23,21,24]
[25] [25] [25] [25] [25] [25] [26]
a
Refs.
Waste data bank in which companies and organizations in the Helsinki region can voluntary report their yearly amounts ant treatment of different waste fractions.
Table 3 Characteristics of materials used in biomethane potential calculations [16,36].
Biowaste from households, industry, private and public services and ferries Bakery waste Milk waste (whey) Fat waste Slaughter waste Vegetable waste Manure of horses Manure from slaughterhouse Solid manure of cattle Liquid manure of cattle Solid manure of pig Liquid manure of pig Solid manure of poultry Sludge from municipality waste water treatment and food industry Sludge from paper and pulp mill, biol. Sludge from paper and pulp mill, primary Straw of cereals Vegetable top Grass silage Straw of rapeseed
radius of 3 km was used taking into account the high availability of biomass sources. 2.4. Candidate plant site selection In order to locate optimal sites for biogas plants two different approaches were applied while taking into consideration the regional distinctions for biogas production. In regions where a natural gas grid is available (Kymenlaakso) that can serve as potential gas storage and distribution system, the main priority during site selection process was to identify areas with high availability of biomass source close to the grid with the help of the Kernel density maps. In absence of a natural gas grid end use points of biomethane are concentrated mainly at city centres (Turku and Salo regions). Therefore one has to consider whether it’s more practicable to produce the biomethane in areas with high raw
CH4 (m3/tVS)
CH4 (m3/tww)
TS (%)
VS/TS (%)
400 400 420 800 600 400 250 250 200 200 300 300 300 300 100 300 230 300 350 250
97 97 18 288 216 97 48 18 23 10 58 10 81 42 14 42 178 28 104 207
27 66 6 40 40 27 32 10 19 6 24 4 38 20 20 20 85 11 35 90
90 90 70 90 90 90 60 70 60 80 80 85 71 70 70 70 91 85 85 92
material supply and transport the biomethane to the consumer market or to produce biomethane close to the demand point and transport the raw material to the production site. Due to the fact that a high proportion of available raw material originates from rural areas where digestate utilisation also takes place, it was assumed that it is more feasible to produce biomethane in areas with high raw material availability. Therefore, site selection in areas without a natural gas grid was mainly driven by the availability of biomass feedstock. Also here, the Kernel density map was used in order to identify areas with high availability of biomass. 2.5. Determination of plants’ collection areas, production capacity and biomass transportation distances In the second step collection areas for each initial candidate site were derived using the Service Area application in ArcGIS
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Network Analyst. This application computes an area that encompasses all accessible streets that lie within a specified maximal transportation distance and assigns each biomass source point to the closest candidate facility in order to take the competition for biomass resources between biogas plants into account. According to Dagnal et al. and Palm [27,28] maximum transportation distances for raw materials vary from ten to 40 km. In the present study the lower and upper end was used to study the impact of varying transportation distance on collection area and associated biogas production potential. Thereafter, for each initial candidate site biomass amounts which were available within the collection area were determined and plant-specific theoretical biomethaneproduction capacities were calculated assuming an annual running time of 8000 h (T) according to following formula:
Ptheo ¼ Eavail =T
ð1Þ
where Ptheo is the theoretical installed power (MW); Eavail is energy of the feedstock available in collection area (MWh) (Table 3); T is the annual running time of plant (h/yr). Feedstock energy (Eavail) was calculated using the coefficients provided in Table 3 and assuming that 1 m3 CH4 equals to 10 kWh Finally, distances and vehicle-specific transportation amounts/ driven kilometres in total between the biomass sources and the
potential site, were calculated. Transportation distances were derived using the OD Cost Matrix application in ArcGIS Network Analyst which uses the Dijkstra’s algorithm in order to determine the shortest path from a starting point to a destination location [29]. 2.6. Variation of the number of biogas plants The initial solution of number and location of biogas plants considers multiple sites, assuming that at each identified suitable location a biogas plant is built that competes with neighboring plants for biomass sources. Biomass collection areas for each plant differ as a function of the presence of competitors. In order to study how the number of plants influence plant specific collection areas and associated availability of biomass sources, collection areas were derived with varying number of plants. For this purpose the number of plants was continuously reduced ending with one centralized plant serving the entire regions. For each reduction step plant specific collection areas and associated biomass availability and biomethane production potential was calculated using the Location–Allocation model. This tool solves the p-median problem by choosing facilities such that the total sum of weighted distances allocated to a facility is minimized [30]. In this study the p-median model was applied in order to
Table 4 Amount of different biomass (tTS/a) in the study regions. Biomass
Turku region
Municipal biowaste Industrial biowaste Municipal WWTP sludge Industrial WWTP sludge Manure Grass silage Straw Agricultural waste and side productsa Total
Salo region
Kymenlaakso region
tTS/yr
tww/yr
GWh (% of total)
tTS/yr
tww/yr
GWh (% of total)
tTS/yr
tww/yr
GWh (% of total)
6200 2000 12,000 0 54,300 201,414 119,200 6800 402,000
23,100 5400 60,000 0 327,400 584,630 140,223 47,563 1188,300
22 (2) 9 (1) 25 (2) 0 (0) 103 (10) 601 (58) 250 (24) 22 (2) 1032
1400 1300 1500 50 26,600 176,515 104,200 8000 320,000
5000 4000 7400 170 200,100 520,000 122,580 66,380 925,500
5 (1) 7 (1) 3 (0.4) 0.1 (0) 46 (6) 525 (63) 220 (27) 23 (3) 827
3400 5900 6000 37,600 38,100 197,256 117,140 91 405,000
12,600 9500 30,100 125,200 316,700 570,000 137,800 780 1202,700
12 (1) 22 (2) 13 (1) 49 (5) 55 (6) 588 (60) 245 (25) 0.2 (0) 984
TS = total solids; ww = wet weight. a Vegetables, greenhouse waste, potato waste, sugar beet.
±
T17
Turku region T1
Mynämäki T19
T22 T11
Kymenlaakso region
T18 T14
T12
K14 K1
T5
Aura T13 T15 T6 T2 T8 T4 T21 T7 T16 Marttila S4 Raisio Lieto T10 S3 Naantali T9 S1 T3 S5 Paimio Turku Kaarina Halikko Salo S7 T20 Sauvo S8 S2 S11 Parainen
S12
Salo region
Perniö S10 S9
K2
S6
S13
K4
K11 K9
Valkeala K3 Kouvola K7 K8
K13
Anjalankoski K5
K12
Loviisa
Candidate Site
Hamina K6
K10
Kotka
Biogas potential
40 Kilometers
w
30
Lo
20
H
0 5 10
ig
h
Natural gas grid
Fig. 3. Biomethane potential intensity and potential biogas plant locations in the study regions (T = Turku region, S = Salo region, K = Kymenlaakso region).
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J. Höhn et al. / Applied Energy 113 (2014) 1–10
determine p facilities in a predefined set with n candidate facilities (n > p) so that total weighted transport distances between each biomass source and facility is minimized. Transport distances were weighted by the amount of vehicle loads which are needed to transport the annual quantity of biomass to the facility. In this study it was assumed that sludge and liquid manure could be transported in 32 m3 tankers and solid manure in 20 t trailers. Biowaste could be carried in 12 t collection vehicles, silage in 9 t trailers and straw and other agricultural side products in 18 t trailers. With this notation, the p-median model can be formulated as follows:
Min
XX j
hi dij Y ij
ð2Þ
i
s:t:
X Y ij ¼ 1 8i 2 I
ð3Þ
j
Y ij X j 6 0 8i 2 I;
8j 2 J
ð4Þ
X Xj ¼ p
ð5Þ
j
X j 2 f0; 1g 8j 2 J Y ij 2 f0; 1g 8j 2 I;
ð6Þ
8j 2 J
ð7Þ
where I is the set of demand nodes and J the set of candidate facilities; i = index of demand nodes and j = index of candidate facilities;
Table 5 Descriptions of biogas plant candidates in the Turku (T), Salo (S) and Kymenlaakso (K) regions. Plant ID
T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 T16 T17 T18 T19 T20 T21 T22
Plant size (MW) 7.0 4.5 7.4 7.1 5.7 3.8 3.1 6.7 6.2 2.6 5.9 4.2 3.2 3.2 4.8 2.7 2.1 2.7 3.4 2.8 5.8 2.8
Feedstock (1000 tTS/yr) 22 14 23 22 18 12 9 21 19 7 19 13 10 10 15 8.5 7 8 11 9 18 9
Feedstock composition (% of TS)
Total transportation distances (km)
Average transportation distance (km)
Tons (FM/km)
28 35 10 31 31 33 36 30 33 28 26 29 31 35 34 31 21 32 28 35 33 29
34,600 15,000 18,000 29,000 19,000 16,100 9300 26,500 23,700 7400 21,200 12,800 11,000 8400 13,600 8800 4900 8000 14,200 12,500 21,700 12,700
6.2 4.8 3.9 5.4 5.0 5.4 4.4 5.7 5.8 4.7 5.2 4.6 4.6 3.8 4.0 5.0 3.5 4.1 5.4 6.3 5.5 5.4
2.1 2.6 5.1 2.2 2.3 2.7 2.5 2.4 1.9 2.5 2.7 2.8 3.0 3.2 3.3 2.3 3.8 3.3 2.7 1.7 2.2 2.7
Biowaste
Sludge
Manure
Silage
Straw
0 0 20 1 0 0 1 1 1 23 0 0 0 0 0 0 0 0 0 2 0 0
0 0 52 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
19 6 2 12 16 10 1 20 10 3 31 22 16 8 9 19 39 13 24 3 11 19
47 58 16 51 51 55 61 49 55 46 43 48 52 58 57 51 36 54 46 58 55 48
TOTAL TURKU S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13
97.7 6.7 6.3 4.3 8.4 8.1 5.4 6.6 6.5 4.7 5.0 4.5 4.2 4.0
305 19 19 14 26 25 17 20 20 14 15 14 13 12
7 0 0 0 0 0 3 1 0 0 0 0 0
0 0 0 0 0 0 7 0 0 0 0 0 0
10 10 19 6 8 16 4 5 2 13 9 4 2
50 50 50 57 55 52 52 56 59 47 56 58 61
30 30 30 35 33 31 31 34 35 28 34 35 37
26,200 32,300 16,400 34,200 36,400 22,600 29,600 30,500 23,800 24,600 19,300 21,600 15,200
5.5 5.9 5.5 5.7 6.0 5.9 6.3 6.1 7.0 5.4 5.9 7.1 5.8
2.3 2.1 2.3 2.0 1.9 2.2 2.0 1.9 1.5 2.2 2.2 1.6 1.8
TOTAL SALO K1 K2 K3 K4 K5 K6 K7 K8 K9 K10 K11 K12 K13 K14
74.6 2.6 6.0 3.8 7.4 7.0 5.9 3.6 4.8 4.8 2.5 6.4 3.3 7.4 3.8
230 8 18 20 23 27 22 11 15 15 8 20 11 23 12
0 0 8 0 0 24 9 1 0 0 0 0 0 0
0 0 59 0 55 70 18 0 0 0 0 0 0 0
9 2 2 9 2 0 2 13 9 17 11 13 8 15
57 61 19 57 26 4 44 54 56 51 55 54 58 53
34 36 11 34 16 2 27 32 34 30 33 33 35 32
8000 22,500 12,000 32,400 19,900 13,000 9900 18,700 20,600 12,400 28,000 12,500 30,100 17,800
4.4 5.6 4.2 6.2 4.6 4.5 4.5 5.4 6.1 6.7 5.8 5.2 5.7 6.3
2.9 1.9 4.8 2.0 4.1 5.4 3.0 2.4 2.1 1.9 2.5 2.5 2.2 2.1
TOTAL KYMENLAAKSO
69.2
234
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3.1. Biomass and biomethane resources The amount of potential biomass (biowaste, wastewater sludge, manure, crop residues and energy crops) in the three regions was estimated to be 3.3 million tons wet weight (tww)/a (1.1 million tTS/a) which corresponds theoretically to 2.8 TWh energy (Table 4). The highest biomethane potential in the study regions is in agro materials, although in cities biowastes and sludges are good resources for bioenergy. Agro materials account for 94, 98, and 91% of the total theoretical biomethane potential in Turku, Salo and Kymenlaakso regions, respectively (Table 4). Different types of manures account for 6 to 10% of the total theoretical biomethane potential in study regions. In the Turku region, manure from poultry, pork and cattle accounts for 43, 30 and 20% of energy potential of manure in the region while in Kymenlaakso 73% of the energy content from manure comes from cattle manure. In Salo the manure from cattle, pork and poultry account for 39, 34 and 18%, respectively, of the theoretical manure energy potential in the region. About 56, 15 and 96 GWh of biomethane energy, respectively, could be produced from municipal and industrial biowaste and sludges in the Turku, Salo and Kymenlaakso regions (Table 4). In the Kymenlaakso region industrial sludges accounts for 80% of the total theoretical biomethane energy potential of sludges (5% of total biomethane potential), which is mainly from the forest industry. The grass silage accounts for more than half of the total theoretical biomethane potential in all study areas. A quarter of the total biomethane potential comes from cereal straws (Table 4). The grass silage and straw are not yet commonly used raw materials in biomethane production but interest in these materials is increasing [31]. In Germany there are over 7000 biogas plants using agro biomasses (mainly manure and maize) as raw materials using some 700,000 ha energy maize with almost 3 TW of installed electric power output [32]. The concepts for sustainable large scale production of biomethane from grass silage is still under development but as grass silage’s energy potential is high the possibilities for utilising grass silage or other boreal crops should be encouraged[33]. 3.2. Location of biogas plants, collection area and transportation distances Kernel density maps were made to point out the regional patterns of biomass distribution (Fig. 3). Hot spots of high biomethane density are clearly visible and are mainly concentrated around the city centres which have high availability of municipal and industrial biowaste and sludge from wastewater treatment plants. Such areas are concentrated around the city centre of Turku, Kouvola, Anjalankoski, and Kotka. In such areas the annual availability of
(a) 1 800 Total transportation distance (1000 km)
3. Results and discussion
biomass can reach up to 20,000 t/km2 with an associated biomethane potential of roughly 820,000 Nm3 (e.g. Turku city centre). In rural areas local hot spots of biomass are identified by high availability of agricultural resources. In Turku and Salo region the greatest portion of such biomass is generated in rural areas and also the south-western part of Kymenlaakso has significant biomass potential. Based on the density maps (Fig. 3) initial suitable candidate sites for biomethane production were derived according to the criteria described in Section 2.4. In total 22 sites for the Turku region and 13 for the Salo region were initially considered as potential production sites. They are all situated in areas with high biomethane density (Fig. 3) whereby two of them are furthermore situated close to the city centre of Turku. In the Kymenlaakso region, initially seven candidate sites were chosen which are all located directly nearby or in close proximity to the natural gas grid and which are characterized by high biomass availability. Nevertheless, since a significant portion of crop production is concentrated in the south-western area of Kymenlaakso region with a high amount of agro residues available this part of the region was also taken into consideration resulting in six additional candidate sites. The production capacity, the used feedstock and the transportation distances of the selected initial biogas plant candidate sites was calculated for maximum transport distances of 10 km (Table 5) and 40 km (data not shown in tables). It can be seen that, when applying a maximum transportation distance of 10 km, 76%, 72% and 56% of the available biomasses could be collected and transported to the selected sites in Turku, Salo and Kymenlaakso regions, respectively. If a maximum transportation distance of 40 km was applied, almost 100% (Turku: 96.5%, Salo: 99%, Kymenlaakso 99.5%) of the available biomasses could be utilised (data not shown). Only a small portion of the available biomass
1 600 1 400 1 200 1 000 800 600 400 200 0
1
4
7
10
13
16
19
22
Number of plants
(b) Average biogas production capacity (MW)
dij = distance between demand node i and candidate facility j; h = weight associated with demand node i; Xj = 1 if the facility is located at candidate point j, and 0 otherwise; Yij = 0 if demand node i is assigned to a candidate facility j, and 0 otherwise. The objective function (2) minimizes the demand-weighted total distance. The first constraint (3) stipulates that each node is assigned, the second constraint (4) limits assignments to open or selected sites. Constraint (5) states that p facilities are to be located. Finally, constraints (6) and (7) are integrality constraints. This type of assessment could be useful for specific regional planning processes where the objective could be to identify most suitable locations for a defined number of intended biogas plants to be built.
120 100 80 60 40 20 0
1
4
7
10
13
16
19
22
Number of plants Fig. 4. Variation of plant capacity and accumulated feedstock transportation distance with different amount of plants for the Turku region.
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J. Höhn et al. / Applied Energy 113 (2014) 1–10
(a)
(b)
(c)
(d)
Chosen Candidate
0 5 10
20
30
Kilometers 40
Lines
Fig. 5. Origin–destination connectivity in Turku region with (a) 22 plants, (b) 15 plants, (c) 8 plants and (d) 1 plant.
resources is further than 40 km away from a potential site and is therefore not allocated to the plants. With maximum of 10 km biomass transportation distance, the production capacity of the planned plants ranges from 2.1 to 7.4 MW, from 4 to 8.4 MW and from 2.5 to 7.4 MW in Turku, Salo and Kymenlaakso regions, respectively (Table 5). If transportation distance was increase to 40 km, the plant capacities could also increase from 2.3 to 10.3 MW, from 4.9 to 13.4 MW and from 3.8 to 16.8 MW in Turku, Salo and Kymenlaakso regions, respectively (data not shown). The highest biomass potential available per transportation kilometre is found for plant T3 in Turku (5.1 t/km) and plant K6 (5.4 t/km) in Kymenlaakso (Table 5). These plants are located in the Turku and Kotka city area and use mainly biowaste and sludge as primary biomethane production feedstock. These plants are located nearby biomass resources as the average transportation distances (3.9 km for T3 and 4.5 km for K6) indicate. Additionally they are situated close to the city centres and therefore biofuel demand market which pronounces the suitability of these locations. In general, transportation distances over which the feedstock has to be transported, are higher for agricultural resources as they are more dispersedly distributed. The highest average transportation distances were found for plants S9 and S12 in the Salo region (from 7.0 to 7.1 km) which use mainly field biomass as primary biomethane production feedstock.
In the case when biogas is used for electricity and heat production, the best biogas plant locations are in areas of high population density and therefore high energy demand [12]. In this study some of the potential biogas plants were located near to city centres and from an energy demand point of view these could be the best locations in the beginning phase of biomethane production. Overall, when the use of renewable energy increases, other biomasses in the regions are needed to fulfill this demand. According to Pöschl et al. the transportation of manure (TS 8%) results in a negative energy input–output ratio if transportation distances are over 20 km because of the low energy content of manure [34]. Even with a distance of 5 km the energy input–output ratio is over 60% meaning that a biogas plant treating manures should be located near the farms. For other feedstocks the transportation distances are not that crucial as with manure as with them the energy content is higher. This is especially true in areas where gas can be transported via pipelines. By reducing the transportation of feedstocks the total energy input output ratio can be minimized. 3.3. The effect of plant number on biomass collection areas and production capacity The results presented in the previous chapter are based on a multiple biogas plant scenario with the objective to allocate the biomass sources to a facility in that way, that the total weighted
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J. Höhn et al. / Applied Energy 113 (2014) 1–10
transportation distances are minimized. Due to the fact, that in specific regional integrated planning the objective could be to find the most suitable location for a pre-defined set of biogas plants, this study provides an approach to identify preference sites for scenarios with varying number of biogas plants. For this purpose, the number of plants under consideration was continuously reduced ending with one centralized plant serving the entire region using the Location-Allocation model. Fig. 5 shows the optimal utilization of available biomass resources for the Turku region for a 22 plants scenario (a), 15 plants scenario (b), 8 plants (c) and a single plant scenario (d). With 22 plants, as provided by the initial solution, the average plant capacity would be 4.4 MW with max. 10 km transportation distance (5.7 MW with 40 km distance, data not shown) and the accumulated transportation distance would be 344,000 km (Fig. 4) (700,000 km with max. 40 km distance, data not shown). With decreasing number of plants, the production capacity per plant naturally increases since there are less competitors, but also transportation distances increase first at a slower rate and then more rapidly. A scenario which would include eight plants would result in an average production capacity of 15.4 MW with an accumulated transportation distance of some 980,000 km/ yr. In a single plant scenario, the average transportation distance would be 25 km resulting in an overall transport distance of 1900,000 km annually with an overall production capacity of 108 MW.
3.4. Assessing feasibility of the method for biogas plant location analyses In this study GIS based methods were developed in order to analyse suitable biogas plant location considering the spatial variation in the distribution of biomass sources and end-use points and applied to a case study for three regions in southern Finland. This approach consists of a detailed and comprehensive evaluation of available biomass sources including location information at a fine scale (i.e. farm and field level for agricultural sources and street addresses for biowaste from the private and public sector and sludge sources). Together with the usage of a real road network this approach provides detailed and accurate information about potential biogas plants characteristics including potential biomethane production capacities and expected transportation distances distinguishing this study from previous work conducted in that field which has been mainly based on aggregated spatial information data. [e.g. 12,13]. Due to the modular framework of the proposed methodology (see Fig. 1) the method can be in general adapted to other case areas and can be adjusted to specific regional planning. However, as mentioned above, this study employs spatial data at a very fine scale which might not be available for a specific selected case area. Furthermore, data collection was for some biomass types time consuming including primary data acquisition through interviews (e.g. biowaste). Furthermore, this study does not consider potential environmental and social constraints when identifying suitable initial locations for biogas plants. In fact, the methodology aims to determine suitable sites for biogas plants as a function of biomass availability and demand locations in order to render biomethane production sites in advance by minimizing transportation distances. Potential negative impacts on social and ecological systems can be taken into account by defining exclusion criteria which restrict site development for energy production purposes [35] and through the use of Multi Criteria Evaluation (MCE) in order to derive a parcel or grid based suitability ranking taking social and environmental constraints such as proximity to settlement areas, distance to waterbodies and parks and recreational areas into consideration [17]. Results of the spatial allocation method can be further used in cost analysis and the proposed
approach can also be part of improving the waste management practices at the municipal level [12]. 4. Conclusions In this study a regional GIS based methodology was developed to analyze suitable locations and number of biogas plants and their capacities based on resource location and transportation distances in three study areas. In total the theoretical biomethane potential was found to be 2.8 TWh in study regions. Most of the biomass potential is located in rural areas as agro feedstocks accounts for more than 90% of the total biomethane potential in the Turku, Salo and Kymenlaakso regions. From agro biomass, grass silage account for almost 50%. In all 49 biogas plants with capacities of between 2 and 8 MW could be introduced according to the feedstock available within a maximum transportation radius of 10 km. Few biogas plants would be located close to city centres due to the high availability of preferential biomass resources (e.g. biowaste) and demand allocation resulting in low transportation distances and transportation costs. The studied GIS base methodology uses the existing road network and the ArcGIS Network Analyst extension in the Site Suitability Analysis, which improves the identification of most suitable locations of biogas plants as it calculates real road transport routes. Accurate transportation distance information can provide valuable information at the planning stage and can be used to calculate CO2 emissions and associated transportation and investment costs accurately. Acknowledgements This study was funded by EU Central Baltic Interreg IV A Programme and Regional Council of Southwest Finland and the project partners. The authors would like to thank HSY Helsinki Region Environmental Services Authority, Turun seudun jätehuolto Oy, Kymen Vesi Oy, Kymenlaakson Jäte Oy, Rouskis Oy, Liikelaitos Salon Vesi and the Finnish Biogas Association. A special thank you to Esa Aro-Heinilä, Hannu Ojanen, Erja Heino, Saana Ahonen, Sanna Marttinen and Maarit Hellstedt for their contribution to the project. Appendix A. See Tables A1 and A2.
Table A1 Excrement amount per animal and year [26]. Livestock
Solid and liquid manure (t/animal/yr)
Livestock
Solid and liquid manure t/ animal/yr
Dairy cows
24–25
1
Suckler cows Heifers
17 10–11
Bulls
13–14
Calves male (<12 months) Calves female (<12 months) Sows + piglets <11 weeks Gilts Boars Fattening pigs 50–110 kg
8
Pigs 20– 50 kg Horses Sheeps and goats Laying hens Broilers Broiler hens Cockerels Chickens Turkeys
0.08
6 5–7 1–2 1–2 1–2
13 1 0.05 0.03
0.09 0.03 0.09
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Table A2 Pasturing period, proportion of pastured animals and proportion of excreted manure on pasture for different types of livestock [22]. Livestock
Pasturing period (d)
Pastured animals (%)
Manure excreted on pasture (%)
Dairy cows Suckler cows Heifers Bulls Calves <12 month Pigs Horses Sheep Goats Poultry
125 140 140 0 100 0 140 130 130 0
90 95 90 0 25 0 95 90 90 0
26 36 35 0 7 0 36 32 32 0
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