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A decision support system for planning biomass-based energy production
ARTICLE IN PRESS Energy 34 (2009) 362–369
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Energy journal homepage: www.elsevier.com/locate/energy
A decision support system for planning biomass-based energy production Francesco Frombo a,b, Riccardo Minciardi a, Michela Robba a,b,, Roberto Sacile a a b
DIST, Department of Communication, Computer and System Sciences, University of Genoa, Via Opera Pia 13, 16145 Genova, Italy Renewable Energy Laboratory, Modelling and Optimization, Via A. Magliotto 2, 17100 Savona, Italy
a r t i c l e in f o
a b s t r a c t
Article history: Received 18 January 2008 Available online 18 December 2008
Environmental decision support systems (EDSS) are recognized as valuable tools for environmental planning and management. In this paper, a geographic information system (GIS)-based EDSS for the optimal planning of forest biomass use for energy production is presented. A user-friendly interface allows the creation of Scenarios and the running of the developed decision and environmental models. In particular, the optimization model regards decisions over a long-term period (e.g. years) and includes decision variables related to plant locations, conversion processes (pyrolisis, gasification, combustion), harvested biomass. Moreover, different energy products and different definitions of the harvesting and pre-treatment operations are taken into account. The correct management of the forest is considered through specific constraints, security factors, and procedures for parcel selection. The EDSS features and capabilities are described in detail, with specific reference to a case study. Discussion and further research are reported. & 2008 Elsevier Ltd. All rights reserved.
Keywords: Environmental decision support system Optimization Biomass Renewable energy Power production
1. Introduction The consequences of the use of traditional fossil fuels in recent years have led the European Union to promote and encourage the development and the use of renewable energies rather than traditional ones. Without any doubt, the energy production from biomass represents an important part of an energy plan based on renewable resources. Biomass is a term which includes all organic material that stems from plants including trees, crops and algae. Also industrial and municipal wastes have sometimes been considered as biomass because of their high percentage of wood and organic matter. In this work, attention is focused on wood biomass and, in particular, on the definition and implementation of an environmental decision support systems (EDSS) for wood (forest and industrial) biomass use for energy production. The exploitation of forest biomass to produce energy is important for the attainment of different goals like the reduction of greenhouse gas emission, the partial replacement of fossil fuels, the reduction of external energies supply. In recent literature, several papers describe the role of EDSS for effective environmental management [1]. One of the most recent
Corresponding author at: DIST, Department of Communication, Computer and System Sciences, University of Genoa, Via Opera Pia 13, 16145 Genova, Italy. Fax: +39 0103532154. E-mail address: [email protected] (M. Robba).
0360-5442/$ - see front matter & 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.energy.2008.10.012
papers on this subject [2] states that an EDSS often consists of various coupled environmental models, databases and assessment tools, which are integrated under a graphical user interface (GUI), often created by using spatial data management functionalities provided by geographical information systems (GIS). Denzer [3] states that environmental information systems (EIS) and EDSS are major building blocks in environmental management and science today. They are used at all levels of public bodies (community, state, national and international level), in science, in management and as public information platforms. EIS and EDSS are usually said to have certain characteristics, which distinguish them from standard information systems, e.g. information complexity in time and space or incompleteness or fuzziness of data items. Different works about EDSS are applied to the case of biomass use. For example, Noon and Daly [4] proposed an EDSS to assist the Tennessee Valley Authority in estimating the costs for supplying wood fuel to its 12 coal-fired power plants. Nagel [5] presented a methodology to allow biomass management for energy supply at a regional level tested in the German state of Brandenburg [6], and dealing with many aspects like the dimensions and typology of heating plants, the fuel costs, and the reduction of carbon emissions. A GIS-based decision support system to estimate the power production potential of agricultural residues has been developed by Voivontas et al. [7]. This analysis handles all possible restrictions and identifies candidate power plants using an iterative procedure that locates bioenergy units and establishes the needed cultivated area for biomass collection. Electricity production cost represents the criterion to identify the sites where biomass potential can be economically exploited.
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Freppaz et al. [8] presented a decision support system for forest biomass exploitation at a regional level in which a GIS system is integrated with mathematical programming methods, investigating biomass exploitation possibilities for both thermal and electrical energy production in a given area. In this work, the design and the implementation of an innovative EDSS for biomass exploitation is presented. Its features are described and a case study is presented to highlight the role of the GIS tool, of the relational database, and of the developed optimization models. The EDSS includes different decision models, according to the various exigencies relevant to the case of biomass use for energy production: the long-term planning, the tactical planning, and the operational level. The necessity of taking into account different levels derives from the different time scales to be considered and from the different decisions to be performed. Long-term decisions refer to plant sizing, location, and selection among the various technology options. Tactical level decisions refer to planning over a mediumshort-term horizon, and are generally considered within a discrete-time setting, with the assumption that the plant capacity and the facilities are known. Finally, the operational level is based on the explicit modelling of the supply-chain process as an ordered sequence of the operations that should be performed from biomass collection to energy conversion. In this work, attention is focused on long-term planning. Specifically, an innovative decision model for plants’ location and technology choice is formalized and described. Binary variables are used to define the presence/absence of a specific plant in a specific location, while continuous variables are represented by biomass harvesting and plant capacity. The benefits and the costs related to the conversion processes (pyrolisis, gasification, combustion) are included in the objective function, as well as all costs related to biomass treatment, transportation, and collection. Since the problem is related to long-term planning, the decision variables are not time-dependent. As regards harvesting in each parcel, it is assumed that each year the same quantity of material is taken, the limits of this quantity being bound to Italian regulations, environmental constraints, and security factors related to forest biomass use in a specific territory. The rest of the paper is organized as follows. In the next section, the EDSS architecture and the long-term planning optimization problem are described. In Section 3, the EDSS is applied to a specific case study. Finally, the conclusion and future developments are reported.
2. The EDSS: methods, models, and optimization of logistic operations 2.1. System architecture A system allowing experts to plan the biomass exploitation in a region has been implemented. This system can be classified as an EDSS [1]. The EDSS is based on three modules [8]: the GIS-based interface for the characterization of the problem and for the determination of the parameters involved in the formulation of the problem; the database where data characterizing the problem is stored; the optimization module, subdivided into strategic planning, tactical planning and the operational level. A user-friendly interface has been developed to integrate all the modules. Communication with the database is managed by a proper ODBC (Open Database Connectivity) interface, while the optimization module is called within the MS Visual Basic 6.0 program by a specific Lingo 8 (Lindo System, http://www.lindo. com/) component. GIS features are exploited through the MapObjects (http://www.esri.com/) tool.
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Fig. 1 shows the interface: on the top there are utilities that can help in saving files, modifying parameters directly linked to the database, zoom, spam, parcels characteristics interrogation, parcels selection for the specific Scenario. Moreover, the pictures of a plant and of a wheel can be used to locate possible plants on the map and insert industrial biomass sources data and location. On the left there is a column where it is possible to select the following GIS layers: provinces, communes, forest biomass parcels, territorial images. Once a layer is selected, it appears on the right, directly on the territorial area. It is also possible to decide which layer to bring to the front or to take to the back. In Fig. 1, the forest parcels relevant to the Val Bormida (Savona Province) forest map are shown. Each parcel is a territorial area in which the forest species is considered homogeneous, but its territory is in general partitioned in areas classified into five different slope classes: 0–20%, 20–40%, 40–60%, 60–80%, and 80–100%. These parcels can be selected for the analysis through the arrow (on the right/upper side of the interface) that can easily select or delete specific parcels. Clicking on the right button of the mouse, it is also possible to invert the selection, i.e. to take all the parcels not selected with the arrow. This feature is particularly important because the experts may create their own Scenario and exclude those parcels that cannot be included because of territorial and environmental constraints (for example, protected areas, private properties, hard to reach parcels). On the left hand side of the interface, there are three buttons (strategic optimization, tactical optimization, and receding horizon), that can be used to call the different optimization models. Results are then displayed directly on the map and stored in the database. In the next subsections, the EDSS planning problem is described in detail and formalized through decision variables, objectives, and constraints.
2.2. The characteristics of the optimization problem: resources, logistics, technologies The EDSS has been developed in order to take into account all issues and costs/benefits related to the forest biomass use, transport and energy production. In this paper, specific attention is given to the formalization of a comprehensive decision model for long-term planning. Consider a territorial area in which there are several possible plant locations. The necessary decisions are to define whether the plant location should be used or not, which technology is to be selected, what the plant size is to be, and which forest parcels (or fractions of them) should be used for each plant. A thermal energy demand (for heating) can be present in each location. In the case of a non-zero thermal energy demand in a certain location, this demand must be entirely satisfied if a plant is set in that location. Moreover, at least a (given) percentage of the energy demand of the overall territorial area should be satisfied by renewable resources. The harvesting is supposed to be performed once a year, taking into account constraints deriving from Italian regulations, environmental constraints, and security factors (on harvestable biomass) that depend on data uncertainty, on the presence of a mixed forest in the same parcel, on the necessity of leaving a biomass quantity (different for each species) in the parcel. Three different issues should be considered to define the optimization problem: biomass collection, conversion technologies, and plants localization. In particular, each type of used biomass (i.e., each parcel i, i ¼ 1; . . . ; N) has specific properties that have an effect
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Fig. 1. The software interface: the Val Bormida (Savona Province, Liguria Region, Italy) study area.
on the process efficiency [9]: the volumetric mass, the moisture content, the lower heating value (LHV) (calculated from the higher heating value [10], subtracting the humidity contribution). The forest operations are represented by the felling, processing, and primary transportation phases. In this work, the felling and processing phases are considered as operations executed by chainsaw. The felling phase is necessary and includes stem cutting, while the processing phase consists of three possible independent forest operations: debarking (removing the bark from the tree), delimbing (elimination of branches), and cross cutting (stem cutting in smaller parts). Depending on the characteristics of the parcels, one or more of such operations may be unnecessary. The forest primary transportation is the transport from the felling areas to the landing points near the first available road. The landing points are areas where the forest biomass is temporarily collected before being transported to a warehouse or to the plant. Forest primary transportation costs depend on the distance the forest parcel travels from the first available road and on the slope of the territory. Different processes, whose costs and characteristics influence the design of the overall system, can be used to obtain energy [11]. In this work, the main attention is focused on the thermochemical processes of combustion, gasification, and pyrolysis. Specifically, the plant technologies considered in the EDSS are: grate firing combustor and steam cycle (GFC); fluid bed combustor and steam cycle (FBC); fluid bed gasification and gas engine (FBG); fast pyrolysis and diesel engine (FP). Each technology k, k ¼ 1; . . . ; Kð¼ 4Þ, can be selected for a specific location j, j ¼ 1; . . . ; J, according to the objective of minimizing the costs and to environmental constraints. In a specific location a maximum of one technology can be chosen. In each location, a given thermal energy demand, Dtj, can be requested. If the plant is present in a location where Dtja0, then it has to entirely satisfy the thermal demand, while the surplus of produced energy should be used to generate electricity. In the following, decision variables, parameters, objectives, and constraints are described. The resulting formulated optimization model can be classified as a non-linear mixed integer programming problem.
2.2.1. Decision variables The decision variables of the optimization problem are:
ui,j, the annual biomass quantity, harvested in the ith parcel,
(m3 y1) (i ¼ 1; . . . ; N), that is used in the jth location (j ¼ 1; . . . ; J) for energy production; CAPj, the power that is generated by the harvested biomass through the plant in the jth location (MW); dk,j, a binary variable that is equal to 1 if technology k is present in location j, and 0 otherwise, j ¼ 1; . . . ; J; k ¼ 1; . . . ; K.
2.2.2. Parameters For the definition of the problem, the following parameters are required. A first set of parameters is related to the biomass properties and collection:
VMi (kg m3), volumetric mass in the ith parcel, is the ratio between the dry mass (kg) and the volume (m3);
MCi, moisture content in the ith parcel, that expresses the
water amount present in the biomass. It is expressed as a percentage of the dry weight (%); LHVi, lower heating value in the ith parcel, expressed in terms of energy content per unit mass, (MJ kg1); PrFF, productivity for forest felling and processing operations (m3 h1), referred to standard working team; Pr FT z;i , productivity for forest primary transportation related to a particular slope class z, z ¼ 1, y, 5 (m3 h1) and forest parcel i, i ¼ 1,y,N. It depends on the distance between the specific parcel centroid and the first available road (or collection point); sDeb,i;sDel,i;sCc,i, binary parameters equal to 1 when the operation (for debarking, Deb, delimbing, Del, and cross cutting, Cc, respectively) is not carried out, 0 otherwise, for the ith parcel; DDeb;DDel;DCc, percentage of extra time (%) needed for the different operations: debarking, Deb, delimbing, Del, and cross cutting, Cc, with respect to the pure felling operation; Accz,i, percentage of surface area of parcel i characterized by slope class z (%);
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ai, fraction of biomass that can be collected in parcel i (adim); di,j, distance between the ith parcel and the jth plant (km). A second set of parameters is related to the costs:
CFF* is the unit cost for the forest felling and processing phase
(h h1), referring to a standard working team; CFT* is the unit cost for the forest primary transportation phase (h h1), referring to a standard working team; CT* is the unit cost for the forest transport phase (h kg1 km1); CkFix is the fixed plant cost for the kth technology (h MW1 y1); CkM is the operational plant coefficient cost for the kth technology, (h kg1); PEl is the price for the sales of electricity energy (h/MWh); PTh is the price for the sales of thermal energy (h/MWh).
A third set of parameters is related to the characteristics of the power plant:
dEl,k is a binary parameter for the kth technology, that is equal
to 1 if the plant can produce some amount of electrical energy, 0 otherwise; dTh,k is a binary parameter for the kth technology: it is equal to 1 if the plant can produce some amount of thermal energy, 0 otherwise; ETAkel is the net thermal efficiency of the kth conversion technology, (%); ETAkth is the net electrical efficiency of the kth conversion technology, (%); ETAk( ¼ ETAkelETAkth) is the net electrical efficiency of the kth conversion technology (%); h is the useful working time in a year (h); Dtj is the required demand of thermal energy (MWh yr1) for each possible plant location j. If Dtja0, the plant has to produce at least this thermal energy, and the surplus will be used for electricity; ˜ is the required energy for the overall territorial area D considered (MWh yr1); b is the required fraction of D˜ to be satisfied by renewable energies (adim).
2.2.3. The objective function The objective function includes the costs and the benefits of the decision problem. In particular, it is necessary to consider felling and processing, primary transportation (i.e., from the forest to the road), transportation (i.e., from the road to the plant), purchasing and plant costs, as well as the benefits deriving from the sales of the products. The objective function is then composed of five terms:
CFF, forest biomass felling and processing cost (h yr1); CFT, forest biomass primary transportation cost (h yr1); CT, transportation cost from the landing points to the plant
(h yr1); CI, plant cost related to construction, installation and management (h y1); B, benefits deriving from the sales of the products (h yr1). The objective function, which has to be minimized, is then
C ¼ C FF þ C FT þ C T þ C I B
productivity is affected by the execution of delimbing, debarking, and cross cutting operations. Depending on many factors, like the territory configuration or the primary transportation techniques, or if one or more of these operations are neglected. The absence of an operation allows an increase in the productivity. The total (felling and processing) costs are given by C FF ¼
The total forest felling and processing costs are dependent on the number of hours required for the operations, and on the number and kinds of operations executed in the forest. The
J N X X
C FF
i¼1 j¼1
ui;j ð1 DDel sDel;i DDeb sDeb;i DCc sCc;i Þ PrFF
(2)
The forest primary transportation is the transportation from the felling areas to the landing points near the first available road. The transportation technique depends on the slope, and, subsequently, the costs are related to the portions of the forest parcel that have homogeneous slope. Thus, the yearly costs can be expressed as ! J X N X 5 X C FT C FT ¼ ui;j (3) Accz;i Pr FT z;i i¼1 j¼1 z¼1 The biomass transportation cost refers to the transportation from the landing points to the plant location. Such (yearly) costs can be expressed as CT ¼
J N X X
di;j ui;j VM i C T
(4)
i¼1 j¼1
Fixed and variable costs are related to the various plants and they change as a function of the plant capacity. Thus, the yearly overall plant cost may be expressed as CP ¼
J K X X
C Fix k CAP j ETAk dk;j þ
k¼1 j¼1
J K X X k¼1 j¼1
CM k
N X ðui;j VM i Þdk;j
(5)
i¼1
Finally, the annual profit deriving from energy production and thermo-chemical transformation can be determined as B¼
J X Th El ðEpTh þ EpEl j P j P Þ
(6)
j¼1
where EpjEl, EpjTh represent the production of electrical and thermal energy, respectively (MWh yr1), in location j. 2.2.4. The constraints Different classes of constraints have been formalized: the constraints on thermal/electrical energy generation, the restrictions upon the forest biomass collection, the energy balance between incoming material and plant capacity, the constraints that impose that in a specific location there is only one plant, the minimum energy production from renewable resources, and the constraints that relate continuous and binary variables. These classes of constraints are described in the following. As, in this work, it is assumed that:
if a non-zero thermal energy demand is present at location j, and if a plant is set in that location, then the technology used by this plant must allow thermal energy generation; if a plant is set in location j, EpjTh (i.e., the thermal energy produced in location j) must be exactly equal to the thermal demand Dtj.
The following constraints must be fulfilled: Dt j
(1)
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K X ðdk;j dTh k;j Þ ¼ 0;
j ¼ 1; . . . ; J
(7)
k¼1
EpTh j ¼ Dt j
K X k¼1
dTh;k dk;j ;
j ¼ 1; . . . ; J
(8)
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For the technologies that can produce electrical energy, EpjEl (MWh/yr1) is given by the total thermal energy produced in 1 location j ðSKk¼1 dk;j CAPj ETAth ) minus the thermal k hÞ (MWh yr energy demand in that location (Dtj), multiplied by the electrical efficiency for the specific technology used. That is EpEl j ¼
K X
Finally, constraints must be introduced in order to impose that if the flow entering such a plant is greater than zero, then the plant exists. That is N X i¼1
el dEl;k ½ðdk;j CAPj ETAth k hÞ Dt j ETAk ;
j ¼ 1; . . . ; J
(9)
ui;j M
K X
dk;j p0;
j ¼ 1; . . . ; J
(14)
k¼1
where M is a big number.
k¼1
where it is also imposed that EpjEl is zero when a technology incompatible with electrical energy generation is adopted in location j. The biomass that can be harvested depends on local regulation, on security factors related to forest species, on uncertainties related to the available data. All these aspects are considered in a cumulative dimensionless parameter ai (%) that gives rise to an upper limit for the available biomass collection. In the case of strategic planning such an upper limit is related to the initial biomass quantity, Xi, i ¼ 1,y,N. Thus J X
ui;j pai X i ;
i ¼ 1; :::; N
(10)
j¼1
It is assumed that the biomass flow through each plant saturates the plant capacity, i.e. CAPj ¼
K X
dk;j
k¼1
j ¼ 1; . . . ; J;
N X
LHV i ui;j VM i
i¼1
k ¼ 1; . . . ; K
1 , 3600 h (11)
where 3600 is the number of seconds in an hour. It is imposed that in each location there may be only a single plant (i.e., at most a plant using one of the available technologies). That is K X
dk;j p1;
j ¼ 1; . . . ; J
(12)
k¼1
The total produced energy from biomass should be at least equal to a fraction b of the energy demand: J X j¼1
˜ CAPj XbD
(13)
3. Application to a case study The system has been applied to the consortium of municipalities in the mountain community of Val Bormida, inside the Savona Province. The Val Bormida community consists of 18 municipalities for a total area of over 530 km2. The study area has a high tree density index (about 75%, i.e., 400 km2) and it is largely covered by natural forest vegetation (mostly homogeneous hardwood forest). The Val Bormida forest map has been elaborated before being inserted in the EDSS. Specifically, the following surface areas have been eliminated: forest territory that cannot be exploited because of the presence of legislative constraints (natural parks and protected areas), areas characterized by forest fires and hydro geological disasters, areas of great bio-naturalistic importance. The total area available for the EDSS testing is about 280 km2. The number of parcels in the considered optimization problem is equal to 2300. Each parcel is characterized by one main typology of biomass out of five typologies of biomass located in various parts of the territory. The size is variable, ranging, on average, from 0.05 to 1 km2, with a few big parcels of around 5 km2. The centroid of each parcel is taken into account with the aim of calculating distances from the first available road and from the plant. For the specific case study, the local authority has decided to install a biomass power plant in the Cairo Montenotte district for electricity production. For this reason, one location for the plant is set a priori, that is J ¼ 1, and Dt1 ¼ 0. Moreover, the fraction b is set to zero. The optimization problem has been applied to the different kinds of plants, that is, with different runs, with a fixed dk,j. Under this assumption, the optimization problem is linear, and the decision variables 2300.
Fig. 2. Plant technology selection in the EDSS interface.
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Fig. 2 represents the Scenario settings: the image showing the plant is taken and put in the desired location. When the picture of the plant is placed by the user in the desired map (see Fig. 2), another window appears (on the top-left). Here, it is possible to insert the name of the plant and to select the plant typology from among four different technologies. The technology brief description appears when the kind is selected. The plant location coordinates appear on the bottom of the window. The system calculates the necessary distances between the parcels and the plants, and selects the parameters relevant to the specific plant. The same procedure can be followed for agro-industrial point sources of biomass. A window appears in which data about biomass type, quantity, heating value, moisture content and price is asked for. All Scenario data is transferred to the database and the software is ready for the use of the optimization modules (that can be run clicking on the different buttons, i.e., in this case, strategic optimization). In this paper, the optimal values of the objective function and of the decision variables have been found for the following plant typologies: GFC, FBC, FBG, FP. Then, for GFC and FBC, the option of the dryer pre-treatment plant has been evaluated in further runs of the optimization problem. The results show that FP plant obtains lower benefits and reaches a lower capacity in comparison with the other plant technologies. The other kinds of plants obtain similar capacities, but the FBC obtain the worst economic value. The choice between GFC and FBC is more complex, but it is possible to observe that, also having the worst net conversion efficiency, the GFC costs are lower than the FBC. Moreover, it should be added that GFC plants need a less specialized supervision and can be more easily managed. Finally, a GFC technology of 5 MWe has been selected, with the suggestion that FBC should be encouraged when other kinds of biomass (such as solid waste) are used. Fig. 3 reports the results for the strategic optimization problem for GFC directly on the interface from where it is also possible to perform queries on the obtained results, directly working with the map. All results are stored in the database module. The selected parcels for the Scenario are shown in different colours in a
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thematic map, on the basis of the degree of use they are subject to. In fact, the thematic map represents, for each parcel, the fraction between the harvested biomass SJj¼1 ui;j and the available biomass that can be harvested aiXi (considering law limits and silvicultural knowledge for each biomass type). Three different main numerical outputs can be found on the interface: the Scenario Characteristics, the Scenario Detail, and the Parcels Detail. They can be viewed by clicking on the correspondent label on the interface. In Fig. 3, the Scenario Detail is showed, while in Fig. 4 the Parcels Detail is highlighted. The part regarding the Scenario Characteristics (hidden in Fig. 3) summarizes the characteristics of the created Scenario working on the map at the beginning of the software use. It includes the energy demand, the number of selected parcels, the overall area, the plant typology, the biomass kinds. Instead, in the Scenario Detail, the optimal results for the strategic planning are shown. They include information about plant size, economic analysis, and resources use: optimal electrical capacity, optimal thermal power, benefits, forest primary transportation costs, transportation costs, forest felling and processing costs, plant maintenance costs, number of used parcels, used forest biomass, used agro-industrial biomass. Both for ‘‘Scenario Characteristics’’ and for ‘‘Scenario Detail’’, on the right hand side, near the map, two figures appear. They represent an analysis for the results and, specifically, the percentage of the overall used biomass with respect to the total that can be harvested yearly from the selected parcels, and the percentage of the surface that is yearly characterized by the harvesting with respect to the total surface area of the Scenario. Finally, Fig. 4 reports the ‘‘Parcels Detail’’ in which, for each parcel, the optimal results are shown. In particular, there is a matrix: the rows are the selected parcels and the columns represent the parcel ID, typology, used biomass, forest felling and processing costs, forest primary transportation costs, transportation costs, respectively. Clicking on a specific row, a link is created with the map and the specific parcel is highlighted in the territory. Then, two different figures appear on the right hand side of the interface for the selected parcel: the used biomass per the available biomass, and the percentage of the used biomass that is harvested in the different slope classes.
Fig. 3. The optimal results for the strategic planning (GFC technology): ‘‘Scenario Characteristics’’ and ‘‘Scenario Detail’’.
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Fig. 4. The optimal results for the strategic planning (GFC technology): ‘‘Parcels Detail’’.
4. Conclusion A GIS-based EDSS for the optimal use of wood biomass and the optimal selection of plant size, location, and technology has been developed and presented. The user-friendly interface is described and its features highlighted. In particular, the users can create Scenarios using maps and tools typical of GIS: for example, spam, zoom, layer selection, map interrogation, territorial distances calculation, surface selection, and elimination. Moreover, it is possible to insert, directly on the map, the objects related to the plant technology, the industrial/agricultural wood biomass and to add data related to a specific Scenario. Through the interface, it is also possible to choose and run the different optimization models linked to the EDSS and to show results directly on the map. All information (data and results) is stored in a relational database. The innovation of this work lies, first of all, in the definition and the development of a user-friendly EDSS allowing users to create Scenarios and work with data and results. Moreover, this paper focuses attention on the formalization of an innovative decision model for long-term planning in which plant size, location, and technology kind are decision variables. The system has been applied to the consortium of municipalities in the mountain community of Val Bormida, inside the Savona Province, and the EDSS tools and results have been described in connection with the case study. The solution of the optimization problem allows the minimization of costs and respects environmental constraints. In particular, the Val Bormida forest map used in the EDSS has been elaborated and areas subject to environmental constraints have been eliminated. Moreover, in the decision model, a parameter (that depends on each parcel) has been determined to define an upper limit on the use of the available biomass. The value of the parameter has been calculated on the basis of limits imposed by regulation for each species, security factors related to mixed forests and/or data availability, presence/absence of local forest roads, security factors related to species characteristics. Future developments regard a more accurate formalization of the forest growth model, the quantification (for different forest ages) of the CO2 absorption by the forest in connection to the growth model, the calculation of the CO2 balance (considering
emissions from the plant, the forest operation and transport), and the modelling of humidity variation in the vegetation. In this way, the optimization problem cannot be static and the described dynamics should be taken into account. The same approach can then be extended to agricultural biomass and energetic crops. Finally, future research regards the implementation in the EDSS of the supply chain of all biomass flows at an operational level, more plant technologies, the optimal control of the processes, and the use of digitalized information about the private properties of the forest parcels. In fact, the EDSS is already organized in such a way that the map layers can be updated when more accurate and/or new information is available for a specific case study.
Acknowledgments The presented work is part of the research activity of the PRAIFESR project, coordinated by the National Council of Research (CNR) and supported by several local enterprises and by the Liguria Region. The authors would like to thank the Liguria Region and the enterprises co-financing the PRAI-FESR project on Dynamic numerical modelling for Renewable Energy (Acrotec, COS(OT), PDC), for the data collection and for the exchange of knowledge necessary for the development of the EDSS. References [1] Rizzoli AE, Young WY. Delivering environmental decision support systems: software tools and techniques. Environmental Modelling & Software 1997; 12(2–3):237–49. [2] Matthies M, Giupponi C, Ostendorf B. Environmental decision support systems: current issues, methods and tools. Environmental Modelling & Software 2007;22:123–7. [3] Denzer R. Generic integration of environmental decision support systemsstate of the art. Environmental Modelling & Software 2005;20(10):1217–23. [4] Noon CE, Daly MJ. GIS-based resource assessment with BRAVO. Biomass and Bioenergy 1996;10:101–9. [5] Nagel J. Determination of an economic energy supply structure based on biomass using a mixed-integer linear optimisation model. Ecological Engineering 2000;16:S91–S102. [6] Nagel J. Biomass in energy supply, especially in the state of Brandenburg, Germany. Ecological Engineering 2000;16:S103–10.
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[7] Voivontas D, Assimacopoulos D, Koukios EG. Assessment of biomass potential for power production: a GIS based method. Biomass and Bioenergy 2001; 20:101–12. [8] Freppaz D, Minciardi R, Robba M, Rovatti M, Sacile R, Taramasso A. Optimizing forest biomass exploitation for energy supply at a regional level. Biomass and Bioenergy 2004;26:15–25.
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[9] McKendry P. Energy production from biomass (part 1): overview of biomass. Bioresource Technology 2002;83:37–46. [10] Sheng Changdong, Azevedo JLT. Estimating the higher heating value of biomass fuels from basic analysis data. Biomass and Bioenergy 2001;28:499–507. [11] McKendry P. Energy production from biomass (part 2): conversion technologies. Bioresource Technology 2002;83:47–54.
Contents lists available at ScienceDirect
Energy journal homepage: www.elsevier.com/locate/energy
A decision support system for planning biomass-based energy production Francesco Frombo a,b, Riccardo Minciardi a, Michela Robba a,b,, Roberto Sacile a a b
DIST, Department of Communication, Computer and System Sciences, University of Genoa, Via Opera Pia 13, 16145 Genova, Italy Renewable Energy Laboratory, Modelling and Optimization, Via A. Magliotto 2, 17100 Savona, Italy
a r t i c l e in f o
a b s t r a c t
Article history: Received 18 January 2008 Available online 18 December 2008
Environmental decision support systems (EDSS) are recognized as valuable tools for environmental planning and management. In this paper, a geographic information system (GIS)-based EDSS for the optimal planning of forest biomass use for energy production is presented. A user-friendly interface allows the creation of Scenarios and the running of the developed decision and environmental models. In particular, the optimization model regards decisions over a long-term period (e.g. years) and includes decision variables related to plant locations, conversion processes (pyrolisis, gasification, combustion), harvested biomass. Moreover, different energy products and different definitions of the harvesting and pre-treatment operations are taken into account. The correct management of the forest is considered through specific constraints, security factors, and procedures for parcel selection. The EDSS features and capabilities are described in detail, with specific reference to a case study. Discussion and further research are reported. & 2008 Elsevier Ltd. All rights reserved.
Keywords: Environmental decision support system Optimization Biomass Renewable energy Power production
1. Introduction The consequences of the use of traditional fossil fuels in recent years have led the European Union to promote and encourage the development and the use of renewable energies rather than traditional ones. Without any doubt, the energy production from biomass represents an important part of an energy plan based on renewable resources. Biomass is a term which includes all organic material that stems from plants including trees, crops and algae. Also industrial and municipal wastes have sometimes been considered as biomass because of their high percentage of wood and organic matter. In this work, attention is focused on wood biomass and, in particular, on the definition and implementation of an environmental decision support systems (EDSS) for wood (forest and industrial) biomass use for energy production. The exploitation of forest biomass to produce energy is important for the attainment of different goals like the reduction of greenhouse gas emission, the partial replacement of fossil fuels, the reduction of external energies supply. In recent literature, several papers describe the role of EDSS for effective environmental management [1]. One of the most recent
Corresponding author at: DIST, Department of Communication, Computer and System Sciences, University of Genoa, Via Opera Pia 13, 16145 Genova, Italy. Fax: +39 0103532154. E-mail address: [email protected] (M. Robba).
0360-5442/$ - see front matter & 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.energy.2008.10.012
papers on this subject [2] states that an EDSS often consists of various coupled environmental models, databases and assessment tools, which are integrated under a graphical user interface (GUI), often created by using spatial data management functionalities provided by geographical information systems (GIS). Denzer [3] states that environmental information systems (EIS) and EDSS are major building blocks in environmental management and science today. They are used at all levels of public bodies (community, state, national and international level), in science, in management and as public information platforms. EIS and EDSS are usually said to have certain characteristics, which distinguish them from standard information systems, e.g. information complexity in time and space or incompleteness or fuzziness of data items. Different works about EDSS are applied to the case of biomass use. For example, Noon and Daly [4] proposed an EDSS to assist the Tennessee Valley Authority in estimating the costs for supplying wood fuel to its 12 coal-fired power plants. Nagel [5] presented a methodology to allow biomass management for energy supply at a regional level tested in the German state of Brandenburg [6], and dealing with many aspects like the dimensions and typology of heating plants, the fuel costs, and the reduction of carbon emissions. A GIS-based decision support system to estimate the power production potential of agricultural residues has been developed by Voivontas et al. [7]. This analysis handles all possible restrictions and identifies candidate power plants using an iterative procedure that locates bioenergy units and establishes the needed cultivated area for biomass collection. Electricity production cost represents the criterion to identify the sites where biomass potential can be economically exploited.
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Freppaz et al. [8] presented a decision support system for forest biomass exploitation at a regional level in which a GIS system is integrated with mathematical programming methods, investigating biomass exploitation possibilities for both thermal and electrical energy production in a given area. In this work, the design and the implementation of an innovative EDSS for biomass exploitation is presented. Its features are described and a case study is presented to highlight the role of the GIS tool, of the relational database, and of the developed optimization models. The EDSS includes different decision models, according to the various exigencies relevant to the case of biomass use for energy production: the long-term planning, the tactical planning, and the operational level. The necessity of taking into account different levels derives from the different time scales to be considered and from the different decisions to be performed. Long-term decisions refer to plant sizing, location, and selection among the various technology options. Tactical level decisions refer to planning over a mediumshort-term horizon, and are generally considered within a discrete-time setting, with the assumption that the plant capacity and the facilities are known. Finally, the operational level is based on the explicit modelling of the supply-chain process as an ordered sequence of the operations that should be performed from biomass collection to energy conversion. In this work, attention is focused on long-term planning. Specifically, an innovative decision model for plants’ location and technology choice is formalized and described. Binary variables are used to define the presence/absence of a specific plant in a specific location, while continuous variables are represented by biomass harvesting and plant capacity. The benefits and the costs related to the conversion processes (pyrolisis, gasification, combustion) are included in the objective function, as well as all costs related to biomass treatment, transportation, and collection. Since the problem is related to long-term planning, the decision variables are not time-dependent. As regards harvesting in each parcel, it is assumed that each year the same quantity of material is taken, the limits of this quantity being bound to Italian regulations, environmental constraints, and security factors related to forest biomass use in a specific territory. The rest of the paper is organized as follows. In the next section, the EDSS architecture and the long-term planning optimization problem are described. In Section 3, the EDSS is applied to a specific case study. Finally, the conclusion and future developments are reported.
2. The EDSS: methods, models, and optimization of logistic operations 2.1. System architecture A system allowing experts to plan the biomass exploitation in a region has been implemented. This system can be classified as an EDSS [1]. The EDSS is based on three modules [8]: the GIS-based interface for the characterization of the problem and for the determination of the parameters involved in the formulation of the problem; the database where data characterizing the problem is stored; the optimization module, subdivided into strategic planning, tactical planning and the operational level. A user-friendly interface has been developed to integrate all the modules. Communication with the database is managed by a proper ODBC (Open Database Connectivity) interface, while the optimization module is called within the MS Visual Basic 6.0 program by a specific Lingo 8 (Lindo System, http://www.lindo. com/) component. GIS features are exploited through the MapObjects (http://www.esri.com/) tool.
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Fig. 1 shows the interface: on the top there are utilities that can help in saving files, modifying parameters directly linked to the database, zoom, spam, parcels characteristics interrogation, parcels selection for the specific Scenario. Moreover, the pictures of a plant and of a wheel can be used to locate possible plants on the map and insert industrial biomass sources data and location. On the left there is a column where it is possible to select the following GIS layers: provinces, communes, forest biomass parcels, territorial images. Once a layer is selected, it appears on the right, directly on the territorial area. It is also possible to decide which layer to bring to the front or to take to the back. In Fig. 1, the forest parcels relevant to the Val Bormida (Savona Province) forest map are shown. Each parcel is a territorial area in which the forest species is considered homogeneous, but its territory is in general partitioned in areas classified into five different slope classes: 0–20%, 20–40%, 40–60%, 60–80%, and 80–100%. These parcels can be selected for the analysis through the arrow (on the right/upper side of the interface) that can easily select or delete specific parcels. Clicking on the right button of the mouse, it is also possible to invert the selection, i.e. to take all the parcels not selected with the arrow. This feature is particularly important because the experts may create their own Scenario and exclude those parcels that cannot be included because of territorial and environmental constraints (for example, protected areas, private properties, hard to reach parcels). On the left hand side of the interface, there are three buttons (strategic optimization, tactical optimization, and receding horizon), that can be used to call the different optimization models. Results are then displayed directly on the map and stored in the database. In the next subsections, the EDSS planning problem is described in detail and formalized through decision variables, objectives, and constraints.
2.2. The characteristics of the optimization problem: resources, logistics, technologies The EDSS has been developed in order to take into account all issues and costs/benefits related to the forest biomass use, transport and energy production. In this paper, specific attention is given to the formalization of a comprehensive decision model for long-term planning. Consider a territorial area in which there are several possible plant locations. The necessary decisions are to define whether the plant location should be used or not, which technology is to be selected, what the plant size is to be, and which forest parcels (or fractions of them) should be used for each plant. A thermal energy demand (for heating) can be present in each location. In the case of a non-zero thermal energy demand in a certain location, this demand must be entirely satisfied if a plant is set in that location. Moreover, at least a (given) percentage of the energy demand of the overall territorial area should be satisfied by renewable resources. The harvesting is supposed to be performed once a year, taking into account constraints deriving from Italian regulations, environmental constraints, and security factors (on harvestable biomass) that depend on data uncertainty, on the presence of a mixed forest in the same parcel, on the necessity of leaving a biomass quantity (different for each species) in the parcel. Three different issues should be considered to define the optimization problem: biomass collection, conversion technologies, and plants localization. In particular, each type of used biomass (i.e., each parcel i, i ¼ 1; . . . ; N) has specific properties that have an effect
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Fig. 1. The software interface: the Val Bormida (Savona Province, Liguria Region, Italy) study area.
on the process efficiency [9]: the volumetric mass, the moisture content, the lower heating value (LHV) (calculated from the higher heating value [10], subtracting the humidity contribution). The forest operations are represented by the felling, processing, and primary transportation phases. In this work, the felling and processing phases are considered as operations executed by chainsaw. The felling phase is necessary and includes stem cutting, while the processing phase consists of three possible independent forest operations: debarking (removing the bark from the tree), delimbing (elimination of branches), and cross cutting (stem cutting in smaller parts). Depending on the characteristics of the parcels, one or more of such operations may be unnecessary. The forest primary transportation is the transport from the felling areas to the landing points near the first available road. The landing points are areas where the forest biomass is temporarily collected before being transported to a warehouse or to the plant. Forest primary transportation costs depend on the distance the forest parcel travels from the first available road and on the slope of the territory. Different processes, whose costs and characteristics influence the design of the overall system, can be used to obtain energy [11]. In this work, the main attention is focused on the thermochemical processes of combustion, gasification, and pyrolysis. Specifically, the plant technologies considered in the EDSS are: grate firing combustor and steam cycle (GFC); fluid bed combustor and steam cycle (FBC); fluid bed gasification and gas engine (FBG); fast pyrolysis and diesel engine (FP). Each technology k, k ¼ 1; . . . ; Kð¼ 4Þ, can be selected for a specific location j, j ¼ 1; . . . ; J, according to the objective of minimizing the costs and to environmental constraints. In a specific location a maximum of one technology can be chosen. In each location, a given thermal energy demand, Dtj, can be requested. If the plant is present in a location where Dtja0, then it has to entirely satisfy the thermal demand, while the surplus of produced energy should be used to generate electricity. In the following, decision variables, parameters, objectives, and constraints are described. The resulting formulated optimization model can be classified as a non-linear mixed integer programming problem.
2.2.1. Decision variables The decision variables of the optimization problem are:
ui,j, the annual biomass quantity, harvested in the ith parcel,
(m3 y1) (i ¼ 1; . . . ; N), that is used in the jth location (j ¼ 1; . . . ; J) for energy production; CAPj, the power that is generated by the harvested biomass through the plant in the jth location (MW); dk,j, a binary variable that is equal to 1 if technology k is present in location j, and 0 otherwise, j ¼ 1; . . . ; J; k ¼ 1; . . . ; K.
2.2.2. Parameters For the definition of the problem, the following parameters are required. A first set of parameters is related to the biomass properties and collection:
VMi (kg m3), volumetric mass in the ith parcel, is the ratio between the dry mass (kg) and the volume (m3);
MCi, moisture content in the ith parcel, that expresses the
water amount present in the biomass. It is expressed as a percentage of the dry weight (%); LHVi, lower heating value in the ith parcel, expressed in terms of energy content per unit mass, (MJ kg1); PrFF, productivity for forest felling and processing operations (m3 h1), referred to standard working team; Pr FT z;i , productivity for forest primary transportation related to a particular slope class z, z ¼ 1, y, 5 (m3 h1) and forest parcel i, i ¼ 1,y,N. It depends on the distance between the specific parcel centroid and the first available road (or collection point); sDeb,i;sDel,i;sCc,i, binary parameters equal to 1 when the operation (for debarking, Deb, delimbing, Del, and cross cutting, Cc, respectively) is not carried out, 0 otherwise, for the ith parcel; DDeb;DDel;DCc, percentage of extra time (%) needed for the different operations: debarking, Deb, delimbing, Del, and cross cutting, Cc, with respect to the pure felling operation; Accz,i, percentage of surface area of parcel i characterized by slope class z (%);
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ai, fraction of biomass that can be collected in parcel i (adim); di,j, distance between the ith parcel and the jth plant (km). A second set of parameters is related to the costs:
CFF* is the unit cost for the forest felling and processing phase
(h h1), referring to a standard working team; CFT* is the unit cost for the forest primary transportation phase (h h1), referring to a standard working team; CT* is the unit cost for the forest transport phase (h kg1 km1); CkFix is the fixed plant cost for the kth technology (h MW1 y1); CkM is the operational plant coefficient cost for the kth technology, (h kg1); PEl is the price for the sales of electricity energy (h/MWh); PTh is the price for the sales of thermal energy (h/MWh).
A third set of parameters is related to the characteristics of the power plant:
dEl,k is a binary parameter for the kth technology, that is equal
to 1 if the plant can produce some amount of electrical energy, 0 otherwise; dTh,k is a binary parameter for the kth technology: it is equal to 1 if the plant can produce some amount of thermal energy, 0 otherwise; ETAkel is the net thermal efficiency of the kth conversion technology, (%); ETAkth is the net electrical efficiency of the kth conversion technology, (%); ETAk( ¼ ETAkelETAkth) is the net electrical efficiency of the kth conversion technology (%); h is the useful working time in a year (h); Dtj is the required demand of thermal energy (MWh yr1) for each possible plant location j. If Dtja0, the plant has to produce at least this thermal energy, and the surplus will be used for electricity; ˜ is the required energy for the overall territorial area D considered (MWh yr1); b is the required fraction of D˜ to be satisfied by renewable energies (adim).
2.2.3. The objective function The objective function includes the costs and the benefits of the decision problem. In particular, it is necessary to consider felling and processing, primary transportation (i.e., from the forest to the road), transportation (i.e., from the road to the plant), purchasing and plant costs, as well as the benefits deriving from the sales of the products. The objective function is then composed of five terms:
CFF, forest biomass felling and processing cost (h yr1); CFT, forest biomass primary transportation cost (h yr1); CT, transportation cost from the landing points to the plant
(h yr1); CI, plant cost related to construction, installation and management (h y1); B, benefits deriving from the sales of the products (h yr1). The objective function, which has to be minimized, is then
C ¼ C FF þ C FT þ C T þ C I B
productivity is affected by the execution of delimbing, debarking, and cross cutting operations. Depending on many factors, like the territory configuration or the primary transportation techniques, or if one or more of these operations are neglected. The absence of an operation allows an increase in the productivity. The total (felling and processing) costs are given by C FF ¼
The total forest felling and processing costs are dependent on the number of hours required for the operations, and on the number and kinds of operations executed in the forest. The
J N X X
C FF
i¼1 j¼1
ui;j ð1 DDel sDel;i DDeb sDeb;i DCc sCc;i Þ PrFF
(2)
The forest primary transportation is the transportation from the felling areas to the landing points near the first available road. The transportation technique depends on the slope, and, subsequently, the costs are related to the portions of the forest parcel that have homogeneous slope. Thus, the yearly costs can be expressed as ! J X N X 5 X C FT C FT ¼ ui;j (3) Accz;i Pr FT z;i i¼1 j¼1 z¼1 The biomass transportation cost refers to the transportation from the landing points to the plant location. Such (yearly) costs can be expressed as CT ¼
J N X X
di;j ui;j VM i C T
(4)
i¼1 j¼1
Fixed and variable costs are related to the various plants and they change as a function of the plant capacity. Thus, the yearly overall plant cost may be expressed as CP ¼
J K X X
C Fix k CAP j ETAk dk;j þ
k¼1 j¼1
J K X X k¼1 j¼1
CM k
N X ðui;j VM i Þdk;j
(5)
i¼1
Finally, the annual profit deriving from energy production and thermo-chemical transformation can be determined as B¼
J X Th El ðEpTh þ EpEl j P j P Þ
(6)
j¼1
where EpjEl, EpjTh represent the production of electrical and thermal energy, respectively (MWh yr1), in location j. 2.2.4. The constraints Different classes of constraints have been formalized: the constraints on thermal/electrical energy generation, the restrictions upon the forest biomass collection, the energy balance between incoming material and plant capacity, the constraints that impose that in a specific location there is only one plant, the minimum energy production from renewable resources, and the constraints that relate continuous and binary variables. These classes of constraints are described in the following. As, in this work, it is assumed that:
if a non-zero thermal energy demand is present at location j, and if a plant is set in that location, then the technology used by this plant must allow thermal energy generation; if a plant is set in location j, EpjTh (i.e., the thermal energy produced in location j) must be exactly equal to the thermal demand Dtj.
The following constraints must be fulfilled: Dt j
(1)
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K X ðdk;j dTh k;j Þ ¼ 0;
j ¼ 1; . . . ; J
(7)
k¼1
EpTh j ¼ Dt j
K X k¼1
dTh;k dk;j ;
j ¼ 1; . . . ; J
(8)
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For the technologies that can produce electrical energy, EpjEl (MWh/yr1) is given by the total thermal energy produced in 1 location j ðSKk¼1 dk;j CAPj ETAth ) minus the thermal k hÞ (MWh yr energy demand in that location (Dtj), multiplied by the electrical efficiency for the specific technology used. That is EpEl j ¼
K X
Finally, constraints must be introduced in order to impose that if the flow entering such a plant is greater than zero, then the plant exists. That is N X i¼1
el dEl;k ½ðdk;j CAPj ETAth k hÞ Dt j ETAk ;
j ¼ 1; . . . ; J
(9)
ui;j M
K X
dk;j p0;
j ¼ 1; . . . ; J
(14)
k¼1
where M is a big number.
k¼1
where it is also imposed that EpjEl is zero when a technology incompatible with electrical energy generation is adopted in location j. The biomass that can be harvested depends on local regulation, on security factors related to forest species, on uncertainties related to the available data. All these aspects are considered in a cumulative dimensionless parameter ai (%) that gives rise to an upper limit for the available biomass collection. In the case of strategic planning such an upper limit is related to the initial biomass quantity, Xi, i ¼ 1,y,N. Thus J X
ui;j pai X i ;
i ¼ 1; :::; N
(10)
j¼1
It is assumed that the biomass flow through each plant saturates the plant capacity, i.e. CAPj ¼
K X
dk;j
k¼1
j ¼ 1; . . . ; J;
N X
LHV i ui;j VM i
i¼1
k ¼ 1; . . . ; K
1 , 3600 h (11)
where 3600 is the number of seconds in an hour. It is imposed that in each location there may be only a single plant (i.e., at most a plant using one of the available technologies). That is K X
dk;j p1;
j ¼ 1; . . . ; J
(12)
k¼1
The total produced energy from biomass should be at least equal to a fraction b of the energy demand: J X j¼1
˜ CAPj XbD
(13)
3. Application to a case study The system has been applied to the consortium of municipalities in the mountain community of Val Bormida, inside the Savona Province. The Val Bormida community consists of 18 municipalities for a total area of over 530 km2. The study area has a high tree density index (about 75%, i.e., 400 km2) and it is largely covered by natural forest vegetation (mostly homogeneous hardwood forest). The Val Bormida forest map has been elaborated before being inserted in the EDSS. Specifically, the following surface areas have been eliminated: forest territory that cannot be exploited because of the presence of legislative constraints (natural parks and protected areas), areas characterized by forest fires and hydro geological disasters, areas of great bio-naturalistic importance. The total area available for the EDSS testing is about 280 km2. The number of parcels in the considered optimization problem is equal to 2300. Each parcel is characterized by one main typology of biomass out of five typologies of biomass located in various parts of the territory. The size is variable, ranging, on average, from 0.05 to 1 km2, with a few big parcels of around 5 km2. The centroid of each parcel is taken into account with the aim of calculating distances from the first available road and from the plant. For the specific case study, the local authority has decided to install a biomass power plant in the Cairo Montenotte district for electricity production. For this reason, one location for the plant is set a priori, that is J ¼ 1, and Dt1 ¼ 0. Moreover, the fraction b is set to zero. The optimization problem has been applied to the different kinds of plants, that is, with different runs, with a fixed dk,j. Under this assumption, the optimization problem is linear, and the decision variables 2300.
Fig. 2. Plant technology selection in the EDSS interface.
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Fig. 2 represents the Scenario settings: the image showing the plant is taken and put in the desired location. When the picture of the plant is placed by the user in the desired map (see Fig. 2), another window appears (on the top-left). Here, it is possible to insert the name of the plant and to select the plant typology from among four different technologies. The technology brief description appears when the kind is selected. The plant location coordinates appear on the bottom of the window. The system calculates the necessary distances between the parcels and the plants, and selects the parameters relevant to the specific plant. The same procedure can be followed for agro-industrial point sources of biomass. A window appears in which data about biomass type, quantity, heating value, moisture content and price is asked for. All Scenario data is transferred to the database and the software is ready for the use of the optimization modules (that can be run clicking on the different buttons, i.e., in this case, strategic optimization). In this paper, the optimal values of the objective function and of the decision variables have been found for the following plant typologies: GFC, FBC, FBG, FP. Then, for GFC and FBC, the option of the dryer pre-treatment plant has been evaluated in further runs of the optimization problem. The results show that FP plant obtains lower benefits and reaches a lower capacity in comparison with the other plant technologies. The other kinds of plants obtain similar capacities, but the FBC obtain the worst economic value. The choice between GFC and FBC is more complex, but it is possible to observe that, also having the worst net conversion efficiency, the GFC costs are lower than the FBC. Moreover, it should be added that GFC plants need a less specialized supervision and can be more easily managed. Finally, a GFC technology of 5 MWe has been selected, with the suggestion that FBC should be encouraged when other kinds of biomass (such as solid waste) are used. Fig. 3 reports the results for the strategic optimization problem for GFC directly on the interface from where it is also possible to perform queries on the obtained results, directly working with the map. All results are stored in the database module. The selected parcels for the Scenario are shown in different colours in a
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thematic map, on the basis of the degree of use they are subject to. In fact, the thematic map represents, for each parcel, the fraction between the harvested biomass SJj¼1 ui;j and the available biomass that can be harvested aiXi (considering law limits and silvicultural knowledge for each biomass type). Three different main numerical outputs can be found on the interface: the Scenario Characteristics, the Scenario Detail, and the Parcels Detail. They can be viewed by clicking on the correspondent label on the interface. In Fig. 3, the Scenario Detail is showed, while in Fig. 4 the Parcels Detail is highlighted. The part regarding the Scenario Characteristics (hidden in Fig. 3) summarizes the characteristics of the created Scenario working on the map at the beginning of the software use. It includes the energy demand, the number of selected parcels, the overall area, the plant typology, the biomass kinds. Instead, in the Scenario Detail, the optimal results for the strategic planning are shown. They include information about plant size, economic analysis, and resources use: optimal electrical capacity, optimal thermal power, benefits, forest primary transportation costs, transportation costs, forest felling and processing costs, plant maintenance costs, number of used parcels, used forest biomass, used agro-industrial biomass. Both for ‘‘Scenario Characteristics’’ and for ‘‘Scenario Detail’’, on the right hand side, near the map, two figures appear. They represent an analysis for the results and, specifically, the percentage of the overall used biomass with respect to the total that can be harvested yearly from the selected parcels, and the percentage of the surface that is yearly characterized by the harvesting with respect to the total surface area of the Scenario. Finally, Fig. 4 reports the ‘‘Parcels Detail’’ in which, for each parcel, the optimal results are shown. In particular, there is a matrix: the rows are the selected parcels and the columns represent the parcel ID, typology, used biomass, forest felling and processing costs, forest primary transportation costs, transportation costs, respectively. Clicking on a specific row, a link is created with the map and the specific parcel is highlighted in the territory. Then, two different figures appear on the right hand side of the interface for the selected parcel: the used biomass per the available biomass, and the percentage of the used biomass that is harvested in the different slope classes.
Fig. 3. The optimal results for the strategic planning (GFC technology): ‘‘Scenario Characteristics’’ and ‘‘Scenario Detail’’.
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Fig. 4. The optimal results for the strategic planning (GFC technology): ‘‘Parcels Detail’’.
4. Conclusion A GIS-based EDSS for the optimal use of wood biomass and the optimal selection of plant size, location, and technology has been developed and presented. The user-friendly interface is described and its features highlighted. In particular, the users can create Scenarios using maps and tools typical of GIS: for example, spam, zoom, layer selection, map interrogation, territorial distances calculation, surface selection, and elimination. Moreover, it is possible to insert, directly on the map, the objects related to the plant technology, the industrial/agricultural wood biomass and to add data related to a specific Scenario. Through the interface, it is also possible to choose and run the different optimization models linked to the EDSS and to show results directly on the map. All information (data and results) is stored in a relational database. The innovation of this work lies, first of all, in the definition and the development of a user-friendly EDSS allowing users to create Scenarios and work with data and results. Moreover, this paper focuses attention on the formalization of an innovative decision model for long-term planning in which plant size, location, and technology kind are decision variables. The system has been applied to the consortium of municipalities in the mountain community of Val Bormida, inside the Savona Province, and the EDSS tools and results have been described in connection with the case study. The solution of the optimization problem allows the minimization of costs and respects environmental constraints. In particular, the Val Bormida forest map used in the EDSS has been elaborated and areas subject to environmental constraints have been eliminated. Moreover, in the decision model, a parameter (that depends on each parcel) has been determined to define an upper limit on the use of the available biomass. The value of the parameter has been calculated on the basis of limits imposed by regulation for each species, security factors related to mixed forests and/or data availability, presence/absence of local forest roads, security factors related to species characteristics. Future developments regard a more accurate formalization of the forest growth model, the quantification (for different forest ages) of the CO2 absorption by the forest in connection to the growth model, the calculation of the CO2 balance (considering
emissions from the plant, the forest operation and transport), and the modelling of humidity variation in the vegetation. In this way, the optimization problem cannot be static and the described dynamics should be taken into account. The same approach can then be extended to agricultural biomass and energetic crops. Finally, future research regards the implementation in the EDSS of the supply chain of all biomass flows at an operational level, more plant technologies, the optimal control of the processes, and the use of digitalized information about the private properties of the forest parcels. In fact, the EDSS is already organized in such a way that the map layers can be updated when more accurate and/or new information is available for a specific case study.
Acknowledgments The presented work is part of the research activity of the PRAIFESR project, coordinated by the National Council of Research (CNR) and supported by several local enterprises and by the Liguria Region. The authors would like to thank the Liguria Region and the enterprises co-financing the PRAI-FESR project on Dynamic numerical modelling for Renewable Energy (Acrotec, COS(OT), PDC), for the data collection and for the exchange of knowledge necessary for the development of the EDSS. References [1] Rizzoli AE, Young WY. Delivering environmental decision support systems: software tools and techniques. Environmental Modelling & Software 1997; 12(2–3):237–49. [2] Matthies M, Giupponi C, Ostendorf B. Environmental decision support systems: current issues, methods and tools. Environmental Modelling & Software 2007;22:123–7. [3] Denzer R. Generic integration of environmental decision support systemsstate of the art. Environmental Modelling & Software 2005;20(10):1217–23. [4] Noon CE, Daly MJ. GIS-based resource assessment with BRAVO. Biomass and Bioenergy 1996;10:101–9. [5] Nagel J. Determination of an economic energy supply structure based on biomass using a mixed-integer linear optimisation model. Ecological Engineering 2000;16:S91–S102. [6] Nagel J. Biomass in energy supply, especially in the state of Brandenburg, Germany. Ecological Engineering 2000;16:S103–10.
ARTICLE IN PRESS F. Frombo et al. / Energy 34 (2009) 362–369
[7] Voivontas D, Assimacopoulos D, Koukios EG. Assessment of biomass potential for power production: a GIS based method. Biomass and Bioenergy 2001; 20:101–12. [8] Freppaz D, Minciardi R, Robba M, Rovatti M, Sacile R, Taramasso A. Optimizing forest biomass exploitation for energy supply at a regional level. Biomass and Bioenergy 2004;26:15–25.
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[9] McKendry P. Energy production from biomass (part 1): overview of biomass. Bioresource Technology 2002;83:37–46. [10] Sheng Changdong, Azevedo JLT. Estimating the higher heating value of biomass fuels from basic analysis data. Biomass and Bioenergy 2001;28:499–507. [11] McKendry P. Energy production from biomass (part 2): conversion technologies. Bioresource Technology 2002;83:47–54.