Research article Special Issues

Economic feasibility of a wood biomass energy system under evolving demand

  • In some European regions, particularly in mountainous areas, the demand for energy is evolving due to the decrease of resident population and the adoption of energy efficiency measures. Such changes are rapid enough to significantly impact on the planning process of wood-to-energy chains that are supposed to work for the following 20–25 years. The paper summarizes a study in an Italian pre-alpine district where some municipality shows a declining resident population together with increasing summer tourism. The planning of conversion plants to exploit the local availability of wood is formulated as a mathematical programming problem that maximizes the economic return of the investment, under time-varying parameters that account for the demand evolution. Such a demand is estimated from current trends, while biomass availability and transport is computed from the local cartography, through standard GIS operations. Altogether, the mixed integer optimization problem has 11 possible plant locations of different sizes and technologies taking their feedstock from about 200 parcels. The problem is solved with a commercial software package and shows that the optimal plan changes if one considers the foreseen evolution of the energy demand. As it always happen in this type of biomass-based plants, while the problem formulation is general and may be applied to other cases, the solution obtained is strongly dependent on local values and thus cannot be extrapolated to different contexts.

    Citation: Giorgio Guariso, Fabio de Maria. Economic feasibility of a wood biomass energy system under evolving demand[J]. AIMS Energy, 2016, 4(1): 104-118. doi: 10.3934/energy.2016.1.104

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  • In some European regions, particularly in mountainous areas, the demand for energy is evolving due to the decrease of resident population and the adoption of energy efficiency measures. Such changes are rapid enough to significantly impact on the planning process of wood-to-energy chains that are supposed to work for the following 20–25 years. The paper summarizes a study in an Italian pre-alpine district where some municipality shows a declining resident population together with increasing summer tourism. The planning of conversion plants to exploit the local availability of wood is formulated as a mathematical programming problem that maximizes the economic return of the investment, under time-varying parameters that account for the demand evolution. Such a demand is estimated from current trends, while biomass availability and transport is computed from the local cartography, through standard GIS operations. Altogether, the mixed integer optimization problem has 11 possible plant locations of different sizes and technologies taking their feedstock from about 200 parcels. The problem is solved with a commercial software package and shows that the optimal plan changes if one considers the foreseen evolution of the energy demand. As it always happen in this type of biomass-based plants, while the problem formulation is general and may be applied to other cases, the solution obtained is strongly dependent on local values and thus cannot be extrapolated to different contexts.


    [1] EurObserv’ER (2015) Solid biomass barometer 2014. Available from: www.eurobserv-er.org. Last accessed Dec 2, 2015.
    [2] Kopetz H (2013) Renewable resources: Build a biomass energy market. Nature 494: 29-31. doi: 10.1038/494029a
    [3] Kosinkova J, Doshi A, Maire J, et al. (2015) Measuring the regional availability of biomass for biofuels and the potential for microalgae. Renew sust energ rev 49: 1271-1285. doi: 10.1016/j.rser.2015.04.084
    [4] Sacchelli S, De Meo I, Paletto A (2012) Bioenergy production and forest multifunctionality: A trade-off analysis using multiscale GIS model in a case study in Italy. Appl energ 104: 10-20.
    [5] Sartor K, Quoilin S, Dewallef P (2014) Simulation and optimization of a CHP biomass plant and district heating network. Appl energ 130: 474-483. doi: 10.1016/j.apenergy.2014.01.097
    [6] Zamora-Cristales R, Sessions J, Boston K, et al. (2015) Economic Optimization of Forest Biomass Processing and Transport in the Pacific Northwest USA. Forest sci 61: 220-234. doi: 10.5849/forsci.13-158
    [7] Andersen F, Iturmendi F, Espinosa S, et al. (2012) Optimal design and planning of biodiesel supply chain with land competition. Comput chem eng 47: 170-182. doi: 10.1016/j.compchemeng.2012.06.044
    [8] Čuček L, Lam HL, Klemeš JJ, et al. (2010) Synthesis of regional networks for the supply of energy and bioproducts. Clean technol environ policy 2: 635-645.
    [9] Leão RRCC, de Campos CL, Hamacher S, et al. (2011) Optimization of biodiesel supply chains based on small farmers: A case study in Brazil. Bioresource technol 102: 8958-8963. doi: 10.1016/j.biortech.2011.07.002
    [10] Sharma B, Ingalls RG, Jones CL, et al. (2013) Biomass supply chain design and analysis: Basis, overview, modeling, challenges, and future. Renew sust energ rev 24: 608-627. doi: 10.1016/j.rser.2013.03.049
    [11] Freppaz D, Minciardi R, Robba M, et al. (2004) Optimizing forest biomass exploitation for energy supply at a regional level. Biomass bioenerg 26: 15-25. doi: 10.1016/S0961-9534(03)00079-5
    [12] Fiorese G, Guariso G (2010) A GIS-based approach to evaluate biomass potential from energy crops at regional scale. Environ modell softw 25: 702-711. doi: 10.1016/j.envsoft.2009.11.008
    [13] Evans A, Strezov V, Evans TJ (2010) Sustainability considerations for electricity generation from biomass. Renew sust energ rev 14: 1419-1427. doi: 10.1016/j.rser.2010.01.010
    [14] Lähtinen K, Myllyviita T, Leskinen P, et al. (2014) A systematic literature review on indicators to assess local sustainability of forest energy production. Renew sust energ rev 40: 1202-1216. doi: 10.1016/j.rser.2014.07.060
    [15] Pérez-Fortes M, Laı́nez-Aguirre JM, Arranz-Piera P, et al. (2012) Design of regional and sustainable bio-based networks for electricity generation using a multi-objective MILP approach. Energy 44: 79-95. doi: 10.1016/j.energy.2012.01.033
    [16] Fiorese G, Gatto M, Guariso G (2013) Optimisation of combustion bioenergy in a farming district under different localisation strategies. Biomass bioenerg 58: 20-30. doi: 10.1016/j.biombioe.2013.07.018
    [17] Baglivi A, Fiorese G, Guariso G, et al. (2015) Energy and GHG Emission Assessments of Biodiesel Production in Mato Grosso, Brazil, In: Bhardwaj AK, Zenone T, Chen J (Eds.) Sustainable Biofuels, Berlin: De gruyter, 267-294.
    [18] Newell R G, Pizer W A, Raimi D (2014) Carbon Market Lessons and Global Policy Outlook. Science 343: 1316-1317. doi: 10.1126/science.1246907
    [19] GSE (Gestore Servizi Energetici) (2015) Rapporto sulle aste di quote europee di emission (in Italian) Available from: http://www.gse.it/it/Gas e servizi energetici/Aste CO2/Pagine/default.aspx.
    [20] Frombo F, Minciardi R, Robba M, et al. (2009) A decision support system for planning biomass-based energy production. Energy 34: 362-369. doi: 10.1016/j.energy.2008.10.012
    [21] CEER (2014) Certificazione energetica regionale, Regione Lombardia (in Italian).Available from: http://certenergy.it/certificazione-energetica-lombardia/502-lombardia­catasto-energetico-regionale-accessibile-a-tutti.html.
    [22] Regione Lombardia (2015) Sistema Informatico Regionale Energia (in Italian). Available from: http://sirena.cestec.eu.
    [23] ISTAT (2015) Database: Servizio Turismo (in Italian). Available from: http://www.istat.it
    [24] Regione Lombardia (2015) Cartografia regionale (in Italian): Available from:
    http://www.geoportale.regione.lombardia.it.
    [25] Fiorese G, Guariso G (2013) Modeling the role of forests in a regional carbon mitigation plan. Renew energy 52: 175-182. doi: 10.1016/j.renene.2012.09.060
    [26] Rentizelas A, Karellas S, Kakaras E, et al. (2009) Comparative techno- economic analysis of ORC and gasification for bioenergy applications. Energ convers manage50: 674-681.
    [27] Dept. of Energy, Politecnico di Milano (2013) Costi di produzione di energia elettrica da fonti rinnovabili (Electric energy production costs from renewable sources),AEEGSI Report (in Italian).
    [28] Mosier N, Wyman C, Dale B, et al. (2005) Features of promising technologies for pretreatment of lignocellulosic biomass. Bioresource technol 96: 673-686.
    [29] Energy and Strategy Group (2013) Biomass Executive Energy Report, Dipartimento di Ingegneria Gestionale, Politecnico di Milano (in Italian).
    [30] AEEGSI, Italian National Energy Authority (2015) Statistical Data. Available at: http://www.autorita.energia.it.
    [31] Moiseyev A, Solberg B, Maarit A, Kallio I (2013) Wood biomass use for energy in Europe under different assumptions of coal, gas and CO2 emission prices and market conditions. J forest econ 19: 432-449. doi: 10.1016/j.jfe.2013.10.001
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