Economic feasibility of a wood biomass energy system under evolving demand

Giorgio Guariso; Fabio de Maria; Giorgio Guariso; Fabio de Maria

doi:10.3934/energy.2016.1.104

AIMS Energy

2016, Volume 4, Issue 1: 104-118. doi: 10.3934/energy.2016.1.104

Previous Article Next Article

Research article Special Issues

Economic feasibility of a wood biomass energy system under evolving demand

Giorgio Guariso ^,,
Fabio de Maria

Department of Electronics, Information, and Bioengineering, Politecnico di Milano, 20133 Milano, Italy

Received: 25 August 2015 Accepted: 07 January 2016 Published: 19 January 2016

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.

Keywords:

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

Related Papers:

[1]	Ana Isabel Muñoz, J. Ignacio Tello . On a mathematical model of bone marrow metastatic niche. Mathematical Biosciences and Engineering, 2017, 14(1): 289-304. doi: 10.3934/mbe.2017019
[2]	Changxiang Huan, Jiaxin Gao . Insight into the potential pathogenesis of human osteoarthritis via single-cell RNA sequencing data on osteoblasts. Mathematical Biosciences and Engineering, 2022, 19(6): 6344-6361. doi: 10.3934/mbe.2022297
[3]	Swadesh Pal, Malay Banerjee, Vitaly Volpert . Spatio-temporal Bazykin’s model with space-time nonlocality. Mathematical Biosciences and Engineering, 2020, 17(5): 4801-4824. doi: 10.3934/mbe.2020262
[4]	Blessing O. Emerenini, Stefanie Sonner, Hermann J. Eberl . Mathematical analysis of a quorum sensing induced biofilm dispersal model and numerical simulation of hollowing effects. Mathematical Biosciences and Engineering, 2017, 14(3): 625-653. doi: 10.3934/mbe.2017036
[5]	Ali Moussaoui, Vitaly Volpert . The impact of immune cell interactions on virus quasi-species formation. Mathematical Biosciences and Engineering, 2024, 21(11): 7530-7553. doi: 10.3934/mbe.2024331
[6]	Chichia Chiu, Jui-Ling Yu . An optimal adaptive time-stepping scheme for solving reaction-diffusion-chemotaxis systems. Mathematical Biosciences and Engineering, 2007, 4(2): 187-203. doi: 10.3934/mbe.2007.4.187
[7]	Hoang Pham . Analyzing the relationship between the vitamin D deficiency and COVID-19 mortality rate and modeling the time-delay interactions between body's immune healthy cells, infected cells, and virus particles with the effect of vitamin D levels. Mathematical Biosciences and Engineering, 2022, 19(9): 8975-9004. doi: 10.3934/mbe.2022417
[8]	Ana I. Muñoz, José Ignacio Tello . Mathematical analysis and numerical simulation of a model of morphogenesis. Mathematical Biosciences and Engineering, 2011, 8(4): 1035-1059. doi: 10.3934/mbe.2011.8.1035
[9]	Marek Bodnar, Urszula Foryś . Time Delay In Necrotic Core Formation. Mathematical Biosciences and Engineering, 2005, 2(3): 461-472. doi: 10.3934/mbe.2005.2.461
[10]	Changwook Yoon, Sewoong Kim, Hyung Ju Hwang . Global well-posedness and pattern formations of the immune system induced by chemotaxis. Mathematical Biosciences and Engineering, 2020, 17(4): 3426-3449. doi: 10.3934/mbe.2020194

Abstract

References

[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

This article has been cited by:

James L. Buchanan, Robert Gilbert, Yvonne Ou, Anja Nohe, Rachel Schaefer, The Kinetics of Vitamin D3 in the Osteoblastic Cell, 2013, 75, 0092-8240, 1612, 10.1007/s11538-013-9861-2

Reader Comments

Your name:*

Email:*
© 2016 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)