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A biobjective chance constrained optimization model to evaluate the economic and environmental impacts of biopower supply chains

  • Hadi KarimiEmail author
  • Sandra D. Ekşioğlu
  • Michael Carbajales-Dale
S.I.: MOPGP 2017
  • 59 Downloads

Abstract

Generating electricity by co-combusting biomass and coal, known as biomass cofiring, is shown to be an economically attractive option for coal-fired power plants to comply with emission regulations. However, the total carbon footprint of the associated supply chain still needs to be carefully investigated. In this study we propose a stochastic biobjective optimization model to analyze the economic and environmental impacts of biopower supply chains. We use a life cycle assessment approach to derive the emission factors used in the environmental objective function. We use chance constraints to capture the uncertain nature of energy content of biomass feedstocks. We propose a cutting plane algorithm which uses the sample average approximation method to model the chance constraints and finds high confidence feasible solutions. In order to find Pareto optimal solutions we propose a heuristic approach which integrates the \(\epsilon \)-constraint method with the cutting plane algorithm. We show that the developed approach provides a set of local Pareto optimal solutions with high confidence and reasonable computational time. We develop a case study using data about biomass and coal plants in North and South Carolina. The results indicate that, cofiring of biomass in these states can reduce emissions by up to 8%. Increasing the amount of biomass cofired will not result in lower emissions due to biomass delivery.

Keywords

Stochastic multiobjective optimization Chance constraints Sample average approximation Biopower supply chain Biomass cofiring Life cycle assessment 

Notes

Acknowledgements

This work is supported by the National Science Foundation, Grant CMMI 1462420; this support is gratefully acknowledged. Clemson University is acknowledged for generous allotment of compute time on Palmetto cluster.

References

  1. Abdelaziz, F. B. (2012). Solution approaches for the multiobjective stochastic programming. European Journal of Operational Research, 216(1), 1–16.CrossRefGoogle Scholar
  2. Aguilar, F., Goerndt, M., Song, N., & Shifley, S. (2012). Internal, external and location factors influencing cofiring of biomass with coal in the U.S. northern region. Energy Economics, 34(6), 1790–1798.CrossRefGoogle Scholar
  3. Ahmed, S., & Shapiro, A. (2008). Solving chance-constrained stochastic programs via sampling and integer programming. Tutorials in Operations Research, 10, 261–269.Google Scholar
  4. Azapagic, A., & Clift, R. (1999). Life cycle assessment and multiobjective optimisation. Journal of Cleaner Production, 7(2), 135–143.CrossRefGoogle Scholar
  5. Ba, B. H., Prins, C., & Prodhon, C. (2016). Models for optimization and performance evaluation of biomass supply chains: An operations research perspective. Renewable Energy, 87, 977–989.CrossRefGoogle Scholar
  6. Bauer, J., Bektas, T., & Crainic, T. G. (2010). Minimizing greenhouse gas emissions in intermodal freight transport: An application to rail service design. Journal of the Operational Research Society, 61(3), 531–542.CrossRefGoogle Scholar
  7. Baxter, L. (2005). Biomass-coal co-combustion: Opportunity for affordable renewable energy. Fuel, 84(10), 1295–1302.CrossRefGoogle Scholar
  8. Benayoun, R., De Montgolfier, J., Tergny, J., & Laritchev, O. (1971). Linear programming with multiple objective functions: Step method (stem). Mathematical Programming, 1(1), 366–375.CrossRefGoogle Scholar
  9. Bilsel, R. U., & Ravindran, A. (2011). A multiobjective chance constrained programming model for supplier selection under uncertainty. Transportation Research Part B: Methodological, 45(8), 1284–1300.CrossRefGoogle Scholar
  10. Boardman, R. D., Cafferty, K. G., Nichol, C., Searcy, E. M., Westover, T., Wood, R., Bearden, M. D., Cabe, J. E., Drennan, C., Jones, S. B., et al. (2014). Logistics, costs, and GHG impacts of utility scale cofiring with 20% biomass. Technical report, Pacific Northwest National Laboratory (PNNL), Richland, WA (US).Google Scholar
  11. Boekhoudt, A., & Behrendt, L. (2015). Taxes and incentives for renewable energy. Amstelveen: International Cooperative KPMG.Google Scholar
  12. Bonnel, H., & Collonge, J. (2014). Stochastic optimization over a pareto set associated with a stochastic multi-objective optimization problem. Journal of Optimization Theory and Applications, 162(2), 405–427.CrossRefGoogle Scholar
  13. Bowyer, J. L., Shmulsky, R., & Haygreen, J. G. (2007). Forest products and wood science: An introduction (5th ed.). Hoboken: Blackwell Publishing.Google Scholar
  14. California. (2017). Unofficial electronic version of the regulation for the California cap on greenhouse gas emissions and market-based compliance mechanisms. https://www.arb.ca.gov/cc/capandtrade/capandtrade/unofficial_ct_100217.pdf. Accessed Aug 2018.
  15. Cambero, C., Sowlati, T., & Pavel, M. (2016). Economic and life cycle environmental optimization of forest-based biorefinery supply chains for bioenergy and biofuel production. Chemical Engineering Research and Design, 107, 218–235. CrossRefGoogle Scholar
  16. Campbell, J., Lobell, D., & Field, C. (2009). Greater transportation energy and ghg offsets from bioelectricity than ethanol. Science, 324(5930), 1055–1057.CrossRefGoogle Scholar
  17. Cardona-Valdés, Y., Álvarez, A., & Ozdemir, D. (2011). A bi-objective supply chain design problem with uncertainty. Transportation Research Part C: Emerging Technologies, 19(5), 821–832.CrossRefGoogle Scholar
  18. Change, I. C. (2014). Mitigation of climate change. Contribution of working group third to the fifth assessment report of the intergovernmental panel on climate change. Cambridge: Cambridge University Press.Google Scholar
  19. Chen, C. W., & Fan, Y. (2012). Bioethanol supply chain system planning under supply and demand uncertainties. Transportation Research Part E: Logistics and Transportation Review, 48(1), 150–164.CrossRefGoogle Scholar
  20. Cherubini, F., Bird, N. D., Cowie, A., Jungmeier, G., Schlamadinger, B., & Woess-Gallasch, S. (2009). Energy-and greenhouse gas-based LCA of biofuel and bioenergy systems: Key issues, ranges and recommendations. Resources, Conservation and Recycling, 53(8), 434–447.CrossRefGoogle Scholar
  21. Cinar, D., Pardalos, P. M., & Rebennack, S. (2015). Evaluating supply chain design models for the integration of biomass co-firing in existing coal plants under uncertainty. In Handbook of bioenergy, Springer, pp. 191–217.Google Scholar
  22. COP21. (2015). United Nations climate change conference. Paris, France.Google Scholar
  23. Čuček, L., Klemeš, J. J., Varbanov, P., & Kravanja, Z. (2011). Life cycle assessment and multi-criteria optimization of regional biomass and bioenergy supply chains. Chemical Engineering Transactions, 25, 575–580.Google Scholar
  24. Čuček, L., Varbanov, P. S., Klemeš, J. J., & Kravanja, Z. (2012). Total footprints-based multi-criteria optimisation of regional biomass energy supply chains. Energy, 44(1), 135–145.CrossRefGoogle Scholar
  25. Cuellar, A. D. (2012). Plant power: The cost of using biomass for power generation and potential for decreased greenhouse gas emissions. PhD thesis, Massachusetts Institute of Technology.Google Scholar
  26. De Meyer, A., Cattrysse, D., Rasinmäki, J., & Van Orshoven, J. (2014). Methods to optimise the design and management of biomass-for-bioenergy supply chains: A review. Renewable and Sustainable Energy Reviews, 31, 657–670.CrossRefGoogle Scholar
  27. Dias, L. C., Passeira, C., Malça, J., & Freire, F. (2016). Integrating life-cycle assessment and multi-criteria decision analysis to compare alternative biodiesel chains. Annals of Operations Research.  https://doi.org/10.1007/s10479-016-2329-7.
  28. Ding, H., Benyoucef, L., & Xie, X. (2006). A simulation-based multi-objective genetic algorithm approach for networked enterprises optimization. Engineering Applications of Artificial Intelligence, 19(6), 609–623.CrossRefGoogle Scholar
  29. Doolittle, E. K., Kerivin, H. L., & Wiecek, M. M. (2018). Robust multiobjective optimization with application to internet routing. Annals of Operations Research, 271, 1–39.CrossRefGoogle Scholar
  30. Dunning, I., Huchette, J., & Lubin, M. (2017). Jump: A modeling language for mathematical optimization. SIAM Review, 59(2), 295–320.  https://doi.org/10.1137/15M1020575.CrossRefGoogle Scholar
  31. Ehrgott, M. (2013). Multicriteria optimization (Vol. 491). Berlin: Springer.Google Scholar
  32. Ekşioğlu, S. D., Acharya, A., Leightley, L. E., & Arora, S. (2009). Analyzing the design and management of biomass-to-biorefinery supply chain. Computers and Industrial Engineering, 57(4), 1342–1352.CrossRefGoogle Scholar
  33. Ekşioğlu, S. D., Karimi, H., & Ekşioğlu, (2016). Optimization models to integrate production and transportation planning for biomass co-firing in coal-fired power plants. IIE Transactions, 48(10), 901–920.  https://doi.org/10.1080/0740817X.2015.1126004.CrossRefGoogle Scholar
  34. Eskandari, H., & Geiger, C. D. (2009). Evolutionary multiobjective optimization in noisy problem environments. Journal of Heuristics, 15(6), 559.CrossRefGoogle Scholar
  35. Fliege, J., & Xu, H. (2011). Stochastic multiobjective optimization: Sample average approximation and applications. Journal of Optimization Theory and Applications, 151(1), 135–162.CrossRefGoogle Scholar
  36. Fonseca, M. C., García-Sánchez, Á., Ortega-Mier, M., & Saldanha-da Gama, F. (2010). A stochastic bi-objective location model for strategic reverse logistics. Top, 18(1), 158–184.CrossRefGoogle Scholar
  37. Gebreslassie, B. H., Yao, Y., & You, F. (2012). Multiobjective optimization of hydrocarbon biorefinery supply chain designs under uncertainty. In IEEE 51st annual conference on decision and control (CDC), IEEE, pp. 5560–5565.Google Scholar
  38. GHG Protocol. (2011). The greenhouse gas protocol: A corporate accounting and reporting standard. Standard, World Business Council for Sustainable Development (WBCSD) and the World Resources Institute (WRI).Google Scholar
  39. Gutiérrez, C., Jiménez, B., & Novo, V. (2012). Equivalent \(\varepsilon \)-efficiency notions in vector optimization. Top, 20(2), 437–455.CrossRefGoogle Scholar
  40. Gutjahr, W. J. (2005). Two metaheuristics for multiobjective stochastic combinatorial optimization. In International symposium on stochastic algorithms, Springer, pp. 116–125.Google Scholar
  41. Gutjahr, W. J., & Pichler, A. (2016). Stochastic multi-objective optimization: A survey on non-scalarizing methods. Annals of Operations Research, 236(2), 475–499.CrossRefGoogle Scholar
  42. Harmon, M. E., Harmon, J. M., Ferrell, W. K., & Brooks, D. (1996). Modeling carbon stores in oregon and washington forest products: 1900–1992. Climatic Change, 33(4), 521–550.CrossRefGoogle Scholar
  43. Heijungs, R., Guinée, J. B., Huppes, G., Lankreijer, R. M., Udo de Haes, H. A., Wegener Sleeswijk, A., et al. (1992). Environmental life cycle assessment of products: Guide and backgrounds (part 1). Leiden, The Netherlands: Center of Environmental Science.Google Scholar
  44. Heller, M. C., Keoleian, G. A., & Volk, T. A. (2003). Life cycle assessment of a willow bioenergy cropping system. Biomass and Bioenergy, 25(2), 147–165. CrossRefGoogle Scholar
  45. Hunter, S. R., Applegate, E. A., Arora, V., Chong, B., Cooper, K., Rincón-Guevara, O., & Vivas-Valencia, C. (2017). An introduction to multi-objective simulation optimization. Optimization.Google Scholar
  46. IEA-ETSAP and IRENA. (2013). Technology brief E21: Biomass cofiring. https://www.irena.org. Accessed May 2015.
  47. ISO14040 I. (2006). 14040: Environmental management—Life cycle assessment—Principles and framework. London: British Standards InstitutionGoogle Scholar
  48. Kalinina, M., Olsson, L., & Larsson, A. (2013). A multi objective chance constrained programming model for intermodal logistics with uncertain time. International Journal of Computer Science Issues, 10(6), 35–44.Google Scholar
  49. Karimi, H., Ekşioğlu, S. D., & Khademi, A. (2018). Analyzing tax incentives for producing renewable energy by biomass cofiring. IISE Transactions, 50(4), 332–344.CrossRefGoogle Scholar
  50. Kemper, J. (2015). Biomass and carbon dioxide capture and storage: A review. International Journal of Greenhouse Gas Control, 40, 401–430.CrossRefGoogle Scholar
  51. Kim, K. K., & Lee, C. G. (2012). Evaluation and optimization of feed-in tariffs. Energy Policy, 49, 192–203.CrossRefGoogle Scholar
  52. Kim, J., Realff, M. J., & Lee, J. H. (2011). Optimal design and global sensitivity analysis of biomass supply chain networks for biofuels under uncertainty. Computers and Chemical Engineering, 35(9), 1738–1751.CrossRefGoogle Scholar
  53. Kutateladze, S. (1979). Convex e-programming. Soviet Mathematics: Doklady, 20, 391–393.Google Scholar
  54. Lagoa, C. M., Li, X., & Sznaier, M. (2005). Probabilistically constrained linear programs and risk-adjusted controller design. SIAM Journal on Optimization, 15(3), 938–951.CrossRefGoogle Scholar
  55. Luedtke, J., & Ahmed, S. (2008). A sample approximation approach for optimization with probabilistic constraints. SIAM Journal on Optimization, 19(2), 674–699.CrossRefGoogle Scholar
  56. Mann, M., & Spath, P. (2001). A life cycle assessment of biomass cofiring in a coal-fired power plant. Clean Products and Processes, 3(2), 81–91.CrossRefGoogle Scholar
  57. Marufuzzaman, M., Eksioglu, S., & Huang, Y. (2014). Two-stage stochastic programming supply chain model for biodiesel production via wastewater treatment. Computers and Operations Research, 49, 1–17.CrossRefGoogle Scholar
  58. Mehmood, S., Reddy, B. V., & Rosen, M. A. (2015). Exergy analysis of a biomass co-firing based pulverized coal power generation system. International Journal of Green Energy, 12(5), 461–478.CrossRefGoogle Scholar
  59. Memişoğlu, G., & Üster, H. (2015). Integrated bioenergy supply chain network planning problem. Transportation Science, 50(1), 35–56.CrossRefGoogle Scholar
  60. Muench, S., & Guenther, E. (2013). A systematic review of bioenergy life cycle assessments. Applied Energy, 112, 257–273.CrossRefGoogle Scholar
  61. Nishio, K., & Asano, H. (2006). Supply amount and marginal price of renewable electricity under the renewables portfolio standard in japan. Energy Policy, 34(15), 2373–2387.CrossRefGoogle Scholar
  62. Norkin, B. (2014). Sample approximations of multiobjective stochastic optimization problems. www.optimization-onlineorg. Accessed Nov 2018.
  63. NREL. (2012). U.s. life cycle inventory database. https://www.nrel.gov/lci/. Accessed Dec 2017.
  64. Oak Ridge National Laboratory. (2013). Knowledge discovery framework (KDF) database. https://bioenergykdf.net. Accesssed December 2013.
  65. Pagnoncelli, B., Ahmed, S., & Shapiro, A. (2009a). Sample average approximation method for chance constrained programming: Theory and applications. Journal of Optimization Theory and Applications, 142(2), 399–416.CrossRefGoogle Scholar
  66. Pagnoncelli, B. K., Ahmed, S., & Shapiro, A. (2009b). Computational study of a chance constrained portfolio selection problem. Journal of Optimization Theory and Applications, 142(2), 399–416.CrossRefGoogle Scholar
  67. Rabl, A., Benoist, A., Dron, D., Peuportier, B., Spadaro, J. V., & Zoughaib, A. (2007). How to account for \(\text{ CO }_2\) emissions from biomass in an LCA. The International Journal of Life Cycle Assessment, 12(5), 281–281.CrossRefGoogle Scholar
  68. Roni, M., Eksioglu, S., Searcy, E., & Jha, K. (2014). A supply chain network design model for biomass co-firing in coal-fired power plants. Transportation Research Part E: Logistics and Transportation Review, 61, 115–134.CrossRefGoogle Scholar
  69. Ruhul-Kabir, M., & Kumar, A. (2012). Comparison of the energy and environmental performances of nine biomass/coal co-firing pathways. Bioresource Technology, 124, 394–405.CrossRefGoogle Scholar
  70. Ruszczynski, A., & Shapiro, A. (2003). Stochastic programming, handbooks in operations research and management science, Vol. 10.Google Scholar
  71. Santibanez-Aguilar, J. E., González-Campos, J. B., Ponce-Ortega, J. M., Serna-González, M., & El-Halwagi, M. M. (2011). Optimal planning of a biomass conversion system considering economic and environmental aspects. Industrial & Engineering Chemistry Research, 50(14), 8558–8570.CrossRefGoogle Scholar
  72. Sebastián, F., Royo, J., & Gómez, M. (2011). Cofiring versus biomass-fired power plants: GHG (greenhouse gases) emissions savings comparison by means of LCA (life cycle assessment) methodology. Energy, 36(4), 2029–2037.CrossRefGoogle Scholar
  73. Shabani, N., & Sowlati, T. (2016). Evaluating the impact of uncertainty and variability on the value chain optimization of a forest biomass power plant using monte carlo simulation. International Journal of Green Energy, 13(7), 631–641.CrossRefGoogle Scholar
  74. Sharma, B., Ingalls, R., Jones, C., & Khanchi, A. (2013). Biomass supply chain design and analysis: Basis, overview, modeling, challenges, and future. Renewable and Sustainable Energy Reviews, 24, 608–627.CrossRefGoogle Scholar
  75. Shmulsky, R., & Jones, P. D. (2011). Forest products and wood science. New York: Wiley.CrossRefGoogle Scholar
  76. Skone, T. J., & Gerdes, K. (2008). Development of baseline data and analysis of life cycle greenhouse gas emissions of petroleum-based fuels, National Energy Technology Laboratory 310.Google Scholar
  77. Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K., Tignor, M., & Miller, H. (2007). Climate change 2007: The physical science basis. In Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change, Cambridge University Press, Cambridge.Google Scholar
  78. Spath, P., Mann, M., & Kerr, D. (1999). Life cycle assessment of coal-fired power production. Technical report, National Renewable Energy Lab. (No. NREL/TP-570-25119), Golden, CO (US).Google Scholar
  79. Tillman, D. (2000). Biomass cofiring: The technology, the experience, the combustion consequences. Biomass and Bioenergy, 19, 365–384.CrossRefGoogle Scholar
  80. Tillman, D., Conn, R., & Duong, D. (2010). Coal characteristics and biomass cofiring in pulverized coal boilers. Technical report, Foster Wheeler North America Corp.Google Scholar
  81. Tricoire, F., Graf, A., & Gutjahr, W. J. (2012). The bi-objective stochastic covering tour problem. Computers & Operations Research, 39(7), 1582–1592.CrossRefGoogle Scholar
  82. Urli, B., & Nadeau, R. (2004). Promise/scenarios: An interactive method for multiobjective stochastic linear programming under partial uncertainty. European Journal of Operational Research, 155(2), 361–372.CrossRefGoogle Scholar
  83. Vargas-Moreno, J., Callejón-Ferre, A., Pérez-Alonso, J., & Velázquez-Martí, B. (2012). A review of the mathematical models for predicting the heating value of biomass materials. Renewable and Sustainable Energy Reviews, 16(5), 3065–3083.CrossRefGoogle Scholar
  84. Wang, M. (2008). The greenhouse gases, regulated emissions, and energy use in transportation (GREET) model: Version 1.5, Center for Transportation Research, Argonne National Laboratory.Google Scholar
  85. Xu, J., Yao, L., & Zhao, X. (2011). A multi-objective chance-constrained network optimal model with random fuzzy coefficients and its application to logistics distribution center location problem. Fuzzy Optimization and Decision Making, 10(3), 255–285.CrossRefGoogle Scholar
  86. You, F., Tao, L., Graziano, D. J., & Snyder, S. W. (2012). Optimal design of sustainable cellulosic biofuel supply chains: Multiobjective optimization coupled with life cycle assessment and input–output analysis. AIChE Journal, 58(4), 1157–1180.CrossRefGoogle Scholar
  87. You, F., & Wang, B. (2011). Life cycle optimization of biomass-to-liquid supply chains with distributed-centralized processing networks. Industrial & Engineering Chemistry Research, 50, 10102–10127.CrossRefGoogle Scholar
  88. Yue, D., You, F., & Snyder, S. (2014). Biomass-to-bioenergy and biofuel supply chain optimization: Overview, key issues and challenges. Computers and Chemical Engineering, 66, 36–56.CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Department of Industrial Engineering, 277 Freeman HallClemson UniversityClemsonUSA
  2. 2.Department of Environmental Engineering and Earth SciencesClemson UniversityClemsonUSA

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