Tri-generation investment analysis using Bayesian network: A case study

References

• Abbaspour, M., and A. Saraei. 2014. An innovative design and cost optimization of a tri-generation (combined cooling, heating and power) system. International Journal of Environmental Research 8 (4):971–78.
   Web of Science ®Google Scholar
• Aguilera, P., A. Fernández, R. Fernández, R. Rumí, and A. Salmerón. 2011. Bayesian networks in environmental modelling. Environmental Modelling and Software 26:1376–88.
   Web of Science ®Google Scholar
• Arcuri, P., P. Beraldi, G. Florio, and P. Fragiacomo. 2015. Optimal design of a small size tri-generation plant in civil users: A MINLP (mixed integer non linear programming model). Energy 80:628–41.
   Web of Science ®Google Scholar
• Arcuri, P., G. Florio, and P. Fragiacomo. 2007. A mixed integer programming model for optimal design of tri-generation in a hospital complex. Energy 32 (8):1430–47.
   Web of Science ®Google Scholar
• Arteconi, A., C. Brandoni, and F. Polonara. 2009. Distributed generation and tri-generation: Energy saving opportunities in Italian supermarket sector. Applied Thermal Engineering 29 (8–9):1735–43.
   Web of Science ®Google Scholar
• Badami, M., M. Mura, P. Campanile, and F. Anzioso. 2008. Design and performance evaluation of an innovative small scale combined cycle cogeneration system. Energy 33 (8):1264–76.
   Web of Science ®Google Scholar
• Bilgen, E. 2000. Exergetic and engineering analyses of gas turbine based cogeneration systems. Energy 25 (12):1215–29.
   Web of Science ®Google Scholar
• Bourgeoisa, T. G., B. Hedmanb, and F. Zalcmanc. 2003. Creating markets for combined heat and power and clean distributed generation in New York State. Environmental Pollution 123 (3):451–62.
   PubMed Web of Science ®Google Scholar
• Bracco, S., G. Dentici, and S. Siri. 2013. Economic and environmental optimization model for the design and the operation of a combined heat and power distributed generation system in an urban area. Energy 55:1014–24.
   Web of Science ®Google Scholar
• Büyüközkan, G., G. Kayakutlu, and İ. S. Karakadılar. 2015. Assessment of lean manufacturing effect on business performance using Bayesian belief networks. Expert Systems with Applications 42:6539–51.
   Web of Science ®Google Scholar
• Calise, F., M. Dentice, and A. Piacentino. 2014. A novel solar trigeneration system integrating PVT (photovoltaic/thermal collectors) and SW (seawater) desalination: Dynamic simulation and economic assessment. Energy 67:129–48.
   Web of Science ®Google Scholar
• Calise, F., A. Palombo, and L. Vanoli. 2011. Design and dynamic simulation of a novel solar trigeneration system based on photovoltaic/thermal collectors. In ECOS 2011. Faculty of Mechanical Engineering, University of Niš, Faculty of Technical Sciences. Serbia: Novi Sad.
   Google Scholar
• Celio, E., T. Koellner, and A. Grêt-Regamey. 2014. Modeling land use decisions with Bayesian networks: Spatially explicit analysis of driving forces on land use change. Environmental Modelling & Software, 52:222–33. doi:10.1016/j.envsoft.2013.10.014.
   Web of Science ®Google Scholar
• Chicco, G., and P. Mancarella 2005. Planning aspects and performance indicators for small-scale trigeneration plants. International Conference on Future Power Systems, October 2005,1–6.
   Google Scholar
• Chicco, G., and P. Mancarella. 2006. From cogeneration to trigeneration: Profitable alternatives in a competitive market. IEEE Transactions on Energy Conversion 21 (1):265–72.
   Web of Science ®Google Scholar
• Chicco, G., and P. Mancarella, 2007. Trigeneration primary energy saving evaluation for energy planning and policy development. Energy Policy 35 (12):6132–44.
   Web of Science ®Google Scholar
• Cho, H., P. J. Mago, R. Luck, and L. M. Chamra. 2009. Evaluation of CCHP systems performance based on operational cost, primary energy consumption, and carbon dioxide emission by utilizing an optimal operation scheme. Applied Energy 86:2540–49.
   Web of Science ®Google Scholar
• Choa, H., A. D. Smith, and P. Magoa. 2014. Combined cooling, heating and power: A review of performance improvement and optimization. Applied Energy 136:168–85.
   Web of Science ®Google Scholar
• Cinar, D., and G. Kayakutlu. 2010. Scenario analysis using Bayesian networks: A case study in energy sector. Knowledge-Based Systems 23:267–76.
   Web of Science ®Google Scholar
• Compernolle, T., N. Witters, S. Van Passel, and T. Thewys. 2011. Analyzing a self-managed CHP system for greenhouse cultivation as a pro table way to reduce CO2-emissions. Energy 36:1940–47.
   Web of Science ®Google Scholar
• Corp, N. S., 1990–2017. Netica. Version 6.04. http://www.norsys.com/
   Google Scholar
• Delcroix, V., K. Sedki, and F.-X. Lepoutre. 2013. A Bayesian network for recurrent multi-criteria and multi-attribute decision problems: Choosing a manual wheelchair. Expert Systems with Applications 40:2541–51. doi:10.1016/j.eswa.2012.10.065.
   Web of Science ®Google Scholar
• Dogan, I., and N. Aydin. 2011. Combining Bayesian networks and total cost of ownership method for supplier selection analysis. Computers & Industrial Engineering 61:1072–85. doi:10.1016/j.cie.2011.06.021.
   Web of Science ®Google Scholar
• Energy for Sustainable Development. 2001. The future of chp in the European market. The European Cogeneration Study Final Publishable Report, UK.
   Google Scholar
• Espirito Santo, D. B. 2012. Energy and exergy efficiency of a building internal combustion engine trigeneration system under two different operational strategies. Energy and Buildings 53:28–38.
   Web of Science ®Google Scholar
• Freschi, F., L. Giaccone, P. Lazzeroni, and M. Repetto. 2013. Economic and environmental analysis of a trigeneration system for food-industry: A case study. Applied Energy 107:157–72.
   Web of Science ®Google Scholar
• Ge, Y., S. Tassou, I. Chaer, and N. Suguartha. 2009. Performance evaluation of a trigeneration system with simulation and experiment. Applied Energy 86:2317–26.
   Web of Science ®Google Scholar
• Gholamhossein Abdollahi, H. S. 2013. Application of the multi-objective optimization and risk analysis for the sizing of a residential small-scale CCHP system. Energy and Buildings 60:330–44.
   Web of Science ®Google Scholar
• Gopisetty, S., and P. Treffinger. 2013. Combined cooling, heat and power (Trigeneration) at Offenburg University of Applied Sciences. Environmental Biotechnology 9 (1):25–37. http://www.environmentalbiotechnology.pl/eb_dzialy/eb_online/2013/vol9_1/eb2013,9(1)_ms156.pdf
   Google Scholar
• Häger, D., and L. B. Andersen. 2010. A knowledge based approach to loss severity assessment in financial institutions using Bayesian networks and loss determinants. European Journal of Operational Research 207:1635–44.
   Web of Science ®Google Scholar
• Hänninen, M. 2014. Bayesian networks for maritime traffic accident prevention: Benefits and challenges. Accident Analysis and Prevention 73:305–12.
   PubMed Web of Science ®Google Scholar
• Heckerman, D., D. Geige, and D. M. Chickering. 1995. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning 20 (3):197–243.
   Web of Science ®Google Scholar
• Houwing, M., A. N. Ajah, P. W. Heijnen, I. Bouwmans, and P. M. Herder. 2008. Uncertainties in the design and operation of distributed energy resources: The case of micro-CHP systems. Energy 33:1518–36.
   Web of Science ®Google Scholar
• Huangfu, Y., J. Wu, R. Wang, X. Kong, and B. Wei. 2007. Evaluation and analysis of novel micro-scale combined cooling, heating and power (MCCHP) system. Energy Conversion & Management 48:1703–09.
   Web of Science ®Google Scholar
• Jensen, F. V., and T. D. Nielsen. 2007. Bayesian networks and decision graphs. New York, NY: Springer.
   Google Scholar
• Jiang-Jiang Wang, Y.-Y. J., C.-F. Zhang, G.-H. Shi, and X.-T. Zhang. 2008. A fuzzy multi-criteria decision-making model for trigeneration system. Energy Policy 36:3823–32.
   Web of Science ®Google Scholar
• Kavvadias, K. C., and Z. B. Maroulis. 2010. Multi-objective optimization of a trigeneration plant. Energy Policy 38:945–54.
   Web of Science ®Google Scholar
• Kavvadias, K. C., A. P. Tosios, and Z. B. Maroulis. 2010. Design of a combined heating, cooling and power system: Sizing, operation strategy selection and parametric analysis. Energy Conversion and Management 51:833–45.
   Web of Science ®Google Scholar
• Kong, X. Q., R. Z. Wang, and X. H. Huang. 2005. Energy optimization model for a CCHP system with available gas turbine. Applied Thermal Engineering 25:377–91.
   Web of Science ®Google Scholar
• Lai, S. M., and C. W. Hu. 2010. Integration of trigeneration system and thermal storage under demand uncertainties. Applied Energy 87:2868–80.
   Web of Science ®Google Scholar
• Lee, K. C., and H. Cho. 2012. Integration of general Bayesian network and ubiquitous decision support to provide context prediction capability. Expert Systems with Applications 39:6116–21.
   Web of Science ®Google Scholar
• Li, C.-Z., Y.-M. Shi, S. Liu, Z. Zheng, and Y. Liu. 2010. Uncertain programming of building cooling heating and power (BCHP) system based on Monte-Carlo method. Energy and Buildings 42 (9):1369–75.
   Web of Science ®Google Scholar
• Lior, N., and N. Zhang. 2007. Energy, exergy, and second law performance criteria. Energy 32:281–96.
   Web of Science ®Google Scholar
• Liu, M., Y. Shi, and F. Fang. 2012. A new operation strategy for CCHP systems with hybrid chillers. Applied Energy 95:164–73.
   Web of Science ®Google Scholar
• Lozano, M., M. Carvalho, and L. M. Serra. 2009. Operational strategy and marginal costs in simple trigeneration systems. Energy 34:2001–08.
   Web of Science ®Google Scholar
• Mago, P. J., and L. M. Chamra. 2009. Analysis and optimization of CCHP systems based on energy, economical, and environmental considerations. Energy and Buildings 41 (10):1099–106.
   Web of Science ®Google Scholar
• Morote, A. N., F. Ruz-Vila, and F. J. Canovas-Rodriguez. 2011. Selection of a trigeneration system using a fuzzy AHP. Int. Journal of Energy Research 35:781–94.
   Web of Science ®Google Scholar
• Namwongse, P., T. Chaiyawat, and Y. Limpiyakorn. 2011. Scenario analysis in expressway management via modeling bayesian networks. International Journal of Computer Applications 30(11):14–20.
   Google Scholar
• Office of Environment and Heritage. 2013. Nsw, energy saver cogeneration feasibility guide, environment and heritage. Office of Environment and Heritage NSW, Sydney.
   Google Scholar
• Oliveira, A. C., C. Afonso, J. Matos, S. Riffat, M. Nguyen, and P. Doherty. 2002. A combined heat and power system for buildings driven by solar energy and gas. Applied Thermal Engineering 22 (6):587–93.
   Web of Science ®Google Scholar
• Pearl, J. 1988. Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc. ISBN 0-934613-73-7.
   Google Scholar
• Perkusich, M., G. Soares, H. Almeida, and A. Perkusich. 2015. A procedure to detect problems of processes in software development projects using Bayesian networks. Expert Systems with Applications 42 (1):437–50.
   Web of Science ®Google Scholar
• Piltan, M., E. Mehmanchi, and S. F. Ghaderi. 2012. Proposing a decision-making model using analytical hierarchy process and fuzzy expert system for prioritizing industries in installation of combined heat and power systems. Expert Systems with Applications 39 (1):1124–33.
   Web of Science ®Google Scholar
• Pollino, C. A., O. Woodberry, A. E. Nicholson, K. B. Korb, and B. T. Hart. 2007. Parameterization and evaluation of a Bayesian network for use in an ecological risk assessment. Environmental Modelling and Software 22:1140–52.
   Web of Science ®Google Scholar
• Sanaye, S., M. Meybodi, and S. Shokrollahi. 2008. Selecting the prime movers and nominal powers in combined heat and power systems. Applied Thermal Engineering 28 (10):1177–88.
   Web of Science ®Google Scholar
• Shuang-Cheng, W., S. Jun, and D. Rui-Jie 2010. The Bayesian network method for scenario prediction, Chinese Conference on Pattern Recognition (CCPR), 1–5.
   Google Scholar
• Smith, A., R. Luck, and P. J. Mago. 2010. Analysis of a combined cooling, heating, and power system model under different operating strategies with input and model data uncertainty. Energy and Buildings 42 (11):2231–40.
   Web of Science ®Google Scholar
• Smith, W. P., J. Doctor, J. Meyer, I. J. Kalet, and M. H. Phillips. 2009. A decision aid for intensity-modulated radiation-therapy plan selection in prostate cancer based on a prognostic Bayesian network and a Markov model. Artificial Intelligence in Medicine 46 (2):119–30.
   PubMed Web of Science ®Google Scholar
• Sun, L., Y. Lu, J. G. Jin, D.-H. Lee, and K. W. Axhausen. 2015. An integrated Bayesian approach for passenger flow assignment in metro networks. Transportation Research Part C: Emerging Technologies 52:116–31.
   Web of Science ®Google Scholar
• Tatsumi, I. 2001. Applicability of micro gas turbine as distributed generation and its connection to utility power grid. Journal of Gas Turbine Society of Japan 29 (3):141–45.
   Google Scholar
• Tichi, S. G., M. M. Ardehali, and M. E. Nazari. 2010. Examination of energy price policies in Iran for optimal configuration of CHP and CCHP systems based on particle swarm optimization algorithm. Energy Policy 38 (10):6240–50.
   Web of Science ®Google Scholar
• Tora, E. A., and M. M. El-Halwagi. 2011. Integrated conceptual design of solar-assisted trigeneration systems. Computers & Chemical Engineering 35 (9):1807–14.
   Web of Science ®Google Scholar
• Trucco, P., E. Cagno, F. Ruggeri, and O. Grande. 2008. A Bayesian belief network modeling of organizational factors in risk analysis: A case study in maritime transportation. Reliability Engineering & System Safety 93 (6):845–56.
   Web of Science ®Google Scholar
• Ulengin, F., Ş. Önsel, E. Aktas, Ö. Kabak, and Ö. Özaydın. 2014. A decision support methodology to enhance the competitiveness of the Turkish automotive industry. European Journal of Operational Research 234 (3):789–801.
   Web of Science ®Google Scholar
• Uusitalo, L. 2007. Advantages and challenges of Bayesian networks in environmental modelling. Ecological Modelling 203:312–18.
   Web of Science ®Google Scholar
• Wang, L., and C. Singh. 2008. Stochastic combined heat and power dispatch based on multi-objective particle swarm optimization. International Journal of Electrical Power Energy Systems 30 (3):226–34.
   Web of Science ®Google Scholar
• Westner, G., and R. Madlener. 2010. The benefit of regional diversification of cogeneration investments in Europe: Amean-variance portfolio analysis. Energy Policy 38:7911–20. doi:10.1016/j.enpol.2010.09.011.
   Web of Science ®Google Scholar
• Wu, D. W., and R. Z. Wang. 2006. Combined cooling, heating and power: A review. Progress in Energy and Combustion Science 32 (5–6):459–95.
   Web of Science ®Google Scholar
• Zhang, L., X. Wu, M. J. Skibniewski, J. Zhong, and Y. Lu. 2014. Bayesian-network-based safety risk analysis in construction projects. Reliability Engineering & System Safety 131:29–39.
   Web of Science ®Google Scholar
• Zhu, J., and A. Deshmukh. 2003. Application of Bayesian decision networks to life cycle engineering in Green design and manufacturing. Engineering Applications of Artificial Intelligence 16 (2):91–103.
   Web of Science ®Google Scholar