Life Long Economic Analysis for Industrial Microgrids: A Case Study in Turkey

  • Cagri Ozturk
  • Irem Duzdar Argun
  • M. Özgür Kayalica
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 149)


Microgrids are used prevalently in isolated sites as a solution for multiple resource usage and distributed energy generation. Industrial Zones are constructed as isolated sites, where expectations include reducing the energy costs, providing local energy supply with fewer fluctuations and reducing greenhouse gas emissions. To encourage the microgrids in a developing country of Small and Medium-sized Enterprises (SMEs) placed in industrial zones, pre-investment studies are to be run. This article aims at minimizing the total energy costs of an organized industrial zone in parallel with mitigation of emission for climate change. The costs depend on the number and power of the Wind Turbines (WT) and the capacity of Photovoltaic (PV) panels when renewable energy sources and power storage construct the resources. A Mixed Integer Nonlinear Programming (MINLP) model is proposed to optimize the number of installations to satisfy the current demand. Lifelong carbon emission and cost analysis are performed to minimize the total cost of ownership. In this initial study, uncertainties caused by the renewable energy supply are smoothed by limited use of one gas tribune and grid connection. A case study of the model is implemented for Gebze Industrial Zone. This project will contribute to the researches on microgrids for a long term optimization model.


  1. Asano, H., & Bando, S. (2008, July). Economic evaluation of microgrids. In Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century (pp. 1–6). IEEE.Google Scholar
  2. Asanol, H., & Bandol, S. (2007, April). Economic analysis of microgrids. In Power Conversion Conference-Nagoya PCC’07 (pp. 654–658). IEEE.Google Scholar
  3. Baghaee, H. R., Mirsalim, M., Gharehpetian, G. B., & Talebi, H. A. (2016). Reliability/cost-based multi-objective Pareto optimal design of stand-alone wind/PV/FC generation microgrid system. Energy, 115, 1022–1041.CrossRefGoogle Scholar
  4. Biczel, P., & Koniak, M. (2011). Design of power plant capacity in DC hybrid system and microgrid. COMPEL—The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 30(1), 336–350.CrossRefzbMATHGoogle Scholar
  5. Bracco, S., Delfino, F., Pampararo, F., Robba, M., & Rossi, M. (2014). A mathematical model for the optimal operation of the University of Genoa Smart Polygeneration Microgrid: Evaluation of technical, economic and environmental performance indicators. Energy, 64, 912–922.CrossRefGoogle Scholar
  6. Breeze, P. (2016). Wind power generation (pp. 67–73).Google Scholar
  7. Bussieck, M. R., & Pruessner, A. (2003). Mixed-integer nonlinear programming (pp. 1–2).Google Scholar
  8. Chen, C., Duan, S., Cai, T., Liu, B., & Hu, G. (2011). Optimal allocation and economic analysis of energy storage system in microgrids. IEEE Transactions on Power Electronics, 26(10), 2762–2773.CrossRefGoogle Scholar
  9. Costa, P. M., & Matos, M. A. (2006, June). Economic analysis of microgrids including reliability aspects. In International Conference on Probabilistic Methods Applied to Power Systems (PMAPS 2006) (pp. 1–8). IEEE.Google Scholar
  10. Dicorato, M., Forte, G., & Trovato, M. (2009, June). A procedure for evaluating microgrids technical and economic feasibility issues. In PowerTech, Bucharest (pp. 1–6). IEEE.Google Scholar
  11. Hu, M. C., Lu, S. Y., & Chen, Y. H. (2016). Stochastic programming and market equilibrium analysis of microgrids energy management systems. Energy, 113, 662–670.CrossRefGoogle Scholar
  12. Li, M., Zhang, X., Li, G., & Jiang, C. (2016). A feasibility study of microgrids for reducing energy use and GHG emissions in an industrial application. Applied Energy, 176, 138–148.CrossRefGoogle Scholar
  13. Mao, M., Jin, P., Zhao, Y., Chen, F., & Chang, L. (2013, September). Optimal allocation and economic evaluation for industrial PV microgrid. In Energy Conversion Congress and Exposition (ECCE) (pp. 4595–4602). IEEE.Google Scholar
  14. Moradi, M. H., Hajinazari, M., Jamasb, S., & Paripour, M. (2013). An energy management system (EMS) strategy for combined heat and power (CHP) systems based on a hybrid optimization method employing fuzzy programming. Energy, 49, 86–101.CrossRefGoogle Scholar
  15. Parisio, A., Rikos, E., & Glielmo, L. (2016). Stochastic model predictive control for economic/environmental operation management of microgrids: An experimental case study. Journal of Process Control, 43, 24–37.CrossRefGoogle Scholar
  16. Shaffie, S. S., & Jaaman, S. H. (2016). Monte Carlo on net present value for capital investment in Malaysia. Procedia-Social and Behavioral Sciences, 219, 688–693.CrossRefGoogle Scholar
  17. Xie, D., Du, Z., Ding, H., Zhang, J., Ma, L., Zhang, S. (2015, July). An integrated configuration optimization and economic evaluation approach for microgrids. In 34th ChineseControl Conference (CCC) (pp. 7877–7882). IEEE.Google Scholar
  18. Yu, N., Kang, J. S., Chang, C. C., Lee, T. Y., & Lee, D. Y. (2016). Robust economic optimization and environmental policy analysis for microgrid planning: An application to Taichung Industrial Park, Taiwan. Energy, 113, 671–682.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Cagri Ozturk
    • 1
  • Irem Duzdar Argun
    • 2
  • M. Özgür Kayalica
    • 1
  1. 1.Istanbul Technical UniversityMackaTurkey
  2. 2.Duzce UniversityDuzceTurkey

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