Operational Planning in Energy Systems: A Literature Review

  • Cengiz Kahraman
  • Sezi Çevik Onar
  • Başar Öztayşi
  • Ali Karaşan
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 149)


Operational planning is the process of planning and organizing the resources to achieve organization’s strategic plan. Planning the supply chain, maintenance, marketing, and production operations are the main parts of operational planning. The operational planning for energy investments is crucial since these investments are costly and the efficiency of the investments necessitates an ample planning process. Also, the type of energy source changes the operational planning need. Understanding these needs and the research gaps can enhance the efficiency of the energy systems. The objective of this chapter is to reveal the primary needs and research focuses on operation planning in energy systems. A comprehensive literature review is conducted to identify the research focuses and the gaps in operational planning in energy systems.


  1. Agusdinata, D. B., Lee, S., Zhao, F., & Thissen, W. (2014). Simulation modeling framework for uncovering system behaviors in the biofuels supply chain network. Simulation, 90(9), 1103–1116.CrossRefGoogle Scholar
  2. Akoka, J., et al. (2017). Research on big data—A systematic mapping study. Computer Standards and Interfaces, 54, 105–115.CrossRefGoogle Scholar
  3. Alippi, C., & Galperti, C. (2008). An adaptive system for opimal solar energy harvesting in wireless sensor network nodes. IEEE Transactions on Circuits and Systems I: Regular Papers, 55(6), 1742–1750.MathSciNetCrossRefGoogle Scholar
  4. Balaman, Ş. Y., & Selim, H. (2014). A fuzzy multiobjective linear programming model for design and management of anaerobic digestion based bioenergy supply chains. Energy, 74, 928–940.CrossRefGoogle Scholar
  5. Beeftink, H. H., Van der Heijden, R. T. J. M., & Heijnen, J. J. (1990). Maintenance requirements: energy supply from simultaneous endogenous respiration and substrate consumption. FEMS Microbiology Letters, 73(3), 203–209.CrossRefGoogle Scholar
  6. Belyaev, L. S., Marchenko, O. V., Filippov, S. P., Solomin, S. V., Stepanova, T. B., & Kokorin, A. L. (2002). World energy and transition to sustainable development. Boston: Kluwer.CrossRefGoogle Scholar
  7. Ben-Daya, M., Ait-Kadi, D., Duffuaa, S. O., Knezevic, J., & Raouf, A. (2009). Handbook of maintenance management and engineering (Vol. 7). London: Springer.CrossRefGoogle Scholar
  8. Bergonzini, C., et al. (2009). Algorithms for harvested energy prediction in batteryless wireless sensor networks. In 3rd International Workshop on Advances in Sensors and Interfaces, IWASI 2009.Google Scholar
  9. Bodansky, D. (2004). Nuclear energy: Principles practices and prospects. Oxford: Springer.Google Scholar
  10. Cambero, C., et al. (2015). Strategic optimization of forest residues to bioenergy and biofuel supply chain. International Journal of Energy Research, 39(4), 439–452.CrossRefGoogle Scholar
  11. Castillo-Villar, K. K. (2014). Metaheuristic algorithms applied to bioenergy supply chain problems: theory, review, challenges, and future. Energies, 7(11), 7640–7672.CrossRefGoogle Scholar
  12. Ceci, M., et al. (2015). Big data techniques for supporting accurate predictions of energy production from renewable sources. In ACM International Conference Proceeding Series.Google Scholar
  13. Chapman, S. N. (2006). The fundamentals of production planning and control. Prentice Hall.Google Scholar
  14. Chen, S., et al. (2012). A simple asymptotically optimal energy allocation and routing scheme in rechargeable sensor networks. In Proceedings—IEEE INFOCOM.Google Scholar
  15. Chopra, S., & Meindl, P. (2007). Supply chain management. Strategy, planning & operation. Das summa summarum des management (pp. 265–275).Google Scholar
  16. Cristaldi, L., Faifer, M., Rossi, M., & Ponci, F. (2011). Monitoring of a PV system: The role of the panel model. In Applied Measurements for Power Systems (s. 90–95). IEEE.Google Scholar
  17. Cuadra, L., Salcedo-Sanz, S., Nieto-Borge, J. C., Alexandre, E., & Rodríguez, G. (2016). Computational intelligence in wave energy: Comprehensive review and case study. Renewable and Sustainable Energy Reviews, 58, 1223–1246.CrossRefGoogle Scholar
  18. Cui, C., Li, X., Sui, H., & Sun, J. (2017). Optimization of coal-based methanol distillation scheme using process superstructure method to maximize energy efficiency. Energy, 119, 110–120.CrossRefGoogle Scholar
  19. Cunico, M. C., Flores, J. R., & Vecchietti, A. (2017). Investment in the energy sector: An optimization model that contemplates several uncertain parameters. Energy, 138, 831–845. (1 Nov 2017).CrossRefGoogle Scholar
  20. Daneshi-Far, Z., et al. (2010). Review of failures and condition monitoring in wind turbine generators. In 19th International Conference on Electrical Machines, ICEM 2010.Google Scholar
  21. Daut, M. A. M., Hassan, M. Y., Abdullah, H., Rahman, H. A., Abdullahab, M. P., & Hussin, F. (2017). Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review. Renewable and Sustainable Energy Reviews, 70, 1108–1118.CrossRefGoogle Scholar
  22. Dehghani, E., Jabalameli, M. S., & Jabbarzadeh, A. (2018). Robust design and optimization of solar photovoltaic supply chain in an uncertain environment. Energy, 142, 139–156.CrossRefGoogle Scholar
  23. Demirel, Y. (2012). Green energy and technology. Berlin: Springer.Google Scholar
  24. Dondi, D., et al. (2008). Modeling and optimization of a solar energy harvester system for self-powered wireless sensor networks. IEEE Transactions on Industrial Electronics, 55(7), 2759–2766.CrossRefGoogle Scholar
  25. Drummond, J., & Hanna, F. (2001). Selling power: Marketing energy under deregulation.Google Scholar
  26. EIA (2011) Renewable energy consumption and electricity preliminary statistics 2010. Release date: 28 June 2011. Accessed July 2011.
  27. Elia, J. A., et al. (2011). Optimal energy supply network determination and life cycle analysis for hybrid coal, biomass, and natural gas to liquid (CBGTL) plants using carbon-based hydrogen production. Computers & Chemical Engineering, 35(8), 1399–1430.CrossRefGoogle Scholar
  28. Felzien, D., et al. (2003). IT requirements for market participant interaction with ISOs/RTOs. IEEE Transactions on Power Systems, 18(2), 517–519.CrossRefGoogle Scholar
  29. Ferrari, S., Lazzaroni, M., Piuri, V., Salman, A., Cristaldi, L., Faifer, M., et al. (2016). Solar panel modelling through computational intelligence techniques. Measurement, 93, 572–580.CrossRefGoogle Scholar
  30. Flodén, J., & Williamsson, J. (2016). Business models for sustainable biofuel transport: The potential for intermodal transport. Journal of Cleaner Production, 113, 426–437.CrossRefGoogle Scholar
  31. Frackowiak, E., & Beguin, F. (2001). Carbon materials for the electrochemical storage of energy in capacitors. Carbon, 39(6), 937–950.CrossRefGoogle Scholar
  32. Gabaldón, A., et al. (2008). Development of a methodology for improving the effectiveness of customer response policies through electricity-price patterns. In IEEE Power and Energy Society 2008 General Meeting: Conversion and Delivery of Electrical Energy in the 21st Century, PES.Google Scholar
  33. García-Valverde, R., et al. (2010). Life cycle analysis of organic photovoltaic technologies. Progress in Photovoltaics: Research and Applications, 18(7), 535–538.CrossRefGoogle Scholar
  34. Ghaffari, R., & Venkatesh, B. (2015). Network constrained model for options based reserve procurement by wind generators using binomial tree. Renewable Energy, 80, 348–358.CrossRefGoogle Scholar
  35. Gossling, S., et al. (2005). A target group-specific approach to “green” power retailing: Students as consumers of renewable energy. Renewable and Sustainable Energy Reviews, 9(1), 69–83.CrossRefGoogle Scholar
  36. Gupta, P. K. (1999). Renewable energy sources-a longway to go in India. Renewable Energy, 16(1–4), 1216–1219.CrossRefGoogle Scholar
  37. Gupta, S. C., et al. (2007). Optimal sizing of solar-wind hybrid system. In IET Seminar Digest.Google Scholar
  38. Hashemian, H. M. (2011). On-line monitoring applications in nuclear power plants. Progress in Nuclear Energy, 53(2), 167–181.CrossRefGoogle Scholar
  39. Hashemian, H. M., & Bean, W. C. (2011). State-of-the-art predictive maintenance techniques. IEEE Transactions on Instrumentation and Measurement, 60(10), 3480–3492.CrossRefGoogle Scholar
  40. Hashemian, H. M., et al. (2011). Wireless sensor applications in nuclear power plants. Nuclear Technology, 173(1), 8–16.CrossRefGoogle Scholar
  41. 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, ISSN 0360–5442.Google Scholar
  42. Hugo, A., Rutter, P., Pistikopoulos, E. N., Amorelli, A., & Zoia, G. (2005). Hydrogen infrastructure strategic planning using multi-objective optimization. International Journal of Hydrogen Energy, 30(15), 1523–1534.CrossRefGoogle Scholar
  43. Iakovou, E., Karagiannidis, A., Vlachos, D., Toka, A., & Malamakis, A. (2010). Waste biomass-to-energy supply chain management: A critical synthesis. Waste Management, 30(10), 1860–1870.CrossRefGoogle Scholar
  44. Irizarry, F., & Seemer, R. H. (1986). Human resource planning: A nuclear application. In Proceedings—Fall Industrial Engineering Conference (Institute of Industrial Engineers).Google Scholar
  45. Ismail, M., & Sanavullah, M. Y. (2008). Security topology in wireless sensor networks with routing optimisation. In Proceedings of the 4th International Conference on Wireless Communication and Sensor Networks, WCSN 2008.Google Scholar
  46. Jauhari, V., & Dutta, K. (2009). Services: Marketing, operations, and managment. Oxford University Press.Google Scholar
  47. Jha, S. K., Bilalovic, J., Jha, A., Patel, N., & Zhang, H. (2017). Renewable energy: Present research and future scope of artificial intelligence. Renewable and Sustainable Energy Reviews, 77, 297–317.CrossRefGoogle Scholar
  48. Kayakutlu, G., & Mercier-Laurent, E. (2017). Intelligence for Energy. In Intelligence in energy (pp. 79–116).Google Scholar
  49. Kebede, K. Y., et al. (2014). After-sales service and local presence: Key factors for solar energy innovations diffusion in developing countries. In PICMET 2014—Portland International Center for Management of Engineering and Technology, Proceedings: Infrastructure and Service Integration.Google Scholar
  50. Kejariwal, A., & Orsini, F. (2016). On the definition of real-time: Applications and systems. In Proceedings—15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016.Google Scholar
  51. Kwon, S., Won, W., & Kim, J. (2016). A superstructure model of an isolated power supply system using renewable energy: Development and application to Jeju Island, Korea. Renewable Energy, 97, 177–188.CrossRefGoogle Scholar
  52. Lei, X., & Sandborn, P. A. (2018). Maintenance scheduling based on remaining useful life predictions for wind farms managed using power purchase agreements. Renewable Energy, 116, 188–198.CrossRefGoogle Scholar
  53. Lin, K., & Holbert, K. E. (2009). Blockage diagnostics for nuclear power plant pressure transmitter sensing lines. Nuclear Engineering and Design, 239(2), 365–372.CrossRefGoogle Scholar
  54. Liu, P., Pistikopoulos, E. N., & Li, Z. (2009a). A mixed-integer optimization approach for polygeneration energy systems design. Computers & Chemical Engineering, 33(3), 759–768.CrossRefGoogle Scholar
  55. Liu, P., Pistikopoulos, E. N., & Li, Z. (2009b). A multi-objective optimization approach to polygeneration energy systems design. AIChE Journal, 56(5), 1218–1234.CrossRefGoogle Scholar
  56. Liu, P., Psitikopoulos, N. E., & Li, Z. (2010). Energy systems engineering: methodologies and applications. Frontiers in Energy, 4(2), 131–142.Google Scholar
  57. Lusby, R., et al. (2013). A solution approach based on Benders decomposition for the preventive maintenance scheduling problem of a stochastic large-scale energy system. Journal of Scheduling, 16(6), 605–628.MathSciNetCrossRefzbMATHGoogle Scholar
  58. Martinot, E., et al. (2001). World Bank/GEF solar home system projects: Experiences and lessons learned 1993–2000. Renewable and Sustainable Energy Reviews, 5(1), 39–57.CrossRefGoogle Scholar
  59. Meadows, J., et al. (2014). The potential supply of biomass for energy from Hardwood Plantations in the Sunshine Coast Council Region of South-East Queensland, Australia. Small-scale Forestry, 13(4), 461–481.CrossRefGoogle Scholar
  60. Medidi, M., & Zhou, Y. (2006). Maintaining an energy-efficient bluetooth scatternet. In Performance, Computing, and Communications Conference (s. 8). IEEE.Google Scholar
  61. Mirabella, N., et al. (2013). Life cycle assessment of bio-based products: A disposable diaper case study. International Journal of Life Cycle Assessment, 18(5), 1036–1047.CrossRefGoogle Scholar
  62. Mohamed, M. A., Eltamaly, A. M., & Alolah, A. I. (2017). Swarm intelligence-based optimization of grid-dependent hybrid renewable energy systems. Renewable and Sustainable Energy Reviews, 77, 515–524.CrossRefGoogle Scholar
  63. Mont, O. K. (2002). Clarifying the concept of product–service system. Journal of Cleaner Production, 10(3), 237–245.CrossRefGoogle Scholar
  64. Mueller, T. S. (2017). Consumer perceptions of electric utilities: Insights from the center for Analytics Research & Education Project in the United States. Energy Research and Social Science, 26, 34–39.CrossRefGoogle Scholar
  65. Murilo, P. S., Alexandre, S., & Davi, M. V. (2017). On the solution variability reduction of stochastic dual dynamic programming applied to energy planning. European Journal of Operational Research, 258(2), 743–760.MathSciNetCrossRefGoogle Scholar
  66. Mydock III, S., Pervan, S. J., Almubarak, A. F., Lester, J., & Kortt, M. (2017). Influence of made with renewable energy appeal on consumer behaviour. Marketing Intelligence & Planning., artical in press.
  67. Ni, M., Leung, M. K., Sumathy, K., & Leung, D. Y. (2006). Potential of renewable hydrogen production for energy supply in Hong Kong. International Journal of Hydrogen Energy, 31(10), 1401–1412.CrossRefGoogle Scholar
  68. Paulo, H., et al. (2015). Supply chain optimization of residual forestry biomass for bioenergy production: The case study of Portugal. Biomass and Bioenergy, 83, 245–256.CrossRefGoogle Scholar
  69. Pereira, C. M. N. A., et al. (2010). A particle swarm optimization (PSO) approach for non-periodic preventive maintenance scheduling programming. Progress in Nuclear Energy, 52(8), 710–714.CrossRefGoogle Scholar
  70. Phillips, B. R., & Middleton, R. S. (2012). SimWIND: A geospatial infrastructure model for optimizing wind power generation and transmission. Energy Policy, 43, 291–302.CrossRefGoogle Scholar
  71. Presencia, C. E., & Shafiee, M. (2017). Risk analysis of maintenance ship collisions with offshore wind turbines. International Journal of Sustainable Energy, 1–21.Google Scholar
  72. Richard, T. L. (2010). Challenges in scaling up biofuels infrastructure. Science, 329(5993), 793–796.CrossRefGoogle Scholar
  73. Rodriguez, C. P., & Anders, G. J. (2004). Energy price forecasting in the Ontario competitive power system market. IEEE Transactions on Power Systems, 19(1), 366–374.CrossRefGoogle Scholar
  74. Rogers, J. H. (1999). Learning reliability lessons from PV leasing. Progress in Photovoltaics: Research and Applications, 7(3), 235–241.CrossRefGoogle Scholar
  75. Rulkens, W. (2007). Sewage sludge as a biomass resource for the production of energy: Overview and assessment of the various options. Energy & Fuels, 22(1), 9–15.CrossRefGoogle Scholar
  76. Sarja, J., & Halonen, V. (2012). Case study of wind turbine sourcing: Manufacturer selection criteria. In 2012 IEEE Electrical Power and Energy Conference, EPEC 2012.Google Scholar
  77. Schaffner, D., Ohnmacht, T., Weibel, C., & Mahrer, M. (2017). Moving into energy-efficient homes: A dynamic approach to understanding residents’ decision-making. Building and Environment, 123, 211–222.CrossRefGoogle Scholar
  78. Shafiee, M. (2015). Maintenance logistics organization for offshore wind energy: Current progress and future perspectives. Renewable Energy, 77(1), 182–193.CrossRefGoogle Scholar
  79. Sherrard, J. R., & Horner, T. A. (2007). Developing a quality entry level technician workforce for the twenty-first century commercial nuclear industry. In CONTE 2007: Conference on Nuclear Training and Education.Google Scholar
  80. Sikkema, R., et al. (2014). Legal harvesting, sustainable sourcing and cascaded use of wood for bioenergy: Their coverage through existing certification frameworks for sustainable forest management. Forests, 5(9), 2163–2211.CrossRefGoogle Scholar
  81. Tascikaraoglu, A., et al. (2014). An adaptive load dispatching and forecasting strategy for a virtual power plant including renewable energy conversion units. Applied Energy, 119, 445–453.CrossRefGoogle Scholar
  82. Teeuwsen, S. P., Erlich, I., & El-Sharkawi, M. A. (2005). Fast eigenvalue assessment for large interconnected powers systems. In Power Engineering Society General Meeting (s. 1727–1733). IEEE.Google Scholar
  83. Thiaw, L., Sow, G., & Fall, S. (2014). Application of neural networks technique in renewable energy systems. In First International Conference on Systems Informatics, Modelling and Simulation, IEEE Computer Society, Sheffield, United Kingdom, 29 April–1 May 2014.Google Scholar
  84. Tryfonidou, R., & Wagner, H. J. (2004). Multi-megawatt wind turbines for offshore use: Aspects of Life Cycle Assessment. International Journal of Global Energy Issues, 21(3), 255–262.CrossRefGoogle Scholar
  85. Tsoutsos, T. D. (2002). Marketing solar thermal technologies: Strategies in Europe, experience in Greece. Renewable Energy, 26(1), 33–46.CrossRefGoogle Scholar
  86. Verdú, S. V., et al. (2006). Classification, filtering, and identification of electrical customer load patterns through the use of self-organizing maps. IEEE Transactions on Power Systems, 21(4), 1672–1682.CrossRefGoogle Scholar
  87. Voigt, T., et al. (2003). Utilizing solar power in wireless sensor networks. In Proceedings—Conference on Local Computer Networks, LCN.Google Scholar
  88. Voll, P., Klaffke, C., Hennen, M., & Bardow, A. (2013). Automated superstructure-based synthesis and optimization of distributed energy supply systems. Energy, 50, 374–388.CrossRefGoogle Scholar
  89. Wang, S., & Song, M. (2017). Influences of reverse outsourcing on green technological progress from the perspective of a global supply chain. Science of the Total Environment, 595, 201–208.CrossRefGoogle Scholar
  90. Wiggelinkhuizen, E., et al. (2008). Assessment of condition monitoring techniques for offshore wind farms. Journal of Solar Energy Engineering, Transactions of the ASME, 130(3), 0310041–0310049.CrossRefGoogle Scholar
  91. Wirl, F. (1989). Analytics of demand-side conservation programs. Energy systems and policy, 13(4), 285–300.Google Scholar
  92. Xie, L., & Ilić, M. D. (2008). Model predictive dispatch in electric energy systems with intermittent resources. In Conference Proceedings—IEEE International Conference on Systems, Man and Cybernetics.Google Scholar
  93. Xie, L., & Ilić, M. D. (2009). Model predictive economic/environmental dispatch of power systems with intermittent resources. In 2009 IEEE Power and Energy Society General Meeting, PES ‘09.Google Scholar
  94. Yang, R., et al. (2015). An enhanced preventive maintenance optimization model based on a three-stage failure process. In Science and Technology of Nuclear Installations 2015.Google Scholar
  95. Yang, W., et al. (2014). Wind turbine condition monitoring: Technical and commercial challenges. Wind Energy, 17(5), 673–693.CrossRefGoogle Scholar
  96. Yeomans, H., & Grossmann, I. E. (1999). A systematic modeling framework of superstructure optimization in process synthesis. Computers & Chemical Engineering, 23(6), 709–731.CrossRefGoogle Scholar
  97. Yokoyama, R., Nakamura, R., & Wakui, T. (2017). Performance comparison of energy supply systems under uncertain energy demands based on a mixed-integer linear model. In Energy, 137, 878–887.CrossRefGoogle Scholar
  98. Zahraee, S. M., Assadi, M. K., & Saidur, R. (2016). Application of artificial intelligence methods for hybrid energy system optimization. Renewable and Sustainable Energy Reviews, 66, 617–630.CrossRefGoogle Scholar
  99. Zéphyr, L., Lang, P., Lamond, B. F., & Côté, P. (2017). Approximate stochastic dynamic programming for hydroelectric production planning. European Journal of Operational Research, 262(2), 586–601.MathSciNetCrossRefzbMATHGoogle Scholar
  100. Zhang, C., et al. (2017). A spare parts demand prediction method for wind farm based on periodic maintenance strategy. In American Society of Mechanical Engineers, Power Division (Publication) POWER.Google Scholar
  101. Zhang, H., Liang, Y., Liao, Q., Wu, M., & Yan, X. (2017). A hybrid computational approach for detailed scheduling of products in a pipeline with multiple pump stations. Energy, 119, 612–628.CrossRefGoogle Scholar
  102. Zhou, W., Xia, X., & Wang, B. (2015). Improving building energy efficiency by multiobjective neighborhood field optimization. Energy and Buildings, 87, 45–56.Google Scholar
  103. Zhu, J., et al. (2014). Survey of condition indicators for condition monitoring systems. In PHM 2014—Proceedings of the Annual Conference of the Prognostics and Health Management Society 2014.Google Scholar
  104. Zorić, J., & Hrovatin, N. (2012). Household willingness to pay for green electricity in Slovenia. Energy Policy, 47, 180–187.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Cengiz Kahraman
    • 1
  • Sezi Çevik Onar
    • 1
  • Başar Öztayşi
    • 1
  • Ali Karaşan
    • 1
    • 2
  1. 1.Industrial Engineering Department, Management FacultyIstanbul Technical UniversityMacka, IstanbulTurkey
  2. 2.Institute of Natural and Applied SciencesYildiz Technical UniversityEsenler, IstanbulTurkey

Personalised recommendations