IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v88y2015icp837-848.html
   My bibliography  Save this article

Joint cost of energy under an optimal economic policy of hybrid power systems subject to uncertainty

Author

Listed:
  • Díaz, Guzmán
  • Planas, Estefanía
  • Andreu, Jon
  • Kortabarria, Iñigo

Abstract

Economical optimization of hybrid systems is usually performed by means of LCoE (levelized cost of energy) calculation. Previous works deal with the LCoE calculation of the whole hybrid system disregarding an important issue: the stochastic component of the system units must be jointly considered. This paper deals with this issue and proposes a new fast optimal policy that properly calculates the LCoE of a hybrid system and finds the lowest LCoE. This proposed policy also considers the implied competition among power sources when variability of gas and electricity prices are taken into account. Additionally, it presents a comparative between the LCoE of the hybrid system and its individual technologies of generation by means of a fast and robust algorithm based on vector logical computation. Numerical case analyses based on realistic data are presented that valuate the contribution of technologies in a hybrid power system to the joint LCoE.

Suggested Citation

  • Díaz, Guzmán & Planas, Estefanía & Andreu, Jon & Kortabarria, Iñigo, 2015. "Joint cost of energy under an optimal economic policy of hybrid power systems subject to uncertainty," Energy, Elsevier, vol. 88(C), pages 837-848.
    • Handle: RePEc:eee:energy:v:88:y:2015:i:c:p:837-848
    DOI: 10.1016/j.energy.2015.07.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544215008968
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2015.07.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Yuanyuan & Wang, Jianzhou & Zhao, Ge & Dong, Yao, 2012. "Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China," Energy Policy, Elsevier, vol. 48(C), pages 284-294.
    2. Levitt, Andrew C. & Kempton, Willett & Smith, Aaron P. & Musial, Walt & Firestone, Jeremy, 2011. "Pricing offshore wind power," Energy Policy, Elsevier, vol. 39(10), pages 6408-6421, October.
    3. Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, vol. 27(5), pages 791-817, September.
    4. Kitapbayev, Yerkin & Moriarty, John & Mancarella, Pierluigi, 2015. "Stochastic control and real options valuation of thermal storage-enabled demand response from flexible district energy systems," Applied Energy, Elsevier, vol. 137(C), pages 823-831.
    5. Cho, Heejin & Smith, Amanda D. & Mago, Pedro, 2014. "Combined cooling, heating and power: A review of performance improvement and optimization," Applied Energy, Elsevier, vol. 136(C), pages 168-185.
    6. Fathima, A. Hina & Palanisamy, K., 2015. "Optimization in microgrids with hybrid energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 431-446.
    7. Ramli, Makbul A.M. & Hiendro, Ayong & Twaha, Ssennoga, 2015. "Economic analysis of PV/diesel hybrid system with flywheel energy storage," Renewable Energy, Elsevier, vol. 78(C), pages 398-405.
    8. Helyette Geman & A. Roncoroni, 2006. "Understanding the Fine Structure of Electricity Prices," Post-Print halshs-00144198, HAL.
    9. repec:dau:papers:123456789/1433 is not listed on IDEAS
    10. Abdullah, M.A. & Muttaqi, K.M. & Agalgaonkar, A.P., 2015. "Sustainable energy system design with distributed renewable resources considering economic, environmental and uncertainty aspects," Renewable Energy, Elsevier, vol. 78(C), pages 165-172.
    11. Hélyette Geman & Andrea Roncoroni, 2006. "Understanding the Fine Structure of Electricity Prices," The Journal of Business, University of Chicago Press, vol. 79(3), pages 1225-1262, May.
    12. van Goor, Harm & Scholtens, Bert, 2014. "Modeling natural gas price volatility: The case of the UK gas market," Energy, Elsevier, vol. 72(C), pages 126-134.
    13. Kaikko, Juha & Backman, Jari, 2007. "Technical and economic performance analysis for a microturbine in combined heat and power generation," Energy, Elsevier, vol. 32(4), pages 378-387.
    14. Bouabdallah, A. & Olivier, J.C. & Bourguet, S. & Machmoum, M. & Schaeffer, E., 2015. "Safe sizing methodology applied to a standalone photovoltaic system," Renewable Energy, Elsevier, vol. 80(C), pages 266-274.
    15. Mallikarjun, Sreekanth & Lewis, Herbert F., 2014. "Energy technology allocation for distributed energy resources: A strategic technology-policy framework," Energy, Elsevier, vol. 72(C), pages 783-799.
    16. Dufo-López, Rodolfo & Bernal-Agustín, José L. & Yusta-Loyo, José M. & Domínguez-Navarro, José A. & Ramírez-Rosado, Ignacio J. & Lujano, Juan & Aso, Ismael, 2011. "Multi-objective optimization minimizing cost and life cycle emissions of stand-alone PV–wind–diesel systems with batteries storage," Applied Energy, Elsevier, vol. 88(11), pages 4033-4041.
    17. Sim, Kyuho & Koo, Bonjin & Kim, Chang Ho & Kim, Tae Ho, 2013. "Development and performance measurement of micro-power pack using micro-gas turbine driven automotive alternators," Applied Energy, Elsevier, vol. 102(C), pages 309-319.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Thomas, Dimitrios & Deblecker, Olivier & Ioakimidis, Christos S., 2016. "Optimal design and techno-economic analysis of an autonomous small isolated microgrid aiming at high RES penetration," Energy, Elsevier, vol. 116(P1), pages 364-379.
    2. Konneh, Keifa Vamba & Masrur, Hasan & Konneh, David A. & Senjyu, Tomonobu, 2022. "Independent or complementary power system configuration: A decision making approach for sustainable electrification of an urban environment in Sierra Leone," Energy, Elsevier, vol. 239(PD).
    3. Díaz, Guzmán & Moreno, Blanca, 2016. "Valuation under uncertain energy prices and load demands of micro-CHP plants supplemented by optimally switched thermal energy storage," Applied Energy, Elsevier, vol. 177(C), pages 553-569.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nowotarski, Jakub & Tomczyk, Jakub & Weron, Rafał, 2013. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," Energy Economics, Elsevier, vol. 39(C), pages 13-27.
    2. Michel Culot & Valérie Goffin & Steve Lawford & Sébastien de Meten & Yves Smeers, 2013. "Practical stochastic modelling of electricity prices," Post-Print hal-01021603, HAL.
    3. Shi Chen & Wolfgang Karl Hardle & Brenda L'opez Cabrera, 2020. "Regularization Approach for Network Modeling of German Power Derivative Market," Papers 2009.09739, arXiv.org.
    4. Algieri, Bernardina & Leccadito, Arturo & Tunaru, Diana, 2021. "Risk premia in electricity derivatives markets," Energy Economics, Elsevier, vol. 100(C).
    5. Smith, Michael Stanley & Shively, Thomas S., 2018. "Econometric modeling of regional electricity spot prices in the Australian market," Energy Economics, Elsevier, vol. 74(C), pages 886-903.
    6. Timothy Christensen & Stan Hurn & Kenneth Lindsay, 2009. "It Never Rains but it Pours: Modeling the Persistence of Spikes in Electricity Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 25-48.
    7. Per B. Solibakke, 2022. "Step‐ahead spot price densities using daily synchronously reported prices and wind forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 17-42, January.
    8. Fanone, Enzo & Gamba, Andrea & Prokopczuk, Marcel, 2013. "The case of negative day-ahead electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 22-34.
    9. Chan, Kam Fong & Gray, Philip & van Campen, Bart, 2008. "A new approach to characterizing and forecasting electricity price volatility," International Journal of Forecasting, Elsevier, vol. 24(4), pages 728-743.
    10. Le Pen, Yannick & Sévi, Benoît, 2010. "Volatility transmission and volatility impulse response functions in European electricity forward markets," Energy Economics, Elsevier, vol. 32(4), pages 758-770, July.
    11. Janczura, Joanna & Trück, Stefan & Weron, Rafał & Wolff, Rodney C., 2013. "Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling," Energy Economics, Elsevier, vol. 38(C), pages 96-110.
    12. Nadja Klein & Michael Stanley Smith & David J. Nott, 2020. "Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices," Papers 2010.01844, arXiv.org, revised May 2021.
    13. Adam Clements & Joanne Fuller & Stan Hurn, 2013. "Semi-parametric Forecasting of Spikes in Electricity Prices," The Economic Record, The Economic Society of Australia, vol. 89(287), pages 508-521, December.
    14. Fred Espen Benth & Jūratė Šaltytė Benth & Steen Koekebakker, 2008. "Stochastic Modeling of Electricity and Related Markets," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 6811, August.
    15. Bannör, Karl & Kiesel, Rüdiger & Nazarova, Anna & Scherer, Matthias, 2016. "Parametric model risk and power plant valuation," Energy Economics, Elsevier, vol. 59(C), pages 423-434.
    16. Rubin, Ofir David, 2010. "Equilibrium pricing in electricity markets with wind power," ISU General Staff Papers 201001010800002361, Iowa State University, Department of Economics.
    17. Nomikos, Nikos & Andriosopoulos, Kostas, 2012. "Modelling energy spot prices: Empirical evidence from NYMEX," Energy Economics, Elsevier, vol. 34(4), pages 1153-1169.
    18. Mayer, Klaus & Trück, Stefan, 2018. "Electricity markets around the world," Journal of Commodity Markets, Elsevier, vol. 9(C), pages 77-100.
    19. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601, December.
    20. Ullrich, Carl J., 2012. "Realized volatility and price spikes in electricity markets: The importance of observation frequency," Energy Economics, Elsevier, vol. 34(6), pages 1809-1818.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:88:y:2015:i:c:p:837-848. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.