Impact of Self-consumption and Storage in Low Voltage Distribution Networks: An Economic Outlook

  • Fernando M. Camilo
  • Rui Castro
  • M. E. Almeida
  • V. Fernão Pires
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 470)


A paradigm shift is taking place in Low Voltage (LV) distribution networks, motivated by progressive implementation of renewable micro-generation (µG), mainly Photovoltaic (PV), near household consumers. The concept of self-consumption linked to battery storage is emerging as a way to enhance the quality of electrical network. Smart-Grid (SG) environment comes close to this approach and may have a crucial relevance on management of intelligent power distribution networks, in the framework of a Smart Environment. This paper proposes an additional contribution on the subject by investigating the economic profitability of PV battery systems being analyzed with respect to its impact and economic feasibility, taking into consideration their initial investment and operation costs. The purpose is to verify if prosumer’s investment is financially more interesting than purchasing all electricity needed for consumption from the LV grid. The results of the performed economic analysis show that self-consumption with storage is a potential solution.


Micro-generation Self-consumption Battery storage Economic analysis 



This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with reference UID/CEC/50021/2013.


  1. 1.
    Luque, A., Hegedus, S.: Handbook of Photovoltaic Science and Engineering. John Wiley & Sons, Hoboken (2011)Google Scholar
  2. 2.
    Rathnayaka, A.J.D., Potdar, V.M., Kuruppu, S.J.: An innovative approach to manage prosumers in smart grid. In: 2011 Sustainable Technologies (WCST), pp. 141–146 (2011)Google Scholar
  3. 3.
    Sun, Q., Beach, A., Cotterell, M.E., Wu, Z., Grijalva, S.: An economic model for distributed energy prosumers. In: 2013 46th Hawaii International Conference on System Sciences (HICSS), pp. 2103–2112 (2013)Google Scholar
  4. 4.
    Lampropoulos, I., Vanalme, G.M.A., Kling, W.L.: A methodology for modeling the behavior of electricity prosumers within the smart grid. In: Innovative Smart Grid Technologies Conference Europe (ISGT Europe), 2010 IEEE PES, pp. 1–8 (2010)Google Scholar
  5. 5.
    Pagani, G.A., Aiello, M.: Power grid complex network evolutions for the smart grid. Phys. A Stat. Mech. Appl. 396, 248–266 (2014)CrossRefGoogle Scholar
  6. 6.
    Putrus, G.A., Bentley, E., Binns, R., Jiang, T., Johnston, D.: Smart grids: energising the future. Int. J. Environ. Stud. 70, 691–701 (2013)CrossRefGoogle Scholar
  7. 7.
    Miceli, R., Favuzza, S., Genduso, F.: A perspective on the future of distribution: Smart grids, state of the art, benefits and research plans. Energy Power Eng. 5, 36 (2013)CrossRefGoogle Scholar
  8. 8.
    Borlase, S.: Smart Grids: Infrastructure, Technology, and Solutions. CRC Press, Boca Raton (2012)Google Scholar
  9. 9.
    Darghouth, N.R., Barbose, G., Wiser, R.: The impact of rate design and net metering on the bill savings from distributed PV for residential customers in California. Energy Policy. 39, 5243–5253 (2011)CrossRefGoogle Scholar
  10. 10.
    Darghouth, N.R., Barbose, G., Wiser, R.H.: Customer-economics of residential photovoltaic systems (Part 1): The impact of high renewable energy penetrations on electricity bill savings with net metering. Energy Policy 67, 290–300 (2014)CrossRefGoogle Scholar
  11. 11.
    Campoccia, A., Dusonchet, L., Telaretti, E., Zizzo, G.: An analysis of feed’in tariffs for solar PV in six representative countries of the European Union. Sol. Energy 107, 530–542 (2014)CrossRefGoogle Scholar
  12. 12.
    Forbes, I., Pearsall, N., Georgitsioti, T.: Simplified levelised cost of the domestic photovoltaic energy in the UK: the importance of the feed-in tariff scheme. IET Renew. Power Gener. 8, 451–458 (2014)CrossRefGoogle Scholar
  13. 13.
    Eid, C., Guillén, J.R., Marín, P.F., Hakvoort, R.: The economic effect of electricity net-metering with solar PV: Consequences for network cost recovery, cross subsidies and policy objectives. Energy Policy 75, 244–254 (2014)CrossRefGoogle Scholar
  14. 14.
    Dusonchet, L., Telaretti, E.: Comparative economic analysis of support policies for solar PV in the most representative EU countries. Renew. Sustain. Energy Rev. 42, 986–998 (2015)CrossRefGoogle Scholar
  15. 15.
    Baker, H.K., Powell, G.: Understanding Financial Management: A Practical Guide. Blackwell Publishing, Hoboken (2009)Google Scholar
  16. 16.
    Abad, F.A.T., Caccamo, M., Robbins, B.: A fault resilient architecture for distributed cyber-physical systems. In: 2012 IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, pp. 222–231. IEEE (2012)Google Scholar
  17. 17.
    Khaitan, S.K., McCalley, J.D.: Design techniques and applications of cyberphysical systems: a survey. IEEE Syst. J. 9, 350–365 (2015)CrossRefGoogle Scholar
  18. 18.
    Alippi, C.: Intelligence for Embedded Systems. Springer, Berlin (2014)CrossRefGoogle Scholar
  19. 19.
    NIST: National Institute of Standards and Technology.
  20. 20.
    Karnouskos, S.: Cyber-physical systems in the smartgrid. In: 9th International Conference on Industrial Informatics (INDIN), pp. 20–23. IEEE (2011)Google Scholar
  21. 21.
    Hu, F.: Cyber-Physical Systems: Integrated Computing and Engineering Design. CRC Press, Boca Raton (2013)CrossRefGoogle Scholar
  22. 22.
    Peters, M., Schmidt, T.S., Wiederkehr, D., Schneider, M.: Shedding light on solar technologies—a techno-economic assessment and its policy implications. Energy Policy 39, 6422–6439 (2011)CrossRefGoogle Scholar
  23. 23.
    NREL - National Renewable Energy Laboratory: Best Practices in PV System Operations and Maintenance (2015)Google Scholar
  24. 24.
    Jordan, D., Kurtz, S.: Photovoltaic degradation rates—an analytical review. Prog. Photovoltaics Res. Appl. 21(1), 12–29 (2013)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  • Fernando M. Camilo
    • 1
  • Rui Castro
    • 1
    • 2
  • M. E. Almeida
    • 1
    • 2
  • V. Fernão Pires
    • 2
    • 3
  1. 1.ISTUniversity of LisbonLisbonPortugal
  2. 2.INESC-ID/ISTUniversity of LisbonLisbonPortugal
  3. 3.EstSetúbalPolythecnics of SetúbalSetúbalPortugal

Personalised recommendations