Abstract
The perception of the associated impacts among possible management schemes introduces a new way to assess energy storage systems. The ability to define a specific management scheme considering the different stakeholder objectives, both technical and economic, will increase the perception of available installation options. This paper presents a multiobjective feasibility assessment methodology using an improved version of the Non-dominated Sorting Genetic Algorithm II, to optimize the placement of electric energy storage units in order to improve the operation of distribution networks. The model is applied to a case study, using lithium-ion battery technology as an example. The results show the influence of different charging/discharging profiles on the choice of the best battery location, as well as the influence that these choices may have on the different network management objectives, e.g. increasing the integration of renewable generation. As an additional outcome, the authors propose a pricing scheme for filling the present regulatory gap regarding the pricing scheme to be applied to energy storage in order to allow the exploitation of viable business models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
EPRI: Electricity Energy Storage Technology Options., California (2010)
Divya, K.C., Østergaard, J.: Battery energy storage technology for power systems—an overview. Electr. Power Syst. Res. 79, 511–520 (2009)
Schoenung, S.: Energy Storage Systems Cost Update A Study for the DOE Energy Storage Systems Program. Sandia National Laboratories, California (2011)
Kempton, W., Tomić, J.: Vehicle-to-grid power fundamentals: calculating capacity and net revenue. J. Power Sour. 144, 268–279 (2005)
Lassila, J., Haakana, J., Tikka, V., Partanen, J.: Methodology to analyze the economic effects of electric cars as energy storages. IEEE Trans. Smart Grid. 3, 506–516 (2012)
Battistelli, C., Baringo, L., Conejo, A.J.: Optimal energy management of small electric energy systems including V2G facilities and renewable energy sources. Electr. Power Syst. Res. 92, 50–59 (2012)
Gonçalves, J.A.R., Martins, A.G., Neves, L.M.P.: Potential role of stationary urban distributed storage on the management of power systems. In: International Conference on Energy & Environment (ICEE 2013), Porto, p. 11 (2013)
Gonçalves, J.A.R., Vitorino, R.M., Neves, L.M.P., Martins, A.G.: Assessment of best location of distributed storage using improved genetic algorithms. Energy for Sustainability 2013, Sustainable Cities: Designing for People and the Planet, p. 7. Energy for Sustainability 2013, Coimbra (2013)
Vitorino, R.M., Jorge, H.M., Neves, L.P.: Loss and reliability optimization for power distribution system operation. Electr. Power Syst. Res. 96, 177–184 (2013)
Vitorino, R.M., Neves, L.P., Jorge, H.M.: Network reconfiguration to improve reliability and efficiency in distribution systems. In: 2009 IEEE Bucharest PowerTech, pp. 1–7 (2009)
Vitorino, R.M., Jorge, H.M., Neves, L.P.: Multi-objective optimization using NSGA-II for power distribution system reconfiguration. Int. Trans. Electr. Energy Syst. (2013). doi:10.1002/etep.1819
Sahoo, N.C., Prasad, K.: A fuzzy genetic approach for network reconfiguration to enhance voltage stability in radial distribution systems. Energy Convers. Manag. 47, 3288–3306 (2006)
Systems, A.: Nanophosphate Basics: An Overview of the Structure, Properties and Benefits of A123 Systems’ Proprietary Lithium Ion Battery Technology (2013)
Systems, A.: Nanophosphate Lithium Ion Prismatic Pouch Cell (2012)
BuyA123baterries: buyA123batteries. http://www.buya123batteries.com/category_s/1825.htm
Vasconcelos, J., Ruester, S., He, X., Chong, E., Glachant, J.-M.: Seventh Framework Programme (European Commission): Electricity Storage: How to Facilitate its Deployment and Operation in the EU Final Report. European University Institute, European Union Centre in Taiwan, Firenze, Italy, Taipei, Taiwan (2012)
Acknowledgments
This work has been framed under the Energy for Sustainability Initiative of the University of Coimbra, and supported by the Energy and Mobility for Sustainable Regions Project CENTRO-07-0224-FEDER-002004, co-funded by the European Regional Development Fund (ERDF) through the «Programa Operacional Regional do Centro 2007–2013 (PORC)», in the framework of the «Sistema de Apoio a Entidades do Sistema Científico e Tecnológico Nacional». The work was also funded by the «Fundação para a Ciência e Tecnologia» under PEst-OE/EEI/UI0308/2014.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Gonçalves, J., Neves, L., Martins, A.G. (2015). Multiobjective Methodology for Assessing the Location of Distributed Electric Energy Storage. In: Mora, A., Squillero, G. (eds) Applications of Evolutionary Computation. EvoApplications 2015. Lecture Notes in Computer Science(), vol 9028. Springer, Cham. https://doi.org/10.1007/978-3-319-16549-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-16549-3_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16548-6
Online ISBN: 978-3-319-16549-3
eBook Packages: Computer ScienceComputer Science (R0)