Next Article in Journal
Effect of Coal Grain Size on Sorption Capacity with Respect to Propylene and Acetylene
Previous Article in Journal
Economic Analysis of Flat-Plate and U-Tube Solar Collectors Using an Al2O3 Nanofluid
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Energies 2017, 10(11), 1916;

Aging Cost Optimization for Planning and Management of Energy Storage Systems

Dipartimento di Ingegneria Elettrica ed Elettronica, Università di Cagliari, Italy, Via Marengo 1, 09123 Cagliari, Italy
IMT School for Advanced Studies Lucca, Piazza S. Francesco 19, 55100 Lucca, Italy
Author to whom correspondence should be addressed.
Received: 10 October 2017 / Revised: 11 November 2017 / Accepted: 12 November 2017 / Published: 21 November 2017
Full-Text   |   PDF [2238 KB, uploaded 21 November 2017]   |  


In recent years, many studies have proposed the use of energy storage systems (ESSs) for the mitigation of renewable energy source (RES) intermittent power output. However, the correct estimation of the ESS degradation costs is still an open issue, due to the difficult estimation of their aging in the presence of intermittent power inputs. This is particularly true for battery ESSs (BESSs), which have been proven to exhibit complex aging functions. Unfortunately, this collides with considering aging costs when performing ESS planning and management procedures, which are crucial for the exploitation of this technology. In order to overcome this issue, this paper presents the genetic algorithm-based multi-period optimal power flow (GA-MPOPF) procedure, which aims to economically optimize the management of ESSs by taking into account their degradation costs. The proposed methodology has been tested in two different applications: the planning of the correct positioning of a Li-ion BESS in the PG& E 69 bus network in the presence of high RES penetration, and the definition of its management strategy. Simulation results show that GA-MPOPF is able to optimize the ESS usage for time scales of up to one month, even for complex operative costs functions, showing at the same time excellent convergence properties. View Full-Text
Keywords: energy storage systems; renewable energy; multi-period optimization; genetic algorithms energy storage systems; renewable energy; multi-period optimization; genetic algorithms

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Korjani, S.; Mureddu, M.; Facchini, A.; Damiano, A. Aging Cost Optimization for Planning and Management of Energy Storage Systems. Energies 2017, 10, 1916.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top