Aging Cost Optimization for Planning and Management of Energy Storage Systems

Saman Korjani, Mario Mureddu, Angelo Facchini and Alfonso Damiano
Additional contact information
Saman Korjani: Dipartimento di Ingegneria Elettrica ed Elettronica, Università di Cagliari, Italy, Via Marengo 1, 09123 Cagliari, Italy
Mario Mureddu: Dipartimento di Ingegneria Elettrica ed Elettronica, Università di Cagliari, Italy, Via Marengo 1, 09123 Cagliari, Italy
Angelo Facchini: IMT School for Advanced Studies Lucca, Piazza S. Francesco 19, 55100 Lucca, Italy
Alfonso Damiano: Dipartimento di Ingegneria Elettrica ed Elettronica, Università di Cagliari, Italy, Via Marengo 1, 09123 Cagliari, Italy

Energies, 2017, vol. 10, issue 11, 1-17

Abstract: 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.

Keywords: energy storage systems; renewable energy; multi-period optimization; genetic algorithms (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/10/11/1916/pdf (application/pdf)
https://www.mdpi.com/1996-1073/10/11/1916/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:10:y:2017:i:11:p:1916-:d:119704

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().