A robust optimization model for Optimal Feeder Routing and Battery Storage Systems design

Vasko Zdraveski and Mirko Todorovski

Applied Energy, 2024, vol. 374, issue C, No S0306261924013618

Abstract: The increasing integration of small-scale distributed generation units and electric vehicle present significant challenges to the design of urban electrical distribution networks (DN), introducing considerable uncertainties. This paper addresses these complex challenges by proposing an Optimal Feeder Routing and Battery Storage System (OFRBSS) model. The model offers a single-stage, long-term optimization framework that simplifies load uncertainty to two values – minimum and maximum power demand – thereby enhancing simplicity and efficiency in modelling power demand uncertainty. Leveraging the Column and Constraint Generation algorithm, the OFRBSS model optimizes feeder routing and battery storage system installation, enabling distribution system operators to meet operational constraints under worst power demand conditions. This research bridges existing gaps in integrating optimal DN planning, battery storage system sizing, and placement while addressing the critical aspect of power demand uncertainty. The three-level OFRBSS model is transformed into a two-level counterpart through the application of Karush–Kuhn–Tucker conditions.

Keywords: Three-level robust optimization; C&CG; Uncertainty set; Uncertainty budget; Energy storage systems (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924013618
Full text for ScienceDirect subscribers only

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:eee:appene:v:374:y:2024:i:c:s0306261924013618

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.123978

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().