Optimal Planning and Operation of the Smart Electrical Distribution Network Considering Stochastic Optimization Modeling and Energy Storage Systems

Ming Hung Lin (), Mohammad Sassani, Navid Golchin, Yeganeh Jabbari, Zulxumorxon Boymatova, Jabbarov Umarbek Rustambekovich, Yuldoshev Jushkinbek Erkaboy Ugli, Saodat Atajanova and Yakitjon Turdiyeva
Additional contact information
Ming Hung Lin: National Cheng Kung University
Mohammad Sassani: University of Sistan and Baluchestan
Navid Golchin: University of Nevada, Las Vegas
Yeganeh Jabbari: University of Padua
Zulxumorxon Boymatova: Andijan Branch of Kokand University
Jabbarov Umarbek Rustambekovich: Mamun University
Yuldoshev Jushkinbek Erkaboy Ugli: Urgench Innovation University
Saodat Atajanova: Urgench State University Named After Abu Rayhan Biruni
Yakitjon Turdiyeva: Tashkent State University of Economy

SN Operations Research Forum, 2025, vol. 6, issue 3, 1-22

Abstract: Abstract This study proposes a stochastic multi-objective optimization method to enhance the energy storage systems (ESSs), along with wind and photovoltaic renewable energy sources in distribution networks, accounting for uncertainties in renewable power and network load. The multi-objective function involves reducing the costs of energy loss, emissions, and both investment and operational expenses related to energy resources and storage systems. An enhanced meta-heuristic optimization technique, called the improved artificial hummingbird algorithm, is introduced based on a spiral motion approach to address premature convergence and determine the decision variables, including the location and size of the energy storage system, as well as the photovoltaic and wind energy resources in the distribution network. The simulation outcomes are obtained in various scenarios that incorporate renewable resources. With the participation of the stationary ESS, the costs of energy loss and emission are reduced by 31.47% and 1.11%, respectively. Also, the placement of the Mobile ESS with solar and wind resources was presented, and the energy loss cost and cost of emission were reduced by 36.50% and 1.29%, respectively.

Keywords: Multi-objective optimization; Energy storage systems; Enhanced meta-heuristic optimization technique; Energy resources; Fuzzy method (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s43069-025-00503-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:snopef:v:6:y:2025:i:3:d:10.1007_s43069-025-00503-3

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/43069

DOI: 10.1007/s43069-025-00503-3

Access Statistics for this article

SN Operations Research Forum is currently edited by Marco Lübbecke

More articles in SN Operations Research Forum from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().