Long-term chronological load modeling in power system studies with energy storage systems

Abbas Marini, Mohammad Amin Latify, Mohammad Sadegh Ghazizadeh and Ahmad Salemnia

Applied Energy, 2015, vol. 156, issue C, 436-448

Abstract: Smartening and restructuring of power industry lead to introduction of new energy resources in both supply and demand sides of energy sectors. In this regard, energy storage systems (ESSs) are appropriate alternatives for reducing the utilization of current declining non-renewable energy resources. Consequently, it is essential to consider various aspects of ESS application and face its related implementation challenges. This paper investigates the simulation of ESSs in long-term power system studies and proposes two long-term chronological load modeling methods. At first, a review of current load modeling methods in long-term studies including ESSs is provided and then two new load modeling methods are proposed. The proposed models are implemented in a typical unit commitment problem and solved for IEEE reliability test system (RTS) and IEEE 118-bus test system. Finally, a comparative study among examined load modeling methods is presented. The key feature of the proposed load models is that they are able to provide a tradeoff between computational burden and model accuracy in terms of calculating the desired requirements of the system planner.

Keywords: Chronological simulation; Energy storage system; Long-term load model; Mixed integer linear programming; Smart grid (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261915008764
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:156:y:2015:i:c:p:436-448

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.2015.07.047

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 ().