A Robust Computational Framework for Mid-Term Techno-Economical Assessment of Energy Storage
|Title:||A Robust Computational Framework for Mid-Term Techno-Economical Assessment of Energy Storage||Authors:||Maghouli, Pourya
|Permanent link:||http://hdl.handle.net/10197/7241||Date:||Oct-2015||Online since:||2015-11-23T14:45:22Z||Abstract:||Rapid expansion and integration of wind energy is restrained due to transmission capacity constraints and conventional generation technologies limited operational flexibility in today's power systems. Energy storage is an attractive option to integrate and utilise more renewable energy without major and timely upgrade of existing transmission infrastructure. Moreover, it can be considered as a means for differing the reinforcement plans. The evaluation of energy storage deployment projects is a challenging task due to severe uncertainty of wind power generation. In this study, a robust techno-economic framework is proposed for energy storage evaluation based on information gap decision theory for handling wind generation uncertainty. The total social cost of the system including conventional generators’ fuel and pollution cost and wind power curtailment cost is optimised considering generators operational constraints and transmission system capacity limitations based on the DC model of the power grid. The effect of storage devices on system performance is evaluated taking into account wind power uncertainty. The proposed method is conducted on the modified IEEE reliability test system and the modified IEEE-118-bus test system to assess its applicability and performance in mid-term robust evaluation of energy storage implementation plans.||Type of material:||Journal Article||Publisher:||IET||Journal:||IET Generation Transmission and Distribution||Volume:||10||Issue:||3||Start page:||822||End page:||831||Copyright (published version):||2015 The Institution of Engineering and Technology||Keywords:||DC optimal power flow; Storage devices; Transmission capacity constraints; Wind power generation; Information gap decisiontheory||DOI:||10.1049/iet-gtd.2015.0453||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Electrical and Electronic Engineering Research Collection|
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