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Distributed demand response market model for facilitating wind power integration

Citation

Saebi, J and Nguyen, DT, Distributed demand response market model for facilitating wind power integration, IET Smart Grid, 3, (3) pp. 394-405. ISSN 2515-2947 (2020) [Refereed Article]


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Copyright Statement

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)

DOI: doi:10.1049/iet-stg.2019.0214

Abstract

To cope with wind power uncertainty, balancing authorities are required to procure adequate ancillary services (ASs) with the aim of maintaining the security of the power system operation. The transmission system operator (TSO) is responsible for maintaining the balance between supply and demand in delivery hours. Besides the generating units, demand response (DR) has the potential capabilities to be considered as a source of AS. The demand‐side AS can be used both locally (by the local entities in distribution networks) and system‐wide (by the TSO). However, the optimal coordination between the local and global beneficiaries is a challenging task. This study proposes a distributed DR market model, in which the DR is traded as a public good among the providers and beneficiaries through the local DR markets. The local DR markets can be run in each load bus to trade the DR provided by retail customers connected to that bus with the buyers. To include the interactions between the energy/reserve market and the local DR markets, a bi‐level programming model is proposed. The bi‐level problem is translated into a single‐level mixed‐integer linear programming problem using the duality theorem. The proposed model is verified by simple and realistic case studies.

Item Details

Item Type:Refereed Article
Keywords:linear programming, power markets, integer programming, demand side management, wind power plants, power generation economics
Research Division:Information and Computing Sciences
Research Group:Distributed computing and systems software
Research Field:Distributed systems and algorithms
Objective Division:Energy
Objective Group:Renewable energy
Objective Field:Wind energy
UTAS Author:Nguyen, DT (Dr Thanh Nguyen)
ID Code:143656
Year Published:2020
Web of Science® Times Cited:4
Deposited By:Engineering
Deposited On:2021-03-29
Last Modified:2021-06-30
Downloads:13 View Download Statistics

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