Coordination of wind generation and demand response to minimise operation cost in day‐ahead electricity markets using bi‐level optimisation framework. Issue 16 (5th July 2018)

• Record Type: Journal Article
• Authors: Mohammad, Nur; Mishra, Yateendra
• Is Part Of: IET generation, transmission & distribution. Volume 12:Issue 16(2018)
• Page Start: 3793
• Page End: 3802
• Publication Date: 2018-07-05
• Subjects: power generation economics -- power generation scheduling -- wind power plants -- power markets -- demand side management -- mathematical programming -- minimisation; associated Karush‐Kuhn‐Tucker optimality conditions -- Lagrangian relaxation -- complementarity constraints -- mathematical programming problem -- monetary benefit maximisation -- DR‐capable loads -- social welfare maximisation -- DR exchange operator -- electricity market operator -- plausible wind generation scenarios -- wind variability -- wind profile -- DR requirement -- wind‐integrated electricity market -- bilevel market model -- power system -- bilevel optimisation framework -- day‐ahead electricity markets -- day‐ahead operation cost minimisation -- demand response -- wind generation; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2018.0110
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : Demand response (DR) can play an important role when dealing with the increasing variability of renewables in power system. This study proposes a bi‐level market model for wind‐integrated electricity market, where the DR requirement is paired with the wind profile to deal with wind variability. At the upper level, an electricity market operator aims to minimise the day‐ahead operation cost considering plausible wind generation scenarios. At the lower level, the DR exchange operator aims to maximise social welfare by trading aggregated DR among several aggregators. The solution at this level determines the optimal DR amount and price setting for each aggregator. The DR from the flexible loads is modelled from the end‐users' perspective considering their willingness parameter. The market model is formulated as a bi‐level optimisation problem using Lagrangian relaxation with Karush–Kuhn–Tucker optimality conditions. The effectiveness of the proposed scheme is demonstrated on sample 4‐bus and IEEE 24‐bus systems. Different scenarios such as high, medium and low levels of wind and DR are investigated. The high‐wind low‐DR scenario leads to minimum operation cost and is least inconvenient for end users as it sets the DR at a minimum level while keeping the higher levels of wind generation.