Revenue and ancillary benefit maximisation of multiple non‐collocated wind power producers considering uncertainties. Issue 3 (1st February 2016)

• Record Type: Journal Article
• Authors: Majumder, Subir; Khaparde, Shrikrishna A.
• Is Part Of: IET generation, transmission & distribution. Volume 10:Issue 3(2016)
• Page Start: 789
• Page End: 797
• Publication Date: 2016-02-01
• Subjects: wind power plants -- power generation scheduling -- wind turbines -- power markets -- power generation economics -- distributed power generation -- nonlinear programming -- load forecasting; ancillary benefit maximisation -- noncollocated wind power producer -- revenue maximisation -- optimal scheduling -- day‐ahead distribution electricity market -- WPP -- wind turbine generator -- battery storage device -- cost equivalent -- network losses -- noncollocated WTG placement -- BSD -- distribution network -- ancillary services -- nonlinear programming technique -- profit map -- profit maximisation -- wind power forecast uncertainty -- DA energy schedule; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2015.0480
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : In this study, optimal scheduling of multiple non‐colocated, price taker, independent wind power producers (WPPs) participating in forward day‐ahead (DA) distribution electricity market is described; where, a WPP is comprised of multiple wind turbine generator (WTG) and battery storage device (BSD). Cost equivalent of reduction in network losses and improvement in voltage profile for non‐colocated placement of WTG and BSD in Distribution Network (termed as ancillary benefit) is included in the objective function resulting in a scheduling strategy dependent upon location of WPP in the network. Objective function comprises of following sub‐objectives: (i) maximize return from energy market, (ii) maximize benefit obtained from providing ancillary services, and (iii) minimize uncertainties in schedule by providing reserve from BSDs. Non‐linear programming (NLP) technique is used for scheduling. Location of a WPP is varied to obtain a 'profit map'; which can be used as an 'offline‐tool' to find out relative location of WTG and BSD for profit maximization. Proposed formulation is extended to participation of multiple WPP, where ancillary benefit is proportionally shared. Wind power forecast uncertainty leads to risk of not meeting the schedule and is probabilistically modeled in this work. Impact of reserve on DA energy schedule of is also studied.