Open AccessRevenue and ancillary benefit maximisation of multiple non‐collocated wind power producers considering uncertainties
Author(s) -
Majumder Subir,
Khaparde Shrikrishna A.
Publication year - 2016
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2015.0480
Subject(s) - wind power , computer science , maximization , schedule , profit maximization , revenue , linear programming , electricity market , scheduling (production processes) , mathematical optimization , profit (economics) , reliability engineering , operations research , electricity , renewable energy , engineering , economics , finance , electrical engineering , microeconomics , mathematics , algorithm , operating system
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.