Economic risk‐based bidding strategy for profit maximisation of wind‐integrated day‐ahead and real‐time double‐auctioned competitive power markets. Issue 2 (18th December 2018)

• Record Type: Journal Article
• Authors: Panda, Rajesh; Tiwari, Prashant Kumar
• Is Part Of: IET generation, transmission & distribution. Volume 13:Issue 2(2019)
• Page Start: 209
• Page End: 218
• Publication Date: 2018-12-18
• Subjects: power markets -- power generation economics -- risk management -- minimisation -- power generation reliability -- wind power plants -- Monte Carlo methods -- profitability; economic risk‐based bidding strategy -- profit maximisation -- wind‐integrated day‐ahead double‐auctioned competitive power market -- wind‐integrated real‐time double‐auctioned competitive power market -- bilevel optimisation model -- generation companies -- distribution companies -- GENCO -- bilevel optimisation problem -- lower‐level problem -- upper‐level problem -- market clearing model -- locational marginal price minimisation -- load buses -- net generation cost minimisation -- optimal wind generator placement -- conditional value‐at‐risk -- CVaR -- risk assessment tool -- Monte Carlo simulation -- scenario‐based approach -- random line outages -- generator outages -- load variations -- modified IEEE 30‐bus system; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2018.5905
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : This study proposes a unique method of bidding strategy which is based on bi‐level optimisation model for analysing the profits of the generation companies (GENCOs) and distribution companies in the day‐ahead and real‐time competitive power markets. The bi‐level optimisation problem includes lower‐level and upper‐level problems. In the lower‐level problem, market clearing model is presented, in which market clearing problem is solved, locational marginal price is minimised at load buses, net generation cost is also minimised with a simultaneous increase in profit with optimal placement of wind generator by assessing the conditional value‐at‐risk (CVaR). In the upper level, the profit is maximised by selecting the proper bidding strategy based on double‐auctioned market mechanism. To check the effectiveness of the proposed strategy, Monte Carlo simulation and scenario‐based approach are used to generate the scenarios considering the random line outages, generator outages and load variations. The CVaR is used as a risk assessment tool for the uncertainties that would inadvertently affect the profit of the GENCOs. The proposed approach is applied to the modified IEEE 30‐bus system to show the effectiveness of the proposed model and its impact on the bidders and suppliers.