Risk assessment by security‐constrained unit commitment for hybrid wind‐thermal by pair copula approach in reserve power market: a stochastic approach. Issue 26 (17th February 2021)

• Record Type: Journal Article
• Authors: Panda, Rajesh; Tiwari, Prashant Kumar
• Is Part Of: IET generation, transmission & distribution. Volume 14:Issue 26(2020)
• Page Start: 6639
• Page End: 6649
• Publication Date: 2021-02-17
• Subjects: wind power plants -- power generation economics -- power markets -- power generation scheduling -- linear programming -- risk management -- power system security -- integer programming -- power generation dispatch -- thermal power stations -- hybrid power systems -- load (electric) -- profitability -- stochastic programming; risk assessment -- reserve power market -- stochastic approach -- market participants -- bivariate pair copula‐based security‐constrained unit commitment economic dispatch -- wind power generations -- ancillary service market -- wind farms -- conditional value‐at‐risk -- optimal placement -- risk‐based SCUCED model -- optimal bidding strategy -- profit -- GENCO -- independent system operator -- mixed integer linear programming -- Pareto tail factor -- bivariate marginal distribution -- modified IEEE 30 bus system -- ramp‐down optimal reserve curves -- ramp‐up optimal reserve curves; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2020.1128
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : High volatility renewable penetration increases the security risk of the market participants. In order to ensure the security of the reserve market under uncertainties, the authors have proposed a novel optimal bidding strategy based on bivariate pair copula‐based security‐constrained unit commitment economic dispatch (SCUCED) for the generation companies (GENCOs) while considering the volatility of wind power generations and load variations under different ( N − k ) contingencies in ancillary service market. The expected profit of GENCOs are based on the ramp‐up and ramp‐down optimal reserve curves in each time interval are submitted to the independent system operator. The profit of wind farms is obtained and the risks due to uncertainties with wind farms are also analysed while participating in the reserve market. Conditional value‐at‐risk is considered for the optimal placement of wind farms in the network. The risk‐based SCUCED model is solved by mixed integer linear programming. Moreover, a factor called joint Pareto tail factor has been also developed to provide information regarding the fat tails in the lower end of the bivariate marginal distribution of different GENCOs. The effectiveness of the proposed approach has been tested on a modified IEEE 30 bus system.