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<p>This paper proposes a risk-based optimal bidding strategy for generation companies (GENCOs) while considering the optimal placement of wind farms in a highly uncertain environment of energy and reserve markets. The proposed approach consists of a bi-level optimization method in which the lower level consists of scheduling of stochastic security-constrained unit commitment economic dispatch (SCUCED) of the GENCOs in the day-ahead (DA) market to obtain the optimal bids while the upper level consists of profit maximization and risk sensitivity analysis of the GENCOs in a highly uncertain environment. The lower-level problem consisting of optimal scheduling of the GENCOs has been solved by the mixed integer linear programming (MILP) method. The stochastic parameters considered are wind power and <i>(N-k)</i> contingencies such as line outages, generator outages, and load outages. Conditional value-at-risk (CVaR) is used as a risk assessment tool and decision-making parameter for optimal placement of wind farms in the DA market. The GENCO's risk sensitivities have been determined by interval estimation method using the student’s t-distribution by considering the confidence interval ‘<i>α</i>’ as 95%. A factor named Dynamic Locational Risk Factor (DLRF) has been also developed and proposed to check the risk level of GENCOs. The effectiveness of the proposed approach has been tested on a modified IEEE 30 bus system.</p>

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Estimation of Risk Sensitivity and Level for GENCOs in Stochastic Wind Integrated Reserve Power Markets

  • 期刊论文

  • 发表日期：

    2025年8月23日

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  • Rajesh Panda,
  • Prashant Kumar Tiwari

摘要

This paper proposes a risk-based optimal bidding strategy for generation companies (GENCOs) while considering the optimal placement of wind farms in a highly uncertain environment of energy and reserve markets. The proposed approach consists of a bi-level optimization method in which the lower level consists of scheduling of stochastic security-constrained unit commitment economic dispatch (SCUCED) of the GENCOs in the day-ahead (DA) market to obtain the optimal bids while the upper level consists of profit maximization and risk sensitivity analysis of the GENCOs in a highly uncertain environment. The lower-level problem consisting of optimal scheduling of the GENCOs has been solved by the mixed integer linear programming (MILP) method. The stochastic parameters considered are wind power and (N-k) contingencies such as line outages, generator outages, and load outages. Conditional value-at-risk (CVaR) is used as a risk assessment tool and decision-making parameter for optimal placement of wind farms in the DA market. The GENCO's risk sensitivities have been determined by interval estimation method using the student’s t-distribution by considering the confidence interval ‘α’ as 95%. A factor named Dynamic Locational Risk Factor (DLRF) has been also developed and proposed to check the risk level of GENCOs. The effectiveness of the proposed approach has been tested on a modified IEEE 30 bus system.