A novel optimization model for biding in the deregulated power market with pay as a bid settlement mechanism, based on the stochastic market clearing price

In this paper, a novel optimization-based bidding strategy will propose for an electricity generation company in the day-ahead market with a pay-as-bid settlement system. In this proposed model, Market Clearing Price is considered as a stochastic parameter with different distribution functions per hour. Moreover, days are also clustered in three different clusters based on the historical data by using the K-Means Algorithm. In addition, three types of linear, quadratic, and cubic cost functions are taken into account in the proposed model, where the results indicated that the cubic function is the best estimation model for the generation cost. Finally, the League Championship Algorithm is applied in order to solve our proposed model. The proposed model has been applied to real case studies where the obtained results indicated that the proposed model is able to improve the expected revenue by 17% in comparison with the basic model.

Automated summary

This paper proposes an optimization-based bidding strategy for an electricity generation company in the day-ahead market. The proposed model takes into account the stochastic Market Clearing Price, historical data clustering using the K-Means Algorithm, and three types of cost functions. The results show that the proposed model can significantly improve the expected revenue.