Optimal Bidding of Price-Maker Retailers With Demand Price Quota Curves Under Price Uncertainty

This paper proposes an optimal bidding method of price-maker retailers in the electricity market with demand price quota curves (DPQC)-based probability distribution function (PDF) estimation of the market price. Different from traditional game-theory methods or agent-based methods, the proposed DPQC-based PDF estimation method unnecessity to have full knowledge of the strategies of each rival or the market operation. In detail, the DPQC method is applied to consider the impacts of the market clearing price from the price-maker retailers themselves, and the PDF model is utilized to consider the market price uncertainty. The technical key point of the proposed method is to amend the PDF along with the PQCs dynamically. Moreover, the DPQC-based PDF estimation with one-segment and multi-segment bidding rules are presented, respectively. The optimization model of the bidding problem is formulated then, and we use the genetic algorithm to solve it. The case study shows that the proposed method can help the price-maker retailers better to consider the price impacts from their bidding behaviors, and enable them to make a higher profit in the electricity market.