Abstract
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.
Highlights
This paper addresses the optimal bidding problem faced by a price-maker retailer in a pay-as-clear electric energy market
(1) We propose a demand price quota curve (PQC) (DPQC)-based probability distribution function (PDF) method to reflect the price impacts from the bidding behavior of the price-maker retailers
The main conclusions are: (1) We propose a DPQC-based PDF method to reflect the price impacts from the bidding behavior of the price-maker retailers and consider the price uncertainty at the same time
Summary
This paper addresses the optimal bidding problem faced by a price-maker retailer in a pay-as-clear electric energy market. Bi-level optimization, and agent-based simulation methods usually necessitate that each strategic market player has full knowledge of the strategies of each rival and the operating parameters of the market clearing process This unrealistic assumption, along with the modeling and computational complexities, renders such approaches less applicable for conducting practical use of the bidders. We consider the price impacts from the price-maker retailer by DPQC method and incorporate the forecast uncertainty of the market clearing price by the PDF model. (2) We build up an optimal bidding model for retailers in China’s monthly energy market Both the one-segment and multi-segment bidding rules are considered for the pay-as-clear market, and the solving algorithm for the proposed model is presented. Where λc is the clearing price, Qb is the bidding quantity, uk is a binary variable indicating whether the bidder will choose block k, qmk ax is the maximal quantity of block k, λck is the clearing price of the block k of the price quota curve
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