A Decision-Making Model for Monthly or Ten-Day Time-Sharing Transactions

With the acceleration of the national electric power reform process, the domestic electric energy market is further improved. Among them, in order to realize time-sharing price of full electricity, the medium and long-term time-sharing trading mechanism of electricity market has been gradually carried out. From this, this paper proposes a method and system for assisting decision making of monthly or ten-day timeshare trading. Firstly, the historical data and the data of the medium and long-term contracts are preprocessed by statistical analysis. Secondly, an auxiliary decision model is established with the goal of maximizing day-ahead settlement income. Then, according to the transaction probability of the model, the valuation of the declared electricity quantity is carried out, and the model is constrained by the monthly or ten-day timeshare trading market rules. Finally, combined with operational research method and genetic algorithm, the monthly or ten-day trading assisted decision model is solved. Assist power generation side users to complete monthly or ten-day Bidding on time-sharing and monthly or ten-day Rolling time-sharing trading declaration decision. Through the method proposed in this paper, using the data of a power plant in Shanxi Province, the rolling matching transaction in August and the centralized bidding transaction in the early August are respectively used to make auxiliary decisions. For the rolling matching trading in August, the model's daily income increased by RMB30,700 compared with that reported by the power plant based on manual experience. At the same time, compared with the results of centralized bidding in early August, the estimated earnings increased by RMB53,800. Therefore, it can be seen that the method proposed in this paper is scientific and effective, which can greatly help power plants to improve revenue, maximize economic benefits and liberate human resources.