Interactive pricing optimization of multi-microgrid based on deep learning

With the development of distributed energy microgrid and the interaction of multi-microgrid in the power market, optimizing the interactive price of multi-microgrid has become an important means to adjust network supply and demand and improve social benefits. The traditional physical model methods are widely used to solve the microgrid pricing problem. However, the calculation is growing sharply with the increase in the equipment number, and there is a lack of consideration of uncertainty in the traditional model. In this paper, a deep learning-based interactive pricing optimization method for multi-microgrid is proposed. Based on the deep learning method, an interactive encapsulation model for multi-microgrid is established. Uncertain factors are taken into account while improving the model accuracy, and the interactive pricing optimization strategy is optimized based on the model. The deep learning interaction model is applied in the retail market pricing scenario to optimize the interactive price under the interaction of multi-microgrid. The results show that the proposed interactive pricing method of multi-microgrid can optimize the interactive price based on the deep learning interactive model of multi-microgrid, avoid the deviation between the electricity consumption curve and the day-ahead curve, improve the economic benefits of both multi-microgrid and retailer, and provide a reference for the optimization of interactive pricing strategy of multi-microgrid.