Multi-objective collaborative operation optimization of park-level integrated energy system clusters considering green power forecasting and trading

In order to achieve digital and intelligent upgrading of traditional industrial parks, and support high-quality regional development and renewable energy consumption, there is an urgent need for intelligent scheduling models in parks. This paper combines the smart park management system and the physical model of the park-level integrated energy system to establish a cluster architecture, and a three-stage collaborative operation method for green power trading in park-level integrated energy system cluster is proposed to solve the problem of green power trading in multi park integrated energy systems, achieving accurate prediction and on-site consumption of distributed green power. Firstly, based on the big language model LLAMA-7B, green power prediction is implemented, and further the purchase and sale types of power parks are divided by green power. Secondly, based on the green electricity price quota curve prediction model and dynamic green electricity pricing strategy, the differential prices for green electricity transactions between parks are formulated. On this basis, a multi-objective low-carbon economic optimization operation model is established to solve the contradiction between economic and environmental factors brought by green electricity exchanges. The case analysis shows that compared with the three scenarios of no green electricity-fixed carbon price-park operation alone, no green electricity-tiered carbon trading-park operation alone, and fixed green electricity price-tiered carbon trading-park cluster operation, the comprehensive cost of the green electricity dynamic pricing-tiered carbon trading-park cluster operation scenario proposed in this paper decreased by 24.76 %, 24.28 %, and 0.97 %, respectively; Compared with single objective optimization, the multi-objective optimization model proposed in this paper can comprehensively plan the economic cost, actual carbon emissions, and new energy utilization rate of the park-level integrated energy system cluster, and has a positive promoting effect on the intelligent scheduling of multi park integrated energy systems.