LLM-assisted value assessment and strategy design for virtual power plant participation in coupled electricity–carbon markets

As power systems transition toward low-carbon operation, virtual power plants (VPPs) increasingly aggregate distributed renewable generation, energy storage, electric vehicles, and flexible loads to participate in coupled day-ahead energy, ancillary-service, and carbon markets. However, existing carbon-accounting methods may double count low-carbon electricity because they overlook the distinct emission-reduction mechanisms of heterogeneous resources. Conventional optimization models also cannot readily incorporate natural-language user preferences or other unstructured information. This paper proposes a large language model (LLM)-assisted electricity–carbon co-optimization method for a source–load–storage–vehicle VPP. The method introduces a carbon-credit mechanism that traces renewable-energy utilization and explicitly prevents double counting. This mechanism quantifies emission reductions associated with directly accommodated renewable electricity, renewable-origin energy shifted through storage, rescheduled electric-vehicle charging, and controllable-load curtailment or shifting. The LLM converts users’ natural-language preferences into hard constraints, soft constraints, prohibited time windows, and penalty parameters. These elements are then embedded in a deterministic day-ahead co-optimization model. The resulting mixed-integer linear programming model maximizes net revenue subject to operational, user-preference, carbon-credit, and market-performance constraints. Case-study results show that the proposed method improves economic returns, carbon-market revenue, emission reductions, renewable-energy accommodation, and the quality of demand-side response.