With the increasing variety of electric vehicles (EVs) joining virtual power plants (VPPs), VPP operators increasingly require precise and tailored models for schedulable EV energy. Based on a publicly available anonymous EV charging power dataset, EV users are clustered through a weighted K-means++ algorithm. Secondly, based on the results of clustering, we analyzed the daily traveling patterns of various types of EVs, including commuting EVs, electric light-duty trucks (ELDTs) and electric tractors (ETs), and then customized the all-day schedulable energy domain model (SEDM) for each category. Subsequently, an optimal bidding strategy for a VPP consisting of diversified-member EVs, air conditionings (ACs), energy storage (ES) and distributed energy resources (DERs) is constructed. By modifying the levels of participation in supplementation and absorption of DERs among VPP members, while integrating considerations such as user comfort, EV defying rate, and seasonal variability, diverse VPP operational frameworks are established. Finally, using the Gurobi solver, the optimal bidding strategies and profit results under different scenarios are derived. The results indicate that (1) increasing the VPP members’ participation in the supplementation and absorption of DERs will bring higher benefits to both the VPP and its members; (2) with the increased sensitivity of users to room temperature and range anxiety, the demand response capacity of AC clusters decreases, reducing EV clusters’ market participation and VPP profits; and (3) among various types of EVs, ELDTs and ETs have a larger battery energy adjustment range, which can fully supplement the output shortfalls of DERs. Therefore, these EVs prove to be a good supplement for the improvement of VPP’s schedule capability and profitability.

China’s nuclear power plants traditionally operate to meet baseload needs, with minimal involvement in peak load regulation. However, as the share of renewable energy generation rapidly increases, the volatility of the power system and the demand for peak load regulation have significantly risen, necessitating greater nuclear power flexibility to meet the new power system’s requirements. Our study forecasts the energy structure and load demand for the Province of Liaoning in Northeastern China in 2035. Under this vision, it analyzes the flexibility challenges faced by nuclear generation units. A joint clearing model for spot electricity and ancillary services, along with an energy storage revenue model, was established. Based on this, this study analyzed the clearing results for various typical scenarios in the Province of Liaoning in 2035. The simulation results demonstrate that nuclear units will participate in peak shaving by the target year. This study demonstrates the feasibility of solid-state thermal storage in improving both flexibility and economic efficiency of nuclear generation. Based on these findings, policy recommendations are proposed, including improving regulation compensation mechanisms and promoting multi-energy coupling, providing crucial theoretical and practical support for the role transformation of nuclear generation entities in the new power system. This study establishes a full lifecycle economic assessment model for combined heat and power revenue versus thermal storage investment costs, considering integrated nuclear power–solid thermal energy storage heating systems as the primary technical pathway. Taking a configuration plan with a 715 MW heating capacity and a 6000 MWh thermal storage capacity as an example under Liaoning Province’s 2035 long-term scenario, the simulation results indicate that introducing solid thermal energy storage can significantly improve the revenue structure of nuclear units while meeting deep peak shaving demands, reducing the project’s static payback period to under 11 years.