Optimal scheduling of multiple entities in virtual power plant based on the master-slave game

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As the energy market evolves into a dynamic and interactive landscape, the distributed nature of virtual power plant (VPP) becomes increasingly significant. This shift highlights the limitations of traditional centralized optimization approaches in capturing the complex interplay among various stakeholders. In response, this article introduces a novel multi-entity distributed collaborative optimization strategy for an integrated energy system (IES) that incorporates a VPP, electric vehicles, and carbon capture technologies, under the Stackelberg master-slave game framework. Within this framework, the VPP operator (VPPO) assumes the role of the leader, while the energy supply aggregator (ESA), customer side residential load aggregator (CSRLA), electric vehicles aggregator (EVA), and carbon treatment system aggregator (CTSA) are positioned as the followers. The paper delves into the development of interaction strategies for each entity, aiming at achieving optimal objectives. The study undergoes by presenting the mathematical model of the VPP-integrated energy system, which is seamlessly integrated into the master-slave game structure. This integration facilitates the creation of a distributed multi-entity cooperative optimization model featuring a single leader and multiple followers, with the uniqueness of the Stackelberg equilibrium being rigorously established. Therefore, the research employs a hybrid nutcracker optimization algorithm and quadratic programming (NOA-QP) method to address the engineering challenge of optimizing operator energy pricing. The case study conducted demonstrates that the proposed scheduling methodology improves the VPP system profit by 10.80%, increases the aggregation profit by up to 1317.22%, optimizes the carbon emissions by 15.59%, and significantly improves the solution efficiency of the VPP by 99.29%.