Intelligent Decision Formulation and Composite Neural Network-Driven Optimization Scheduling for Multi-Agent Collaboration in the Electricity Market for Unit Commitment

With the relentless expansion of installed capacity in renewable energy (RE) sources, including wind and photovoltaic power, the operational landscape of the power system has witnessed a substantial surge in uncertainty and complexity. The conventional unit commitment (UC) model falls short when it comes to addressing the challenges posed by a high proportion of RE integration and the collaborative involvement of multiple entities, especially within the electricity market framework. UC now faces a dual challenge: it must not only grapple with the pronounced uncertainty on the source–load side but also harmonize the operational characteristics of emerging entities, such as independent energy storage (ES) systems and virtual power plants. All of this must be achieved while strictly adhering to market regulations and grid safety constraints. To tackle these issues, this paper proposes an intelligent scheduling model built upon a composite neural network. This model enables real-time optimization of scheduling for thermal power, renewable energy, and ES systems through multi-agent collaboration. By doing so, it effectively mitigates the uncertainty associated with RE sources and enhances the safety, economic efficiency, and flexibility of the power system.