Abstract
Under the backdrop of the “dual carbon” strategy, the rapid increase in renewable energy penetration has exacerbated challenges such as widening peak–valley load gaps and insufficient grid regulation capacity, highlighting the urgent need to establish a market-oriented collaborative dispatching mechanism. This paper proposes a peak-shaving and valley-filling dispatching approach based on a multi-agent system (MAS) to enhance both the regulatory capability and economic efficiency of power grids. A multi-agent collaborative architecture is established on the generation side, where behavioral modeling and interaction simulations of generation, load, and energy storage agents are conducted using the NetLogo platform to emulate dynamic responses under market conditions. On the grid side, dynamic electricity pricing and energy storage control strategies are implemented. An integrated time-of-use electricity pricing mechanism is designed that incorporates environmental pollution factors, supply–demand state factors, and price-smoothing factors to dynamically adjust tariffs. A price-responsive load demand model and a dynamic threshold-based energy storage control strategy are developed to facilitate flexible regulation. On the load side, an optimized dispatch model is formulated with dual objectives of minimizing system operating costs and reducing the standard deviation of the net load profile. The Beetle Antennae Search (BAS) algorithm is employed to solve the model, striking a balance between economic efficiency and stability. Case study results demonstrate that, compared with traditional dispatch methods, the coordinated optimization of the BAS algorithm and the dynamic pricing mechanism proposed in this paper achieves a dual improvement in solution efficiency and economy. This ultimately reduces the system’s peak-to-valley difference by 10.92% and operating costs by 66.2%, proving its effectiveness and superiority in power grids with high renewable energy penetration.