Distributed Intelligent Decision-making Technology for Virtual Power Plant Aggregators

Although energy storage and demand response strategies are widely used to balance loads, the capacity, economy, and response of energy storage systems to different load changes are still not fully optimized in many studies. To this end, this paper studies the distributed intelligent decision-making technology of Virtual Power Plant (VPP) aggregator, focusing on its application in resource scheduling, energy optimization, and load management. VPP integrates various Distributed Energy Resources (DER), including wind, solar, gas turbines, and energy storage systems to balance supply and demand while improving reliability, flexibility, and sustainability. The proposed method introduces a two-level optimization strategy for VPP load management, using a clustering algorithm for efficient electric vehicle aggregation and a predictive model for energy demand forecasting. The VPPO-LA (Virtual Power Plant Optimization-Load Allocation) model integrates gas-electric coupling, storage system and hydrogen production to improve system stability and optimize operating efficiency. Finally, the experiment simulated the changes in wind power, photovoltaic and other fluctuating power sources and load demands under different scenarios. Testing conducted in different scenarios has shown that the proposed method significantly reduces the operational costs of VPP, enhances overall economic benefits, and effectively addresses the challenges of renewable energy integration and the uncertainties brought by distributed resources.