With the large-scale integration of distributed photovoltaics, energy storage, and electric vehicle charging stations, the power system is shifting from "centralized power supply by large units" to "massive distributed resource collaboration". The volatility of the power grid has increased, and the complexity of scheduling has also risen accordingly. Virtual power plants (VPPs) are designed to solve this problem: they aggregate dispersed energy storage, charging stations, air conditioning loads, and small distributed power sources through digital platforms to participate in the electricity market and grid scheduling as a whole - essentially "assembling" a power plant in the software world.
The core capability of virtual power plants is prediction and scheduling, which is precisely where AI comes in handy. Firstly, prediction: load forecasting at the polymer level and distributed photovoltaic output forecasting directly determine the declaration and quotation strategy, and time series models are already quite mature in this field. Next is scheduling optimization: Faced with hundreds or thousands of adjustable resources, how to minimize the response cost and maximize the benefits of each resource while meeting the grid instructions is a typical combinatorial optimization problem. Intelligent optimization algorithms can provide solutions that are close to optimal. Going up to the next level, the big language model begins to enter the auxiliary decision-making stage: translating scheduling plans into understandable explanations, generating analysis reports of abnormal alarms, and even assisting operations personnel in troubleshooting problems, making complex scheduling logic more user-friendly for frontline personnel.
At the technical implementation level, several key points are worth paying attention to. First, edge computing: massive resource side real-time data is not suitable for all cloud processing, edge side completes data preprocessing and local decision-making, and cloud side is responsible for global optimization, which is a common layered architecture. Secondly, data quality: The scheduling decisions of virtual power plants highly rely on the accuracy and real-time nature of measurement data, and dirty data can directly erode the effectiveness of the model. The third is the incentive mechanism and standards: the profit distribution rules between aggregators, resource owners, and the power grid determine whether resources are willing to participate in the response - no matter how advanced the technology is, it cannot bypass the rationality of the business model.
Virtual power plants can be seen as a microcosm of "software defined power systems" - the flexibility of power systems increasingly comes from algorithms and data, rather than physical units. AI plays the role of the brain in this: making predictions more accurate, scheduling better, and responding faster. With the advancement of electricity marketization reform and the expansion of distributed resource scale, the combination of virtual power plants and AI will move from pilot to normal, which is also one of the most solid landing points for AI powered energy transformation.
This article comprehensively compiles technical information related to the energy industry and power system that has been publicly released.