A Virtual Power Plant (VPP) is not a real power plant, but a system that aggregates and schedules dispersed energy storage, charging stations, air conditioning loads, rooftop photovoltaics, and other resources. It does not burn coal or gas, but can "squeeze out" the regulating capacity equivalent to a power plant during peak electricity consumption. In the past few years, virtual power plants have moved from concept to pilot, and the emergence of large-scale models is taking their scheduling capabilities to the next level.
The core challenge of virtual power plants is prediction and scheduling. On the one hand, the output of photovoltaic and wind power fluctuates with weather conditions; On the other hand, the load on the user side is also affected by temperature, daily routine, and electricity prices, making it difficult to accurately predict. Traditional physical models combined with statistical methods perform well in stationary scenarios, but often struggle to cope with nonlinear changes in extreme weather and sudden events.
The value of a large model is reflected in three aspects.
One is stronger temporal prediction. A time-series model trained on massive historical data can simultaneously absorb multi-source signals such as weather, electricity prices, holidays, and regional economy, and make more fine-grained predictions on photovoltaic output and load curves. The more accurate the prediction, the more daring the virtual power plant is to report competitive regulation capacity in the electricity market.
The second is scheduling instructions for natural language interaction. Traditional scheduling relies on manually writing rules and scripts, while large models can automatically translate natural language instructions such as "reduce commercial building air conditioning load by 10% from 2pm to 4pm tomorrow" into executable aggregate control strategies, greatly reducing the scheduling threshold.
The third is the dynamic portrait of aggregated resources. Charging stations, energy storage, and building air conditioning have different response characteristics for each resource. Large models can continuously learn the actual response behavior of each resource, establish dynamic profiles for them, and accurately call the most suitable resources at critical moments, rather than a one size fits all approach.
Of course, the large-scale implementation of virtual power plants still faces many practical constraints: the communication standards for aggregating resources have not been unified, the spot trading rules in the electricity market are still being improved, and the revenue model for small and medium-sized users is not clear enough. The big model solves the problem of "scheduling intelligence", while the infrastructure of market-oriented mechanisms and standard interoperability requires the joint promotion of the industry.
From the trend, the combination of AI and new power systems is already a major direction. Whether it is power prediction on the generation side, intelligent inspection on the transmission side, or virtual power plants on the consumption side, AI is pushing the power system from "experience driven" to "data-driven". For ordinary users, in the future, charging stations and energy storage batteries at home may also participate in grid regulation through virtual power plant platforms, saving electricity costs while contributing to grid stability.
This article is a comprehensive compilation of policy interpretations and industry information publicly released by the National Energy Administration and industry media.