With the rapid increase in the proportion of photovoltaic and wind power in the power structure, the operation mode of the power system is undergoing profound changes. The volatility and dispersion of power generation output make the traditional dispatch mode of "source following load" increasingly difficult, while the massive small resources on the demand side - rooftop photovoltaics, industrial and commercial energy storage, charging piles, air conditioning loads - are also isolated from the electricity market for a long time due to their small size and scattered location. Virtual Power Plant (VPP) is the product of entrusting this contradiction to digitization and artificial intelligence for resolution.
A virtual power plant is not a real power plant in the true sense. It aggregates power generation, energy storage, and adjustable loads scattered in different locations into a whole through cloud computing, the Internet of Things, and intelligent control platforms, and participates in grid scheduling and power trading uniformly. For the power grid, it is like an "elastic power source" that can respond to instructions at any time; For resource owners, it provides opportunities for small resources that originally had no bargaining power to participate in market transactions.
In the operation of virtual power plants, AI runs through almost every core link.
Firstly, the prediction. Both short-term fluctuations in photovoltaic output and changes in the load of industrial and commercial users are highly dependent on complex factors such as weather and production plans. The AI prediction model based on historical data and meteorological forecasts can control the prediction error of output and load at a level far superior to manual experience, leaving a safety margin for subsequent scheduling.
Next is optimizing scheduling. When the electricity price signal changes in real-time with supply and demand, the optimization problems of "when to charge and release" energy storage, "when to adjust the power of charging piles", and "when to temporarily transfer air conditioning load" are essentially constrained. AI optimization algorithms can provide near optimal strategies in seconds, allowing each kilowatt hour of electricity to be used or stored at the most suitable time.
Finally, there is market trading. When virtual power plants participate in the spot market and ancillary service market, their pricing strategies directly affect their returns. AI can learn historical market price patterns and develop more competitive declaration strategies based on its own resource response capabilities.
From the perspective of practical progress, virtual power plants have moved from concept to implementation. Internationally, Tesla's Autobidding platform is a representative commercial practice that helps energy storage assets automatically participate in electricity market transactions; Domestically, multiple cities have also launched large-scale pilot projects around demand response and virtual power plants, exploring the potential for demand side regulation through market-oriented means.
Of course, the large-scale promotion of virtual power plants still faces challenges: the communication reliability of massive devices, data security and privacy protection, and further improvement of electricity market mechanisms are engineering and institutional issues that need to be continuously addressed. But the direction is already clear - the deep integration of AI and the power system is making "invisible power plants" an important component of the new power system.
[Reference source] This article comprehensively compiles information and publicly available product materials from the industry.