2026 Top 100 Potential New Energy Storage Companies in China

Excerpt from Baihua Season DBC Deben Consulting

Energy storage is transitioning from a 'cost item' to a 'strategic asset' in the AI era.

If there is one thing that is most important in the energy storage industry from 2025 to 2026, it is: energy storage is evolving from a passive supporting device into a core infrastructure of the AI era.

This sounds like a slogan, but it hides a profound paradigm shift.

For the past decade, the story of energy storage has been simple: it is an 'appendage' of renewable energy. Photovoltaics and wind power are unstable; we pair them with energy storage to store excess electricity and smooth out the output. At this time, energy storage is a cost item. The core logic of the industry is to reduce the cost of electricity and increase the cycle life.

However, this logic is being disrupted by a 'too romantic' force, which is, of course, AI.

The demand for electricity from AI has already exceeded the design boundaries of traditional power systems. The annual electricity consumption of a large intelligent computing center is comparable to that of a small city with hundreds of thousands of people. Moreover, the requirements of AI computational power on power quality are nearly harsh. Voltage fluctuations, frequency deviations, and even millisecond-level power interruptions can render weeks of model training worthless.

More critically, the power load of AI is not flat. It fluctuates wildly with the peaks and valleys of model training and inference requests. Traditional power grids have never faced such a dual impact of 'digitalization + electrification'.

In the future, the role of energy storage will change. It will no longer be 'batteries that store electricity', but 'systems that regulate power'.

A number of 'new faces' are rising. They may not have the largest battery cell production capacity, but they definitely understand 'power systems + AI loads' the best. For example, companies like Kehua Data and KSTAR, which are rooted in data center UPS, inherently understand what 'high reliability, zero interruption' means; companies like Huawei Digital Energy, leveraging intelligent string architecture, are enabling energy storage systems to possess the ability to 'actively respond to the grid' for the first time; companies like Guoneng Rixin and Hengshi Technology are not making batteries, but energy storage EMS and virtual power plant platforms—using algorithms to predict load, optimize charging and discharging, and participate in electricity market transactions.

Regarding the core capabilities of energy storage in the AI era, it should not only be about how much electricity is stored, but also how to make electricity 'obey'.

Another change that many people overlook is that long-duration energy storage is shifting from 'technical reserve' to 'essential need'.

AI computing centers, large manufacturing bases, and even entire cities' zero-carbon parks require not just 4 hours of peak-valley arbitrage, but a stable supply of green electricity for continuous hours or even across days. Lithium batteries can do this, but cost, safety, and resource constraints will become increasingly tight. Thus, we will see that all-vanadium flow batteries from Dalian Rongke, zinc-iron flow batteries from Weijing Storage, and compressed air storage from China Energy Storage are transitioning from 'marginal technologies' to 'mainstream alternatives'. What they are solving is the Achilles' heel of 'long-cycle stable power supply' in the AI era.

At the same time, the business model of energy storage is being reshaped by AI.

In the past, energy storage made money through 'peak-valley price differences'. Charge during the day, discharge at night, and earn the price difference. Now, with the deepening of electricity market reforms, the sources of income for energy storage have diversified. Capacity leasing, ancillary services, demand response, and even participation in the spot market. And all of this relies on one core capability: forecasting and scheduling.

AI not only creates demand but also provides solutions.

Companies like Meike Shengen and Lechuang Energy in the list represent the use of AI algorithms to optimize energy storage operations. They do not produce batteries but can help users increase the revenue of each kilowatt-hour by several percentage points. In the future of energy storage asset securitization, this 'operational capability' will be more scarce than 'manufacturing capability'.

Therefore, when looking ahead to the next few years, the most important factor might not be which company has the largest capacity, but who can transform energy storage from 'equipment' into 'service'. Who can turn battery stacks into intelligent nodes will occupy a core position in the energy system in the AI era.

Behind this is no longer just competition over materials, processes, and production capacity; the focus is shifting to software, algorithms, and system integration capabilities.

The answers to this transformation are likely hidden in those companies that are 'currently not yet seen'. They may not be located in industrial parks of first-tier cities, and they may not produce battery cells or inverters, but they are quietly building the next generation of energy infrastructure using code, algorithms, and a profound understanding of the electricity market.

AI is too romantic, so romantic that it needs a new power world to support it. And energy storage is becoming the 'foundation' of that world. This is what we see as the most important thing.

(Written by Baihua Season)