Views: 0 Author: Site Editor Publish Time: 2026-03-23 Origin: Site
Recently, the phenomenon of "raising lobsters" has been heating up continuously and has become a hot topic of public discussion.
The "lobster" here is not the aquatic lobster used in the catering industry, but the open-source agent "OpenClaw" jointly developed by OpenAI, Microsoft, Amazon, Google and other global tech giants. It is affectionately called "Lobster" by people because its product icon is in the shape of a lobster.
The core advantage of "Lobster" lies in its ability to make autonomous decisions and execute on devices, breaking through the limitation of traditional AI that can only provide dialogue suggestions, and thereby promoting the upgrade of AI from pure chat interaction to scenario-based execution and implementation.
With its influence rapidly rising, OpenClaw's popularity has gradually permeated the energy sector. Relying on its cross-platform scheduling and intelligent execution capabilities, it is regarded by many photovoltaic industry practitioners as an important breakthrough to solve the pain points and bottlenecks in the industry, and has been highly anticipated by the industry.
So, what substantive value can this tech "lobster" that has crossed over from other fields bring to the photovoltaic industry, and what kind of waves will it stir up in the process of industry transformation?
From lobsters to computing power collaboration
Before delving into the impact of OpenClaw on photovoltaics, we need to first understand how it differs from other AI products.
The core advantage of "lobster" lies in its ability to make autonomous decisions and execute equipment. It can not only think but also act. This qualitative change means: When a user issues a target command, OpenClaw no longer merely tells you what to do. Instead, it directly mobilizes system resources and cross-platform coordination tools to complete the task step by step, completely breaking through the limitation of traditional AI that can only provide dialogue suggestions. This further promotes the upgrade of AI from pure chat interaction to scenario-based execution and implementation.
Just as food doesn't grow out of thin air on supermarket shelves, the driver of OpenClaw also requires energy support. Every time OpenClaw mobiles plugins, calls large models for reasoning, and executes multi-step task streams, it consumes a vast amount of tokens.
Behind these tokens lies the trillions of operations per second of cloud computing power centers. And behind these calculations lies the electricity consumption measured in kilowatt-hours in the real world. It is estimated that the computing power consumed by a mature AI agent to perform complex tasks is dozens of times that of traditional conversational AI.
It is easy to imagine how much pressure the power grid that supports them will be under when millions of "lobsters" simultaneously wake up in the cloud and start handling emails, writing reports and managing schedules for humans.
In addition to the possible power shortage, the greater challenge brought by AI data centers lies in the sharp fluctuations in power load. Unlike traditional data centers that handle relatively stable loads such as web browsing and video streaming, AI data centers carry typical pulse loads. The GPU has relatively low power consumption when idle, but once it receives a computing task, its power will reach full capacity in milliseconds, with the peak reaching several times the average value. This kind of sharply fluctuating load poses a far more challenging test to the power grid than simply consuming a large amount of electricity.
The "computing and power synergy" proposed in this year's government work report aims to address the mismatch between the supply and demand of computing power and electricity, and promote the coordinated development of computing power and energy supply. This also opens up deeper opportunities for the photovoltaic industry to get involved in the supply of computing power and energy and achieve high-quality development.
What new possibilities will arise when electricity meets computing power
In this year's government work report, "computing and power synergy" was included in the new infrastructure for the first time, marking that the deep integration of computing power and power has been elevated to a national strategy. However, the synergy between computing power and electricity is not merely about computing power using electricity and green power providing electricity. Instead, it forms a virtuous cycle where computing power dispatching optimizes power utilization and photovoltaic power supply supports the development of computing power. This model has brought three core opportunities to the photovoltaic industry.
First, computing power centers are becoming stable major customers of photovoltaic power. The explosive growth in computing power demand, coupled with the strict requirements of the state on the proportion of green electricity used by data centers, has driven an increasing number of data centers to seek large-scale direct supply of green electricity. At present, photovoltaic power generation has a significant cost advantage. Meanwhile, it can smooth out the power shortage at night by being equipped with energy storage, achieving a deep integration of photovoltaic power generation, energy storage and AI data centers.
At present, many technology enterprises have begun to build their own photovoltaic power stations and energy storage facilities, and supply power to data centers through microgrids. The cross-platform dispatching capability of AI can also achieve precise matching between photovoltaic output and computing power load, enabling photovoltaic power to serve computing power demands more efficiently.
Meanwhile, photovoltaic manufacturers are also making targeted plans and developing a new generation of photovoltaic modules for data centers. These products focus on the special demands of data centers such as high load and long-term operation, further adapting to the scenarios of computing and power collaboration, and promoting the precise penetration of photovoltaic products into the computing power field.
The second is to address the intermittent pain point of photovoltaic power and enhance the efficiency of consumption. The photovoltaic industry has long been confronted with the problem of being dependent on the weather. Power generation is characterized by obvious intermittency and volatility, which contradicts the demand for stable electricity from computing power centers. The autonomous decision-making and intelligent scheduling capabilities of AI can precisely address this pain point from both the temporal and spatial dimensions.
From the perspective of time dimension, the collaboration between computing and power enables flexible scheduling of computing power tasks. Some non-real-time computing tasks, such as the background computing for large model training, can be arranged for centralized processing during the midday period when photovoltaic power generation is high. When photovoltaic power generation is high, more computing power consumes electricity; when photovoltaic power generation is low, less computing power consumes electricity, reducing the phenomenon of abandoned light from the source.
The autonomous decision-making and intelligent scheduling capabilities of AI can precisely address this pain point and help photovoltaic power better adapt to the demands of computing and power coordination. On the one hand, Coco collects the output data of photovoltaic power stations and the power load data of AIDC in real time. Through intelligent analysis, it can predict the fluctuations in photovoltaic output and changes in computing power load in advance, and dispatch the supporting energy storage for charging and discharging regulation.
From a spatial perspective, AI intelligent scheduling can link cross-regional photovoltaic resources and computing power centers, solving the spatial mismatch problem of "east computing, west power". By matching the abundant photovoltaic resources in the western region with the vigorous computing power demand in the eastern region, the utilization efficiency of photovoltaic resources across the country can be further enhanced.
Third, it can promote the digital and intelligent upgrade of the photovoltaic industry. Traditional photovoltaic power stations mostly rely on manual operation and maintenance, which is inefficient and difficult to meet the precise dispatching requirements in the scenario of computing and power coordination. The introduction of AI is changing this situation. On the one hand, AI can monitor the operational status of photovoltaic modules in real time, automatically identify faults and accurately locate problems, significantly reducing manual operation and maintenance costs. On the other hand, in the spot electricity market transactions, AI agents can analyze the trends of electricity prices and power generation forecasts in real time, automatically decide on the output strategies of power stations, and help photovoltaic power stations increase their trading profits.
The deeper change lies in the fact that the collaboration between computing and power is driving photovoltaic power to transform from a mere role on the power generation side to an integrated role of source-grid-load-storage. In the past, photovoltaic power stations only needed to generate electricity and feed it into the grid to complete their tasks. Nowadays, the emergence of computing power centers has provided photovoltaic power generation with a more closely linked object. Photovoltaic power stations can be combined with computing power centers and energy storage systems to form microgrids. While meeting their own electricity demands, they can also participate in grid interaction and obtain multiple benefits.
Written at the end
From the popularity of "lobsters" to the writing of the integration of computing and power in the government work report, a clear logical chain is emerging: the autonomous execution ability of AI agents drives the explosive demand for computing power, the power pressure on computing power centers forces the transformation of the power system, and photovoltaic power, as the most flexible green power source, has ushered in an unprecedented development space in this transformation.
However, it must be noted that the implementation of computing and power integration still faces many practical challenges.
From the perspective of the collaborative mechanism, the collaborative dispatching system between computing power centers and photovoltaic power stations has not yet been established. There is a lack of real-time information exchange channels between the computing power center and the power generation side. The division of dispatching permissions among the power grid, the computing power dispatching platform, and the photovoltaic power station is ambiguous. How the computing power load responds to changes in photovoltaic output, who responds, and to what extent, these key issues are still under exploration.
From the perspective of market rules, the specific details of electricity market-based trading have not yet been fully adapted to the scenarios of power calculation and electricity coordination. The green power trading between photovoltaic power stations and computing power centers also lacks a stable price mechanism and long-term contract guarantees, and there is considerable uncertainty in the expected investment returns.
From the perspective of project implementation, there are still barriers in the connection between photovoltaic and computing power projects in the planning and approval process. The location selection of computing power centers often prioritizes factors such as network latency and land costs, resulting in spatial misalignment with areas rich in photovoltaic resources. In addition, the construction standards, safety regulations, and grid connection procedures for the supporting energy storage projects of computing power centers have not yet been unified, which affects the efficiency of project advancement.
The direction has been clear, but the road ahead is still long. The transition of computing and power integration from a policy concept to industrial practice requires multiple supports including mechanism design, market rules, and technological breakthroughs. For the photovoltaic industry, the window of opportunity has opened, but whether it can truly seize this round of growth dividends depends on whether all links in the industrial chain can work together to solve the above-mentioned practical problems.
