smart energy industry is currently undergoing an in-depth transformation. With the integration of a high proportion of new energy sources and the deep convergence of multi-source heterogeneous energy systems, the pain points of the industry's digital transformation are exploding in tandem with the surging demand for computing power.
On one hand, the installed capacity of new energy sources such as wind power and photovoltaic continues to expand. The strong volatility and randomness of their output pose unprecedented challenges to the millisecond-level regulation capability of the power grid, making the traditional model relying on manual experience and rigid scheduling increasingly unable to meet the demand for real-time power balance. On the other hand, massive distributed terminals including distributed photovoltaics, energy storage systems, virtual power plants and charging piles are widely deployed in industrial parks, buildings and mining enterprises. This leads to fragmented data collection, insufficient edge-side computing power and high cloud response latency, leaving a large amount of real-time generated energy data "dormant" that fails to be effectively converted into scheduling instructions or optimization value. Meanwhile, AI applications in the energy sector are rapidly evolving from auxiliary analysis and decision-making to autonomous perception, optimization and execution, putting forward clear and urgent demands for a "cloud-edge-terminal" collaborative, hierarchically classified, flexible and elastic AI computing power system.
However, the industry is generally faced with the dilemma of mismatch between computing power layout and the spatiotemporal distribution of energy resources: Computing power demand is concentrated and tight in eastern load centers, while the computing power potential in western regions rich in renewable energy has not been fully unleashed; Edge-side devices are generally weak in computing power, making it impossible to process and analyze a large number of high-value real-time data locally. This not only increases the data backhaul cost, but also fails to meet the low-latency requirements of scenarios such as instant fault diagnosis and rapid load regulation. Therefore, building a hierarchical AI computing power solution that is deeply adapted to smart energy business scenarios and realizes "computing power - power" collaboration has become the core infrastructure to break down data silos, unlock data value and support the safe, stable and efficient operation of the new power system.
This solution follows the design concept of "terminal-side perception, edge intelligence, regional collaboration and cloud optimization", builds a three-dimensional AI computing power deployment system covering full scenarios from lightweight access, medium-level aggregation to high-performance core, and deeply integrates networked control capabilities.
For massive, scattered and lightweight edge scenarios such as distributed photovoltaic stations, single-point charging piles and small energy storage cabinets, the core demand is low-cost and highly reliable data access and local preprocessing. Deployed Device: USR-EG218 ARM Architecture Industrial PC Core Positioning: Lightweight Edge AI Perception and Protocol Gateway Key Features: Computing Platform: Built on Rockchip RK3562 processor, with standard 4GB LPDDR4 memory and 64GB eMMC storage. Open System: Pre-installed with Ubuntu system (clearly specified as one of the default adapted systems in the manual), with full Linux ecosystem opened for convenient secondary development. Rich Interfaces: Equipped with 1 Gigabit Ethernet port, 4 RS485 serial ports, 2 USB 3.0 ports, and supports Wi-Fi and Bluetooth wireless communication. Edge Computing: As stated in the manual, it has powerful multi-core computing capability to carry edge computing services. It supports real-time data cleaning, statistics, limit judgment and instant alarm for collected data. Application Value: Unified Protocols: Converts on-site protocols such as Modbus and DL/T645 into MQTT, IEC 104 and other standard protocols, reducing the complexity of cloud access. Cloud Burden Reduction: After completing data preprocessing, only key data is uploaded, saving network bandwidth and cloud storage costs.
For medium-scale scenarios with complex business logic such as industrial park microgrids and building smart energy management, it is necessary to aggregate data from dozens of devices and realize rapid local optimization. Deployed Device: USR-EG528 Edge Computing Gateway Core Positioning: Regional Energy Data Aggregation and Intelligent Regulation Hub Key Features: Visual Programming: Official documents indicate that it can be pre-installed with the USR WukongEdge edge computing framework to realize graphical, low-code application orchestration, allowing engineers to quickly build energy business logic through drag-and-drop operations. Powerful Access and Computing: Equipped with 2 Gigabit Ethernet ports and 4 RS485 interfaces, it can simultaneously access dozens of devices such as photovoltaic inverters and energy storage converters (PCS). Powered by Rockchip RK3562 quad-core A55 processor (main frequency 2.0GHz), it supports lightweight AI inference. Local Intelligent Decision-making: It can realize 15-minute-level new energy output prediction and load optimization scheduling locally, supporting the rapid response of park-level "source-load interaction". Application Value: Microgrid Autonomy: Realizes smooth grid-connected/off-grid switching of park-level microgrids and maximum revenue calculation based on real-time electricity prices. Rapid Response: Receives superior regulation instructions and completes aggregated regulation of controllable loads or energy storage systems within its jurisdiction within hundreds of milliseconds.
For scenarios requiring visual monitoring such as photovoltaic power stations, wind farms and substations, intelligent equipment inspection and safety monitoring need to be realized. Deployed Device: USR AI Vision Edge Industrial PC Core Positioning: Dedicated Computing Node for Unstructured Visual Data Analysis and Recognition Key Features: High-performance AI Computing Power: The models listed in the manual are built on RK3576/RK3588 platforms, with the maximum whole-machine AI computing power up to 46TOPS (INT8). Multi-channel Video Access: The manual points out that it supports up to 32 cameras to meet the concurrent monitoring needs of multiple scenarios. Multi-algorithm Integration: The manual mentions that the device can run algorithms such as object detection, classification and segmentation, which are suitable for tasks such as photovoltaic panel hot spot identification, automatic meter reading and safety helmet wearing detection. Application Value: Intelligent Operation and Maintenance: Upgrades regular manual inspection to 7×24-hour uninterrupted automatic monitoring, realizing early fault warning. Safety Management and Control: Monitors the wearing of safety equipment by operators and intrusions into dangerous areas in real time, and triggers linked alarms.
In scenarios such as virtual power plant regulation centers and large wind-solar-storage base control centers, high-load tasks such as massive data fusion and complex model inference are faced. Deployed Device: USR-EG928A High-performance Fanless Industrial PC Core Positioning: High-performance, Highly Reliable Core AI Computing Power and Centralized Control Unit Key Features: Powerful Computing Platform: Adopts Rockchip RK3588 processor (8 cores, maximum 2.4GHz), with a built-in NPU of 6TOPS computing power, supporting multi-channel 4K video decoding and AI inference. Industrial-grade Reliability: Adopts a fanless, fully enclosed metal shell design, with an operating temperature range of -10℃ ~ 55℃, adapting to complex and harsh industrial environments. Rich Expansion: The manual shows that it has 2 Gigabit Ethernet ports, 4 USB3.0 ports, and can expand 5G/4G modules through M.2 and other interfaces. Application Value: Intelligent Operation of Virtual Power Plant (VPP): Aggregates a large number of distributed energy resources within its jurisdiction, runs optimized scheduling algorithms and participates in power market transactions. Advanced AI Applications: Can deploy complex models such as new energy power prediction, equipment health management and AI visual analysis.
The above four levels of computing power nodes are closely connected through an efficient and secure energy Internet of Things to form an organic whole working in collaboration. Network Architecture: Adopts a hybrid "wired + wireless" networking mode, and recommends the SD-WAN intelligent networking solution to realize safe and convenient interconnection of cross-regional devices. Protocol Stack: Downlink supports Modbus, IEC 61850 and other protocols, while uplink adopts standard protocols such as MQTT, IEC 104 and HTTPs. Platform Collaboration: Edge Platform: Such as WukongEdge, provides graphical configuration for data collection, calculation and forwarding rules. Cloud Management Platform: Provides functions such as device management, data visualization and AI model distribution, and can be connected with platforms such as Huawei NetNumen and ZTE NetView.
This smart energy networked control solution based on hierarchical AI computing power realizes maximum value mining of data near the source and precise and fast execution of control instructions by deploying appropriate computing power in appropriate positions. It effectively solves the core pain points faced by the smart energy industry, including data fragmentation, high response latency and heavy cloud load. At present, this solution has been successfully applied in multiple smart energy demonstration projects, helping users reduce operation and maintenance costs and improve new energy consumption rate. Looking ahead, with the continuous development of AI algorithms and computing power technologies, this solution will contribute a key "computing power base" and "smart brain" to the construction of the new power system.
