Deep Optimization of Collaborative Scheduling Between Energy Storage and Wind-Solar Systems by Industrial Computers in Multi-Energy Complementary Scenarios
Driven by the "dual carbon" goals, multi-energy complementary systems are transitioning from conceptual validation to large-scale deployment. Wind-solar-storage integration, representing a new energy network paradigm, constructs solutions for high-proportion renewable energy consumption by combining the intermittent generation characteristics of wind and solar power with the flexible regulation capabilities of energy storage. However, the stochastic nature of wind-solar output, dynamic response requirements of energy storage systems, and complexity of grid scheduling pose multiple challenges for collaborative optimization. Industrial computers, serving as the system's "nervous center," are reshaping operational paradigms through data fusion, edge computing, and intelligent decision-making.
Collaborative Scheduling Challenges in Multi-Energy Complementary Systems
1.1 Spatiotemporal Mismatch of Wind-Solar Output
Wind and solar power generation exhibit distinct characteristics: solar output follows diurnal cycles influenced by daylight and weather, while wind power responds to stochastic wind speed variations, potentially showing prolonged low output or sudden high-power surges. In a Jiangsu industrial park, the solar system peaks at 14:00 on sunny days, whereas wind power reaches maximum output between 22:00 and 04:00, with daily generation curve overlap below 30%. This mismatch makes it difficult for a single energy storage system to simultaneously smooth both energy sources.
1.2 Multi-Objective Optimization Dilemma for Energy Storage
Energy storage systems must fulfill multiple roles including peak shaving, frequency regulation, and backup power supply. In a Gansu wind-solar-storage plant, the storage system absorbs excess solar power during the day and discharges to compensate for wind power deficits at night while responding to grid frequency modulation commands. This multi-task switching demands millisecond-level controller response capabilities, but traditional centralized control architectures often lag behind actual needs due to data transmission delays.
1.3 Dynamic Constraints in Grid Scheduling
As renewable energy penetration increases, grid scheduling requirements for multi-energy systems shift from "passive following" to "active support." The State Grid Zhangbei VSC-HVDC project requires energy storage systems to respond to frequency regulation commands within 100ms and complete power ramping within 15 minutes, imposing stringent demands on controller real-time computing capabilities and protocol compatibility.
Core Technological Breakthroughs of Industrial Computers
2.1 High-Precision Data Fusion and Edge Computing
Industrial computers achieve comprehensive data acquisition through multi-type sensor integration. The USR-EG628 industrial computer, for example, supports 16 ADC channels and 4 CAN buses, enabling simultaneous collection of over 200 parameters including photovoltaic module temperature, inverter power, battery SOC, wind speed/direction at 1kHz sampling rates. Its ARM Cortex-M7 core with hardware accelerators performs local data cleaning, feature extraction, and anomaly detection. In a Qinghai solar plant, edge computing reduced data upload delays from 3s to 200ms, creating critical time windows for storage scheduling.
2.2 Multi-Protocol Compatibility and Real-Time Communication
To address device heterogeneity, industrial computers must support over 20 industrial protocols including Modbus RTU/TCP, IEC 101/103/104, and DL/T 645. The USR-EG628's protocol parsing engine automatically identifies device types and performs conversions, integrating wind turbines, solar inverters, and storage BMS from different manufacturers into unified control platforms. In a Shandong industrial park project, it achieved seamless interconnection between Huawei inverters, Goldwind turbines, and CATL storage systems with 99.99% communication success rates.
2.3 Dynamic Model Prediction and Intelligent Decision-Making
Machine learning-based prediction algorithms enable proactive wind-solar output forecasting. The USR-EG628's WukongEdge platform supports LSTM neural network training. In an Inner Mongolia wind farm, the controller reduced 72-hour wind power prediction errors from 25% to 8% by analyzing historical wind speed data and SCADA records. Combined with battery SOH assessment models, it dynamically adjusts storage charge/discharge strategies to extend battery life by over 30%.
Typical Collaborative Scheduling Applications
3.1 Wind-Solar-Storage Joint Frequency Regulation
During grid frequency fluctuations, industrial computers coordinate wind-solar output with storage response. In a China Southern Grid frequency regulation plant, the USR-EG628 achieves millisecond-level response through:
Frequency monitoring: Real-time grid frequency collection via PMU at 10ms intervals
Strategy switching: Automatic activation of frequency regulation mode when deviations exceed ±0.05Hz
Power allocation: Prioritizing storage systems (<50ms response), then engaging wind turbine pitch control (<200ms) if storage capacity is insufficient
Closed-loop optimization: Feedback of actual regulation effects to refine control parameters
The system participated in frequency regulation markets 127 times during Guangdong's 2024 summer peak, earning over RMB 5 million in compensation.
3.2 Peak Shaving and Demand Response
Industrial computers optimize storage charge/discharge timing by combining electricity price signals with load forecasting. In a Jiangsu commercial complex project, the USR-EG628 maximizes energy efficiency through:
Load forecasting: Predicting next-day load curves based on historical data and weather forecasts
Strategy formulation: Full storage charging during off-peak periods (23:00-7:00) and discharging during peak periods (10:00-12:00, 18:00-20:00)
Flexible adjustment: Dynamic solar inverter output modulation to reduce grid purchases when actual loads exceed predictions
Effectiveness verification: Strategy validation through before/after electricity bill comparisons
The project reduced annual electricity costs by 22% and shortened storage system payback periods to 4.2 years.
3.3 Microgrid Islanding Operation
During grid failures, industrial computers must rapidly switch to islanding mode to sustain critical loads. During Typhoon Yagi in 2024, a Hainan hospital microgrid using USR-EG628 performed the following operations:
Fault detection: Identifying grid voltage loss within 0.1s
Mode switching: Disconnecting from grid and activating diesel generators/storage within 0.5s
Load management: Shedding non-critical loads (AC, lighting) to prioritize operating rooms and ICUs
Grid resynchronization: Automatic phase matching and reconnection after voltage stabilization
The system maintained hospital operations during 72 hours of outage, preventing over RMB 30 million in direct losses.
Technological Evolution Trends and Industry Outlook
4.1 Deep Application of Digital Twin Technology
Built on NVIDIA Omniverse, industrial computers create high-fidelity energy system digital twins. Tesla's Megapack project achieved 98% accuracy in predicting thermal runaway propagation paths, offering new safety solutions. Future digital twins will extend to device-level, enabling real-time mapping of battery cell health status.
4.2 Continuous Optimization of AI Algorithms
Reinforcement learning is replacing traditional PID control as the core of collaborative scheduling. Google DeepMind's "Energy Neural Network" improved frequency regulation response speeds by 5x in UK grid tests. New-generation controllers like USR-EG628 incorporate AI acceleration modules supporting custom model training.
4.3 International Alignment of Standard Systems
IEC 62933-5-2:2025 reduces thermal runaway propagation time requirements from 24 to 12 hours, accelerating technological upgrades among Chinese energy storage enterprises. The UL9540A-certified USR-EG628's safety architecture enables smooth adaptation to European and American markets, providing Chinese solutions for global energy transition.
Building the Foundation for Intelligent Energy Ecosystems
Industrial computers are evolving from simple data acquisition devices into autonomous energy decision centers. In multi-energy complementary scenarios, models like USR-EG628 elevate wind-solar-storage system collaboration efficiency through closed-loop "perception-decision-execution" control. With continued breakthroughs in 5G, digital twins, and AI technologies, industrial computers will drive energy systems toward intelligent agents capable of "self-sensing, self-optimization, and self-healing," providing critical support for the global energy revolution.
