Guangzhou Zhiguang Electric Co., Ltd

The energy storage cloud network is a one-stop energy storage big data management platform integrating real-time operation monitoring, operation and maintenance management and operation management of energy storage stations. Based on the big data distributed architecture of the Zhiguang Industrial Internet platform, real-time data processing of millions of points and PB-level data storage capabilities are realized, and cloud-side collaboration is realized through big data and artificial intelligence algorithm analysis, providing energy storage station operation, operation and maintenance, and operation Optimization provides auxiliary decision-making support, comprehensively guarantees the operation safety of energy storage stations, and improves operating income.

Combined with the company's "digitalization and platform" strategy, the company innovatively proposed a "1+N+1" model framework based on a big data-driven industrial Internet platform for comprehensive energy services.

"1": A unified proprietary cloud platform to realize the unified storage and management of company data resources;

"N": Multiple specialized business applications, complete the staged development, deployment and unified management of diversified applications;

"1": An intelligent innovation drive center, dedicated to global industrial big data analysis and industrial BI cultivation scenarios and innovative applications.

Cell-level status awareness: By collecting full battery data in real time, monitoring the temperature and voltage data of the battery in real time, combined with 3D rendering digital twin technology, quickly discovering and locating abnormal batteries.

Second-level analysis of equipment failure: Provides second-level data for 30 minutes before and after equipment failure to assist in failure attribution analysis.

Equipment lifecycle management: Records from equipment commissioning to decommissioning The data of the whole life cycle of equipment, including equipment ledger, operating parameters, fault/alarm, maintenance, overhaul records, etc., constructs a digital life course of equipment.

Battery preventive diagnosis safety system: Through big data, artificial intelligence Algorithm, conduct neural network prediction and intelligent analysis and early warning of battery degradation characteristics and trends, and establish a preventive maintenance mechanism.

Data-based operation and maintenance system: Establish standardized inspections and defects Management, work order and other processes, build an online/offline closed-loop operation and maintenance management system, and improve operation and maintenance efficiency.

Auxiliary decision-making for energy storage operations: Through historical market data, market Boundary and other information analyzes and predicts multi-market prices, and combines energy storage operation capabilities to assist in decision-making in multi-market bids such as energy storage spot markets and power auxiliary service markets.

Large screen monitoring board

The large-screen monitoring displays the overall business status of the energy storage station, monitors the status of the energy storage station in real time, and helps managers quickly understand all key business indicators of the energy storage station, including overall operation indicators, fault overview, work order overview, etc.
Operation monitoring

Real-time monitoring of the operation of key equipment of the energy storage station, including PCS, BMS, cooling system, fire protection system, etc., display cell voltage and temperature data through 3D rendering and other digital twin technologies, and conduct real-time temperature and pressure difference analysis to ensure the safety of power station operation .
big data analysis
- The temperature difference statistical analysis function monitors the temperature data of the battery cell, captures the abnormal temperature difference and gives a timely warning to avoid the safety risk caused by the thermal runaway of the battery cell.
- The differential pressure statistical analysis function monitors the minute-level voltage data of the battery in real time, captures the abnormal differential pressure and gives an early warning, so as to avoid the loss caused by the circulation caused by the excessive differential pressure.
- Based on the SOC data of all battery clusters of the energy storage unit, an early warning algorithm is provided to provide early warning for those beyond the safe operation range, control the consistency of battery capacity within a reasonable range, and ensure the operation performance of the power station.
- Combined with various factors such as cell voltage, temperature, SOC, etc., the battery consistency evaluation model is established for different levels of evaluation, and three-level early warnings are given for different levels to guide the operation and maintenance of preventive maintenance.
Power station operation analysis report

Automatically generate power station operation analysis reports, conduct big data statistical analysis of energy storage station overall operation data, fault alarms, battery health, battery life, etc. to form a comprehensive evaluation report, and provide auxiliary decision-making for energy storage station operation, operation and maintenance.

DMS energy storage big data operation management system

Energy storage smart cloud network

System Introductionarchitecturesystem functionHardware products

Large screen monitoring board

Operation monitoring

big data analysis

Power station operation analysis report

DMS energy storage big data operation management system

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