New Energy Data Intelligence Platform_Innovative Digital Intelligent Energy Operation Management Ecosystem

New Energy Data Intelligence Platform

The new energy industry faces numerous challenges on its digitalization path due to complex and intertwined information systems, cumbersome and drawn-out business processes, and complex and variable personnel structures. Building a new energy data foundation based on a data-intelligence platform can provide comprehensive data capabilities, enhance production efficiency, promote the sustainable development of new energy, and solidify the foundation for industry digitalization.

A new energy data foundation built on a data-intelligence platform can provide comprehensive data capabilities, improve production efficiency, promote the sustainable development of new energy, and solidify the foundation for industry digitalization.

Industry Pain Points

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Low degree of data integration, lack of efficient data asset management and utilization, absence of data governance and analysis, resulting in low data value.

Low Data Timeliness

Lacks real-time data collection and computing capabilities, unable to efficiently mine and utilize data collected by front-end devices like IoT sensors.

Difficulties in Grid Integration Coordination

Weak data interaction capabilities between equipment and equipment, equipment and people, and people and people.

Value Proposition

Innovation in New Energy Digital Applications

Using the new energy data intelligence platform as the digital foundation, combined with professional new energy power generation technology and operational technology, integrating big data, artificial intelligence, and other technologies, to achieve digital operation, predictive maintenance, power dispatch optimization, short-term new energy power prediction, a model system for new energy power prediction and electricity trading decisions, and the expansion of more algorithms & application scenarios.

Improving Power System Economy and Environmental Friendliness

Economically, through big data analysis, accurately predict electricity prices and load changes in real-time, optimize the operation of generating units and loads, reduce electricity leakage and shortage losses, achieve comprehensive economic operation, and lower power costs by approximately 3-5%. Environmentally, based on power predictions for photovoltaics and wind power, intelligently control the integration and consumption of new energy, increase the grid's capacity for new energy, reduce wind and solar curtailment, promote the consumption and utilization of renewable energy, and reduce environmental pollution.

Enhancing Power System Security and Intelligence

In terms of security, monitor the operational status of the entire grid in real-time, perform power flow redistribution, voltage and reactive power optimization, effectively alleviate system overload and voltage security issues, and conduct hot and cold state simulations to assess the impact of different operational schemes on system stability, improving the security and reliability of system operation. In terms of intelligence, achieve fully automatic operation, significantly reducing errors and uncertainties brought by manual dispatch. Especially as the proportion of new energy increases, intelligent automatic dispatch becomes particularly important; this is the necessary path to achieving power system intelligence.

Innovation in New Energy Digital Applications

Using the new energy data intelligence platform as the digital foundation, combined with professional new energy power generation technology and operational technology, integrating big data, artificial intelligence, and other technologies, to achieve digital operation, predictive maintenance, power dispatch optimization, short-term new energy power prediction, a model system for new energy power prediction and electricity trading decisions, and the expansion of more algorithms & application scenarios.

Improving Power System Economy and Environmental Friendliness

Economically, through big data analysis, accurately predict electricity prices and load changes in real-time, optimize the operation of generating units and loads, reduce electricity leakage and shortage losses, achieve comprehensive economic operation, and lower power costs by approximately 3-5%. Environmentally, based on power predictions for photovoltaics and wind power, intelligently control the integration and consumption of new energy, increase the grid's capacity for new energy, reduce wind and solar curtailment, promote the consumption and utilization of renewable energy, and reduce environmental pollution.

Enhancing Power System Security and Intelligence

In terms of security, monitor the operational status of the entire grid in real-time, perform power flow redistribution, voltage and reactive power optimization, effectively alleviate system overload and voltage security issues, and conduct hot and cold state simulations to assess the impact of different operational schemes on system stability, improving the security and reliability of system operation. In terms of intelligence, achieve fully automatic operation, significantly reducing errors and uncertainties brought by manual dispatch. Especially as the proportion of new energy increases, intelligent automatic dispatch becomes particularly important; this is the necessary path to achieving power system intelligence.

Solution Architecture

Customer Cases

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Data Intelligence Platform for a Wind Power Plant

A wind power plant hopes to use digital technologies to enhance traditional wind power management capabilities and the operational efficiency of the wind farm, thereby reducing management and operation and maintenance costs. In this case, the new energy data foundation is used to monitor the wind farm's real-time data, store historical data, achieving real-time monitoring of indicators and status, making on-site production data fully visible. Simultaneously, through layer-by-layer drilling down in process monitoring, it meets the usage needs from management to frontline employees. The layer-by-layer aggregation of production data also meets the requirements for statistical and diagnostic analysis.

A wind power plant hopes to use digital technologies to enhance traditional wind power management capabilities and the operational efficiency of the wind farm, thereby reducing management and operation and maintenance costs. In this case, the new energy data foundation is used to monitor the wind farm's real-time data, store historical data, achieving real-time monitoring of indicators and status, making on-site production data fully visible. Simultaneously, through layer-by-layer drilling down in process monitoring, it meets the usage needs from management to frontline employees. The layer-by-layer aggregation of production data also meets the requirements for statistical and diagnostic analysis.