Enterprise AI Analysis
Research on Optimisation Pathways for Power Enterprise Management Based on Data Centralisation and Digital Twin Synergy
With the accelerating convergence of energy restructuring and digital technologies, power enterprises' management systems face the profound challenge of contemporary transformation. Addressing critical issues such as fragmented systems, substandard data quality, and inefficient management, this paper builds upon traditional management optimisation research. It introduces a data-centric architecture and digital twin models to construct an intelligent management optimisation framework integrating edge computing, cyber-physical systems, and predictive maintenance. This framework encompasses four tiers: data governance, equipment sensing, intelligent platforms, and talent support. By unifying data standards, constructing integrated virtual-physical models, and enhancing real-time perception and intelligent decision-making, it propels power enterprise management systems from “information-driven support" to "intelligence-driven transformation”.
Transforming Power Enterprise Management: Key Outcomes
The comprehensive framework leverages data centralisation and digital twin synergy to drive significant improvements across key operational metrics for power enterprises.
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Operational Cost Reduction
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Failure Prediction F1-Score
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Unplanned Outage Reduction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Key Issues in Digital Transformation
The power industry faces significant challenges in its digital transformation journey. Despite widespread deployment of smart meters and monitoring devices, systems were developed independently, leading to significant heterogeneity in data structures, interface protocols, and business logic. This results in a siloed system architecture, hindering information integration and operational coordination.
Furthermore, while data resources are growing rapidly, their quality and utilization rates are low. Vast quantities of data are used solely for basic visualization or archiving, failing to effectively inform business decisions. The Data Quality Impact Coefficient (Q) commonly falls below 0.5, indicating compromised analytical accuracy.
Management practices remain predominantly manual, with digitalisation confined to auxiliary tools. Equipment inspections still rely on periodic manual checks, lacking real-time monitoring. These issues lead to statistical discrepancies, distorting management's assessment of operational conditions, and inhibiting deeper application of digital tools.
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Current Data Quality (Q Value)
Power enterprises commonly exhibit Q values below 0.5, significantly weakening data's ability to support decision-making and leading to skewed analytical outcomes.
| Challenge Area | Current Issues (Core Causes & Potential Impacts) | Optimised Pathways (Key Implementation Points & Expected Outcomes) |
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| Data Silos & Integration |
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| Data Quality & Utilisation |
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| Traditional Management Model |
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| Equipment Management |
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Driving Enterprise Transformation
Digital technologies are no longer just supporting tools but are deeply embedded within management systems, playing a prominent role in core functions like decision-making, resource allocation, and emergency response. This transformation promotes a more scientific and forward-looking approach to corporate strategy.
Promoting Scientific Decision-Making
Digitalization provides data foundations and algorithmic support, allowing management to transcend traditional reliance on experience. By integrating multi-source data, enterprises can construct data warehouses and analysis platforms for trend forecasting, risk assessment, and scenario simulation, enhancing scientific rigor and foresight.
Optimising Cost Control and Resource Allocation
Digitalization establishes transparent, efficient, and traceable resource management. Through comprehensive monitoring of equipment, energy consumption, and personnel, enterprises can achieve dynamic control over operational costs. An equipment maintenance cost optimisation model aims to minimise annual maintenance costs. For example, a substation's transformer inspection cycle was optimised from monthly to once every two months, reducing annual operational and maintenance costs by 18%.
Enhancing Emergency Response
Intelligent monitoring systems, underpinned by sensor networks and real-time data analysis, enable round-the-clock perception of grid status, allowing early identification of anomalies and generation of warnings. Coupled with digital emergency command platforms, the system can automatically generate optimal response plans, significantly reducing incident resolution times and enhancing power supply continuity.
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Reduction in Operational & Maintenance Costs
Optimising equipment maintenance cycles, like changing substation transformer inspection from monthly to once every two months, reduced annual operational and maintenance costs by 18%.
The Significance of Digital Management in Power Enterprises (Figure 1)
Multi-source data acquisition
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Data Governance and Integration
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Implementation of core business scenarios
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Transformation Value Presentation
A Four-Dimensional Optimisation Framework
This paper proposes a four-dimensional optimisation framework: data governance, equipment sensing (Digital Twin), intelligent platforms, and talent support. This system is driven by data centralisation and digital twin synergy, integrating edge computing, cyber-physical systems, and AI algorithms to achieve deep convergence of data, information, and business processes, shifting power enterprises to an 'intelligence-driven' paradigm.
Data Governance: Establishing a Trusted Foundation
This is the cornerstone for optimised digital management. It involves establishing unified semantic standards, data formats, and master data systems to overcome data silos. AI-driven ETL processes and data quality monitoring algorithms perform dynamic cleansing, establishing a closed-loop mechanism from collection to feedback. Blockchain traceability ensures data security and compliance.
Digital Twin: Intelligent Operation & Maintenance
Digital twin technology enhances operational efficiency and safety. Three-dimensional and mechanistic modelling integrates monitoring data (voltage, current, temperature, vibration, meteorological parameters) to establish a virtual-physical twin. Time-series anomaly detection algorithms and health index models quantitatively assess equipment condition, facilitating predictive maintenance. This approach shifts management from 'passive response' to 'proactive optimisation'.
Intelligent Platform: CPS-Driven Coordination Hub
The platform integrates CPS architecture, edge computing, and cloud-based intelligent engines. It enables data aggregation, business collaboration, and decision analysis. A data middle platform unifies data access and sharing, with AI algorithm engines for load forecasting, operational optimisation, and equipment health assessment. Visualised interfaces and natural language interaction modules allow managers to obtain key metrics and recommendations.
Digital Talent Framework: Shaping Data-Driven Culture
Digital transformation requires reshaping organisational capabilities, talent structures, and change management. This involves cultivating a multidisciplinary workforce with digital awareness, data literacy, and cross-domain collaboration. Structured change management initiatives, comprehensive digital talent frameworks (multi-tiered training), and fostering cross-functional collaboration are key. Embedding data usage metrics into performance appraisal systems institutionalizes data-driven behavior.
Optimised Roadmap for Digital Transformation (Optimise the Route) (Figure 2)
Data Governance
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Data Application Platform Development
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Digital Twin + Intelligent Applications
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Digital Talent Framework
Predictive Maintenance Success with Digital Twin
The proposed digital-twin-based maintenance strategy achieved an average F1-score of 0.87 for failure event prediction and reduced unplanned outages by over 20%. This was validated through historical maintenance records and asset downtime logs from 2022-2024, demonstrating its effectiveness in anticipating critical failures and shifting from reactive to proactive maintenance.
Considerations for DERs and Microgrid Integration
While the framework shows strong potential in centralized grids, adapting it to distributed energy resources (DERs) and microgrid environments is crucial. The increasing penetration of photovoltaic systems, energy storage, electric vehicles, and flexible loads introduces high heterogeneity, stochastic behavior, and dynamic topology changes.
To support plug-and-play interoperability, the framework needs to extend semantic standardization efforts to cover DER-specific protocols (e.g., IEC 61850-7-420, IEEE 2030.5) and lightweight data models for resource-constrained edge devices. Microgrid switching scenarios (islanded vs. grid-connected) require real-time synchronization of virtual models.
Mitigating Cybersecurity Risks
The proliferation of edge nodes and twin models significantly expands the attack surface. To mitigate risks, the framework must incorporate:
- Device-level identity attestation using lightweight TPM or blockchain-based credentials.
- End-to-end data integrity verification via cryptographic signatures or hash chains.
- Anomaly detection algorithms embedded at both edge and cloud layers to flag suspicious behavioral deviations.
- Zero-trust architecture principles to enforce least-privilege access across all layers.
Future work will focus on validating the framework's scalability in simulated DER-rich environments and hardening its security posture against emerging threats.
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Current Data Quality (Q Value)
Power enterprises commonly exhibit Q values below 0.5, significantly weakening data's ability to support decision-making and leading to skewed analytical outcomes.
| Current Situation and Issues | Core Causes | Potential Impacts |
|---|---|---|
| The issue of data silos is particularly acute. | Lacking an overarching plan, systems are developed in isolation; data interfaces, protocols, and structures are inconsistent. | Difficulties in integrating information and inefficient business collaboration constrain the overall level of digital management. |
| Data of poor quality and low efficiency. | Lack of efficient data mining tools; insufficient data capabilities among business personnel. | Data fails to support decision-making and process optimisation; Analytical accuracy is compromised, thereby misleading management judgements. |
| Traditional Management Model. | Traditional management thinking exhibits strong inertia; a lack of data-driven culture prevails; digitalisation remains poorly integrated with business operations. | Management inefficiencies lead to significant statistical discrepancies; Digital tools are applied superficially, remaining largely superficial. |
The Significance of Digital Management in Power Enterprises (Figure 1)
Multi-source data acquisition
→
Data Governance and Integration
→
Implementation of core business scenarios
→
Transformation Value Presentation
Digital Twin for Predictive Maintenance in Wind Farms
In a wind farm application, the digital twin system continuously monitored gearbox vibration indicators, detecting potential anomalies 72 hours in advance. This proactive approach successfully prevented major equipment failures, realizing a fundamental shift from reactive maintenance to proactive optimization. The model achieved an average F1-score of 0.87 for failure event prediction and reduced unplanned outages by over 20%.
Optimised Roadmap for Digital Transformation (Optimise the Route) (Figure 2)
Data Governance
→
Data Application Platform Development
→
Digital Twin + Intelligent Applications
→
Digital Talent Framework
Calculate Your Potential ROI
Estimate the significant efficiency gains and cost savings your enterprise could achieve by adopting data centralisation and digital twin synergy.
Estimated Annual Savings
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Reclaimed Employee Hours
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Your Digital Transformation Roadmap
A strategic, phased approach to integrating data centralisation and digital twin technologies into your power enterprise.
Phase 1: Assessment & Data Foundation
Conduct a comprehensive assessment of existing systems and data landscape. Establish unified data standards, master data management, and initial data governance frameworks. Implement AI-driven ETL processes and quality monitoring.
Phase 2: Digital Twin PoC & Platform Development
Develop initial digital twin models for critical equipment with edge computing integration. Begin building the data middle platform, ensuring unified access and sharing of diverse data resources. Foster cross-functional teams.
Phase 3: Intelligent Applications & Pilot Rollout
Integrate AI algorithm engines for load forecasting and operational optimization. Roll out predictive maintenance pilots based on digital twin insights. Start multi-tiered training programs for digital talent.
Phase 4: Full-Scale Integration & Cultural Shift
Expand digital twin coverage to broader enterprise assets. Fully integrate intelligent platforms with core business processes. Embed data-driven behavior into performance appraisals. Establish continuous learning and innovation cycles.
Phase 5: Scalability & Security Enhancement
Extend framework to DERs and microgrids, addressing specific protocols and lightweight models. Implement advanced cybersecurity measures including identity attestation and anomaly detection. Continuously monitor and adapt to emerging threats.
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