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市場調查報告書
商品編碼
1702390

全球電氣數位孿生市場 - 2025-2032

Global Electrical Digital Twin Market - 2025-2032
出版日期: | 出版商: DataM Intelligence | 英文 180 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

2024 年全球電氣數位孿生市場規模達到 12.1 億美元，預計到 2032 年將達到 35.7 億美元，在 2025-2032 年預測期內的複合年成長率為 14.50%。

受電力公用事業、工業自動化和智慧電網日益普及的推動，電氣數位孿生市場正在快速成長。它使用虛擬副本實現電氣系統的即時監控、預測性維護和最佳化。再生能源整合、電網現代化和工業物聯網 (IIoT) 的投資不斷增加，推動了市場的發展。

電氣數位孿生市場趨勢

一個主要趨勢是人工智慧和機器學習的日益普及，增強了電力系統的即時監控和預測分析。與再生能源的整合正在加速，從而實現更好的電網穩定性和高效的能源分配。基於雲端的數位孿生解決方案的擴展正在提高公用事業和工業的可訪問性和可擴展性。監管支持和電網現代化項目投資正在推動成長，

例如，2024 年 1 月啟動了 TwinEU 項目，旨在創建整個歐洲電網的數位孿生。該計劃將電網和市場營運商、技術提供商和研究中心聚集在一起，透過本地孿生聯盟開發泛歐數位孿生。

動力學

再生能源的日益普及

根據國際能源總署的數據，再生能源在電力產業的佔有率預計將從 2023 年的 30% 成長到 2030 年的 46%。這將推動電力數位孿生市場的發展，因為即時監控、預測分析和電網最佳化的需求正在增加。數位孿生可協助公用事業公司預測發電波動、最佳化電網營運並加強能源儲存管理。它們能夠即時監控和控制分散式能源資源（DER），提高電網穩定性和彈性。

隨著分散化的不斷增加，數位孿生促進了聚合和協調多種可再生資產的虛擬發電廠 (VPP) 的發展。人工智慧和物聯網驅動的數位孿生增強了預測性維護，減少了停機時間和營運成本。此外，它們還支援需量反應計劃和點對點能源交易，使再生能源系統更有效率。

整合的複雜性

將數位孿生與現有電力系統、SCADA、物聯網設備和人工智慧平台整合的複雜性是電力數位孿生市場發展的一大限制因素。許多公用事業公司仍然依賴缺乏與先進數位孿生解決方案相容性的傳統基礎設施，這使得無縫資料同步變得困難。整合過程需要高度客製化的解決方案，從而增加成本和部署時間。此外，不同供應商之間不一致的資料格式和互通性挑戰也造成了進一步的複雜情況。

目錄

第1章：方法論和範圍

第 2 章：定義與概述

第3章：執行摘要

第4章：動態

  • 影響因素
    • 驅動程式
      • 再生能源的日益普及
    • 限制
      • 整合的複雜性
    • 機會
    • 影響分析

第5章：產業分析

  • 波特五力分析
  • 供應鏈分析
  • 定價分析
  • 監理與合規分析
  • 永續性分析
  • 技術分析
  • DMI 意見

第6章：雙胞胎類型

  • 數位燃氣和蒸汽發電廠
  • 數位化風力發電場
  • 數位電網
  • 數位化水力發電廠
  • 其他

第7章：依用途類型

  • 產品數位孿生
  • 流程數位孿生
  • 系統數位孿生

第 8 章：按部署模式

  • 雲
  • 本地

第9章：按應用

  • 資產績效管理
  • 業務與營運最佳化
  • 故障檢測、預測性維護
  • 效能最佳化
  • 其他

第 10 章：按最終用戶

  • 實用工具
  • 電網基礎設施營運商
  • 其他

第 11 章：按地區

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 法國
    • 義大利
    • 西班牙
    • 歐洲其他地區
  • 南美洲
    • 巴西
    • 阿根廷
    • 南美洲其他地區
  • 亞太
    • 中國
    • 印度
    • 日本
    • 澳洲
    • 亞太其他地區
  • 中東和非洲

第12章：公司簡介

  • General Electric
    • 公司概況
    • 產品組合和描述
    • 財務概覽
    • 關鍵進展
  • ABB
  • Siemens
  • Wipro
  • Schneider Electric
  • Microsoft Corporation
  • SAP SE
  • IBM
  • Bentley Systems, Incorporated
  • Emerson Electric Co.

第 13 章：附錄

簡介目錄
Product Code: CH9457

Global electrical digital twin market reached US$ 1.21 billion in 2024 and is expected to reach US$ 3.57 billion by 2032, growing with a CAGR of 14.50% during the forecast period 2025-2032.

The electrical digital twin market is growing rapidly, driven by the increasing adoption of power utilities, industrial automation, and smart grids. It enables real-time monitoring, predictive maintenance, and optimization of electrical systems using virtual replicas. The market is fueled by rising investments in renewable energy integration, grid modernization, and the Industrial Internet of Things (IIoT).

Electrical Digital Twin Market Trend

A major trend is the increasing adoption of AI and machine learning, enhancing real-time monitoring and predictive analytics for power systems. Integration with renewable energy sources is accelerating, enabling better grid stability and efficient energy distribution. The expansion of cloud-based digital twin solutions is improving accessibility and scalability for utilities and industries. Regulatory support and investments in grid modernization projects are fueling growth,

For instance, in January 2024, the TwinEU project was launched to create a digital twin of the entire European electricity grid. The initiative brings together grid and market operators, technology providers, and research centers to develop a pan-European digital twin through the federation of local twins.

Dynamics

Growing Adoption of Renewable Energy

According to the IEA, the share of renewable energy in the electricity sector is projected to grow from 30% in 2023 to 46% by 2030. This is driving the electrical digital twin market by increasing the need for real-time monitoring, predictive analytics, and grid optimization. Digital twins help utilities predict fluctuations in power generation, optimize grid operations, and enhance energy storage management. They enable real-time monitoring and control of distributed energy resources (DERs), improving grid stability and resilience.

With increasing decentralization, digital twins facilitate virtual power plants (VPPs) that aggregate and coordinate multiple renewable assets. AI and IoT-powered digital twins enhance predictive maintenance, reducing downtime and operational costs. Additionally, they support demand response programs and peer-to-peer energy trading, making renewable energy systems more efficient.

Complexity in Integration

The complexity of integrating digital twins with existing power systems, SCADA, IoT devices, and AI platforms is a major restraint in the electrical digital twin market. Many utilities still rely on legacy infrastructure that lacks compatibility with advanced digital twin solutions, making seamless data synchronization difficult. The integration process requires highly customized solutions, increasing costs and deployment time. Additionally, inconsistent data formats and interoperability challenges across different vendors create further complications.

Segment Analysis

The global electrical digital twin market is segmented based on twin type, usage type, deployment mode, application, end-user and region.

Advancements in Cloud-Based Twin are Expected to Drive the Segment Growth

Cloud-based operations hold a significant share in the electrical digital twin market by enabling scalability, real-time data processing, and remote accessibility. Cloud platforms allow seamless integration of AI, IoT, and big data analytics, enhancing predictive maintenance and energy optimization. They support real-time monitoring of electrical systems, improving grid stability and operational efficiency. With cloud computing, utilities and industries can simulate, test, and optimize power systems without heavy on-premise infrastructure investments.

Collaborations and acquisitions play a major role in expanding the electrical digital twin market by leveraging AI, IoT, and real-time data analytics. In March 2025, Schneider Electric and ETAP introduced the world's first AI Factory digital twin to simulate power requirements from the grid to chip level. Built on NVIDIA Omniverse Cloud APIs, the solution integrates mechanical, thermal, networking, and electrical systems for enhanced insight and control. These initiatives drive scalability, predictive maintenance, and real-time monitoring, making digital twins more accessible.

Geographical Penetration

Government Initiatives and Investment in Smart Grid Systems Drive the Market in Europe

Europe holds a significant share of the global electrical digital twin market due to its strong focus on grid modernization, renewable energy integration, and smart infrastructure. European governments are offering strong initiatives to boost the adoption of electrical digital twin technology as part of their energy transition and smart grid modernization efforts.

For instance, in January 2025, the Horizon Europe DSO4DT project was launched to enhance digital twin adoption for Europe's Distribution System Operators (DSOs), improving grid management and operations. Coordinated by the DSO Entity, the project aims to mobilize DSO members, boost digital twin uptake, and strengthen expertise in smart grid innovations. This type of initiative drives innovation in digital twin technology in the region.

Technological Analysis

The electrical digital twin market is rapidly evolving with advancements in AI, IoT, and 5G connectivity, enabling real-time monitoring, predictive maintenance, and automated decision-making for power grids. Cloud computing and edge computing enhance data processing, ensuring low-latency performance and secure operations. Blockchain technology is emerging for decentralized energy trading, improving transparency and security. Digital twins integrate with smart grids, optimizing renewable energy distribution and stabilizing fluctuating power demand.

In November 2021, Hitachi Energy launched IdentiQ, a digital twin solution for HVDC and power quality systems, enhancing sustainability, flexibility, and security in power grids. Built on Hitachi's Lumada platform, IdentiQ provides a customizable, interactive 3D dashboard with real-time data, asset information, and analytics for improved grid management and decision-making.

Competitive Landscape

The major global players in the market include General Electric, ABB, Siemens, Wipro, Schneider Electric, Microsoft Corporation, SAP SE, IBM, Bentley Systems, Incorporated, Emerson Electric Co. and others.

Key Developments

  • In December 2024, Schneider Electric launched EcoConsult in India to help businesses optimize efficiency, enhance safety, and achieve sustainability goals through electrical and automation system consulting. The service offers audits, digital twins, and system studies to identify issues and improve asset performance.

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Target Audience 2024

  • Manufacturers/ Buyers
  • Industry Investors/Investment Bankers
  • Research Professionals
  • Emerging Companies

Table of Contents

1. Methodology and Scope

  • 1.1. Research Methodology
  • 1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

  • 3.1. Snippet by Twin Type
  • 3.2. Snippet by Usage Type
  • 3.3. Snippet by Deployment Mode
  • 3.4. Snippet by Application
  • 3.5. Snippet by End-User
  • 3.6. Snippet by Region

4. Dynamics

  • 4.1. Impacting Factors
    • 4.1.1. Drivers
      • 4.1.1.1. Growing Adoption of Renewable Energy
    • 4.1.2. Restraints
      • 4.1.2.1. Complexity in Integration
    • 4.1.3. Opportunity
    • 4.1.4. Impact Analysis

5. Industry Analysis

  • 5.1. Porter's Five Force Analysis
  • 5.2. Supply Chain Analysis
  • 5.3. Pricing Analysis
  • 5.4. Regulatory and Compliance Analysis
  • 5.5. Sustainability Analysis
  • 5.6. Technological Analysis
  • 5.7. DMI Opinion

6. By Twin Type

  • 6.1. Introduction
    • 6.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Twin Type
    • 6.1.2. Market Attractiveness Index, By Twin Type
  • 6.2. Digital Gas & Stream -Power Plant*
    • 6.2.1. Introduction
    • 6.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 6.3. Digital Wind Farm
  • 6.4. Digital Grid
  • 6.5. Digital Hydropower Plant
  • 6.6. Others

7. By Usage Type

  • 7.1. Introduction
    • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Usage Type
    • 7.1.2. Market Attractiveness Index, By Usage Type
  • 7.2. Product Digital Twin*
    • 7.2.1. Introduction
    • 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 7.3. Process Digital Twin
  • 7.4. System Digital Twin

8. By Deployment Mode

  • 8.1. Introduction
    • 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
    • 8.1.2. Market Attractiveness Index, By Deployment Mode
  • 8.2. Cloud*
    • 8.2.1. Introduction
    • 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 8.3. On-premises

9. By Application

  • 9.1. Introduction
    • 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 9.1.2. Market Attractiveness Index, By Application
  • 9.2. Asset Performance Management*
    • 9.2.1. Introduction
    • 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 9.3. Business & Operations Optimization
  • 9.4. Fault Detection, Predictive Maintenance
  • 9.5. Performance Optimization
  • 9.6. Others

10. By End-User

  • 10.1. Introduction
    • 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.1.2. Market Attractiveness Index, By End-User
  • 10.2. Utilities*
    • 10.2.1. Introduction
    • 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 10.3. Grid Infrastructure Operators
  • 10.4. Others

11. By Region

  • 11.1. Introduction
    • 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
    • 11.1.2. Market Attractiveness Index, By Region
  • 11.2. North America
    • 11.2.1. Introduction
    • 11.2.2. Key Region-Specific Dynamics
    • 11.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Twin Type
    • 11.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Usage Type
    • 11.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
    • 11.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 11.2.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.2.8.1. US
      • 11.2.8.2. Canada
      • 11.2.8.3. Mexico
  • 11.3. Europe
    • 11.3.1. Introduction
    • 11.3.2. Key Region-Specific Dynamics
    • 11.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Twin Type
    • 11.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Usage Type
    • 11.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
    • 11.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 11.3.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.3.8.1. Germany
      • 11.3.8.2. UK
      • 11.3.8.3. France
      • 11.3.8.4. Italy
      • 11.3.8.5. Spain
      • 11.3.8.6. Rest of Europe
  • 11.4. South America
    • 11.4.1. Introduction
    • 11.4.2. Key Region-Specific Dynamics
    • 11.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Twin Type
    • 11.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Usage Type
    • 11.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
    • 11.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 11.4.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.4.8.1. Brazil
      • 11.4.8.2. Argentina
      • 11.4.8.3. Rest of South America
  • 11.5. Asia-Pacific
    • 11.5.1. Introduction
    • 11.5.2. Key Region-Specific Dynamics
    • 11.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Twin Type
    • 11.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Usage Type
    • 11.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
    • 11.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 11.5.8.
    • 11.5.9. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.5.9.1. China
      • 11.5.9.2. India
      • 11.5.9.3. Japan
      • 11.5.9.4. Australia
      • 11.5.9.5. Rest of Asia-Pacific
  • 11.6. Middle East and Africa
    • 11.6.1. Introduction
    • 11.6.2. Key Region-Specific Dynamics
    • 11.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Twin Type
    • 11.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Usage Type
    • 11.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
    • 11.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.6.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

12. Company Profiles

  • 12.1. General Electric*
    • 12.1.1. Company Overview
    • 12.1.2. Product Portfolio and Description
    • 12.1.3. Financial Overview
    • 12.1.4. Key Developments
  • 12.2. ABB
  • 12.3. Siemens
  • 12.4. Wipro
  • 12.5. Schneider Electric
  • 12.6. Microsoft Corporation
  • 12.7. SAP SE
  • 12.8. IBM
  • 12.9. Bentley Systems, Incorporated
  • 12.10. Emerson Electric Co.

LIST NOT EXHAUSTIVE

13. Appendix

  • 13.1. About Us and Services
  • 13.2. Contact Us