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封面
市场调查报告书
商品编码
1702390

全球电气数位孪生市场 - 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个工作天内

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简介目录

2024 年全球电气数位孪生市场规模达到 12.1 亿美元，预计到 2032 年将达到 35.7 亿美元，在 2025-2032 年预测期内的复合年增长率为 14.50%。

受电力公用事业、工业自动化和智慧电网日益普及的推动，电气数位孪生市场正在快速成长。它使用虚拟副本实现电气系统的即时监控、预测性维护和最佳化。再生能源整合、电网现代化和工业物联网 (IIoT) 的投资不断增加，推动了市场的发展。

电气数位孪生市场趋势

一个主要趋势是人工智慧和机器学习的日益普及，增强了电力系统的即时监控和预测分析。与再生能源的整合正在加速，从而实现更好的电网稳定性和高效的能源分配。基于云端的数位孪生解决方案的扩展正在提高公用事业和工业的可访问性和可扩展性。监管支持和电网现代化项目投资正在推动成长，

例如，2024 年 1 月启动了 TwinEU 项目，旨在创建整个欧洲电网的数位孪生。该计划将电网和市场运营商、技术提供商和研究中心聚集在一起，透过本地孪生联盟开发泛欧数位孪生。

动力学

再生能源的日益普及

根据国际能源总署的数据，再生能源在电力产业的份额预计将从 2023 年的 30% 成长到 2030 年的 46%。这将推动电力数位孪生市场的发展，因为即时监控、预测分析和电网优化的需求正在增加。数位孪生可协助公用事业公司预测发电波动、优化电网营运并加强能源储存管理。它们能够即时监控和控制分散式能源资源（DER），提高电网稳定性和弹性。

随着分散化的不断增加，数位孪生促进了聚合和协调多种可再生资产的虚拟发电厂 (VPP) 的发展。人工智慧和物联网驱动的数位孪生增强了预测性维护，减少了停机时间和营运成本。此外，它们还支援需求响应计划和点对点能源交易，使再生能源系统更有效率。

整合的复杂性

将数位孪生与现有电力系统、SCADA、物联网设备和人工智慧平台整合的复杂性是电力数位孪生市场发展的一大限制因素。许多公用事业公司仍然依赖缺乏与先进数位孪生解决方案相容性的传统基础设施，这使得无缝资料同步变得困难。整合过程需要高度客製化的解决方案，从而增加成本和部署时间。此外，不同供应商之间不一致的资料格式和互通性挑战也造成了进一步的复杂情况。

目录

第一章：方法论和范围

第 2 章：定义与概述

第三章：执行摘要

第四章：动态

  • 影响因素
    • 驱动程式
      • 再生能源的日益普及
    • 限制
      • 整合的复杂性
    • 机会
    • 影响分析

第五章：产业分析

  • 波特五力分析
  • 供应链分析
  • 定价分析
  • 监理与合规分析
  • 可持续性分析
  • 技术分析
  • DMI 意见

第六章：双胞胎类型

  • 数位燃气和蒸汽发电厂
  • 数位化风力发电场
  • 数位电网
  • 数位化水力发电厂
  • 其他的

第七章：依用途类型

  • 产品数位孪生
  • 流程数位孪生
  • 系统数位孪生

第 8 章：按部署模式

  • 云
  • 本地

第九章：按应用

  • 资产绩效管理
  • 业务与营运优化
  • 故障检测、预测性维护
  • 效能最佳化
  • 其他的

第 10 章：按最终用户

  • 实用工具
  • 电网基础设施营运商
  • 其他的

第 11 章：按地区

  • 北美洲
    • 我们
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 法国
    • 义大利
    • 西班牙
    • 欧洲其他地区
  • 南美洲
    • 巴西
    • 阿根廷
    • 南美洲其他地区
  • 亚太
    • 中国
    • 印度
    • 日本
    • 澳洲
    • 亚太其他地区
  • 中东和非洲

第十二章：公司简介

  • General Electric
    • 公司概况
    • 产品组合和描述
    • 财务概览
    • 关键进展
  • ABB
  • Siemens
  • Wipro
  • Schneider Electric
  • Microsoft Corporation
  • SAP SE
  • IBM
  • Bentley Systems, Incorporated
  • Emerson Electric Co.

第 13 章：附录

简介目录
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