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電気デジタルツインの世界市場（2025年～2032年）

Global Electrical Digital Twin Market - 2025-2032

出版日
ページ情報
英文 180 Pages
納期
即日から翌営業日
カスタマイズ可能
適宜更新あり
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価格表記: USDを日本円(税抜)に換算
本日の銀行送金レート: 1USD=143.57円
電気デジタルツインの世界市場（2025年～2032年）
出版日: 2025年04月10日
発行: DataM Intelligence
ページ情報: 英文 180 Pages
納期: 即日から翌営業日
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  • 概要
  • 目次
概要

世界の電気デジタルツインの市場規模は、2024年に12億1,000万米ドルに達し、2032年までに35億7,000万米ドルに達すると予測され、予測期間の2025年～2032年にCAGRで14.50%の成長が見込まれます。

市場は、電力企業、産業オートメーション、スマートグリッドの採用の増加によって急成長しています。電気デジタルツインは、仮想のレプリカを使用して電気システムのリアルタイムモニタリング、予知保全、最適化を可能にします。再生可能エネルギーの統合、送電網の近代化、産業用IoT（IIoT）への投資の高まりが市場を後押ししています。

電気デジタルツイン市場の動向

大きな動向は、AIと機械学習の採用が増加し、電力システムのリアルタイムモニタリングと予測分析が強化されていることです。再生可能エネルギー源との統合が加速しており、より高い送電網の安定性と効率的なエネルギー配分が可能になっています。クラウドベースデジタルツインソリューションの拡大により、電力企業や産業界にとってのアクセシビリティとスケーラビリティが向上しています。規制当局の支援とグリッド近代化プロジェクトへの投資が成長を後押ししており、例えば2024年1月、欧州の電力網全体のデジタルツインを構築するTwinEUプロジェクトが開始されました。この取り組みでは、送電網と市場の業者、技術プロバイダー、研究センターが結集し、ローカルツインの連合を通じて欧州全土のデジタルツインを開発します。

力学

再生可能エネルギーの採用の拡大

IEAによると、電力部門における再生可能エネルギーの割合は、2023年の30%から2030年までに46%に増加すると予測されています。これにより、リアルタイムモニタリング、予測分析、グリッド最適化のニーズが高まり、電気デジタルツイン市場を牽引しています。デジタルツインは、電力企業が発電量の変動を予測し、グリッド運用を最適化し、エネルギー貯蔵管理を強化するのに役立ちます。また、分散型エネルギー資源（DER）のリアルタイムモニタリングと制御を可能にし、グリッドの安定性とレジリエンスを向上させます。

分散化が進む中、デジタルツインは複数の再生可能なアセットを集約し調整する仮想発電所（VPP）を促進します。AIとIoTによるデジタルツインは、予知保全を強化し、ダウンタイムと運用コストを削減します。さらに、需要応答プログラムやピアツーピアのエネルギー取引をサポートし、再生可能エネルギーシステムをより効率的にします。

統合の複雑性

デジタルツインを既存の電力システムやSCADA、IoTデバイス、AIプラットフォームと統合する際の複雑性が、電気デジタルツイン市場の大きな抑制要因となっています。多くの電力企業は、先進のデジタルツインソリューションとの互換性に欠けるレガシーインフラに現在も依存しており、このことはシームレスなデータの同期を困難にしています。統合プロセスには高度にカスタマイズされたソリューションが必要となり、コストと展開時間が増大します。さらに、ベンダーによってデータの形式が異なることや相互運用性の課題が、さらなる複雑性を生み出しています。

当レポートでは、世界の電気デジタルツイン市場について調査し、市場力学、地域とセグメントの分析、競合情勢、企業プロファイルなどを提供しています。

目次

第1章 調査手法と範囲

第2章 定義と概要

第3章 エグゼクティブサマリー

第4章 市場力学

  • 影響要因
    • 促進要因
      • 再生可能エネルギーの採用の拡大
    • 抑制要因
      • 統合の複雑性
    • 機会
    • 影響の分析

第5章 産業の分析

  • ポーターのファイブフォース分析
  • サプライチェーン分析
  • 価格分析
  • 規制とコンプライアンスの分析
  • 持続可能性分析
  • 技術分析
  • DMIの見解

第6章 ツインタイプ別

  • デジタルガス・ストリーム - 発電所
  • デジタル風力発電所
  • デジタルグリッド
  • デジタル水力発電所
  • その他

第7章 使用タイプ別

  • プロダクトデジタルツイン
  • プロセスデジタルツイン
  • システムデジタルツイン

第8章 展開方式別

  • クラウド
  • オンプレミス

第9章 用途別

  • 資産パフォーマンス管理
  • ビジネス・経営最適化
  • 故障検出、予知保全
  • パフォーマンス最適化
  • その他

第10章 エンドユーザー別

  • 電力企業
  • グリッドインフラ業者
  • その他

第11章 地域別

  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 欧州
    • ドイツ
    • 英国
    • フランス
    • イタリア
    • スペイン
    • その他の欧州
  • 南米
    • ブラジル
    • アルゼンチン
    • その他の南米
  • アジア太平洋
    • 中国
    • インド
    • 日本
    • オーストラリア
    • その他のアジア太平洋
  • 中東・アフリカ

第12章 企業プロファイル

  • General Electric
  • ABB
  • Siemens
  • Wipro
  • Schneider Electric
  • Microsoft Corporation
  • SAP SE
  • IBM
  • Bentley Systems, Incorporated
  • Emerson Electric Co.

第13章 付録

目次
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