Energy & Power
Global Smart Grid Market - Industry Trends and Forecast to 2032
REPORT OVERVIEW
Global Smart Grid Market, By Component (Software, Hardware, and Services), Application (Generation, Transmission, Distribution, and Consumption), Communication Technology (Wireline and Wireless), Region (North America, Europe, Asia-Pacific, South America, Middle East and Africa) – Industry Trends and Forecast to 2032.
Market Insights
The global Smart Grid market size is valued to be USD xx million in 2023 and is expected to reach USD xx million by 2032, and it is expected to register a CAGR of xx% over the forecast period 2024-2032.
A Smart Grid refers to an advanced electricity distribution network that uses digital communication technology to improve efficiency, reliability, and sustainability of electricity delivery and consumption. It integrates information and communication technology (ICT) with power generation, transmission, distribution, and consumption to enhance grid operations and optimize energy use. The use of smart grid technology offers a wide range of benefits and applications across the electricity sector, transforming traditional power grids into modern, intelligent systems.
The global smart grid market encompasses the infrastructure, technologies, and solutions aimed at modernizing and improving the efficiency of electrical grids worldwide.
The respective global report analyses market trends, consumer behaviour and industry dynamics to guide towards entry into new markets with ease. Also, it assists in tailoring market specific and related products and services to meet the needs, preferences, and expectations of target audience by delving into their psychology. The report also specializes with comprehensive and extensive competitive analysis which offers useful insights into competitor strengths, weaknesses, opportunities, and threats. The respective report offers exclusive insights into the potential impact of disruptive developments and technologies that are expected to completely transform corporate operations. The context includes tailor-made research solutions to create a stronger footprint in their particular industries thereby offering dedicated customized solutions according to the client needs which helps in addressing unique business challenges with more simplified and efficient decision-making solutions.
Market Dynamics
DRIVERS
- Improved grid reliability
- Efficient outage response
RESTRAINTS
- Low awareness of protocols and interoperability standards
- Integrating interchangeable parts from various global providers
OPPORTUNITIES
- Smart city projects in developing countries
- Renewable energy integration
CHALLENGES
- Proper storage and management of complex data generated by smart grid infrastructure
- Enhancing grid resilience and reliability
SEGMENTATION
- Component
- Software
- Hardware
- Services
- Application
- Generation
- Transmission
- Distribution
- Consumption
- Communication Technology
- Wireline
- Wireless
The respective global report is completely customizable specific to regions (North America, Europe, Asia-Pacific, South America, Middle East and Africa), countries, and segments as per the client requirements.
REGIONAL SEGMENTATION
- North America
- U.S.
- Canada
- Mexico
- Rest of North America
- Europe
- Germany
- U.K.
- France
- Italy
- Spain
- Russia
- The Netherlands
- Belgium
- Turkey
- Rest of Europe
- Asia-Pacific
- China
- India
- Japan
- South Korea
- Singapore
- Malaysia
- Australia
- Thailand
- Philippines
- Rest of Asia-Pacific
- South America
- Brazil
- Argentina
- Chile
- Colombia
- Rest of South America
- Middle East and Africa
- Kingdom of Saudi Arabia
- South Africa
- U.A.E.
- Egypt
- Rest of Middle East and Africa
KEY MARKET PLAYERS
- General Electric Company
- ABB
- Siemens
- Itron
- IBM
- Xylem
- Schneider Electric
- Globema
- Trilliant Holdings
- Wipro
Table OF CONTENTS
- SECTION 1 - INTRODUCTION
- 1.1 Taxonomy
- 1.2 Market Overview
- 1.3 Currency and Limitations
- 1.3.1 Currency
- 1.3.2 Limitations
- 1.4 Key Competitors
- SECTION 2 - RESEARCH METHODOLOGY
- 2.1 Research Approach
- 2.2 Data Collection and Validation
- 2.2.1 Secondary Research
- 2.2.2 Primary Research
- 2.3 Market Assessment
- 2.3.1 Market Size Estimation
- 2.3.2 Bottom-up Approach
- 2.3.3 Top-down Approach
- 2.3.4 Growth Forecast
- 2.4 Market Study Assumptions
- 2.5 Data Sources
- SECTION 3 - EXECUTIVE SUMMARY
- 3.1 Global Smart Grid Market, by Component
- 3.2 Global Smart Grid Market, by Application
- 3.3 Global Smart Grid Market, by Communication Technology
- 3.4 Global Smart Grid Market, by Geography
- 3.5 Market Position Grid
- SECTION 4 - PREMIUM INSIGHTS
- 4.1 Regulatory Framework
- 4.1.1 Standards
- 4.1.2 Regulatory Landscape
- 4.2 Value Chain Analysis
- 4.3 Supply Chain Analysis
- 4.4 COVID-19 Impact
- 4.5 Russia-Ukraine War Impact
- 4.6 PORTER's Five Force Analysis
- 4.7 PESTLE Analysis
- 4.8 SWOT Analysis
- 4.9 Go to Market Strategy
- 4.10 Opportunity Orbit
- 4.11 Multivariate Modelling
- 4.12 Pricing Analysis
- SECTION 5 - MARKET DYNAMICS
- 5.1 Trends
- 5.1.1 Rise in electric vehicles and charging infrastructure
- 5.1.2 Grid modernization initiatives
- 5.1.3 Trend 3
- 5.2 Drivers
- 5.2.1 Improved grid reliability
- 5.2.2 Efficient outage response
- 5.2.3 Driver 3
- 5.2.4 Driver 4
- 5.3 Restraints
- 5.3.1 Low awareness of protocols and interoperability standards
- 5.3.2 Integrating interchangeable parts from various global providers
- 5.3.3 Restraint 3
- 5.4 Opportunities
- 5.4.1 Smart city projects in developing countries
- 5.4.2 Renewable energy integration
- 5.4.3 Opportunity 3
- 5.4.4 Opportunity 4
- 5.5 Challenges
- 5.5.1 Proper storage and management of complex data generated by smart grid infrastructure
- 5.5.2 Enhancing grid resilience and reliability
- 5.5.3 Challenge 3
- SECTION 6 - GLOBAL SMART GRID MARKET, BY COMPONENT
- 6.1 Component Summary
- 6.2 Market Attractive Index
- 6.3 Global Smart Grid Market, by Component (2019-2032)
- SECTION 7 - GLOBAL SMART GRID MARKET, BY APPLICATION
- 7.1 Application Summary
- 7.2 Market Attractive Index
- 7.3 Global Smart Grid Market, by Application (2019-2032)
- SECTION 8 - GLOBAL SMART GRID MARKET, BY COMMUNICATION TECHNOLOGY
- 8.1 Communication Technology Summary
- 8.2 Market Attractive Index
- 8.3 Global Smart Grid Market, by Communication Technology (2019-2032)
- SECTION 9 - GLOBAL SMART GRID MARKET, BY GEOGRAPHY
- 9.1 Regional Summary
- 9.2 Market Attractive Index
- 9.3 Global Smart Grid Market, by Geography (2019-2032)
- SECTION 10 - NORTH AMERICA SMART GRID MARKET
- 10.1 North America Summary
- 10.2 Market Attractive Index
- 10.3 North America Smart Grid Market, by Component (2019-2032)
- 10.4 North America Smart Grid Market, by Application (2019-2032)
- 10.5 North America Smart Grid Market, by Communication Technology (2019-2032)
- 10.6 North America Smart Grid Market, by Country (2019-2032)
- 10.6.1 U.S.
- 10.6.2 Canada
- 10.6.3 Mexico
- 10.6.4 Rest of North America
- SECTION 11 - EUROPE SMART GRID MARKET
- 11.1 Europe Summary
- 11.2 Market Attractive Index
- 11.3 Europe Smart Grid Market, by Component (2019-2032)
- 11.4 Europe Smart Grid Market, by Application (2019-2032)
- 11.5 Europe Smart Grid Market, by Communication Technology (2019-2032)
- 11.6 Europe Smart Grid Market, by Country (2019-2032)
- 11.6.1 Germany
- 11.6.2 U.K.
- 11.6.3 France
- 11.6.4 Italy
- 11.6.5 Spain
- 11.6.6 Russia
- 11.6.7 The Netherlands
- 11.6.8 Belgium
- 11.6.9 Turkey
- 11.6.10 Rest of Europe
- SECTION 12 - ASIA-PACIFIC SMART GRID MARKET
- 12.1 Asia-Pacific Summary
- 12.2 Market Attractive Index
- 12.3 Asia-Pacific Smart Grid Market, by Component (2019-2032)
- 12.4 Asia-Pacific Smart Grid Market, by Application (2019-2032)
- 12.5 Asia-Pacific Smart Grid Market, by Communication Technology (2019-2032)
- 12.6 Asia-Pacific Smart Grid Market, by Country (2019-2032)
- 12.6.1 China
- 12.6.2 India
- 12.6.3 Japan
- 12.6.4 South Korea
- 12.6.5 Singapore
- 12.6.6 Malaysia
- 12.6.7 Australia
- 12.6.8 Thailand
- 12.6.9 Philippines
- 12.6.10 Rest of Asia-Pacific
- SECTION 13 - SOUTH AMERICA SMART GRID MARKET
- 13.1 South America Summary
- 13.2 Market Attractive Index
- 13.3 South America Smart Grid Market, by Component (2019-2032)
- 13.4 South America Smart Grid Market, by Application (2019-2032)
- 13.5 South America Smart Grid Market, by Communication Technology (2019-2032)
- 13.6 South America Smart Grid Market, by Country (2019-2032)
- 13.6.1 Brazil
- 13.6.2 Argentina
- 13.6.3 Chile
- 13.6.4 Colombia
- 13.6.5 Rest of South America
- SECTION 14 - MIDDLE EAST AND AFRICA SMART GRID MARKET
- 14.1 Middle East and Africa Summary
- 14.2 Market Attractive Index
- 14.3 Middle East and Africa Smart Grid Market, by Component (2019-2032)
- 14.4 Middle East and Africa Smart Grid Market, by Application (2019-2032)
- 14.5 Middle East and Africa Smart Grid Market, by Communication Technology (2019-2032)
- 14.6 Middle East and Africa Smart Grid Market, by Country (2019-2032)
- 14.6.1 Kingdom of Saudi Arabia
- 14.6.2 South Africa
- 14.6.3 U.A.E.
- 14.6.4 Egypt
- 14.6.5 Rest of Middle East and Africa
- SECTION 15 - COMPANY SHARE ANALYSIS
- 15.1 Global Smart Grid Market, Company Share Analysis
- 15.2 North America Smart Grid Market, Company Share Analysis
- 15.3 Europe Smart Grid Market, Company Share Analysis
- 15.4 Asia-Pacific Smart Grid Market, Company Share Analysis
- SECTION 16 - COMPANY PROFILES
- 16.1 General Electric Company
- 16.1.1 Company Snapshot
- 16.1.2 Financial Overview
- 16.1.3 Product Portfolio
- 16.1.4 Recent Developments
- 16.2 ABB
- 16.2.1 Company Snapshot
- 16.2.2 Financial Overview
- 16.2.3 Product Portfolio
- 16.2.4 Recent Developments
- 16.3 Siemens
- 16.3.1 Company Snapshot
- 16.3.2 Financial Overview
- 16.3.3 Product Portfolio
- 16.3.4 Recent Developments
- 16.4 Itron
- 16.4.1 Company Snapshot
- 16.4.2 Financial Overview
- 16.4.3 Product Portfolio
- 16.4.4 Recent Developments
- 16.5 IBM
- 16.5.1 Company Snapshot
- 16.5.2 Financial Overview
- 16.5.3 Product Portfolio
- 16.5.4 Recent Developments
- 16.6 Xylem
- 16.6.1 Company Snapshot
- 16.6.2 Financial Overview
- 16.6.3 Product Portfolio
- 16.6.4 Recent Developments
- 16.7 Schneider Electric
- 16.7.1 Company Snapshot
- 16.7.2 Financial Overview
- 16.7.3 Product Portfolio
- 16.7.4 Recent Developments
- 16.8 Globema
- 16.8.1 Company Snapshot
- 16.8.2 Financial Overview
- 16.8.3 Product Portfolio
- 16.8.4 Recent Developments
- 16.9 Trilliant Holdings
- 16.9.1 Company Snapshot
- 16.9.2 Financial Overview
- 16.9.3 Product Portfolio
- 16.9.4 Recent Developments
- 16.10 Wipro
- 16.10.1 Company Snapshot
- 16.10.2 Financial Overview
- 16.10.3 Product Portfolio
- 16.10.4 Recent Developments
- SECTION 17 - RELATED REPORTS
- SECTION 18 - DISCLAIMER
RESEARCH METHODOLOGY
The research methodology employed in Uniprism Market Research involves four basic steps namely research and data collection, data pre-processing, modeling and forecasting, quality assurance and output.
RESEARCH AND DATA COLLECTION
A tripod model research technique is followed for research and data collection in which various approaches such as primary research, secondary research, and product mapping are considered.
Primary research basically involves the process of conducting personalized interviews with market related professionals of major market players, investors, distributors, vendors and many more.
The secondary research include data published by government, annual reports, press releases, investor presentations of companies, white papers, certified publications, annual manufacturing limit of the respective industries related to the market, production consumption analysis of certain products respective to the market and many more.
Below mention are few of the sources which we have considered while estimating the market size:
For instance,
- Research articles published on Technium
- Science and MDPI
- Research publications by government approved associations and societies
Product mapping means the process of mapping the list of products that a key player contributes to the market as well as estimating the revenue of those products in order to define the Global Company share analysis of the respective Global Company in global, regional, and country level markets.
DATA PRE-PROCESSING
The term "data pre-processing" refers to the collection of procedures and methods used to clean, modify, and make ready for analysis the raw data gathered during research and data collection. The completion of this phase is necessary to guarantee that the data are reliable, consistent, and appropriate for statistical analysis and other data-driven tasks. The data pre-processing ensures that the information gathered from research and data collection is comparable and expressed in standard units, by the integration of missing data pointers and algorithmic approaches.
MODELING AND FORECASTING
The process of developing mathematical, statistical, or computational representations of real-world occurrences or relationships is known as modelling. These models are intended to replicate and explain market interactions, interdependence, and dynamics. These models are used by Uniprism Market Research to acquire a better knowledge of numerous market characteristics such as customer preferences, pricing elasticity, competition dynamics, and more. Depending on the individual study aims, many types of models are utilized, such as regression models, econometric models, decision tree models, and machine learning models.
Forecasting is the process of predicting future market conditions, trends, and occurrences using past data and models. Forecasting is used by Uniprism Market Research to estimate future sales, demand for products or services, market growth, and other important performance metrics. Forecasting accurately can assist organizations in making educated decisions about resource allocation, pricing, inventory management, and marketing tactics.
We create standardized bottom-up or top-down models that scale by leveraging data science and machine learning technology. All our market models consider the unique market characteristics of each country. Forecasting is based on major market indicators and a combination of traditional methodologies, such as exponential smoothing, time series analysis, regression analysis, and more modern techniques such as machine learning algorithms are all forecasting methodologies. The method chosen is determined on the nature of the data and the specific forecasting aims.
QUALITY ASSURANCE AND OUTPUT
Quality assurance and output involves the process of validation, adjustments, further publications of key market indicators. Extensive plausibility and consistency tests are performed on derived time series to ensure the high degree of quality of our market analysis. This quality assurance procedure also includes rigorous inspection, validation, and editing by an experienced management team to assure the dependability of the published data.