Global Virtual Power Plant Market, By Technology (Distribution Generation, Demand Response, and Mixed Asset), By End User (Commercial, Industrial, and Residential), By Region (North America, Europe, Asia Pacific, South America, and the Middle East & Africa); Trend Analysis, Market Competition Scenario & Outlook, 2016-2026
- Published Date: August 2020
- Report ID: BWC20116
- Available Format: PDF
- Page: 0
Global Virtual Power Plant Market – Market Outlook & Trends
Rising demand for renewable energy in the power generation sector, changes in dynamics of power networks from centralized to dispersed, and restraining costs and easy availability of energy storage are some of the issues triggering the development of the virtual power plant market. Moreover, the virtual power plant market size is increasing due to the growing share of renewable energy in the power generation sector and the dropping costs of solar and energy storing. The deployment of renewable energy sources and energy storage systems is on a rising spree due to the promotion of new directives and directions for energy industries.
Significant growth in the global market in the coming future is anticipated due to rising awareness among governments of many countries about the necessity to ease power outages simultaneously preserving the environment. The growing knowledge in the market regarding the changes in renewable energy and battery storage systems has encouraged large-scale investments in the sector over the last years. Moreover, there is an increased concern for decentralized power generators in the electricity distribution supply chain to reduce electricity demand.
Global Virtual Power Plant Market – Overview
Virtual Power Plant (VPP) refers to a group of small-scale generators, such as joint heat and power (CHP) units, backup generator sets, and biogas plants, which collectively act as a single large power plant. A virtual power plant does not physically exist. The pool of distributed energy resources transforms as a sizable individual capacity using urbane software to operate them as a group.
Every single generator that goes to the VPP needs an entry to connect with the remote control software handling the whole network. In the VPP model, an energy aggregator gathers a collection of smaller generators, and operates them as a united and flexible resource in the energy market or sells their power as a standby system. A virtual power plant aims to let go of the load on the network by smartly allocating the power generated by the individual units during periods of peak load.
Additionally, the joint power generation and power consumption of the networked units in the virtual power plant is negotiated on the energy interchange. The power traders within a virtual power plant can use real-time data to enhance future trading of renewable energies. As a result, virtual power plants are gradually taking over the role of traditional power plants – by selling their output on large markets and assuming accountability for a balanced grid.
A shift from Centralized to Distributed Power Generation Drives the Growth of Market
Dispersed power generation plant refers to geographically scattered power generation sources, which are usually less than 10 MW. It includes both manageable sources, such as generators, and non-controllable sources, such as wind, solar energy plants. The demand for energy from dispersed energy resources is rising internationally due to several reasons, such as planned rolling blackouts, power quality problems, unforeseen power outages, and surges in power costs, among others.
Increasing Demand for Power Generation from Renewable Sources of Energy
The growing demand for power generation from renewable sources drive across the sphere has pushed the need for virtual power plants. Furthermore, there is increased pressure for dispersed power generators in the electricity distribution supply chain to reduce electricity demand. The development of the market is likely to be supported by growing requests for dispersed power generation, rising demand for renewable sources of energy, and favorable government initiatives to alleviate power outages.
Global Virtual Power Plant Market: Segmentation
The VPP market can be classified based on technology, viz., Demand response, Dispersed Generation, and Asset Mix. Based on end-use, the market can be segregated into residential, industrial, and commercial. In terms of geography, the VPP market can be segmented into North America, Europe, Asia Pacific, South America, and the Middle East & Africa.
Currently, the demand response section by technology holds the largest share and is likely to maintain its supremacy in the virtual power plant market during the prediction period. Demand response is highly profitable for savings due to lasting benefits for end-users and refined energy competence of the grid. Hence, this segment is expected to show a high growth rate. In end-user segmentation, the manufacturing sector held the leading market share in 2016 and is anticipated to do so over the prediction period. Industrial end-users are among the topmost adopters and consumers of the virtual power plant setups and services and thus, significantly influence the market growth.
Global Virtual Power Plant Market: Regional insights
The Middle East & African (MEA) region is likely to register the most substantial revenue growth in the virtual power plant market, during the prediction period. It can be mainly attributed to the increasing new renewable control projects in countries such as South Africa, Saudi Arabia, and the U.A.E. as they aim to diversify their power sector through the deployment of renewable energy. Therefore, the demand for setting up VPPs will surge to achieve dispersed energy efficiently.
The leading companies in this market are ABB Ltd (Switzerland), Siemens AG (Germany), Schneider Electric SE (France), EnerNOC, Inc. (US), Comverge (US), Limejump (UK), and Flexitricity (UK).
Ø In October 2016, ABB Ltd. (Switzerland) launched a microgrid solution to address the demand for flexible technology for distributed power generation. The technology would help maximize the use of renewable energy sources while reducing dependency on fossil fuels used by generator sets.
Based on Technology
Ø Demand Response
Ø Dispersed Generation
Ø Asset Mix
Based on End-Use
Ø North America
Ø Asia Pacific
Ø South America
Ø Middle East & Africa
The objective of the Study:
Ø To analyze and estimate the Global Virtual Power Plant Market size, in terms of value.
Ø To examine the market segmentation carefully and estimate the market size, in terms of value, based on the region by segmenting the Global Virtual Power Plant market into North America, Europe, Asia Pacific, Middle East & Africa, Latin America, and their leading countries
Ø To outline, categorize and project the Global Virtual Power Plant Market based on the Technology, End user and Region.
Ø To study competitive developments like technological advancement, requirements from different end-users, and others within the Global Virtual Power Plant Market.
Ø To highlight market dynamics such as drivers, restraints, opportunities, and challenges and their impact.
Ø To provide a detailed competitive landscape, including major players, strategic profiles, and market shares.
Business Questions Answered by the Report
Ø How will the market drivers, restraints, and opportunities affect the market dynamics?
Ø What will be the market size in terms of value and volume and market statistics with a detailed classification?
Ø Which segment dominates the market or region, and which one will be the fastest-growing, and why?
Ø Who are the key players in the market, and what is their share?
Ø What is the strategy adopted by key players, and how does it affect the existing and new players?
Customization Scope for the Client
Client satisfaction is our first and last priority, and that is why BlueWeave Consulting offers customization as per client’s specific needs. The following customization options are available for the report:
Ø Additional Company Information
Ø Detail Analysis of five additional companies
Ø Additional country analysis
Ø Detailed segment analysis
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