Leveraging System Dynamics to Predict the Commercialization Success of Emerging Energy Technologies: Lessons from Wind Energy
The United States urgently needs to tackle the climate crisis while enhancing energy security and resiliency. The complexity of the U.S. energy system, with its interconnected elements, makes predicting future states challenging, especially with the introduction of novel energy systems like wind, so...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/1996-1073/18/8/2048 |
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| author | Svetlana Lawrence Daniel R. Herber Kamran Eftekhari Shahroudi |
| author_facet | Svetlana Lawrence Daniel R. Herber Kamran Eftekhari Shahroudi |
| author_sort | Svetlana Lawrence |
| collection | DOAJ |
| description | The United States urgently needs to tackle the climate crisis while enhancing energy security and resiliency. The complexity of the U.S. energy system, with its interconnected elements, makes predicting future states challenging, especially with the introduction of novel energy systems like wind, solar, clean hydrogen, and advanced nuclear technologies. Modern systems engineering methods and tools can provide deeper insights into these dynamics and future behaviors. This research aims to develop a comprehensive model that captures the main elements and behaviors of new energy technologies within the existing energy system. We hypothesized that the market uptake of novel energy systems is influenced by multiple diverse factors, such as technological learning, availability of resources, and economic incentives; examined the history of electricity generation using land-based wind technologies; and developed a system dynamics model to investigate the relationships between capacity growth and influencing factors, both internal and external. The developed model yielded outcomes that confirmed the hypothesized dynamics of wind energy system diffusion through a quantitative comparison of installed capacity and highlighted the significant influence of resource availability, federal incentives (production tax credits), and technological learning on capacity growth and cost reduction. This research aims to support informed decision-making for investments in novel energy systems and aid in developing effective policies for technology deployment. |
| format | Article |
| id | doaj-art-868f70e8b9ed476d8ecadd586cef753f |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-868f70e8b9ed476d8ecadd586cef753f2025-08-20T02:28:28ZengMDPI AGEnergies1996-10732025-04-01188204810.3390/en18082048Leveraging System Dynamics to Predict the Commercialization Success of Emerging Energy Technologies: Lessons from Wind EnergySvetlana Lawrence0Daniel R. Herber1Kamran Eftekhari Shahroudi2Idaho National Laboratory, 1955 Fremont Ave., Idaho Falls, ID 83415, USADepartment of Systems Engineering, Walter Scott, Jr. College of Engineering, Colorado State University, Fort Collins, CO 80523, USADepartment of Systems Engineering, Walter Scott, Jr. College of Engineering, Colorado State University, Fort Collins, CO 80523, USAThe United States urgently needs to tackle the climate crisis while enhancing energy security and resiliency. The complexity of the U.S. energy system, with its interconnected elements, makes predicting future states challenging, especially with the introduction of novel energy systems like wind, solar, clean hydrogen, and advanced nuclear technologies. Modern systems engineering methods and tools can provide deeper insights into these dynamics and future behaviors. This research aims to develop a comprehensive model that captures the main elements and behaviors of new energy technologies within the existing energy system. We hypothesized that the market uptake of novel energy systems is influenced by multiple diverse factors, such as technological learning, availability of resources, and economic incentives; examined the history of electricity generation using land-based wind technologies; and developed a system dynamics model to investigate the relationships between capacity growth and influencing factors, both internal and external. The developed model yielded outcomes that confirmed the hypothesized dynamics of wind energy system diffusion through a quantitative comparison of installed capacity and highlighted the significant influence of resource availability, federal incentives (production tax credits), and technological learning on capacity growth and cost reduction. This research aims to support informed decision-making for investments in novel energy systems and aid in developing effective policies for technology deployment.https://www.mdpi.com/1996-1073/18/8/2048investment and policy decision-makingsystem dynamicsenergy transitionnovel energy technology commercializationinvestment uncertainty |
| spellingShingle | Svetlana Lawrence Daniel R. Herber Kamran Eftekhari Shahroudi Leveraging System Dynamics to Predict the Commercialization Success of Emerging Energy Technologies: Lessons from Wind Energy Energies investment and policy decision-making system dynamics energy transition novel energy technology commercialization investment uncertainty |
| title | Leveraging System Dynamics to Predict the Commercialization Success of Emerging Energy Technologies: Lessons from Wind Energy |
| title_full | Leveraging System Dynamics to Predict the Commercialization Success of Emerging Energy Technologies: Lessons from Wind Energy |
| title_fullStr | Leveraging System Dynamics to Predict the Commercialization Success of Emerging Energy Technologies: Lessons from Wind Energy |
| title_full_unstemmed | Leveraging System Dynamics to Predict the Commercialization Success of Emerging Energy Technologies: Lessons from Wind Energy |
| title_short | Leveraging System Dynamics to Predict the Commercialization Success of Emerging Energy Technologies: Lessons from Wind Energy |
| title_sort | leveraging system dynamics to predict the commercialization success of emerging energy technologies lessons from wind energy |
| topic | investment and policy decision-making system dynamics energy transition novel energy technology commercialization investment uncertainty |
| url | https://www.mdpi.com/1996-1073/18/8/2048 |
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