Technological Innovation Systems and Energy Transitions

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Engineering".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 992

Special Issue Editors


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Guest Editor
Kaunas Faculty, Vilnius University, Muitines 8, LT-44280 Kaunas, Lithuania
Interests: MCDM; low-carbon energy transition; just transition; sustainable development

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Guest Editor
Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
Interests: microgrid; energy management system; machine learning; forecasting energy; load forecasting; optimal power flow; renewable energy sources modelling; multi objective optimization
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Special Issue Information

Dear Colleagues,

The transition towards sustainable energy systems is a global priority, and technological innovation is crucial. Technological innovation systems offer a comprehensive framework to understand how new energy technologies emerge, develop, and diffuse. This Special Issue delves into the complex interactions between technological innovation systems and energy transitions, aiming to enhance our understanding of the processes driving transformative change. Areas of interest include integrating renewable energy sources into existing power grids, developing advanced energy storage solutions, and using artificial intelligence and machine learning to optimize energy systems. Other key areas are the impact of digital technologies on energy efficiency, innovations in smart grid technology, and the use of advanced materials to improve the performance of renewable energy technologies. Additionally, it is crucial to explore the dynamics of technological innovation networks and their influence on energy transitions.

This Special Issue seeks the latest advancements and challenges in integrating renewable energy sources into existing power grids and developing innovative energy storage solutions. We invite contributions that examine the role of artificial intelligence, machine learning, and digital technologies in optimizing energy systems and improving efficiency. Additionally, papers focusing on the impact of policy, socio-technical transitions, and multi-criteria decision-making applications in driving sustainable energy innovations are highly encouraged. Papers are being sought in the following areas:

  • Integration of renewable energy sources into power grids;
  • Advanced energy storage solutions;
  • Artificial intelligence in energy systems;
  • Machine learning for energy optimization;
  • Impact of digital technologies on energy efficiency;
  • Innovations in smart grid technology;
  • Advanced materials for renewable energy technologies;
  • Technological innovation networks in energy transitions;
  • Policy impacts on technological innovation in energy;
  • Market formation for sustainable energy technologies;
  • Socio-technical transitions in energy systems;
  • Barriers to technological innovation in energy;
  • Public–private partnerships in energy innovation;
  • Comparative studies of technological innovation systems;
  • Multi-criteria decision-making applications in energy transitions.

We look forward to receiving your contributions.

Dr. Mahyar Kamali Saraji
Dr. Vishnu Suresh
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • renewable energy integration
  • energy storage solutions
  • artificial intelligence in energy
  • machine learning for energy optimization
  • digital technologies in energy
  • smart grid innovations
  • advanced energy materials
  • technological innovation networks
  • energy policy impacts
  • multi-criteria decision-making in energy

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Published Papers (2 papers)

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Research

22 pages, 2641 KiB  
Article
Digital Oasis: How Green Infrastructure Is Reshaping China’s Energy Resilience Landscape
by Xue Lei, Jian Xu, You Chen, Chang Liu and Kunjian Zhao
Systems 2025, 13(5), 306; https://doi.org/10.3390/systems13050306 - 22 Apr 2025
Viewed by 280
Abstract
In the context of global energy transition and climate change, energy system resilience has become critical for countries worldwide. While green digital infrastructure—emerging from the integration of digitalization and low-carbon development—shows theoretical potential to strengthen energy resilience, empirical evidence remains limited. This study [...] Read more.
In the context of global energy transition and climate change, energy system resilience has become critical for countries worldwide. While green digital infrastructure—emerging from the integration of digitalization and low-carbon development—shows theoretical potential to strengthen energy resilience, empirical evidence remains limited. This study utilizes China’s 2015 Green Data Center Pilot Policy as a quasi-natural experiment to examine this relationship through comprehensive panel data from 30 Chinese provinces spanning 2011–2021. We developed an integrated energy resilience evaluation framework across four dimensions: economic resilience, engineering resilience, resource resilience, and ecological resilience, applying the CRITIC method to determine objective indicator weights. Our difference-in-differences analysis demonstrates that green digital infrastructure significantly enhances regional energy resilience, with pilot regions experiencing a 2.83% improvement compared to non-pilot areas. This impact shows regional heterogeneity, with stronger effects in economically developed areas with better digital foundations. We identify two primary mechanisms through which green digital infrastructure influences energy resilience: industrial structure optimization (particularly through service industry growth) and enhanced innovation capacity. These findings provide robust empirical support for green digital infrastructure’s role in strengthening energy system stability and adaptability, offering valuable policy insights for promoting both digitalization and low-carbon transition under global climate governance. Full article
(This article belongs to the Special Issue Technological Innovation Systems and Energy Transitions)
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20 pages, 1694 KiB  
Article
Dynamic Optimal Control Strategy of CCUS Technology Innovation in Coal Power Stations Under Environmental Protection Tax
by Chang Su, Xinxin Zha, Jiayi Ma, Boying Li and Xinping Wang
Systems 2025, 13(3), 193; https://doi.org/10.3390/systems13030193 - 10 Mar 2025
Cited by 6 | Viewed by 503
Abstract
Carbon capture, utilization, and storage (CCUS) technology is an essential technology for achieving low-carbon transformation and upgrading of the coal power industry. This study applies optimal control theory to analyze the dynamic optimization of CCUS technological innovation investment in coal power stations under [...] Read more.
Carbon capture, utilization, and storage (CCUS) technology is an essential technology for achieving low-carbon transformation and upgrading of the coal power industry. This study applies optimal control theory to analyze the dynamic optimization of CCUS technological innovation investment in coal power stations under environmental protection tax. A dynamic control model is constructed to analyze the investment decisions of firms at system steady-state equilibrium, and numerical simulations are performed. The study shows that under both profit maximization and social welfare maximization conditions, a distinct saddle-point steady-state; the environmental protection tax affects technological innovation investment in coal power stations, which in turn affects electricity prices; the learning rate of knowledge accumulation also impacts technological innovation investment: under the social welfare maximization condition, the investment levels in technological innovation, technology, and knowledge accumulation are higher than those under profit maximization. Full article
(This article belongs to the Special Issue Technological Innovation Systems and Energy Transitions)
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