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Multi-Energy Systems Operation, Economics and Policy to Facilitate Low-Carbon Energy Transition

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: 22 January 2025 | Viewed by 5525

Special Issue Editors


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Guest Editor
College of Science and Engineering, James Cook University, Townsville, QLD 4810, Australia
Interests: power system operation and optimization; smart grid; electricity markets; network modelling/planning for renewable integration
Special Issues, Collections and Topics in MDPI journals
Department of Data Science and AI, Monash University, Melbourne, VIC 3800, Australia
Interests: smart grids; power systems optimisation; energy and low-carbon management; renewable and sustainable systems; electric vehicles
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Electrical and Computer Engineering, Temple University, Philadelphia, PA 19122, USA
Interests: electric power grid modernization; energy systems integration; de-centralized and autonomous power architectures; data-driven analytics; renewable integration

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Guest Editor
School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Interests: power distribution system planning and operation; power system big data

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Guest Editor
College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
Interests: power system operation and control; power system economic dispatch and optimization; electricity markets; distribition network optimization

Special Issue Information

Dear Colleagues,

In response to the global challenge of climate change, countries worldwide are actively pursuing strategies to reduce carbon emissions and adopt renewable energy sources. Governments have set ambitious targets to achieve net-zero emissions by the mid-century, reflecting a growing recognition of the importance of sustainable energy and the environmental impact of traditional fossil fuels. Significant changes in our energy systems will be necessary in order to meet these targets and enhance system security and reliability. One promising approach in the low-carbon energy transition is the integration of multiple energy carriers, integrating different forms of energy, such as electricity, heat, cooling, and natural gas, through multi-energy networks. This integration offers a viable solution that enables the efficient utilization of variable renewable energy sources. Furthermore, it aligns with the strict greenhouse gas reduction targets put forth. However, the adoption of multi-energy systems (MES) requires substantial changes to our current energy infrastructure, as well as comprehensive planning and coordination.

In order to advance research and knowledge in this pivotal field, we invite researchers and experts to contribute to the upcoming Special Issue of Energies on “Multi-Energy Systems Operation, Economics, and Policy to Facilitate Low-Carbon Energy Transition.” This Special Issue aims to explore the multifaceted challenges and opportunities associated with the design, operation, economics, and policy aspects of multi-energy systems in the context of achieving low-carbon energy goals. Topics of interest include, but are not limited to:

  1. System design and optimization approaches for integrating multi-energy carriers in low-carbon energy systems.
  2. Techno-economic analysis of MES for enhanced energy efficiency and reduced carbon emissions.
  3. Planning and management strategies to ensure the stability, reliability, and resilience of MES.
  4. Policy frameworks and regulatory mechanisms to support the deployment and integration of low-carbon MES.
  5. Role of energy markets and transactive energy mechanisms in facilitating the transition to MES.
  6. Assessment of the environmental and socio-economic impacts of MES in achieving sustainability goals.
  7. Innovative energy storage technologies and management systems for reliable and flexible multi-energy operations.
  8. Application of artificial intelligence, machine learning, and data analytics in optimizing MES performance.

Dr. Jiajia Yang
Dr. Yunqi Wang
Dr. Liang Du
Dr. Xiangjing Su
Dr. Yumin Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • net-zero emission
  • multi-energy systems
  • energy transition

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Published Papers (6 papers)

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Research

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17 pages, 6362 KiB  
Article
Electric Vehicle Charging Load Prediction Based on Weight Fusion Spatial–Temporal Graph Convolutional Network
by Jun Zhang, Huiluan Cong, Hui Zhou, Zhiqiang Wang, Ziyi Wen and Xian Zhang
Energies 2024, 17(19), 4798; https://doi.org/10.3390/en17194798 - 25 Sep 2024
Viewed by 440
Abstract
The rapid increase in electric vehicles (EVs) poses significant impacts on multi-energy system (MES) operation and energy management. Accurately assessing EV charging demand becomes crucial for maintaining MES stability, making it an urgent issue to be studied. Therefore, this paper proposes a novel [...] Read more.
The rapid increase in electric vehicles (EVs) poses significant impacts on multi-energy system (MES) operation and energy management. Accurately assessing EV charging demand becomes crucial for maintaining MES stability, making it an urgent issue to be studied. Therefore, this paper proposes a novel deep learning-based EV charging load prediction framework to assess the impact of EVs on the MES. First, to model the EV traffic flow, a modified weight fusion spatial–temporal graph convolutional network (WSTGCN) is proposed to capture the inherent spatial–temporal characteristics of traffic flow. Specifically, to enhance the WSTGCN performance, the modified residual modules and weight fusion mechanism are integrated into the WSTGCN. Then, based on the predicted traffic flow, an improved queuing theory model is introduced to predict the charging load. In this improved queuing theory model, special consideration is given to subjective EV user behaviors, such as refusing to join queues and leaving impatiently, making the queuing model more realistic. Additionally, it should be noted that the proposed charging load predicting method relies on traffic flow data rather than historical charging data, which successfully addresses the data insufficiency problem of newly established charging stations, thereby offering significant practical value. Experimental results demonstrate that the proposed WSTGCN model exhibits superior accuracy in predicting traffic flow compared to other benchmark models, and the improved queuing theory model further enhances the accuracy of the results. Full article
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20 pages, 7801 KiB  
Article
Joint Planning Method of Shared Energy Storage and Multi-Energy Microgrids Based on Dynamic Game with Perfect Information
by Qibo He, Changming Chen, Xin Fu, Shunjiang Yu, Long Wang and Zhenzhi Lin
Energies 2024, 17(19), 4792; https://doi.org/10.3390/en17194792 - 25 Sep 2024
Viewed by 367
Abstract
Under the background of the Energy Internet and the shared economy, it is of great significance to explore the collaborative planning strategies of multi-energy microgrids (MEMGs) and a shared energy storage operator (SESO) supported by shared energy storage resources. In this context, a [...] Read more.
Under the background of the Energy Internet and the shared economy, it is of great significance to explore the collaborative planning strategies of multi-energy microgrids (MEMGs) and a shared energy storage operator (SESO) supported by shared energy storage resources. In this context, a joint planning method of SESO and MEMG alliances based on a dynamic game with perfect information is proposed in this paper. First, an upper-level model for energy storage capacity configuration and pricing strategy planning of SESO is proposed to maximize the total planning and operational income of SESO. Then, a lower-level model for the optimal configuration of MEMGs’ alliance considering SES is proposed to minimize the total planning and operational costs of the MEMG alliance. On this basis, a solving algorithm based on the dynamic game theory with perfect information and the backward induction method is proposed to obtain the Nash equilibrium solution of the proposed bi-level optimization models. Finally, a case study with one SESO and an alliance consisting of five MEMGs is conducted, and the simulation results show that the proposed bi-level optimization method can increase SESO’s net income by 1.47%, reduce the average planning costs for each MEMG at least by 1.7%, and reduce model solving time by 62.9% compared with other counterpart planning methods. Full article
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13 pages, 2633 KiB  
Article
Optimal Scheduling Strategy for Urban Distribution Grid Resilience Enhancement Considering Renewable-to-Ammonia Coordination
by Li Jiang, Fei Hu, Shaolei Zong, Hui Yan, Wei Kong, Xiaoguang Chai and Lu Zhang
Energies 2024, 17(18), 4540; https://doi.org/10.3390/en17184540 - 10 Sep 2024
Viewed by 488
Abstract
The integration of numerous distributed energy sources into the power system offers exciting opportunities to enhance the resilience of distribution networks. It is worth noting that the renewable-to-ammonia system has the potential to alleviate the multi-temporal and spatial imbalance of the power system. [...] Read more.
The integration of numerous distributed energy sources into the power system offers exciting opportunities to enhance the resilience of distribution networks. It is worth noting that the renewable-to-ammonia system has the potential to alleviate the multi-temporal and spatial imbalance of the power system. Therefore, this paper proposes a mathematical model for a renewable-to-ammonia system, taking into account the material balance and power balance of each unit. Based on this, this paper further explores the optimization scheduling method for flexible ammonia loads in distribution networks. A relaxation method for branch flow models in distribution networks based on second-order cone programming is proposed. An optimization scheduling model for flexible ammonia loads in distribution networks is constructed to minimize network loss. Moreover, considering the environmental advantages of the renewable-to-ammonia system, this paper compares the changes in hydrogen production technologies under different carbon emission constraints. Finally, a case study of the IEEE 33-node system is adopted to verify the effectiveness of the proposed model and method. It indicates that the renewable-to-ammonia system has environmental benefits and can reduce network loss to a certain extent. Full article
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22 pages, 3129 KiB  
Article
Optimal Energy Management Strategy of Clustered Industry Factories Considering Carbon Trading and Supply Chain Coupling
by Jiaying Wang, Chunguang Lu, Shuai Zhang, Huajiang Yan and Changsen Feng
Energies 2023, 16(24), 8041; https://doi.org/10.3390/en16248041 - 13 Dec 2023
Cited by 3 | Viewed by 1048
Abstract
Industrial parks, characterized by the clustering of multiple factories and interconnected energy sources, require optimized operational strategies for their Integrated Energy Systems (IES). These strategies not only aim to conserve energy for industrial users but also relieve the burden on the power supply, [...] Read more.
Industrial parks, characterized by the clustering of multiple factories and interconnected energy sources, require optimized operational strategies for their Integrated Energy Systems (IES). These strategies not only aim to conserve energy for industrial users but also relieve the burden on the power supply, reducing carbon emissions. In this context, this paper introduces an optimization strategy tailored to clustered factories, considering the incorporation of carbon trading and supply chain integration throughout the entire production process of each factory. First, a workshop model is established for each factory, accompanied by an energy consumption model that accounts for the strict sequencing of the production process and supply chain integration. Furthermore, energy unit models are devised for the IES and then a low-carbon and economically optimized scheduling model is outlined for the IES within the industrial park, aiming to minimize the total operational cost, including the cost of carbon trading. Finally, case studies are conducted within a paper-making industrial park located in the Zhejiang Province. Various scenarios are compared and analyzed. The numerical results underscore the model’s economic and low-carbon merits, and it offers technical support for energy conservation and emission reduction in paper-making fields. Full article
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13 pages, 1700 KiB  
Article
Power Side Risk Assessment of Multi-Energy Microgrids Considering Risk Propagation between Interconnected Energy Networks
by Yan Ma, Yumin Chen, Zhengwei Chang, Qian Li, Hongli Liu and Yang Wei
Energies 2023, 16(22), 7525; https://doi.org/10.3390/en16227525 - 10 Nov 2023
Cited by 1 | Viewed by 1059
Abstract
Traditional power systems only contain a single energy type, namely, electrical energy, and involve no interaction with other networks with different energy types, such as gas networks and heat networks. With the rapid development of the Energy Internet, the coupling between various energy [...] Read more.
Traditional power systems only contain a single energy type, namely, electrical energy, and involve no interaction with other networks with different energy types, such as gas networks and heat networks. With the rapid development of the Energy Internet, the coupling between various energy types has become increasingly tight, making traditional risk assessment methods no longer suitable for multi-energy microgrids. To this end, this paper proposes a microgrid risk assessment method that considers the impact of multiple interconnected networks with different energy types. First, respectively from the equipment and system levels, a risk transfer integrated energy conversion model is built, depicting the output of equipment under risk conditions and describing the process of risk transfer using energy coupling equipment in the microgrid. Thereafter, from the perspective of the energy flow distribution and considering the microgrid grid energy flow characteristics, a microgrid energy flow distribution model is built, based on which a microgrid risk analysis model that simulates the microgrid risk propagation mechanism is established by introducing risk factors that characterize equipment risk statuses. In addition, based on the system structure and the operational characteristics, a microgrid-oriented risk assessment process is designed. Finally, a numerical simulation confirms that considering the impact of multiple different energy networks to the power side in the risk assessment is necessary. Full article
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Review

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17 pages, 2219 KiB  
Review
Progress of Photovoltaic DC Fault Arc Detection Based on VOSviewer Bibliometric Analysis
by Lei Song, Chunguang Lu, Chen Li, Yongjin Xu, Lin Liu and Xianbo Wang
Energies 2024, 17(11), 2450; https://doi.org/10.3390/en17112450 - 21 May 2024
Viewed by 728
Abstract
This paper presents a review of research progress on photovoltaic direct current arc detection based on VOSviewer bibliometric analysis. This study begins by introducing the basic concept and hazards of photovoltaic DC arcing faults, followed by a summary of commonly used arc detection [...] Read more.
This paper presents a review of research progress on photovoltaic direct current arc detection based on VOSviewer bibliometric analysis. This study begins by introducing the basic concept and hazards of photovoltaic DC arcing faults, followed by a summary of commonly used arc detection techniques. Utilizing VOSviewer, the relevant literature is subjected to clustering and visualization analysis, offering insights into research hotspots, trends, and interconnections among different fields. Based on the bibliometric analysis method of VOSviewer software, this paper analyzes the articles published in the last 10 years (2014–2023) on photovoltaic DC fault diagnosis. We analyzed the specific characteristics of 2195 articles on arc failures, including year of publication, author, institution, country, references, and keywords. This study reveals the development trend, global cooperation model, basic knowledge, research hotspots, and emerging frontier of PV DC arc. Future research directions and development trends for photovoltaic DC arc detection are proposed which provides valuable references for further studies and applications in this domain. This comprehensive analysis indicates that photovoltaic DC arc detection technology is expected to find broader applications and greater promotion in the future. Full article
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