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Energies
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Emerging Technologies and Methods for Future Energy Markets
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Emerging Technologies and Methods for Future Energy Markets

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "K: State-of-the-Art Energy Related Technologies".

Deadline for manuscript submissions: closed (10 July 2023) | Viewed by 6496

Special Issue Editors

Dr. Weifeng Zhong

E-Mail Website
Guest Editor
Associate Professor, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Interests: smart grid; vehicle-to-grid; vehicular networks
Special Issues, Collections and Topics in MDPI journals
Dr. Xumin Huang

E-Mail Website
Guest Editor
School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Interests: Internet of Things; smart grid; blockchain; resource allocation and network optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Su Wang

E-Mail Website
Guest Editor
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
Interests: electricity market analysis and design; power system operation and optimization; Internet of Things; mechanism design
Prof. Dr. Jin Ye

E-Mail Website
Guest Editor
School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
Interests: energy management; blockchain; network optimization; artificial intelligence

Special Issue Information

Dear Colleagues,

Driven by the ongoing digitalization, decentralization, and decarbonization of energy systems, novel goods, services, participants, and connections are emerging in energy markets. The high penetration of renewables, the massive use of grid-edge energy resources, and the coupling of multiple energy carriers will profoundly change the energy mix and infrastructure in the future and, in turn, restructure business models and strategies in the energy sector, bringing unprecedented challenges to resource management, risk analysis, and economic assessment in energy markets. This introduces the need for innovative technologies and methodologies able to support the effective arrangement and analysis of the energy, data, and currency flows in future energy markets at different layers. The technologies and methodologies aim to achieve positive interactions among all stakeholders and enhance system reliability, flexibility, and environmental friendliness under the increasing complexity and dynamics in future energy markets. This Special Issue calls for original research articles, reviews, and case studies from multiple disciplines in energy market topics, seeking novel perspectives, models, theories, and frameworks for the promotion of future energy markets.

The topics of interest include, but are not limited to, the following:

  • Energy economics and business models for smart grid, smart cities, smart buildings, transportation, and multienergy systems;
  • Energy pricing, sharing, and peer-to-peer trading;
  • Demand-side management and incentive mechanisms;
  • Design and operation of local and distribution energy markets;
  • Coordination mechanisms for local and wholesale energy markets;
  • Forecasting and analytics in energy markets;
  • Stochastic economic dispatch;
  • Coordination of carbon and energy markets;
  • Modeling strategic behavior in energy markets;
  • Communications, computing, and control in energy markets;
  • Artificial intelligence, blockchain, digital twin technologies supporting energy markets;
  • Cybersecurity and privacy of energy markets.

Dr. Weifeng Zhong
Dr. Xumin Huang
Dr. Su Wang
Prof. Dr. Jin Ye
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

  • energy markets
  • energy economics
  • energy management
  • energy system optimization
  • transactive energy

Published Papers (4 papers)

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Research

15 pages, 7652 KiB  
Article
Adaptive Clustering Long Short-Term Memory Network for Short-Term Power Load Forecasting
by Yuanhang Qi, Haoyu Luo, Yuhui Luo, Rixu Liao and Liwei Ye
Energies 2023, 16(17), 6230; https://doi.org/10.3390/en16176230 - 28 Aug 2023
Viewed by 771
Abstract
Short-term load forecasting (STLF) plays an important role in facilitating efficient and reliable operations of power systems and optimizing energy planning in the electricity market. To improve the accuracy of power load prediction, an adaptive clustering long short-term memory network is proposed to [...] Read more.
Short-term load forecasting (STLF) plays an important role in facilitating efficient and reliable operations of power systems and optimizing energy planning in the electricity market. To improve the accuracy of power load prediction, an adaptive clustering long short-term memory network is proposed to effectively combine the clustering process and prediction process. More specifically, the clustering process adopts the maximum deviation similarity criterion clustering algorithm (MDSC) as the clustering framework. A bee-foraging learning particle swarm optimization is further applied to realize the adaptive optimization of its hyperparameters. The prediction process consists of three parts: (i) a 9-dimensional load feature vector is proposed as the classification feature of SVM to obtain the load similarity cluster of the predicted days; (ii) the same kind of data are used as the training data of long short-term memory network; (iii) the trained network is used to predict the power load curve of the predicted day. Finally, experimental results are presented to show that the proposed scheme achieves an advantage in the prediction accuracy, where the mean absolute percentage error between predicted value and real value is only 8.05% for the first day. Full article
(This article belongs to the Special Issue Emerging Technologies and Methods for Future Energy Markets)
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18 pages, 2922 KiB  
Article
A Novel Cost Allocation Mechanism for Local Flexibility in the Power System with Partial Disintermediation
by Ádám Sleisz, Dániel Divényi, Beáta Polgári, Péter Sőrés and Dávid Raisz
Energies 2022, 15(22), 8646; https://doi.org/10.3390/en15228646 - 17 Nov 2022
Viewed by 1406
Abstract
Electricity markets are going through a comprehensive transformation that includes the large-scale appearance of intermittent renewable generators (RGs). To handle the local effects of new RGs on the distribution grid, the more efficient utilization of distributed local flexibility (LF) resources is necessary. However, [...] Read more.
Electricity markets are going through a comprehensive transformation that includes the large-scale appearance of intermittent renewable generators (RGs). To handle the local effects of new RGs on the distribution grid, the more efficient utilization of distributed local flexibility (LF) resources is necessary. However, the optimal market design is not yet known for LF products. This paper investigates a novel cost allocation mechanism in the context of this market challenge. The mechanism is designed to provide several important advantages of peer-to-peer trading without creating barriers to practical application. It provides partial disintermediation. The acquisition of LF remains the responsibility of the DSO, while the financial costs of the transaction are covered on power exchanges (PXs). To provide this functionality, the clearing algorithm of the PX in question has to incorporate a novel feature we call the Payment Redistribution Technique. This technique allows the buyers’ expenses to be larger than the sellers’ income, and the difference is used to finance flexibility costs. Its mathematical formulation is presented and analyzed in detail, considering computational efficiency and accuracy. Afterward, a realistic case study is constructed to demonstrate the operation of the algorithm and its energy market effects. Full article
(This article belongs to the Special Issue Emerging Technologies and Methods for Future Energy Markets)
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15 pages, 3430 KiB  
Article
Design and Implementation of Demand Side Response Based on Binomial Distribution
by Ming Li and Jin Ye
Energies 2022, 15(22), 8431; https://doi.org/10.3390/en15228431 - 11 Nov 2022
Cited by 3 | Viewed by 1011
Abstract
The application of microgrids (MG) is more and more extensive, therefore it is important to improve the system management method of microgrids. The intended costs can be further minimized when the energy management system is unified with demand side response (DSR) strategies. In [...] Read more.
The application of microgrids (MG) is more and more extensive, therefore it is important to improve the system management method of microgrids. The intended costs can be further minimized when the energy management system is unified with demand side response (DSR) strategies. In this work, we propose a generic method of modeling the equipment in a microgrid including multiple stochastic loads. The microgrid model can be generated on a computer by converting the energy circuit diagram into a signal flow diagram. Then, a demand side response method based on binomial distribution is introduced, and loads are set to different probabilities according to importance. By applying the probability of loads and changing the return coefficient of loads, the problem of individual differences in demand side responses is solved, so as to improve consumer satisfaction. The proposed model is constructed as a mixed-integer linear program (MILP). Cases studies demonstrate feasibility of the proposed modeling method. The demand side response achieves the expected goal. The system management method reduces the operation cost of the energy system of microgrids. Full article
(This article belongs to the Special Issue Emerging Technologies and Methods for Future Energy Markets)
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11 pages, 995 KiB  
Article
A Hybrid Algorithm for Short-Term Wind Power Prediction
by Zhenhua Xiong, Yan Chen, Guihua Ban, Yixin Zhuo and Kui Huang
Energies 2022, 15(19), 7314; https://doi.org/10.3390/en15197314 - 5 Oct 2022
Cited by 7 | Viewed by 1304
Abstract
Accurate and effective wind power prediction plays an important role in wind power generation, distribution, and management. Inthis paper, a hybrid algorithm based on gradient descent and meta-heuristic optimization is designed to improve the accuracy of prediction and reduce the computational burden. The [...] Read more.
Accurate and effective wind power prediction plays an important role in wind power generation, distribution, and management. Inthis paper, a hybrid algorithm based on gradient descent and meta-heuristic optimization is designed to improve the accuracy of prediction and reduce the computational burden. The hybrid algorithm includes three steps: in the first step, we use the gradient descent algorithm to get the initial parameters. Secondly, we input the initial parameters into the meta-heuristic optimization algorithm to search for the “best parameters” (high-quality inferior solutions). Finally, we input optimized parameters into the RMSProp optimization algorithm and conduct gradient descent again to find a better solution. We used 2021 wind power data from Guangxi, China for the experiment. The results show that the hybrid prediction algorithm has better performance than the traditional Back Propagation (BP) in accuracy, stability, and efficiency. Full article
(This article belongs to the Special Issue Emerging Technologies and Methods for Future Energy Markets)
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