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Flexible and Secure Operation of Multi-Scenario Integrated Energy System Coupled with Electricity and Hydrogen

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 524

Special Issue Editors

Dr. Xingtang He

E-Mail Website
Guest Editor
Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Interests: integrated energy system energy management microgrid secure operation; deep reinforcement learning
Dr. Xiaolong Jin

E-Mail Website
Guest Editor
Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Interests: flexible regulation of large-scale public buildings and market mechanisms for buildings-to-grid
Special Issues, Collections and Topics in MDPI journals
Dr. Sheng Wang

E-Mail Website
Guest Editor
School of Engineering, Newcastle University, Newcastle, UK
Interests: hydrogen integration in energy systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The combined utilization of hydrogen and electric energy is becoming an important research topic for integrated energy systems (IESs). The electric–hydrogen-coupled IES presents the more abundant adjustment potential in all aspects of supply, storage, transportation and consumption, which is of great significance for enhancing the flexibility of land grid-connected IESs and the security of off-grid island IESs.

This Special Issue aims to introduce and disseminate advanced technologies and pilot studies that exploit the advantages of electric–hydrogen coupling to enhance the ability of IESs to cope with the challenges of different scenarios. We invite you to submit high-quality original research papers, case studies and reviews related to the operation of IESs coupled with electricity and hydrogen.

Dr. Xingtang He
Dr. Xiaolong Jin
Dr. Sheng Wang
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

  • modeling, operation and trading of integrated energy systems coupled with electricity and hydrogen
  • modeling, operation and trading of micro-energy grids on islands or island clusters
  • flexible potential exploitation and multi-energy management of integrated energy systems
  • security and resilience enhancement of integrated energy systems in extreme scenarios
  • advanced decision making algorithms (analytical and artificial intelligence) in integrated energy systems
  • carbon emission evaluation and mitigation in integrated energy systems

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Published Papers (1 paper)

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Research

36 pages, 4428 KiB  
Article
An Evolutionary Deep Reinforcement Learning-Based Framework for Efficient Anomaly Detection in Smart Power Distribution Grids
by Mohammad Mehdi Sharifi Nevisi, Mehrdad Shoeibi, Francisco Hernando-Gallego, Diego Martín and Sarvenaz Sadat Khatami
Energies 2025, 18(10), 2435; https://doi.org/10.3390/en18102435 - 9 May 2025
Viewed by 225
Abstract
The increasing complexity of modern smart power distribution systems (SPDSs) has made anomaly detection a significant challenge, as these systems generate vast amounts of heterogeneous and time-dependent data. Conventional detection methods often struggle with adaptability, generalization, and real-time decision-making, leading to high false [...] Read more.
The increasing complexity of modern smart power distribution systems (SPDSs) has made anomaly detection a significant challenge, as these systems generate vast amounts of heterogeneous and time-dependent data. Conventional detection methods often struggle with adaptability, generalization, and real-time decision-making, leading to high false alarm rates and inefficient fault detection. To address these challenges, this study proposes a novel deep reinforcement learning (DRL)-based framework, integrating a convolutional neural network (CNN) for hierarchical feature extraction and a recurrent neural network (RNN) for sequential pattern recognition and time-series modeling. To enhance model performance, we introduce a novel non-dominated sorting artificial bee colony (NSABC) algorithm, which fine-tunes the hyper-parameters of the CNN-RNN structure, including weights, biases, the number of layers, and neuron configurations. This optimization ensures improved accuracy, faster convergence, and better generalization to unseen data. The proposed DRL-NSABC model is evaluated using four benchmark datasets: smart grid, advanced metering infrastructure (AMI), smart meter, and Pecan Street, widely recognized in anomaly detection research. A comparative analysis against state-of-the-art deep learning (DL) models, including RL, CNN, RNN, the generative adversarial network (GAN), the time-series transformer (TST), and bidirectional encoder representations from transformers (BERT), demonstrates the superiority of the proposed DRL-NSABC. The proposed DRL-NSABC model achieved high accuracy across all benchmark datasets, including 95.83% on the smart grid dataset, 96.19% on AMI, 96.61% on the smart meter, and 96.45% on Pecan Street. Statistical t-tests confirm the superiority of DRL-NSABC over other algorithms, while achieving a variance of 0.00014. Moreover, DRL-NSABC demonstrates the fastest convergence, reaching near-optimal accuracy within the first 100 epochs. By significantly reducing false positives and ensuring rapid anomaly detection with low computational overhead, the proposed DRL-NSABC framework enables efficient real-world deployment in smart power distribution systems without major infrastructure upgrades and promotes cost-effective, resilient power grid operations. Full article
(This article belongs to the Special Issue Flexible and Secure Operation of Multi-Scenario Integrated Energy System Coupled with Electricity and Hydrogen)
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