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

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

Deadline for manuscript submissions: 5 June 2026 | Viewed by 8804

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Special Issue Editors

Dr. Xingtang He

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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

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Guest Editor

School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: demand response; load control; electricity markets
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Dr. Sheng Wang

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Guest Editor

School of Engineering, Newcastle University, Newcastle, UK
Interests: hydrogen integration in energy systems
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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 250 words) can be sent to the Editorial Office for assessment.

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 (7 papers)

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Research

32 pages, 1364 KB

Open AccessArticle

XRL-LLM: Explainable Reinforcement Learning Framework for Voltage Control

by Shrenik Jadhav, Birva Sevak and Van-Hai Bui

Energies 2026, 19(7), 1789; https://doi.org/10.3390/en19071789 - 6 Apr 2026

Viewed by 701

Abstract

Reinforcement learning (RL) agents are increasingly deployed for voltage control in power distribution networks. However, their opaque decision-making creates a significant trust barrier, limiting their adoption in safety-sensitive operational settings. This paper presents XRL-LLM, a novel framework that generates natural language explanations for RL control decisions by combining game-theoretic feature attribution (KernelSHAP) with large language model (LLM) reasoning grounded in power systems domain knowledge. We deployed a Proximal Policy Optimization (PPO) agent on an IEEE 33-bus network to coordinate capacitor banks and on-load tap changers, successfully reducing voltage violations by 90.5% across diverse loading conditions. To make these decisions interpretable, KernelSHAP identifies the most influential state features. These features are then processed by a domain-context-engineered LLM prompt that explicitly encodes network topology, device specifications, and ANSI C84.1 voltage limits.Evaluated via G-Eval across 30 scenarios, XRL-LLM achieves an explanation quality score of 4.13/5. This represents a 33.7% improvement over template-based generation and a 67.9% improvement over raw SHAP outputs, delivering statistically significant gains in accuracy, actionability, and completeness (

p < 0.001

, Cohen’s d values up to 4.07). Additionally, a physics-grounded counterfactual verification procedure, which perturbs the underlying power flow model, confirms a causal faithfulness of 0.81 under critical loading. Finally, five ablation studies yield three broader insights. First, structured domain context engineering produces synergistic quality gains that exceed any single knowledge component, demonstrating that prompt composition matters more than the choice of foundational model. Second, even an open source 8B-parameter model outperforms templates given the same prompt, confirming the framework’s backbone-agnostic value. Most importantly, counterfactual faithfulness increases alongside load severity, indicating that post hoc attributions are most reliable in the high-stakes regimes where trustworthy explanations matter most. 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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25 pages, 2728 KB

Open AccessArticle

A Full-Time-Domain Analysis Based Method for Fault Transient Characteristic and Optimization Control in New Distribution System

by Wanxing Sheng, Xiaoyu Yang, Dongli Jia, Keyan Liu, Chengfeng Li and Qing Han

Energies 2026, 19(3), 669; https://doi.org/10.3390/en19030669 - 27 Jan 2026

Viewed by 411

Abstract

In new distribution systems with high penetration of renewable energy, inverter-based sources exhibit significant differences in fault characteristics compared to traditional power sources due to the absence of a constant electromotive force and their operation under nonlinear control links, rendering conventional fault current calculation methods inadequate. To address these challenges, a full-time-domain analysis-based method for modelling and calculating fault transient characteristics is proposed. First, a dynamic model of inverter-based sources accounting for current loop saturation effects is established, and phase plane analysis is employed to resolve nonlinear control regions. On this basis, a full-time-domain fault current calculation method is proposed, wherein the steady-state operating point after a fault is determined by iteratively solving the network node voltage equations. By integrating control strategies and derived transient differential equations, the fault current expression across the full-time-domain scope is formulated. Furthermore, a multi-objective optimization control strategy is proposed to achieve effective fault current suppression, and an improved Simulated Annealing-Particle Swarm Optimization (SA-IPSO) hybrid algorithm is adopted for efficient solution. Finally, SIMULINK-based simulation experiments validate the accuracy and effectiveness of the proposed method in transient characteristic analysis and current suppression. 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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37 pages, 2000 KB

Open AccessArticle

An Optimized DRL-GAN Approach for Robust Anomaly Detection in Multi-Scale Energy Systems: Insights from PSML and LEAD1.0

by Anita Ershadi Oskouei, Maral Keramat Dashliboroun, Pardis Sadatian Moghaddam, Nuria Serrano, Francisco Hernando-Gallego, Diego Martín and José Vicente Álvarez-Bravo

Energies 2026, 19(1), 198; https://doi.org/10.3390/en19010198 - 30 Dec 2025

Viewed by 743

Abstract

The increasing complexity of multi-scale energy systems makes robust anomaly detection essential to ensure system resilience and operational continuity. Recent advances in DL enable effective modeling of high-dimensional, non-linear energy data by capturing latent spatio-temporal patterns. In this paper, we proposed an optimized deep reinforcement learning–generative adversarial network (ODRL-GAN) framework for reliable anomaly detection in multi-scale energy systems. The integration of DRL and GAN brings a key innovation: while DRL enables adaptive decision-making under dynamic operating conditions, GAN enhances detection by reconstructing normal patterns and exposing subtle deviations. To further strengthen the model, a novel multi-objective chimp optimization algorithm (NMOChOA) is employed for hyper-parameter tuning, improving accuracy, and convergence. This design allows the ODRL–GAN to effectively capture high-dimensional spatio-temporal dependencies while maintaining robustness against diverse anomaly patterns. The framework is validated on two benchmark datasets, PSML and LEAD1.0, and compared against state-of-the-art baselines including transformer, deep belief network (DBN), convolutional neural network (CNN), gated recurrent unit (GRU), and support vector machines (SVM). Experimental results demonstrate that the proposed method achieves a maximum detection accuracy of 99.58% and recall of 99.75%, significantly surpassing all baselines. Furthermore, the model exhibits superior runtime efficiency, faster convergence, and lower variance across trials, highlighting both robustness and scalability. The optimized DRL–GAN framework provides a powerful and generalizable solution for anomaly detection in complex energy systems, offering a pathway toward secure and resilient next-generation energy infrastructures. 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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19 pages, 1036 KB

Open AccessArticle

A Hydrogen Energy Storage Configuration Method for Enhancing the Resilience of Distribution Networks Within Integrated Energy Systems

by Song Zhang, Yongxiang Cai, Xinyu You, Mingjun He, Ke Fan and Yutao Xu

Energies 2025, 18(23), 6355; https://doi.org/10.3390/en18236355 - 4 Dec 2025

Viewed by 760

Abstract

To address the challenges of renewable energy curtailment under normal conditions and severe power outages under extreme scenarios, this paper proposes a hydrogen-integrated comprehensive energy system (H-IES) configuration method aimed at enhancing the resilience of distribution networks. The proposed method improves energy utilization efficiency while achieving a balance between economic performance and resilience. First, an operational model of the H-IES is established considering the operating characteristics of distribution networks under extreme conditions. On this basis, a Nash bargaining-based equilibrium model is developed, where economic performance and resilience act as game participants negotiating toward equilibrium. By applying the particle swarm optimization algorithm, the Nash equilibrium solution is obtained, realizing a Pareto-optimal trade-off between the two objectives. Finally, case studies demonstrate that the proposed configuration improves the resilience index by 3.13% and reduces total cost by 10.86% compared with mobile battery energy storage. Under the Nash bargaining framework, the equilibrium configuration increases renewable energy utilization and provides up to 21.6% higher resilience compared with an economy-only optimization scheme. 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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25 pages, 5439 KB

Open AccessArticle

Hydrogen Carriers for Renewable Microgrid System Applications

by Dionissios D. Papadias, Rajesh K. Ahluwalia, Jui-Kun Peng, Peter Valdez, Ahmad Tbaileh and Kriston Brooks

Energies 2025, 18(21), 5775; https://doi.org/10.3390/en18215775 - 1 Nov 2025

Cited by 1 | Viewed by 1675

Abstract

Utility-scale energy storage can help improve grid reliability, reduce costs, and promote faster adoption of intermittent sources such as solar and wind. This paper analyzes the technical aspects and economics of standalone microgrids operating on intermittent power combined with hydrogen energy storage. It explores the feasibility of using dibenzyltoluene (DBT) as a liquid organic hydrogen carrier to absorb excess energy during periods of high supply and polymer electrolyte fuel cells to generate electrical energy during periods of low supply. A comparative analysis is conducted on three power demand scenarios (industrial, residential, and office), in conjunction with three alternative energy sources: solar, wind and wind–solar mix. A mixed system of solar and wind energy can maintain an annual average efficiency above 70%, except for residential power demand, which lowered the efficiency to 67%. A balanced combination of wind and solar power was the most cost-effective option. The current levelized cost of electricity (LCOE) for industrial power demand was estimated to 15 ¢/kWh, and it is projected to decrease to 9 ¢/kWh in the future. For residential power demand, the LCOE was 45% higher due to the demand profile. In comparison, battery storage is significantly more expensive than hydrogen storage, even with future cost projections, increasing the LCOE between 60 and 120 ¢/kWh. 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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31 pages, 3563 KB

Open AccessArticle

Research on Flexible Operation Control Strategy of Motor Operating Mechanism of High Voltage Vacuum Circuit Breaker

by Dongpeng Han, Weidong Chen and Zhaoxuan Cui

Energies 2025, 18(17), 4593; https://doi.org/10.3390/en18174593 - 29 Aug 2025

Cited by 1 | Viewed by 1125

Abstract

In order to solve the problem that it is difficult to take into account the performance constraints between the core functions of insulation, current flow and arc extinguishing of high-voltage vacuum circuit breakers at the same time, this paper proposes a flexible control strategy for the motor operating mechanism of high-voltage vacuum circuit breakers. The relationship between the rotation angle of the motor and the linear displacement of the moving contact of the circuit breaker is analyzed, and the ideal dynamic curve is planned. The motor drive control device is designed, and the phase-shifted full-bridge circuit is used as the boost converter. The voltage and current double closed-loop sliding mode control strategy is used to simulate and verify the realization of multi-stage and stable boost. The experimental platform is built and the experiment is carried out. The results show that under the voltage conditions of 180 V and 150 V, the control range of closing speed and opening speed is increased by 31.7% and 25.9% respectively, and the speed tracking error is reduced by 51.2%. It is verified that the flexible control strategy can meet the ideal action curve of the operating mechanism, realize the precise control of the opening and closing process and expand the control range. The research provides a theoretical basis for the flexible control strategy of the high-voltage vacuum circuit breaker operating mechanism, and provides new ideas for the intelligent operation technology of power transmission and transformation projects. 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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35 pages, 4428 KB

Open AccessEditor’s ChoiceArticle

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

Cited by 13 | Viewed by 2304

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 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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