Advancing the Energy Internet: Innovations and Solutions for a Sustainable Energy Future

Topic Information

Dear Colleagues,

The Energy Internet represents a transformative paradigm integrating advanced power systems, distributed renewable energy, and digital technologies to achieve efficient, resilient, and sustainable energy management. As global decarbonization efforts intensify, the Energy Internet’s core components—including smart grid situational awareness, renewable integration optimization, AI-driven microgrid control, and cloud-based big data analytics—are critical to addressing challenges in grid stability, energy equity, and carbon neutrality. Notably, the convergence of artificial intelligence, edge computing, and IoT technologies with power systems has unlocked unprecedented opportunities for real-time decision-making, predictive maintenance, and demand-side management. However, the complexity of multi-energy synergies, cyber-physical security risks, and the volatility of renewable generation necessitate innovative solutions in data-driven modeling, adaptive control, and scalable infrastructure design. This Topic invites cutting-edge research on theoretical advancements, empirical case studies, and technological innovations to propel the Energy Internet toward scalability and interoperability.

Contributions may address, but are not limited to, the following areas:

• Situational awareness and dynamic stability analysis for smart distribution grids;
• Optimization control strategies for high-penetration renewable energy integration;
• AI/ML applications in microgrid energy management and fault diagnosis;
• Blockchain-enabled peer-to-peer energy trading and decentralized governance;
• Big data analytics for load forecasting, asset management, and grid anomaly detection;
• Cyber security and resilience in cyber–physical power systems;
• Edge-cloud collaborative computing for distributed energy resource coordination;
• Standardization and policy frameworks for Energy Internet deployment;
• Socio-economic impacts and business models for transactive energy systems;
• Digital twins and virtual power plants for grid flexibility enhancement.

Dr. Leijiao Ge
Dr. Dou An
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electricity electricity	1.8	5.1	2020	26.9 Days	CHF 1200	Submit
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
Energies energies	3.2	7.3	2008	16.8 Days	CHF 2600	Submit
Future Internet futureinternet	3.6	8.3	2009	16.1 Days	CHF 1800	Submit
IoT IoT	2.8	8.7	2020	25.5 Days	CHF 1400	Submit
Sensors sensors	3.5	8.2	2001	17.8 Days	CHF 2600	Submit

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Published Papers (6 papers)

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All Electricity Electronics Energies Future Internet IoT Sensors

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22 pages, 3757 KB  

Open AccessArticle

Electric Vehicle Cluster Charging Scheduling Optimization: A Forecast-Driven Multi-Objective Reinforcement Learning Method

by Yi Zhao, Xian Jia, Shuanbin Tan, Yan Liang, Pengtao Wang and Yi Wang

Energies 2026, 19(3), 647; https://doi.org/10.3390/en19030647 - 27 Jan 2026

Viewed by 473

Abstract

The widespread adoption of electric vehicles (EVs) has posed significant challenges to the security of distribution grid loads. To address issues such as increased grid load fluctuations, rising user charging costs, and rapid load surges around midnight caused by uncoordinated nighttime charging of [...] Read more.

The widespread adoption of electric vehicles (EVs) has posed significant challenges to the security of distribution grid loads. To address issues such as increased grid load fluctuations, rising user charging costs, and rapid load surges around midnight caused by uncoordinated nighttime charging of household electric vehicles in communities, this paper first models electric vehicle charging behavior as a Markov Decision Process (MDP). By improving the state-space sampling mechanism, a continuous space mapping and a priority mechanism are designed to transform the charging scheduling problem into a continuous decision-making framework while optimizing the dynamic adjustment between state and action spaces. On this basis, to achieve synergistic load forecasting and charging scheduling decisions, a forecast-augmented deep reinforcement learning method integrating Gated Recurrent Unit and Twin Delayed Deep Deterministic Policy Gradient (GRU-TD3) is proposed. This method constructs a multi-objective reward function that comprehensively considers time-of-use electricity pricing, load stability, and user demands. The method also applies a single-objective pre-training phase and a model-specific importance-sampling strategy to improve learning efficiency and policy stability. Its effectiveness is verified through extensive comparative and ablation validation. The results show that our method outperforms several benchmarks. Specifically, compared to the Deep Deterministic Policy Gradient (DDPG) and Particle Swarm Optimization (PSO) algorithms, it reduces user costs by 11.7% and the load standard deviation by 12.9%. In contrast to uncoordinated charging strategies, it achieves a 42.5% reduction in user costs and a 20.3% decrease in load standard deviation. Moreover, relative to single-objective cost optimization approaches, the proposed algorithm effectively suppresses short-term load growth rates and mitigates the “midnight peak” phenomenon. Full article

(This article belongs to the Topic Advancing the Energy Internet: Innovations and Solutions for a Sustainable Energy Future)

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25 pages, 969 KB  

Open AccessArticle

H-CLAS: A Hybrid Continual Learning Framework for Adaptive Fault Detection and Self-Healing in IoT-Enabled Smart Grids

by Tina Babu, Rekha R. Nair, Balamurugan Balusamy and Sumendra Yogarayan

IoT 2026, 7(1), 12; https://doi.org/10.3390/iot7010012 - 27 Jan 2026

Cited by 3 | Viewed by 879

Abstract

The rapid expansion of Internet of Things (IoT)-enabled smart grids has intensified the need for reliable fault detection and autonomous self-healing under non-stationary operating conditions characterized by frequent concept drift. To address the limitations of static and single-strategy adaptive models, this paper proposes [...] Read more.

The rapid expansion of Internet of Things (IoT)-enabled smart grids has intensified the need for reliable fault detection and autonomous self-healing under non-stationary operating conditions characterized by frequent concept drift. To address the limitations of static and single-strategy adaptive models, this paper proposes H-CLAS, a novel Hybrid Continual Learning for Adaptive Self-healing framework that unifies regularization-based, memory-based, architectural, and meta-learning strategies within a single adaptive pipeline. The framework integrates convolutional neural networks (CNNs) for fault detection, graph neural networks for topology-aware fault localization, reinforcement learning for self-healing control, and a hybrid drift detection mechanism combining ADWIN and Page–Hinkley tests. Continual adaptation is achieved through the synergistic use of Elastic Weight Consolidation, memory-augmented replay, progressive neural network expansion, and Model-Agnostic Meta-Learning for rapid adaptation to emerging drifts. Extensive experiments conducted on the Smart City Air Quality and Network Intrusion Detection Dataset (NSL-KDD) demonstrate that H-CLAS achieves accuracy improvements of 12–15% over baseline methods, reduces false positives by over 50%, and enables 2–3× faster recovery after drift events. By enhancing resilience, reliability, and autonomy in critical IoT-driven infrastructures, the proposed framework contributes to improved grid stability, reduced downtime, and safer, more sustainable energy and urban monitoring systems, thereby providing significant societal and environmental benefits. Full article

(This article belongs to the Topic Advancing the Energy Internet: Innovations and Solutions for a Sustainable Energy Future)

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19 pages, 8941 KB  

Open AccessArticle

Physical Information-Guided Kolmogorov–Arnold Networks for Battery State of Health Estimation

by Zeye Liu, Songtao Ye, Feifei Cui and Yu Ma

Energies 2025, 18(22), 5865; https://doi.org/10.3390/en18225865 - 7 Nov 2025

Cited by 5 | Viewed by 1966

Abstract

Against the backdrop of the rapid development of the energy internet, the role of energy storage systems in grid stability, energy balance, and renewable energy integration has become increasingly important. Among these systems, estimating the state of health (SOH) of battery storage systems, [...] Read more.

Against the backdrop of the rapid development of the energy internet, the role of energy storage systems in grid stability, energy balance, and renewable energy integration has become increasingly important. Among these systems, estimating the state of health (SOH) of battery storage systems, particularly lithium batteries, is crucial for ensuring system reliability and safety. While data-driven methods have poor interpretability and physics-based models are computationally expensive, physics-informed neural networks (PINNs) offer a compromise but struggle with high-dimensional inputs and dynamic variable coupling. This paper proposed a novel Kolmogorov–Arnold networks with physics-informed neural network (KAN-PINN) framework for lithium-ion battery SOH estimation. By leveraging KANs’ superior high-dimensional approximation capabilities and embedding the Verhulst model as a physical constraint, the framework enhances nonlinear representation while ensuring predictions adhere to degradation physics. Experimental results on a public dataset demonstrate the model’s superiority, achieving an RMSPE of 0.300 and MAE of 1.342%, along with strong interpretability and robustness across battery chemistries and operating conditions. Full article

(This article belongs to the Topic Advancing the Energy Internet: Innovations and Solutions for a Sustainable Energy Future)

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20 pages, 1690 KB  

Open AccessArticle

Hybrid Drive Simulation Architecture for Power Distribution Based on the Federated Evolutionary Monte Carlo Algorithm

by Dongli Jia, Xiaoyu Yang, Wanxing Sheng, Keyan Liu, Tingyan Jin, Xiaoming Li and Weijie Dong

Energies 2025, 18(21), 5595; https://doi.org/10.3390/en18215595 - 24 Oct 2025

Cited by 2 | Viewed by 870

Abstract

Modern active distribution networks are increasingly characterized by high complexity, uncertainty, and distributed clustering, posing challenges for traditional model-based simulations in capturing nonlinear dynamics and stochastic variations. This study develops a data–model hybrid-driven simulation architecture that integrates a Federated Evolutionary Monte Carlo Optimization [...] Read more.

Modern active distribution networks are increasingly characterized by high complexity, uncertainty, and distributed clustering, posing challenges for traditional model-based simulations in capturing nonlinear dynamics and stochastic variations. This study develops a data–model hybrid-driven simulation architecture that integrates a Federated Evolutionary Monte Carlo Optimization (FEMCO) algorithm for distribution network optimization. The model-driven module employs spectral clustering to decompose the network into multiple autonomous subsystems and performs distributed reconstruction through gradient descent. The data-driven module, built upon Long Short-Term Memory (LSTM) networks, learns temporal dependencies between load curves and operational parameters to enhance predictive accuracy. These two modules are fused via a Random Forest ensemble, while FEMCO jointly leverages Monte Carlo global sampling, Federated Learning-based distributed training, and Genetic Algorithm-driven evolutionary optimization. Simulation studies on the IEEE 33 bus distribution system demonstrate that the proposed framework reduces power losses by 25–45% and voltage deviations by 75–85% compared with conventional Genetic Algorithm and Monte Carlo approaches. The results confirm that the proposed hybrid architecture effectively improves convergence stability, optimization precision, and adaptability, providing a scalable solution for the intelligent operation and distributed control of modern power distribution systems. Full article

(This article belongs to the Topic Advancing the Energy Internet: Innovations and Solutions for a Sustainable Energy Future)

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20 pages, 448 KB  

Open AccessArticle

Toward Scalable and Sustainable Detection Systems: A Behavioural Taxonomy and Utility-Based Framework for Security Detection in IoT and IIoT

by Ali Jaddoa, Hasanein Alharbi, Abbas Hommadi and Hussein A. Ismael

IoT 2025, 6(4), 62; https://doi.org/10.3390/iot6040062 - 21 Oct 2025

Cited by 1 | Viewed by 1135

Abstract

Resource-constrained IoT and IIoT systems require detection architectures that balance accuracy with energy efficiency, scalability, and contextual awareness. This paper presents a conceptual framework informed by a systematic review of energy-aware detection systems (XDS), unifying intrusion and anomaly detection systems (IDS and ADS) [...] Read more.

Resource-constrained IoT and IIoT systems require detection architectures that balance accuracy with energy efficiency, scalability, and contextual awareness. This paper presents a conceptual framework informed by a systematic review of energy-aware detection systems (XDS), unifying intrusion and anomaly detection systems (IDS and ADS) within a single framework. The proposed taxonomy captures six key dimensions: energy-awareness, adaptivity, modularity, offloading support, domain scope, and attack coverage. Applying this framework to the recent literature reveals recurring limitations, including static architectures, limited runtime coordination, and narrow evaluation settings. To address these challenges, we introduce a utility-based decision model for multi-layer task placement, guided by operational metrics such as energy cost, latency, and detection complexity. Unlike review-only studies, this work contributes both a synthesis of current limitations and the design of a novel six-dimensional taxonomy and utility-based layered architecture. The study concludes with future directions that support the development of adaptable, sustainable, and context-aware XDS architectures for heterogeneous environments. Full article

(This article belongs to the Topic Advancing the Energy Internet: Innovations and Solutions for a Sustainable Energy Future)

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23 pages, 2258 KB  

Open AccessArticle

A High-Precision Short-Term Photovoltaic Power Forecasting Model Based on Multivariate Variational Mode Decomposition and Gated Recurrent Unit-Attention with Crested Porcupine Optimizer-Enhanced Vector Weighted Average Algorithm

by Jinxiang Pian and Xianliang Chen

Sensors 2025, 25(19), 5977; https://doi.org/10.3390/s25195977 - 26 Sep 2025

Cited by 2 | Viewed by 1081

Abstract

The increasing reliance on renewable energy sources, such as photovoltaic (PV) systems, is pivotal for achieving sustainable development and addressing global energy challenges. However, short-term power forecasting for distributed PV systems often faces accuracy limitations, hindering their efficient grid integration. To address this, [...] Read more.

The increasing reliance on renewable energy sources, such as photovoltaic (PV) systems, is pivotal for achieving sustainable development and addressing global energy challenges. However, short-term power forecasting for distributed PV systems often faces accuracy limitations, hindering their efficient grid integration. To address this, a novel hybrid prediction model is proposed, combining multivariate variational mode decomposition (MVMD) with a gated recurrent unit (GRU) network, an attention mechanism (ATT), and an enhanced vector weighted average algorithm (cINFO). The MVMD first decomposes historical data to reduce volatility. The INFO algorithm is then improved by integrating the crested porcupine optimizer (CPO), forming the cINFO algorithm to optimize GRU-ATT hyperparameters. An attention mechanism is incorporated to accentuate key influencing factors. The model was evaluated using the DKASC Alice Springs dataset. Results demonstrate high predictive accuracy, with mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) values of 0.0249, 0.0693, and 99.79%, respectively, under sunny conditions, significantly outperforming benchmark models. This confirms the model’s feasibility and superiority for short-term PV power forecasting. Full article

(This article belongs to the Topic Advancing the Energy Internet: Innovations and Solutions for a Sustainable Energy Future)

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