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Electronics
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IoT Applications for Renewable Energy Management and Control, 2nd Edition
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IoT Applications for Renewable Energy Management and Control, 2nd Edition

A special issue of Electronics (ISSN 2079-9292).

Deadline for manuscript submissions: 15 September 2026 | Viewed by 7490

Special Issue Editors

Prof. Dr. Shengqing Li

E-Mail Website
Guest Editor
Department of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China
Interests: smart grid; electric engineering; smart energy
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jiazhu Xu

E-Mail Website
Guest Editor
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Interests: AC/DC hybrid distribution network; high-speed railway traction drive; new energy grid-connected control and optimization; hybrid power system with multiple converters
Prof. Dr. Jianqi Li

E-Mail Website
Guest Editor
School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
Interests: computer control and detection technology; IoT; AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There has been a paradigm shift in energy production and distribution after the emergence of the Internet of Things (IoT). The IoT is now used in all areas of renewable energy production, i.e., generation and transmission, as well as distribution equipment. These devices enable monitoring and controlling the operation of equipment remotely in real time. This reduces operational costs and lowers dependence on already-limited fossil fuels.

The use of renewable energy resources already provides a variety of benefits over conventional ones. The implementation of IoT will help to utilize these clean energy sources further.

Although the use of IoT in renewable energy management provides many benefits, it is not without its challenges and obstacles. The challenge with utilizing IoT devices is their vulnerability to hacking. Since devices are connected to a network, a cyberattack could occur if the network is not secured properly. This can lead to hazardous and unfavorable conditions.

This Special Issue covers IoT applications for renewable energy management and control, as well as its security aspects and associated issues and challenges.

Suggested Topics:

  • IoT application in smart grid energy management;
  • IoT application in renewable power generation;
  • Forecasting demand with IoT;
  • IoT application in power distribution;
  • IoT application in power grid automation;
  • IoT application in domestic power;
  • Security of IoT networks;
  • Advanced technology of electrical engineering.

Prof. Dr. Shengqing Li
Prof. Dr. Jiazhu Xu
Prof. Dr. Jianqi Li
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. Electronics 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 2400 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

  • IoT sensors
  • IoT-based actuators
  • smart grid and renewable energy
  • electrical engineering

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Related Special Issue

Published Papers (6 papers)

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Research

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20 pages, 2092 KB  
Article
Research on Adaptive Reconfigurable Control Strategy for EV Charging Stack in Complex Scenarios
by Si-Yang Hu, Ping Liu, Zheng Lan and Xuan-Yi Tang
Electronics 2026, 15(8), 1670; https://doi.org/10.3390/electronics15081670 - 16 Apr 2026
Viewed by 429
Abstract
This study proposes an adaptive variable structure control strategy for charging stacks to address the issues of reduced conversion efficiency during wide-voltage-range operation and insufficient module allocation flexibility in multi-vehicle scenarios. By dynamically adjusting the number and series/parallel configurations of modules, the strategy [...] Read more.
This study proposes an adaptive variable structure control strategy for charging stacks to address the issues of reduced conversion efficiency during wide-voltage-range operation and insufficient module allocation flexibility in multi-vehicle scenarios. By dynamically adjusting the number and series/parallel configurations of modules, the strategy ensures that modules consistently operate in high-efficiency regions, thereby achieving high energy conversion efficiency across a wide voltage range. First, the operational characteristics of the three-phase PWM rectifier and the dual active bridge (DAB) converters are analyzed, and their corresponding mathematical and loss models are established. Subsequently, the charging demands acquired by the charging stack are analyzed, and an adaptive variable structure control strategy is designed based on the module margin of the charging stack. When modules are surplus, the feasible range of series/parallel configurations for each port is constrained, and module combinations are optimized with the objective of minimizing system losses. When modules are insufficient, an adaptive module reservation scheduling strategy is employed to ensure temporal fairness in vehicle connection order while supplying power to multiple vehicles, effectively reducing the average charging time. Finally, the effectiveness of the proposed control strategy is validated through simulations conducted on the Matlab/Simulink platform. Results demonstrate that compared to traditional fixed-structure systems, the proposed strategy improves peak efficiency by up to 2.53% at 400 V and 1.12% at 800 V, while reducing the average charging time by 3.07% in the disconnection scenario and 12.1% in the asynchronous access scenario. Full article
(This article belongs to the Special Issue IoT Applications for Renewable Energy Management and Control, 2nd Edition)
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16 pages, 841 KB  
Article
Optimal Capacity Configuration of Photovoltaic-Storage Power Stations Based on an Improved Sparrow Search Algorithm
by Luting Zhang, Wei Zhao, Jinhui Zeng and Jie Liu
Electronics 2026, 15(3), 656; https://doi.org/10.3390/electronics15030656 - 2 Feb 2026
Viewed by 340
Abstract
To address the issues of high electricity costs for industrial loads in enterprise parks, significant peak-valley price differences, and insufficient utilization of renewable energy, a multi-objective capacity optimization method for photovoltaic and energy storage systems has been proposed, incorporating price-based demand response (PDR) [...] Read more.
To address the issues of high electricity costs for industrial loads in enterprise parks, significant peak-valley price differences, and insufficient utilization of renewable energy, a multi-objective capacity optimization method for photovoltaic and energy storage systems has been proposed, incorporating price-based demand response (PDR) and cycle life constraints. Firstly, a multi-objective function was constructed by integrating the aforementioned constraints, aiming to minimize the equivalent annualized comprehensive cost and the energy imbalance rate. Then, to overcome the limitations of the traditional sparrow search algorithm (SSA), such as low convergence speed, limited precision, and the tendency to fall into local optima, an improved SSA was proposed. This improved algorithm was enhanced by the integration of chaotic mapping, adaptive inertia weight, Harris Hawks encircling, and predation strategies. Through these improvements, both the convergence speed and accuracy in solving high-dimensional problems were significantly improved. Finally, a case study was conducted using real load data from an enterprise park in Zhuzhou City. The proposed algorithm achieves a maximum economic benefit improvement of 7.32% over conventional intelligent algorithms while further enhancing power supply reliability. Full article
(This article belongs to the Special Issue IoT Applications for Renewable Energy Management and Control, 2nd Edition)
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14 pages, 995 KB  
Article
Operation Efficiency Optimization of Electrochemical ESS with Battery Degradation Consideration
by Bowen Huang, Guojun Xiao, Zipeng Hu, Yong Xu, Kai Liu and Qian Huang
Electronics 2025, 14(21), 4182; https://doi.org/10.3390/electronics14214182 - 26 Oct 2025
Cited by 1 | Viewed by 750
Abstract
In the context of large-scale renewable integration and increasing demand for power-system flexibility, energy-storage systems are indispensable components of modern grids, and their safe, reliable operation is a decisive factor in investment decisions. To mitigate lifecycle degradation and cost increases caused by frequent [...] Read more.
In the context of large-scale renewable integration and increasing demand for power-system flexibility, energy-storage systems are indispensable components of modern grids, and their safe, reliable operation is a decisive factor in investment decisions. To mitigate lifecycle degradation and cost increases caused by frequent charge–discharge cycles, this study puts forward a two-layer energy storage capacity configuration optimization approach with explicit integration of cycle life restrictions. The upper-level model uses time-of-use pricing to economically dispatch storage, balancing power shortfalls while maximizing daily operational revenue. Based on the upper-level dispatch schedule, the lower-level model computes storage degradation and optimizes storage capacity as the decision variable to minimize degradation costs. Joint optimization of the two levels thus enhances overall storage operating efficiency. To overcome limitations of the conventional Whale Optimization Algorithm (WOA)—notably slow convergence, limited accuracy, and susceptibility to local optima—an Improved WOA (IWOA) is developed. IWOA integrates circular chaotic mapping for population initialization, a golden-sine search mechanism to improve the exploration–exploitation trade-off, and a Cauchy-mutation strategy to increase population diversity. Comparative tests against WOA, Gray Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO) show IWOA’s superior convergence speed and solution quality. A case study using measured load data from an industrial park in Zhuzhou City validates that the proposed approach significantly improves economic returns and alleviates capacity degradation. Full article
(This article belongs to the Special Issue IoT Applications for Renewable Energy Management and Control, 2nd Edition)
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23 pages, 7271 KB  
Article
A Hybrid ASW-UKF-TRF Algorithm for Efficient Data Classification and Compression in Lithium-Ion Battery Management Systems
by Bowen Huang, Xueyuan Xie, Jiangteng Yi, Qian Yu, Yong Xu and Kai Liu
Electronics 2025, 14(19), 3780; https://doi.org/10.3390/electronics14193780 - 24 Sep 2025
Viewed by 911
Abstract
Electrochemical energy storage technology, primarily lithium-ion batteries, has been widely applied in large-scale energy storage systems. However, differences in assembly structures, manufacturing processes, and operating environments introduce parameter inconsistencies among cells within a pack, producing complex, high-volume datasets with redundant and fragmented charge–discharge [...] Read more.
Electrochemical energy storage technology, primarily lithium-ion batteries, has been widely applied in large-scale energy storage systems. However, differences in assembly structures, manufacturing processes, and operating environments introduce parameter inconsistencies among cells within a pack, producing complex, high-volume datasets with redundant and fragmented charge–discharge records that hinder efficient and accurate system monitoring. To address this challenge, we propose a hybrid ASW-UKF-TRF framework for the classification and compression of battery data collected from energy storage power stations. First, an adaptive sliding-window Unscented Kalman Filter (ASW-UKF) performs online data cleaning, imputation, and smoothing to ensure temporal consistency and recover missing/corrupted samples. Second, a temporally aware TRF segments the time series and applies an importance-weighted, multi-level compression that formally prioritizes diagnostically relevant features while compressing low-information segments. The novelty of this work lies in combining deployment-oriented engineering robustness with methodological innovation: the ASW-UKF provides context-aware, online consistency restoration, while the TRF compression formalizes diagnostic value in its retention objective. This hybrid design preserves transient fault signatures that are frequently removed by conventional smoothing or generic compressors, while also bounding computational overhead to enable online deployment. Experiments on real operational station data demonstrate classification accuracy above 95% and an overall data volume reduction in more than 60%, indicating that the proposed pipeline achieves substantial gains in monitoring reliability and storage efficiency compared to standard denoising-plus-generic-compression baselines. The result is a practical, scalable workflow that bridges algorithmic advances and engineering requirements for large-scale battery energy storage monitoring. Full article
(This article belongs to the Special Issue IoT Applications for Renewable Energy Management and Control, 2nd Edition)
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19 pages, 6583 KB  
Article
Multiple Fault-Tolerant Control of DC Microgrids Based on Sliding Mode Observer
by Jian Sun, Zewen Li and Minsheng Yang
Electronics 2025, 14(5), 931; https://doi.org/10.3390/electronics14050931 - 26 Feb 2025
Cited by 8 | Viewed by 1439
Abstract
Different locations and types of faults affect the safe and reliable operation of DC microgrids. Therefore, this paper proposes a secondary multiple fault-tolerant control scheme for a DC microgrid based on a sliding mode observer to ensure the voltage is restored to the [...] Read more.
Different locations and types of faults affect the safe and reliable operation of DC microgrids. Therefore, this paper proposes a secondary multiple fault-tolerant control scheme for a DC microgrid based on a sliding mode observer to ensure the voltage is restored to the rated value and realize the proportional current sharing of all sources. Firstly, the secondary control model of the DC microgrid is established, considering the multiple faults of actuators and sensors simultaneously. Secondly, the system model is transformed into two subsystems by bilinear coordinate transformation, and multiple faults decoupling between the sensor and actuator is realized. Then, two sliding mode observers are designed for the two transformed subsystems. The sliding mode variable structure equivalent principle is used to reconstruct the faults at different positions without knowing the fault models in advance, which is convenient for subsequent processing. Then, the fault-tolerant controller based on the sliding mode observer is designed, which uses the reconstructed value to offset the influence of sensor and actuator faults on the DC microgrid and realizes the fault-tolerant control of the DC microgrid. Finally, the effectiveness of the proposed control strategy is verified by experiments. Full article
(This article belongs to the Special Issue IoT Applications for Renewable Energy Management and Control, 2nd Edition)
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Other

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19 pages, 7628 KB  
Technical Note
Distributed Event-Triggered Current Sharing Consensus-Based Adaptive Droop Control of DC Microgrid
by Jinhui Zeng, Tianqi Liu, Chengjie Xu and Zhifeng Sun
Electronics 2025, 14(6), 1217; https://doi.org/10.3390/electronics14061217 - 20 Mar 2025
Cited by 7 | Viewed by 2549
Abstract
Conventional droop control (a decentralized method to regulate power sharing by adjusting voltage–current slopes) in DC microgrids faces challenges in balancing precise current distribution, bus voltage regulation, and communication pressure, especially in distributed energy management scenarios. To address these limitations, this paper proposes [...] Read more.
Conventional droop control (a decentralized method to regulate power sharing by adjusting voltage–current slopes) in DC microgrids faces challenges in balancing precise current distribution, bus voltage regulation, and communication pressure, especially in distributed energy management scenarios. To address these limitations, this paper proposes an adaptive control strategy combining three layers: (1) Primary control achieves power sharing and voltage stabilization via U-I droop characteristics for distributed energy resources (DERs); (2) Secondary control corrects voltage deviations and droop coefficient imbalances through multi-agent consensus algorithms, ensuring global equilibrium; (3) Event-triggered consensus control minimizes communication pressure via a novel protocol with time-varying coupling weights and a hybrid trigger function combining state variables and time-decaying terms rigorously proven to exclude Zeno behavior (i.e., infinite triggering in finite time) using Lyapunov stability theory. Full article
(This article belongs to the Special Issue IoT Applications for Renewable Energy Management and Control, 2nd Edition)
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