Advanced Strategies for Smart Grid Reliability and Energy Optimization

Topic Information

Dear Colleagues,

We are delighted to invite submissions to the thematic collection “Advanced Strategies for Smart Grid Reliability and Energy Optimization”. This topic is dedicated to advancing scholarly discourse on the methodologies and technologies that enhance the reliability, operational efficiency, and energy optimization of smart grid systems. In an era marked by the accelerated adoption of renewable energy sources and the growing complexity of power distribution networks, it is imperative to explore innovative strategies that can address the multifaceted challenges facing modern energy infrastructures. We welcome original research articles and comprehensive reviews that focus on intelligent control systems, data-driven analytics, predictive maintenance, adaptive demand response mechanisms, and the integration of advanced energy storage solutions. Contributions from interdisciplinary perspectives that link electrical engineering, computational modeling, and energy policy are particularly encouraged to drive a deeper understanding of smart grid modernization. Topics of interest encompass, but are not limited to, grid optimization algorithms, advancements in smart metering technology, and the modeling of hybrid energy systems to enhance system resilience and sustainability.

We look forward to receiving your contributions.

Dr. Shuangqi Li
Dr. Alexis Pengfei Zhao
Dr. Yichen Shen
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	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
Eng eng	2.4	3.2	2020	18 Days	CHF 1400	Submit
Sensors sensors	3.5	8.2	2001	17.8 Days	CHF 2600	Submit
Smart Cities smartcities	5.5	14.7	2018	25.2 Days	CHF 2000	Submit
Vehicles vehicles	2.2	5.3	2019	21.4 Days	CHF 1800	Submit

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Published Papers (25 papers)

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28 pages, 5594 KB  

Open AccessArticle

A Novel Adaptive Multiple-Image-Feature Fusion Method for Transformer Winding Fault Diagnosis

by Huan Peng, Binyu Zhu, Zhenlin Yuan, Song Wang, Wei Wang and Jiawei Wang

Eng 2026, 7(5), 193; https://doi.org/10.3390/eng7050193 - 24 Apr 2026

Viewed by 190

Abstract

Frequency response analysis (FRA) is recognized as an effective method in power transformer winding fault diagnosis. However, the traditional numerical index methods focus on the overall features of FRA curves, making it difficult to capture subtle deformations in transformer windings. Similarly, existing digital [...] Read more.

Frequency response analysis (FRA) is recognized as an effective method in power transformer winding fault diagnosis. However, the traditional numerical index methods focus on the overall features of FRA curves, making it difficult to capture subtle deformations in transformer windings. Similarly, existing digital image processing methods rely on a single feature or a simple feature combination without adaptive fusion. These methods ignore differences in the data distributions of features, leading to feature mismatch, the loss of sensitive fault information, and lower diagnostic accuracy. To solve this problem, a novel adaptive multiple-image-feature fusion method for transformer winding fault diagnosis is proposed. First, a multi-dimensional feature space combining image pixel matrix similarity, morphological features, and image texture features is built to decode the difference in fault of FRA images. Second, the multiple kernel learning (MKL) framework is used to dynamically adjust the fusion weights of different kernels to make features compatible and remove redundant information. Finally, comparative and ablation experiments show that the proposed method outperforms the traditional methods in identifying different types and levels of faults. The method achieves over 99% accuracy in fault type identification across SVM, KNN, and RF classifiers. For radial deformation (RD) severity prediction, the accuracy of the proposed model is 93.37% with SVM and 94.85% with KNN, outperforming the full-feature concatenation method. These results confirm the method’s robustness and diagnostic precision. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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24 pages, 2757 KB  

Open AccessArticle

Dynamic Event-Triggered Consensus Algorithm for Economic Dispatch in Microgrids

by Shuming Fan and Chengjie Xu

Electronics 2026, 15(9), 1804; https://doi.org/10.3390/electronics15091804 - 23 Apr 2026

Viewed by 262

Abstract

To address the challenge of limited communication resources in microgrid economic dispatch, this paper proposes an improved dynamic event-triggered mechanism built upon a distributed event-triggered consensus algorithm. Firstly, a dynamic variable is introduced to adaptively adjust the triggering threshold, which effectively reduces the [...] Read more.

To address the challenge of limited communication resources in microgrid economic dispatch, this paper proposes an improved dynamic event-triggered mechanism built upon a distributed event-triggered consensus algorithm. Firstly, a dynamic variable is introduced to adaptively adjust the triggering threshold, which effectively reduces the communication frequency between agents, thereby saving communication bandwidth and energy. Secondly, economic dispatch models are established for two scenarios: without generation constraints and with generation constraints. Corresponding distributed control protocols are designed. Thirdly, rigorous and clear mathematical proofs are provided for the asymptotic stability of the system and the exclusion of Zeno behavior. Simulation results demonstrate that the proposed method converges to the optimal incremental cost and power output. Compared with traditional static event-triggered mechanisms, the frequency of event triggering per unit time is reduced by approximately 51%, thereby effectively validating its effectiveness and superiority under multiple constraints. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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24 pages, 942 KB  

Open AccessArticle

Enhanced Wind Energy Integration and Grid Stability via Adaptive Nonlinear Control with Advanced Energy Management

by Nabil ElAadouli, Adil Mansouri, Abdelmounime El Magri, Rachid Lajouad, Ilyass El Myasse and Karim El Mezdi

Energies 2026, 19(8), 1941; https://doi.org/10.3390/en19081941 - 17 Apr 2026

Viewed by 259

Abstract

This paper proposes an advanced wind energy conversion and management framework for improving grid integration and mitigating frequency and power fluctuations caused by wind intermittency. The studied system combines a permanent magnet synchronous generator (PMSG), a unidirectional Vienna rectifier on the machine side, [...] Read more.

This paper proposes an advanced wind energy conversion and management framework for improving grid integration and mitigating frequency and power fluctuations caused by wind intermittency. The studied system combines a permanent magnet synchronous generator (PMSG), a unidirectional Vienna rectifier on the machine side, a Li-ion battery energy storage system, and a bidirectional Vienna rectifier on the grid side. The main scientific challenge addressed in this work is to ensure efficient wind power extraction, secure battery charging/discharging operation, and stable power exchange with the grid under variable operating conditions. To this end, a comprehensive nonlinear state-space model of the overall system is first established. Then, nonlinear controllers based on integral sliding mode principles are developed to guarantee rotor-speed tracking, DC-bus voltage regulation, battery charging current limitation, and active/reactive power control. In addition, an adaptive observer is designed to estimate the battery open-circuit voltage and support the supervision of the state of charge. An energy management strategy is further proposed to coordinate the operating modes according to grid conditions and battery constraints. Simulation results demonstrate that the proposed approach effectively smooths wind power fluctuations, improves grid support capability, and enhances the overall dynamic performance of the wind energy conversion system. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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25 pages, 6530 KB  

Open AccessArticle

Reinforcement Learning-Based Energy Storage Management for Microgrid Power Exchanges

by Federico Perquoti, Davide Milillo, Lorenzo Sabino, Michele Quercio, Francesco Riganti Fulginei, George Cristian Lazaroiu and Fabio Crescimbini

Eng 2026, 7(3), 126; https://doi.org/10.3390/eng7030126 - 9 Mar 2026

Viewed by 720

Abstract

Intelligent energy management systems are increasingly necessary for integrating renewable energy sources within microgrids. This paper investigates the application of a reinforcement learning (RL) neural network to optimize the operation of an electrochemical storage system in an environment composed of residential loads, commercial [...] Read more.

Intelligent energy management systems are increasingly necessary for integrating renewable energy sources within microgrids. This paper investigates the application of a reinforcement learning (RL) neural network to optimize the operation of an electrochemical storage system in an environment composed of residential loads, commercial loads, and a photovoltaic plant, all connected to the grid. A dataset combining market purchase prices, photovoltaic generation, and residential and commercial load profiles was generated and used to train a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent with the primary goal of deriving a reliable and adaptive post-training policy capable of maximizing photovoltaic self-consumption, minimizing operational costs through intelligent price arbitrage, and ensuring strict compliance with battery physical constraints. The system state includes battery state of charge, load demand, PV generation, and normalized market purchase prices, whereas the action represents the battery’s charge/discharge power, which is restricted from exporting energy to the grid. Results show that the agent learns to effectively store surplus PV energy and minimize grid dependency through dynamic charge management. The proposed approach outperforms strategies based solely on storing surplus self-generated energy and maintains the battery within safe operational limits. Tests with previously unseen data demonstrate robust, adaptive, and economically efficient energy management, highlighting the potential of reinforcement learning in intelligent energy systems. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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22 pages, 365 KB  

Open AccessArticle

Optimal Placement and Sizing of PV-STATCOMs in Distribution Systems for Dynamic Active and Reactive Compensation Using Crow Search Algorithm

by David Steven Cruz-Garzón, Harold Dario Sanchez-Celis, Oscar Danilo Montoya and David Steveen Guzmán-Romero

Eng 2026, 7(3), 110; https://doi.org/10.3390/eng7030110 - 1 Mar 2026

Viewed by 438

Abstract

The proliferation of distributed photovoltaic (PV) generation introduces significant operational challenges for distribution networks, including voltage instability and elevated technical losses. While modern PV inverters capable of static synchronous compensator (STATCOM) functionality—forming PV-STATCOM systems—offer a promising solution, their optimal integration remains a complex [...] Read more.

The proliferation of distributed photovoltaic (PV) generation introduces significant operational challenges for distribution networks, including voltage instability and elevated technical losses. While modern PV inverters capable of static synchronous compensator (STATCOM) functionality—forming PV-STATCOM systems—offer a promising solution, their optimal integration remains a complex mixed-integer non-linear programming (MINLP) problem. This paper addresses this gap by proposing a novel hybrid evaluator–optimizer framework for the optimal daily placement and sizing of PV-STATCOM devices. The framework synergistically integrates the metaheuristic crow search algorithm (CSA) for global exploration of discrete device locations with a high-fidelity, multi-period optimal power flow (OPF) model—implemented efficiently in Julia with the Ipopt solver—for continuous operational evaluation and constraint validation. The methodology incorporates realistic 24 h load and solar irradiance profiles. Extensive validation on standard IEEE 33- and 69-bus test systems demonstrates the efficacy of the proposed approach. The results indicate substantial reductions in daily energy losses—by up to 70.4% and 72.9% for the 33- and 69-bus systems, respectively—and corresponding operational costs, outperforming recent state-of-the-art metaheuristic and convex optimization methods reported in the literature. The CSA also exhibits robust convergence and repeatability across multiple independent runs. This work contributes a computationally efficient, open-source planning tool that leverages modern optimization solvers, providing a scalable and effective strategy for enhancing the power quality and economic performance of PV-rich distribution networks. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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24 pages, 3637 KB  

Open AccessArticle

Privacy-Preserving, Non-Iterative Coordinated Day-Ahead Scheduling of Multi-Area Active Distribution Networks via Equivalent Projection

by Ling Luo, Tiantian Chen, Chenhong Huang, Na Wang, Zhen Zheng, Jiangke Yang, Jian Ping and Zheng Yan

Smart Cities 2026, 9(3), 44; https://doi.org/10.3390/smartcities9030044 - 27 Feb 2026

Viewed by 516

Abstract

A distribution network is transforming into multi-area distribution networks. Traditional iterative multi-area coordination methods protect the privacy of each area but face a high communication burden and convergence issues. To address these challenges, this paper proposes a non-iterative day-ahead scheduling method based on [...] Read more.

A distribution network is transforming into multi-area distribution networks. Traditional iterative multi-area coordination methods protect the privacy of each area but face a high communication burden and convergence issues. To address these challenges, this paper proposes a non-iterative day-ahead scheduling method based on equivalent projection (EP). A deterministic scheduling model is established for multi-area distribution networks that are connected by soft open points (SOPs). An EP-based multi-area coordination method is proposed to transform the scheduling model into a reduced-dimensional problem that eliminates private data from areas. This enables privacy-preserving multi-area scheduling without iterative information exchange, thereby reducing the communication burden and achieving convergence in day-ahead coordination. Simulation results on the IEEE 33-bus five-region system show that the proposed method reduces online coordination time to 0.17 s compared to 181.06 s for the iterative baseline. Furthermore, tests on the larger IEEE 123-bus five-region system confirm its computational scalability in day-ahead scheduling, achieving a solution within 7.20 s with an optimality gap of 0.40%. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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16 pages, 1410 KB  

Open AccessArticle

Digital Twin-Driven Dynamic Reactive Power and Voltage Optimization for Large Grid-Connected PV Stations

by Qianqian Shi and Jinghua Zhou

Electronics 2026, 15(4), 821; https://doi.org/10.3390/electronics15040821 - 13 Feb 2026

Viewed by 444

Abstract

With the increasing penetration of inverter-based photovoltaic (PV) generation, utility-scale grid-connected PV plants are frequently exposed to voltage regulation and voltage stability challenges driven by intermittent irradiance and limited reactive power flexibility under operating constraints. Conventional static Volt/VAR control schemes are typically designed [...] Read more.

With the increasing penetration of inverter-based photovoltaic (PV) generation, utility-scale grid-connected PV plants are frequently exposed to voltage regulation and voltage stability challenges driven by intermittent irradiance and limited reactive power flexibility under operating constraints. Conventional static Volt/VAR control schemes are typically designed for quasi-steady conditions and therefore struggle to respond to fast variations in PV output and network states. This paper presents a digital twin (DT)-enabled framework for dynamic Volt/VAR optimization in large PV plants. A four-layer DT architecture is developed to achieve real-time cyber-physical synchronization through multi-source data acquisition, secure transmission, fusion, and quality control. To balance model fidelity and computational efficiency, a hybrid physics–data-driven model is constructed, and a local voltage stability L-index is incorporated as an explicit security constraint. A multi-objective optimization problem is formulated to minimize node voltage deviations and reactive power losses while maximizing the static voltage stability margin. The problem is solved using an adaptive parameter particle swarm optimization (AP-PSO) algorithm with dynamic inertia and learning coefficients. Case studies on modified IEEE 33-bus and 53-bus systems demonstrate that the proposed method reduces the voltage profile index by up to 68.9%, improves the static voltage stability margin by 76.5%, and shortens optimization time by up to 30.3% compared with conventional control and representative meta-heuristic or learning-based baselines. The framework further shows good scalability and robustness under practical uncertainties, including irradiance forecast errors and measurement noise. Overall, the proposed approach provides a feasible pathway to enhance operational security and efficiency of grid-connected PV plants under high-penetration scenarios. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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26 pages, 4553 KB  

Open AccessArticle

An Explicit Representation Method for Operational Reliability Constraints in Multi-Energy Coupled Low-Carbon Distribution Network

by Taoxing Liu, Changzheng Shao, Mingfeng Yu, Xintong Li and Qinglong Liao

Energies 2026, 19(4), 904; https://doi.org/10.3390/en19040904 - 9 Feb 2026

Viewed by 337

Abstract

Multi-energy coupled low-carbon distribution networks (MEC-LCDNs) face growing risks from extreme weather and high-order contingencies. Traditional deterministic criteria (e.g., N-1) often overlook these low-probability, high-impact events, while existing simulation-based probabilistic methods suffer from excessive computational burdens and a lack of intuitive visualization. To [...] Read more.

Multi-energy coupled low-carbon distribution networks (MEC-LCDNs) face growing risks from extreme weather and high-order contingencies. Traditional deterministic criteria (e.g., N-1) often overlook these low-probability, high-impact events, while existing simulation-based probabilistic methods suffer from excessive computational burdens and a lack of intuitive visualization. To address these challenges, this paper proposes an explicit representation method for MEC-LCDN operational reliability constraints based on the probabilistic reliability region (PRR). This approach transforms the abstract probabilistic reliability criterion—loss of load probability (LOLP)—into a visualizable geometric space. Specifically, a fast contingency screening technique (FCST) is developed to identify a minimal set of boundary scenarios that anchor the target reliability threshold. Subsequently, complex probabilistic constraints are decoupled into deterministic N-k security constraints under these boundary scenarios, enabling the analytical construction of the PRR boundary. A case study demonstrates that the proposed method reduces the number of required contingency scenarios by over 90% and slashes computation time from 78.8 s to 3.1 s compared to traditional N-k truncation methods. Furthermore, the method accurately quantifies the system’s total supply capability (TSC) at 44.501 MW while providing intuitive visualizations of reliability boundaries that satisfy stringent LOLP criterion. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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15 pages, 1766 KB  

Open AccessArticle

Metaheuristic Optimizer-Based Segregated Load Scheduling Approach for Household Energy Consumption Management

by Shahzeb Ahmad Khan, Attique Ur Rehman, Ammar Arshad, Farhan Hameed Malik and Walid Ayadi

Eng 2026, 7(2), 65; https://doi.org/10.3390/eng7020065 - 1 Feb 2026

Cited by 1 | Viewed by 573

Abstract

In the face of escalating energy demand, this research proposes a demand-side management (DSM) strategy that focuses on appliance-level load shifting in residential environments. The proposed approach utilizes detailed energy consumption forecasts that are generated by ensemble machine learning models, which predict usage [...] Read more.

In the face of escalating energy demand, this research proposes a demand-side management (DSM) strategy that focuses on appliance-level load shifting in residential environments. The proposed approach utilizes detailed energy consumption forecasts that are generated by ensemble machine learning models, which predict usage at both whole-household and individual appliance levels. This granular forecasting enables the development of customized load-shifting schedules for controllable devices. These schedules are optimized using a metaheuristic genetic algorithm (GA) with the objectives of minimizing consumer energy costs and reducing peak demand. The iterative nature of GA allows for continuous fine-tuning, thereby adapting to dynamic energy market conditions. The implemented DSM technique yields significant results, successfully reducing the daily energy consumption cost for shiftable appliances. Overall, the proposed system decreases the per-day consumer electricity cost from 237 cents (without DSM) to 208 cents (with DSM), achieving a 12.23% cost saving. Furthermore, it effectively mitigates peak demand, reducing it from 3.4 kW to 1.2 kW, which represents a substantial 64.7% reduction. These promising outcomes demonstrate the potential for substantial consumer savings while concurrently enhancing the overall efficiency and reliability of the power grid. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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17 pages, 1938 KB  

Open AccessArticle

Optimal Scheduling of a Park-Scale Virtual Power Plant Based on Thermoelectric Coupling and PV–EV Coordination

by Ruiguang Ma, Tiannan Ma, Yanqiu Hou, Hao Luo, Jieying Liu, Luoyi Li, Yueping Xiang, Liqing Liao and Dan Tang

Eng 2026, 7(1), 54; https://doi.org/10.3390/eng7010054 - 21 Jan 2026

Viewed by 378

Abstract

This paper presents a closed-loop price–dispatch framework for park-scale virtual power plants (VPPs) with coupled electric–thermal processes under high penetrations of photovoltaics (PVs) and electric vehicles (EVs). The outer layer clears time-varying prices for operator electricity, operator heat, and user feed-in using an [...] Read more.

This paper presents a closed-loop price–dispatch framework for park-scale virtual power plants (VPPs) with coupled electric–thermal processes under high penetrations of photovoltaics (PVs) and electric vehicles (EVs). The outer layer clears time-varying prices for operator electricity, operator heat, and user feed-in using an improved particle swarm optimizer with adaptive coefficients and velocity clamping. Given these prices, the inner layer executes a lightweight linear source decomposition with feasibility projection that enforces transformer limits, combined heat-and-power (CHP) and boiler constraints, ramping, energy balances, and EV state-of-charge requirements. PV uncertainty is represented by a small set of scenarios and a conditional value-at-risk (CVaR) term augments the welfare objective to control tail risk. On a typical winter day case, the coordinated setting aligns EV charging with solar hours, reduces evening grid imports, and improves a social welfare proxy while maintaining interpretable price signals. Measured outcomes include 99.17% PV utilization (95.14% self-consumption and 4.03% routed to EV charging) and a reduction in EV charging cost from CNY 304.18 to CNY 249.87 (−17.9%) compared with an all-from-operator benchmark; all transformer, CHP/boiler, and EV constraints are satisfied. The price loop converges within several dozen iterations without oscillation. Sensitivity studies show that increasing risk weight lowers CVaR with modest welfare trade-offs, while wider price bounds and higher EV availability raise welfare until physical limits bind. The results demonstrate an effective, interpretable, and reproducible pathway to integrate market signals with engineering constraints in park VPP operations. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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25 pages, 3073 KB  

Open AccessArticle

A Two-Stage Intelligent Reactive Power Optimization Method for Power Grids Based on Dynamic Voltage Partitioning

by Tianliang Xue, Xianxin Gan, Lei Zhang, Su Wang, Qin Li and Qiuting Guo

Electronics 2026, 15(2), 447; https://doi.org/10.3390/electronics15020447 - 20 Jan 2026

Viewed by 341

Abstract

Aiming at issues such as reactive power distribution fluctuations and insufficient local support caused by large-scale integration of renewable energy in new power systems, as well as the poor adaptability of traditional methods and bottlenecks of deep reinforcement learning in complex power grids, [...] Read more.

Aiming at issues such as reactive power distribution fluctuations and insufficient local support caused by large-scale integration of renewable energy in new power systems, as well as the poor adaptability of traditional methods and bottlenecks of deep reinforcement learning in complex power grids, a two-stage intelligent optimization method for grid reactive power based on dynamic voltage partitioning is proposed. Firstly, a comprehensive indicator system covering modularity, regulation capability, and membership degree is constructed. Adaptive MOPSO is employed to optimize K-means clustering centers, achieving dynamic grid partitioning and decoupling large-scale optimization problems. Secondly, a Markov Decision Process model is established for each partition, incorporating a penalty mechanism for safety constraint violations into the reward function. The DDPG algorithm is improved through multi-experience pool probabilistic replay and sampling mechanisms to enhance agent training. Finally, an optimal reactive power regulation scheme is obtained through two-stage collaborative optimization. Simulation case studies demonstrate that this method effectively reduces solution complexity, accelerates convergence, accurately addresses reactive power dynamic distribution and local support deficiencies, and ensures voltage security and optimal grid losses. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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30 pages, 7842 KB  

Open AccessArticle

Advanced MPPT Strategy for PV Microinverters: A Dragonfly Algorithm Approach Integrated with Wireless Sensor Networks Under Partial Shading

by Mahir Dursun and Alper Görgün

Electronics 2026, 15(2), 413; https://doi.org/10.3390/electronics15020413 - 16 Jan 2026

Cited by 1 | Viewed by 532 | Correction

Abstract

The integration of solar energy into smart grids requires high-efficiency power conversion to support grid stability. However, Partial Shading Conditions (PSCs) remain a primary obstacle by inducing multiple local maxima on P–V characteristic curves. This paper presents a hardware-aware and memory-enhanced Maximum Power [...] Read more.

The integration of solar energy into smart grids requires high-efficiency power conversion to support grid stability. However, Partial Shading Conditions (PSCs) remain a primary obstacle by inducing multiple local maxima on P–V characteristic curves. This paper presents a hardware-aware and memory-enhanced Maximum Power Point Tracking (MPPT) approach based on a modified Dragonfly Algorithm (DA) for grid-connected microinverter-based photovoltaic (PV) systems. The proposed method utilizes a quasi-switched Boost-Switched Capacitor (qSB-SC) topology, where the DA is specifically tailored by combining Lévy-flight exploration with a dynamic damping factor to suppress steady-state oscillations within the qSB-SC ripple constraints. Coupling the MPPT stage to a seven-level Packed-U-Cell (PUC) microinverter ensures that each PV module operates at its independent Global Maximum Power Point (GMPP). A ZigBee-based Wireless Sensor Network (WSN) facilitates rapid data exchange and supports ‘swarm-memory’ initialization, matching current shading patterns with historical data to seed the population near the most probable GMPP region. This integration reduces the overall response time to 0.026 s. Hardware-in-the-loop experiments validated the approach, attaining a tracking accuracy of 99.32%. Compared to current state-of-the-art benchmarks, the proposed model demonstrated a significant improvement in tracking speed, outperforming the most recent 2025 GWO implementation (0.0603 s) by approximately 56% and conventional metaheuristic variants such as GWO-Beta (0.46 s) by over 94%.These results confirmed that the modified DA-based MPPT substantially enhanced the microinverter efficiency under PSC through cross-layer parameter adaptation. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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18 pages, 14423 KB  

Open AccessArticle

Data-Driven Model-Free Predictive Control for Zero-Sequence Circulating Current Suppression in Parallel NPC Converters

by Lan Cheng, Shiyu Liu, Jianye Rao, Songling Huang, Junjie Chen, Lin Qiu, Yishuang Hu and Youtong Fang

Energies 2026, 19(1), 189; https://doi.org/10.3390/en19010189 - 30 Dec 2025

Cited by 1 | Viewed by 503

Abstract

This paper proposes a data-driven model-free robust predictive control strategy for parallel three-level NPC inverters based on finite control set model predictive control (FCS-MPC), focusing on the zero-sequence circulating current (ZSCC) problem under parameter mismatch conditions. A set of virtual voltage vectors with [...] Read more.

This paper proposes a data-driven model-free robust predictive control strategy for parallel three-level NPC inverters based on finite control set model predictive control (FCS-MPC), focusing on the zero-sequence circulating current (ZSCC) problem under parameter mismatch conditions. A set of virtual voltage vectors with zero average common-mode voltage (CMV) is introduced to effectively suppress ZSCC without adding additional constraints to the cost function. Meanwhile, an Integral Sliding Mode Observer (ISMO) is integrated into the predictive control framework to enhance robustness and enable reliable control using only input–output data. Unlike existing studies that primarily consider ZSCC suppression under an ideal system, this work specifically addresses the practical scenario in which system parameters deviate from their nominal values. Even when ZSCC suppression strategies are employed, parameter mismatch can still lead to noticeable circulating currents, motivating the need for a more robust solution. Simulation and experimental results validate that the proposed approach achieves excellent current tracking, neutral-point voltage balance, and effective ZSCC suppression under parameter variations, demonstrating strong robustness and feasibility for practical applications. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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29 pages, 4559 KB  

Open AccessArticle

A Novel Data-Driven Multi-Agent Reinforcement Learning Approach for Voltage Control Under Weak Grid Support

by Jiaxin Wu, Ziqi Wang, Ji Han, Qionglin Li, Ran Sun, Chenhao Li, Yuehan Cheng, Bokai Zhou, Jiaming Guo and Bocheng Long

Sensors 2025, 25(23), 7399; https://doi.org/10.3390/s25237399 - 4 Dec 2025

Cited by 2 | Viewed by 1244

Abstract

To address active voltage control in photovoltaic (PV)-integrated distribution networks characterized by weak voltage support conditions, this paper proposes a multi-agent deep reinforcement learning (MADRL)-based coordinated control method for PV clusters. First, the voltage control problem is formulated as a decentralized partially observable [...] Read more.

To address active voltage control in photovoltaic (PV)-integrated distribution networks characterized by weak voltage support conditions, this paper proposes a multi-agent deep reinforcement learning (MADRL)-based coordinated control method for PV clusters. First, the voltage control problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP), and a centralized training with decentralized execution (CTDE) framework is adopted, enabling each inverter to make independent decisions based solely on local measurements during the execution phase. To balance voltage compliance with energy efficiency, two barrier functions are designed to reshape the reward function, introducing an adaptive penalization mechanism: a steeper gradient in violation region to accelerate voltage recovery to the nominal range, and a gentler gradient in the safe region to minimize excessive reactive regulation and power losses. Furthermore, six representative MADRL algorithms—COMA, IDDPG, MADDPG, MAPPO, SQDDPG, and MATD3—are employed to solve the active voltage control problem of the distribution network. Case studies based on a modified IEEE 33-bus system demonstrate that the proposed framework ensures voltage compliance while effectively reducing network losses. The MADDPG algorithm achieves a Controllability Ratio (CR) of 91.9% while maintaining power loss at approximately 0.0695 p.u., demonstrating superior convergence and robustness. Comparisons with optimal power flow (OPF) and droop control methods confirm that the proposed approach significantly improves voltage stability and energy efficiency under model-free and communication-constrained weak grid conditions. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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25 pages, 3514 KB  

Open AccessArticle

Hybrid Optimization in Prosumer-to-Grid Energy Management System for Pareto-Optimal Solution

by Celestine Emeka Obi, Rahma Gantassi and Yonghoon Choi

Appl. Sci. 2025, 15(23), 12719; https://doi.org/10.3390/app152312719 - 1 Dec 2025

Viewed by 764

Abstract

Energy management in smart microgrids is critical to achieving sustainable and efficient energy utilization. This study introduces a hybrid optimization framework combining neural networks (NNs) and multi-objective genetic algorithms (MOGAs) (hybrid NN-MOGA) to address the dual objectives of minimizing total energy cost and [...] Read more.

Energy management in smart microgrids is critical to achieving sustainable and efficient energy utilization. This study introduces a hybrid optimization framework combining neural networks (NNs) and multi-objective genetic algorithms (MOGAs) (hybrid NN-MOGA) to address the dual objectives of minimizing total energy cost and maximizing customer satisfaction. The hybrid NN-MOGA approach leverages NNs for predictive modeling of load and renewable energy generation, feeding accurate inputs to the MOGA for enhanced Pareto-optimal solutions. The performance of the proposed method is benchmarked against traditional optimization techniques, including MOGA, multi-objective particle swarm optimization (MOPSO), and the multi-objective firefly algorithm (MOFA). The simulation results demonstrate that hybrid NN-MOGA outperforms the alternative model. The proposed method produces uniformly distributed and highly convergent Pareto frontiers, ensuring robust trade-offs of USD 48.2817 and 81.7898 for total cost and customer satisfaction, respectively. Convexity analysis and the satisfaction of Karush–Kuhn–Tucker (KKT) conditions further validate the optimization model. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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20 pages, 858 KB  

Open AccessArticle

Competition–Cooperation Relationship and Profit Allocation Between Virtual and Traditional Power Plants Under Energy Transition

by Zhilei Huo, Yue Li, Ru Li, Keivan Sadeghzadeh and Dejiang Luo

Energies 2025, 18(23), 6140; https://doi.org/10.3390/en18236140 - 24 Nov 2025

Viewed by 749

Abstract

Virtual power plants (VPPs) can achieve optimized energy management through digital technologies and the integration of diversified energy sources. However, their complex competitive–cooperative dynamics with traditional power plants in market operations, coupled with undefined benefit-sharing mechanisms, require systematic investigation. This study establishes a [...] Read more.

Virtual power plants (VPPs) can achieve optimized energy management through digital technologies and the integration of diversified energy sources. However, their complex competitive–cooperative dynamics with traditional power plants in market operations, coupled with undefined benefit-sharing mechanisms, require systematic investigation. This study establishes a standalone capacity configuration model for independent VPP operations and a cooperative game-theoretic model for collaborative interactions with traditional power plants, focusing on three critical dimensions: energy transition dynamics, symbiotic cooperation mechanisms, and equitable revenue distribution. Through examining optimal distributed resource allocation and cooperative profit-sharing frameworks under market equilibrium conditions, key findings emerge: (1) VPPs demonstrate robust investment attractiveness in independent operation modes. (2) Collaborative scenarios with conventional plants generate mutual economic enhancement, with Shapley value solutions providing equitable benefit apportionment. (3) Intensified governmental intervention induces diminishing marginal returns for VPPs, whereas strengthened collaboration counteracts this effect through enhanced marginal productivity. The conclusions provide a theoretical foundation and decision-support frameworks for the economic operation of VPPs and the grid integration of high-proportion renewable energy sources. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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29 pages, 5471 KB  

Open AccessArticle

Game Theory-Based Bi-Level Capacity Allocation Strategy for Multi-Agent Combined Power Generation Systems

by Zhiding Chen, Yang Huang, Yi Dong and Ziyue Ni

Energies 2025, 18(20), 5338; https://doi.org/10.3390/en18205338 - 10 Oct 2025

Viewed by 1005

Abstract

The wind–solar–storage–thermal combined power generation system is one of the key measures for China’s energy structure transition, and rational capacity planning of each generation entity within the system is of critical importance. First, this paper addresses the uncertainty of wind and photovoltaic (PV) [...] Read more.

The wind–solar–storage–thermal combined power generation system is one of the key measures for China’s energy structure transition, and rational capacity planning of each generation entity within the system is of critical importance. First, this paper addresses the uncertainty of wind and photovoltaic (PV) power outputs through scenario-based analysis. Considering the diversity of generation entities and their complex interest demands, a bi-level capacity optimization framework based on game theory is proposed. In the upper-level framework, a game-theoretic method is designed to analyze the multi-agent decision-making process, and the objective function of capacity allocation for multiple entities is established. In the lower-level framework, multi-objective optimization is performed on utility functions and node voltage deviations. The Nash equilibrium of the non-cooperative game and the Shapley value of the cooperative game are solved to study the differences in the capacity allocation, economic benefits, and power supply stability of the combined power generation system under different game modes. The case study results indicate that under the cooperative game mode, when the four generation entities form a coalition, the overall system achieves the highest supply stability, the lowest carbon emissions at 30,195.29 tons, and the highest renewable energy consumption rate at 53.93%. Moreover, both overall and individual economic and environmental performance are superior to those under the non-cooperative game mode. By investigating the capacity configuration and joint operation strategies of the combined generation system, this study effectively enhances the enthusiasm of each generation entity to participate in the energy market; reduces carbon emissions; and promotes the development of a more efficient, environmentally friendly, and economical power generation model. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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21 pages, 3883 KB  

Open AccessArticle

Control Algorithm for an Inverter-Based Virtual Synchronous Generator with Adjustable Inertia

by Christian A. Villada-Leon, Johnny Posada Contreras, Julio C. Rosas-Caro, Rafael A. Núñez-Rodríguez, Juan C. Valencia and Jesus E. Valdez-Resendiz

Eng 2025, 6(9), 231; https://doi.org/10.3390/eng6090231 - 5 Sep 2025

Cited by 1 | Viewed by 2360

Abstract

This paper presents the design and implementation of a control algorithm for power converters in a microgrid, with the main objective of providing the flexibility to adjust the system inertia. The increasing integration of renewable energy sources in microgrids has driven the development [...] Read more.

This paper presents the design and implementation of a control algorithm for power converters in a microgrid, with the main objective of providing the flexibility to adjust the system inertia. The increasing integration of renewable energy sources in microgrids has driven the development of advanced control techniques to ensure stability and power quality. The proposed algorithm combines droop control, synchronverter dynamics, and virtual impedance to achieve a robust and efficient control strategy. Simulations were conducted to validate the algorithm’s performance, demonstrating its capability to maintain voltage within acceptable limits and improve the inertial response of the microgrid. The results contribute to the advancement of intelligent and resilient microgrid development, which is essential for the transition towards a more sustainable energy system. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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19 pages, 3165 KB  

Open AccessArticle

A Sensor for Multi-Point Temperature Monitoring in Underground Power Cables

by Pedro Navarrete-Rajadel, Pedro Llovera-Segovia, Vicente Fuster-Roig and Alfredo Quijano-López

Sensors 2025, 25(17), 5490; https://doi.org/10.3390/s25175490 - 3 Sep 2025

Viewed by 2326

Abstract

Underground electrical conductors, both medium-and high-voltage, play a crucial role in energy infrastructure. However, they present a maintenance challenge due to their difficult access. Unlike overhead installations, these cables remain hidden, making it harder to obtain key parameters, such as their temperature or [...] Read more.

Underground electrical conductors, both medium-and high-voltage, play a crucial role in energy infrastructure. However, they present a maintenance challenge due to their difficult access. Unlike overhead installations, these cables remain hidden, making it harder to obtain key parameters, such as their temperature or structural condition, in a simple manner. Current temperature measurement methods, including fiber-optic-based systems (DTS and LTS), involve high costs that limit their feasibility in medium-voltage networks, where more economically accessible alternatives are required. This study introduces an alternative system for monitoring the temperature of underground cables using NTC thermistors. Its design allows for reducing the number of connection conductors for sensors to just four regardless of the number of measurement points. The implemented measurement technique is based on the sequential activation of sensors and the integration of the recorded current to achieve an accurate thermal assessment. The tests conducted validate that this proposal represents an efficient, cost-effective, and highly scalable solution for implementation in electrical distribution networks. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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16 pages, 2179 KB  

Open AccessArticle

The Coupling Mechanism of the Electricity–Gas System and Assessment of Attack Resistance Based on Interdependent Networks

by Qingyu Zou and Lin Yan

Eng 2025, 6(8), 193; https://doi.org/10.3390/eng6080193 - 6 Aug 2025

Cited by 1 | Viewed by 1026

Abstract

Natural gas plays a critical role in integrated energy systems. In this context, the present study proposes an optimization model for the electricity–gas coupling system, grounded in the theory of interdependent networks. By integrating network topology parameters with real-time operational metrics, the model [...] Read more.

Natural gas plays a critical role in integrated energy systems. In this context, the present study proposes an optimization model for the electricity–gas coupling system, grounded in the theory of interdependent networks. By integrating network topology parameters with real-time operational metrics, the model substantially enhances system robustness and adaptability. To quantify nodal vulnerability and importance, the study introduces two novel evaluation indicators: the Electric Potential–Closeness Fusion Indicator (EPFI) for power networks and the Pressure Difference–Closeness Comprehensive Indicator (PDCI) for natural gas systems. Leveraging these indicators, three coupling paradigms—assortative, disassortative, and random—are systematically constructed and analyzed. System resilience is assessed through simulation experiments incorporating three attack strategies: degree-based, betweenness centrality-based, and random node removal. Evaluation metrics include network efficiency and the variation in the size of the largest connected subgraph under different coupling configurations. The proposed framework is validated using a hybrid case study that combines the IEEE 118-node electricity network with a 20-node Belgian natural gas system, operating under a unidirectional gas-to-electricity energy flow model. Results confirm that the disassortative coupling configuration, based on EPFI and PDCI indicators, exhibits superior resistance to network perturbations, thereby affirming the effectiveness of the model in improving the robustness of integrated energy systems. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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25 pages, 15607 KB  

Open AccessArticle

A Multi-Objective Optimization Method for Carbon–REC Trading in an Integrated Energy System of High-Speed Railways

by Wei-Na Zhang, Zhe Xu, Ying-Yi Hong, Fang-Yu Liu and Zhong-Qin Bi

Appl. Sci. 2025, 15(15), 8462; https://doi.org/10.3390/app15158462 - 30 Jul 2025

Viewed by 1367

Abstract

The significant energy intensity of high-speed railway necessitates integrating renewable technologies to enhance grid resilience and decarbonize transport. This study establishes a coordinated carbon–green certificate market mechanism for railway power systems and develops a tri-source planning model (grid/solar/energy storage) that comprehensively considers the [...] Read more.

The significant energy intensity of high-speed railway necessitates integrating renewable technologies to enhance grid resilience and decarbonize transport. This study establishes a coordinated carbon–green certificate market mechanism for railway power systems and develops a tri-source planning model (grid/solar/energy storage) that comprehensively considers the full lifecycle carbon emissions of these assets while minimizing lifecycle costs and CO₂ emissions. The proposed EDMOA algorithm optimizes storage configurations across multiple operational climatic regimes. Benchmark analysis demonstrates superior economic–environmental synergy, achieving a 23.90% cost reduction (USD 923,152 annual savings) and 24.02% lower emissions (693,452.5 kg CO₂ reduction) versus conventional systems. These results validate the synergistic integration of hybrid power systems with the carbon–green certificate market mechanism as a quantifiable pathway towards decarbonization in rail infrastructure. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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26 pages, 796 KB  

Open AccessArticle

Distributionally Robust Optimal Scheduling for Integrated Energy System Based on Dynamic Hydrogen Blending Strategy

by Yixiao Xiao, Qianhua Xiao, Keyu Wang, Xiaohui Yang and Yan Zhang

Appl. Sci. 2025, 15(13), 7560; https://doi.org/10.3390/app15137560 - 5 Jul 2025

Viewed by 1496

Abstract

To mitigate challenges arising from renewable energy volatility and multi-energy load uncertainty, this paper introduces a dynamic hydrogen blending (DHB) strategy for an integrated energy system. The strategy is categorized into Continuous Hydrogen Blending (CHB) and Time-phased Hydrogen Blending (THB), based on the [...] Read more.

To mitigate challenges arising from renewable energy volatility and multi-energy load uncertainty, this paper introduces a dynamic hydrogen blending (DHB) strategy for an integrated energy system. The strategy is categorized into Continuous Hydrogen Blending (CHB) and Time-phased Hydrogen Blending (THB), based on the temporal variations in the hydrogen blending ratio. To evaluate the regulatory effect of DHB on uncertainty, a data-driven distributionally robust optimization method is employed in the day-ahead stage to manage system uncertainties. Subsequently, a hierarchical model predictive control framework is designed for the intraday stage to track the day-ahead robust scheduling outcomes. Experimental results indicate that the optimized CHB ratio exhibits step characteristics, closely resembling the THB configuration. In terms of cost-effectiveness, CHB reduces the day-ahead scheduling cost by 0.87% compared to traditional fixed hydrogen blending schemes. THB effectively simplifies model complexity while maintaining a scheduling performance comparable to CHB. Regarding tracking performance, intraday dynamic hydrogen blending further reduces upper- and lower-layer tracking errors by 4.25% and 2.37%, respectively. Furthermore, THB demonstrates its advantage in short-term energy regulation, effectively reducing tracking errors propagated from the upper layer MPC to the lower layer, resulting in a 2.43% reduction in the lower-layer model’s tracking errors. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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48 pages, 986 KB  

Open AccessArticle

A Systematic Mapping Study on Automatic Control Systems of Multi-Port dc/dc Power Converters

by Diego Vargas, Leonardo Ortega, Julio C. Caiza and Danny S. Guamán

Energies 2025, 18(13), 3445; https://doi.org/10.3390/en18133445 - 30 Jun 2025

Cited by 2 | Viewed by 2161

Abstract

In the ongoing transition to renewable energy sources, power converters have become indispensable. Their prevalence is increasing, enabling efficient energy conversion, enhancing reliability and stability, and optimizing power extraction from renewable sources. Multi-port dc/dc power converters are widely used because they offer advantages [...] Read more.

In the ongoing transition to renewable energy sources, power converters have become indispensable. Their prevalence is increasing, enabling efficient energy conversion, enhancing reliability and stability, and optimizing power extraction from renewable sources. Multi-port dc/dc power converters are widely used because they offer advantages in managing multiple sources and loads. However, designing an automatic control system for these converters presents a challenge due to their complexity. Many configurations for multi-port dc/dc power converters have been proposed, featuring diverse combinations of controllers, modulation techniques, and topologies tailored to specific applications. The body of knowledge on these configurations has grown. Yet, papers have been published according to the authors’ areas of specialization, thus generating a scattered and unorganized body of knowledge and making it difficult to discern research trends and open challenges. Previous studies have attempted to organize knowledge about these configurations, but they have not established a systematic mapping process that follows a rigorous and objective methodology. This paper conducts a systematic mapping study on Automatic Control Systems of multi-port dc/dc power converters. Our study analyzed 122 papers from the 777 papers found around the topic to find and organize the body of knowledge on topology, controller, efficiency, number of elements, modulation technique, and practical applications. This systematic mapping provides a foundational framework for researchers, aiming to inspire further exploration and the development of innovative controller systems in multi-port dc/dc power converters. We found the application of machine learning techniques in dc/dc power converters constitutes an open challenge in these devices. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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24 pages, 2110 KB  

Open AccessArticle

Optimizing Hybrid Renewable Energy Systems for Isolated Applications: A Modified Smell Agent Approach

by Manal Drici, Mourad Houabes, Ahmed Tijani Salawudeen and Mebarek Bahri

Eng 2025, 6(6), 120; https://doi.org/10.3390/eng6060120 - 1 Jun 2025

Cited by 2 | Viewed by 2089

Abstract

This paper presents the optimal sizing of a hybrid renewable energy system (HRES) for an isolated residential building using modified smell agent optimization (mSAO). The paper introduces a time-dependent approach that adapts the selection of the original SAO control parameters as the algorithm [...] Read more.

This paper presents the optimal sizing of a hybrid renewable energy system (HRES) for an isolated residential building using modified smell agent optimization (mSAO). The paper introduces a time-dependent approach that adapts the selection of the original SAO control parameters as the algorithm progresses through the optimization hyperspace. This modification addresses issues of poor convergence and suboptimal search in the original algorithm. Both the modified and standard algorithms were employed to design an HRES system comprising photovoltaic panels, wind turbines, fuel cells, batteries, and hydrogen storage, all connected via a DC-bus microgrid. The components were integrated with the microgrid using DC-DC power converters and supplied a designated load through a DC-AC inverter. Multiple operational scenarios and multi-objective criteria, including techno-economic metrics such as levelized cost of energy (LCOE) and loss of power supply probability (LPSP), were evaluated. Comparative analysis demonstrated that mSAO outperforms the standard SAO and the honey badger algorithm (HBA) used for the purpose of comparison only. Our simulation results highlighted that the PV–wind turbine–battery system achieved the best economic performance. In this case, the mSAO reduced the LPSP by approximately 38.89% and 87.50% over SAO and the HBA, respectively. Similarly, the mSAO also recorded LCOE performance superiority of 4.05% and 28.44% over SAO and the HBA, respectively. These results underscore the superiority of the mSAO in solving optimization problems. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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26 pages, 5608 KB  

Open AccessArticle

Natural Gas Consumption Forecasting Model Based on Feature Optimization and Incremental Long Short-Term Memory

by Huilong Wang, Xianjun Gao, Ying Zhang and Yuanwei Yang

Sensors 2025, 25(10), 3079; https://doi.org/10.3390/s25103079 - 13 May 2025

Cited by 2 | Viewed by 2098

Abstract

Natural gas, as a vital component of the global energy structure, is widely utilized as an important strategic resource and essential commodity in various fields, including military applications, urban power generation and heating, and manufacturing. Therefore, accurately assessing energy consumption to ensure a [...] Read more.

Natural gas, as a vital component of the global energy structure, is widely utilized as an important strategic resource and essential commodity in various fields, including military applications, urban power generation and heating, and manufacturing. Therefore, accurately assessing energy consumption to ensure a reliable supply for both military and civilian use has become crucial. Traditional methods have attempted to leverage long-range features guided by prior knowledge (such as seasonal data, weather, and holiday data). However, they often fail to analyze the reasonable correlations among these features. This paper proposes a natural gas consumption forecasting model based on feature optimization and incremental LSTM. The proposed method enhances the robustness and generalization capability of the model at the data level by combining Gaussian Mixture Models to handle missing and anomalous data through modeling and sampling. Subsequently, a weakly supervised cascade network for feature selection is designed to enable the model to adaptively select features based on prior knowledge. Finally, an incremental learning-based regression difference loss is introduced to promote the model’s understanding of the coupled relationships within the data distribution. The proposed method demonstrates exceptional performance in daily urban gas load forecasting for Wuhan over the period from 2011 to 2024. Specifically, it achieves notably low average prediction errors of 0.0556 and 0.0392 on the top 10 heating and non-heating days, respectively. These results highlight the model’s strong generalization capability and its potential for reliable deployment across diverse gas consumption forecasting tasks within real-world deep learning applications. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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