Sheng Bi^1,2,*, Jiayan Wang¹, Dong Su¹, Hui Lu¹, Yu Zhang¹

Energy Engineering, Vol.122, No.10, pp. 4135-4151, 2025, DOI:10.32604/ee.2025.066735 - 30 September 2025

Abstract By comparing price plans offered by several retail energy firms, end users with smart meters and controllers may optimize their energy use cost portfolios, due to the growth of deregulated retail power markets. To help smart grid end-users decrease power payment and usage unhappiness, this article suggests a decision system based on reinforcement learning to aid with electricity price plan selection. An enhanced state-based Markov decision process (MDP) without transition probabilities simulates the decision issue. A Kernel approximate-integrated batch Q-learning approach is used to tackle the given issue. Several adjustments to the sampling and data… More >

Yunhao Yu¹, Meiling Dizha¹, Boda Zhang¹, Ruibin Wen¹, Fuhua Luo¹, Xiang Guo¹, Junjie Song², Bingdong Wang², Zhenyong Zhang^2,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3861-3877, 2025, DOI:10.32604/cmc.2025.066097 - 23 September 2025

Abstract The load profile is a key characteristic of the power grid and lies at the basis for the power flow control and generation scheduling. However, due to the wide adoption of internet-of-things (IoT)-based metering infrastructure, the cyber vulnerability of load meters has attracted the adversary’s great attention. In this paper, we investigate the vulnerability of manipulating the nodal prices by injecting false load data into the meter measurements. By taking advantage of the changing properties of real-world load profile, we propose a deeply hidden load data attack (i.e., DH-LDA) that can evade bad data detection,… More >

Chenxin Lin¹, Qun Zhou¹, Zhan Wang^2,*, Ximing Fan², Yaochang Xu², Yijia Xu²

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5859-5877, 2025, DOI:10.32604/cmc.2025.065496 - 30 July 2025

Abstract False Data Injection Attack (FDIA), a disruptive cyber threat, is becoming increasingly detrimental to smart grids with the deepening integration of information technology and physical power systems, leading to system unreliability, data integrity loss and operational vulnerability exposure. Given its widespread harm and impact, conducting in-depth research on FDIA detection is vitally important. This paper innovatively introduces a FDIA detection scheme: A Protected Federated Deep Learning (ProFed), which leverages Federated Averaging algorithm (FedAvg) as a foundational framework to fortify data security, harnesses pre-trained enhanced spatial-temporal graph neural networks (STGNN) to perform localized model training and More >

Qian Wang¹, Ya Zhou^1,2,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3835-3856, 2025, DOI:10.32604/cmc.2025.065465 - 03 July 2025

Abstract Edge computing has transformed smart grids by lowering latency, reducing network congestion, and enabling real-time decision-making. Nevertheless, devising an optimal task-offloading strategy remains challenging, as it must jointly minimise energy consumption and response time under fluctuating workloads and volatile network conditions. We cast the offloading problem as a Markov Decision Process (MDP) and solve it with Deep Reinforcement Learning (DRL). Specifically, we present a three-tier architecture—end devices, edge nodes, and a cloud server—and enhance Proximal Policy Optimization (PPO) to learn adaptive, energy-aware policies. A Convolutional Neural Network (CNN) extracts high-level features from system states, enabling More >

Zuhaib Nishtar^1,*, Fangzong Wang¹, Fawwad Hassan Jaskani², Hussain Afzaal³

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2919-2956, 2025, DOI:10.32604/cmes.2025.065098 - 30 June 2025

Abstract This research aims to address the challenges of fault detection and isolation (FDI) in digital grids, focusing on improving the reliability and stability of power systems. Traditional fault detection techniques, such as rule-based fuzzy systems and conventional FDI methods, often struggle with the dynamic nature of modern grids, resulting in delays and inaccuracies in fault classification. To overcome these limitations, this study introduces a Hybrid Neuro-Fuzzy Fault Detection Model that combines the adaptive learning capabilities of neural networks with the reasoning strength of fuzzy logic. The model’s performance was evaluated through extensive simulations on the… More >

Luki Septya Mahendra¹, Rezi Delfianti^2,*, Karimatun Nisa¹, Sutedjo¹, Bima Mustaqim³, Catur Harsito⁴, Rafiel Carino Syahroni⁵

Energy Engineering, Vol.122, No.7, pp. 2695-2717, 2025, DOI:10.32604/ee.2025.063807 - 27 June 2025

Abstract Ensuring the reliability of power systems in microgrids is critical, particularly under contingency conditions that can disrupt power flow and system stability. This study investigates the application of Security-Constrained Optimal Power Flow (SCOPF) using the Line Outage Distribution Factor (LODF) to enhance resilience in a renewable energy-integrated microgrid. The research examines a 30-bus system with 14 generators and an 8669 MW load demand, optimizing both single-objective and multi-objective scenarios. The single-objective optimization achieves a total generation cost of $47,738, while the multi-objective approach reduces costs to $47,614 and minimizes battery power output to 165.02 kW.… More >

Irbek Morgoev¹, Roman Klyuev^2,*, Angelika Morgoeva¹

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1381-1399, 2025, DOI:10.32604/cmes.2025.064502 - 30 May 2025

Abstract Non-technical losses (NTL) of electric power are a serious problem for electric distribution companies. The solution determines the cost, stability, reliability, and quality of the supplied electricity. The widespread use of advanced metering infrastructure (AMI) and Smart Grid allows all participants in the distribution grid to store and track electricity consumption. During the research, a machine learning model is developed that allows analyzing and predicting the probability of NTL for each consumer of the distribution grid based on daily electricity consumption readings. This model is an ensemble meta-algorithm (stacking) that generalizes the algorithms of random… More >

Cong Zhang^1,2, Chutong Zhang², Lei Shen¹, Renwei Guo², Wan Chen¹, Hui Huang², Jie Ji^2,*

Energy Engineering, Vol.122, No.5, pp. 2077-2097, 2025, DOI:10.32604/ee.2025.064175 - 25 April 2025

Abstract This paper presents a solution for energy storage system capacity configuration and renewable energy integration in smart grids using a multi-disciplinary optimization method. The solution involves a hybrid prediction framework based on an improved grey regression neural network (IGRNN), which combines grey prediction, an improved BP neural network, and multiple linear regression with a dynamic weight allocation mechanism to enhance prediction accuracy. Additionally, an improved cuckoo search (ICS) algorithm is designed to empower the neural network model, incorporating a gamma distribution disturbance factor and adaptive inertia weight to balance global exploration and local exploitation, achieving… More >

Qinghai Ou¹, Min Yang¹, Jingcai Kong¹, Yang Yang^2,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2429-2447, 2025, DOI:10.32604/cmc.2025.062067 - 16 April 2025

Abstract The lack of communication infrastructure in remote regions presents significant obstacles to gathering data from smart power sensors (SPSs) in smart grid networks. In such cases, a space-air-ground integrated network serves as an effective emergency solution. This study addresses the challenge of optimizing the energy efficiency of data transmission from SPSs to low Earth orbit (LEO) satellites through unmanned aerial vehicles (UAVs), considering both effective capacity and fronthaul link capacity constraints. Due to the non-convex nature of the problem, the objective function is reformulated, and a delay-aware energy-efficient power allocation and UAV trajectory design (DEPATD)… More >

Tien-Wen Sung¹, Wei Li¹, Chao-Yang Lee^2,*, Yuzhen Chen¹, Qingjun Fang¹

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 407-434, 2025, DOI:10.32604/cmc.2025.061694 - 26 March 2025

Abstract To transmit customer power data collected by smart meters (SMs) to utility companies, data must first be transmitted to the corresponding data aggregation point (DAP) of the SM. The number of DAPs installed and the installation location greatly impact the whole network. For the traditional DAP placement algorithm, the number of DAPs must be set in advance, but determining the best number of DAPs is difficult, which undoubtedly reduces the overall performance of the network. Moreover, the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the… More >