Kai Xie¹, Shaoqing Yuan², Dayun Zou¹, Jinran Wang^1,*, Genjun Chen¹, Ciwei Gao³, Yinghao Cao¹

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.070733 - 27 January 2026

Abstract The construction of spot electricity markets plays a pivotal role in power system reforms, where market clearing systems profoundly influence market efficiency and security. Current clearing systems predominantly adopt a single-system architecture, with research focusing primarily on accelerating solution algorithms through techniques such as high-efficiency parallel solvers and staggered decomposition of mixed-integer programming models. Notably absent are systematic studies evaluating the adaptability of primary-backup clearing systems in contingency scenarios—a critical gap given redundant systems’ expanding applications in operational environments. This paper proposes a comprehensive evaluation framework for analyzing dual-system adaptability, demonstrated through an in-depth case… More >

Xiaozhe Guo¹, Suyan Long², Ziyu Yue², Yifan Wang², Guanting Yin¹, Yuyang Wang¹, Zhaoyuan Wu^1,*

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069820 - 27 December 2025

Abstract The increasing complexity of China’s electricity market creates substantial challenges for settlement automation, data consistency, and operational scalability. Existing provincial settlement systems are fragmented, lack a unified data structure, and depend heavily on manual intervention to process high-frequency and retroactive transactions. To address these limitations, a graph-based unified settlement framework is proposed to enhance automation, flexibility, and adaptability in electricity market settlements. A flexible attribute-graph model is employed to represent heterogeneous multi-market data, enabling standardized integration, rapid querying, and seamless adaptation to evolving business requirements. An extensible operator library is designed to support configurable settlement… More >

Yağmur Yılan, Ahad Beykent^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.068440 - 10 November 2025

Abstract Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies, hedge risk and plan generation schedules. By leveraging advanced data analytics and machine learning methods, accurate and reliable price forecasts can be achieved. This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting (XGBoost). We benchmark XGBoost against four alternatives—Support Vector Machines (SVM), Long Short-Term Memory (LSTM), Random Forest (RF), and Gradient Boosting (GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul (EXIST). All models were trained on an identical chronological 80/20 train–test split, with hyperparameters More >

Tariq Ali^1,2,*, Muhammad Ayaz^1,2, Mohammad Hijji², Imran Baig³, MI Mohamed Ershath⁴, Saleh Albelwi²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3839-3858, 2025, DOI:10.32604/cmes.2025.073169 - 23 December 2025

Abstract The integration of renewable energy sources into electricity markets presents significant challenges due to the inherent variability and uncertainty of power generation from wind, solar, and other renewables. Accurate forecasting is crucial for ensuring grid stability, optimizing market operations, and minimizing economic risks. This paper introduces a hybrid forecasting framework incorporating fractional-order statistical models, fractal-based feature engineering, and deep learning architectures to improve renewable energy forecasting accuracy. Fractional autoregressive integrated moving average (FARIMA) and fractional exponential smoothing (FETS) models are explored for capturing long-memory dependencies in energy time-series data. Additionally, multifractal detrended fluctuation analysis (MFDFA) More >

Su Jin Shin¹, Sang Uk Shin^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2787-2819, 2025, DOI:10.32604/cmes.2025.072602 - 26 November 2025

Abstract To reconstruct vehicle accidents, data from the time of the incident—such as pre-collision speed and collision point—is essential. This data is collected and generated through various sensors installed in the vehicle. However, it may contain sensitive information about the vehicle owner. Consequently, vehicle owners tend to be reluctant to provide their vehicle data due to concerns about personal information exposure. Therefore, extensive research has been conducted on secure vehicle data trading models. Existing models primarily utilize centralized approaches, leading to issues such as single points of failure, data leakage, and manipulation. To address these problems,… More >

Alex Segura^*, Joaquim Meléndez

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 649-672, 2025, DOI:10.32604/cmes.2025.070954 - 30 October 2025

Abstract The increasing use of renewable energy sources, combined with the increase in electricity demand, has highlighted the importance of energy flexibility management in electrical grids. Energy flexibility is the capacity that generators and consumers have to change production and/or consumption to support grid operation, ensuring the stability and efficiency of the grid. Thus, Local Flexibility Markets (LFMs) are market-oriented mechanisms operated at different time horizons that support flexibility provision and trading at the distribution level, where the Distribution System Operators (DSOs) are the flexibility-demanding actors, and prosumers are the flexibility providers. This paper investigates the… More >

Cheng Peng^1,*, Hao Qi²

Energy Engineering, Vol.122, No.11, pp. 4579-4602, 2025, DOI:10.32604/ee.2025.067704 - 27 October 2025

Abstract Integrated energy systems (IES) are widely regarded as a key enabler of carbon neutrality, enabling the coordinated use of electricity, heat, and gas to support large-scale renewable integration. Yet their practical deployment still faces major challenges, including rigid thermoelectric coupling, insufficient operational flexibility, and fragmented carbon and certificate market mechanisms. To address these issues, this study proposes a low-carbon economic dispatch model for integrated energy systems (IES) that reduces emissions and costs while improving renewable energy utilization. A coordinated framework integrating carbon capture, utilization, and storage, two-stage power-to-gas, combined heat and power, and ground-source heat… More > Graphic Abstract

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 >

Ganesh Wakte^1,*, Mukesh Kumar², Mohammad Aljaidi³, Ramesh Kumar⁴, Manish Kumar Singla⁴

Energy Engineering, Vol.122, No.6, pp. 2519-2537, 2025, DOI:10.32604/ee.2025.060658 - 29 May 2025

Abstract Nodal pricing is a critical mechanism in electricity markets, utilized to determine the cost of power transmission to various nodes within a distribution network. As power systems evolve to incorporate higher levels of renewable energy and face increasing demand fluctuations, traditional nodal pricing models often fall short to meet these new challenges. This research introduces a novel enhanced nodal pricing mechanism for distribution networks, integrating advanced optimization techniques and hybrid models to overcome these limitations. The primary objective is to develop a model that not only improves pricing accuracy but also enhances operational efficiency and… More > Graphic Abstract

Xingxu Zhu¹, Guiqing Zhao¹, Gangui Yan¹, Junhui Li^1,*, Hongda Dong², Chenggang Li²

Energy Engineering, Vol.122, No.5, pp. 1645-1668, 2025, DOI:10.32604/ee.2025.063192 - 25 April 2025

Abstract The participation of wind farms in the former energy market faces challenges such as power fluctuations and energy storage construction costs. To this end, this paper proposes a joint energy storage operation scheme for multiple wind farms based on a leasing model, which assists wind farms in bidding for participation in the former energy market through leasing services, thereby enhancing energy storage efficiency and maximizing economic benefits. In this paper, based on the Weibull probability distribution to portray the uncertainty of wind power, and considering the lifetime capacity loss caused by charging and discharging of… More > Graphic Abstract