Sk. A. Shezan^*

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.073418 - 27 December 2025

Abstract Modern power systems increasingly depend on interconnected microgrids to enhance reliability and renewable energy utilization. However, the high penetration of intermittent renewable sources often causes frequency deviations, voltage fluctuations, and poor reactive power coordination, posing serious challenges to grid stability. Conventional Interconnection Flow Controllers (IFCs) primarily regulate active power flow and fail to effectively handle dynamic frequency variations or reactive power sharing in multi-microgrid networks. To overcome these limitations, this study proposes an enhanced Interconnection Flow Controller (e-IFC) that integrates frequency response balancing and an Interconnection Reactive Power Flow Controller (IRFC) within a unified adaptive… More >

Yechun Xin¹, Xinyuan Zhao¹, Dong Ding², Shuyu Chen², Chuanjie Wang², Tuo Wang^1,*

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069748 - 27 December 2025

Abstract To enhance power flow regulation in scenarios involving large-scale renewable energy transmission via high-voltage direct current (HVDC) links and multi-infeed DC systems in load-center regions, this paper proposes a hybrid modular multilevel converter–capacitor-commutated line-commutated converter (MMC-CLCC) HVDC transmission system and its corresponding control strategy. First, the system topology is constructed, and a submodule configuration method for the MMC—combining full-bridge submodules (FBSMs) and half-bridge submodules (HBSMs)—is proposed to enable direct power flow reversal. Second, a hierarchical control strategy is introduced, including MMC voltage control, CLCC current control, and a coordination mechanism, along with the derivation of… 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 >

Yukai Li^1,*, Ruixue Zhang¹, Yongfeng Ni¹, Hongkai Qiu¹, Yuning Zhang², Chunming Liu²

Energy Engineering, Vol.122, No.2, pp. 681-707, 2025, DOI:10.32604/ee.2024.058981 - 31 January 2025

Abstract Aiming at the problems of increasing uncertainty of low-carbon generation energy in active distribution network (ADN) and the difficulty of security assessment of distribution network, this paper proposes a two-phase scheduling model for flexible resources in ADN based on probabilistic risk perception. First, a full-cycle probabilistic trend sequence is constructed based on the source-load historical data, and in the day-ahead scheduling phase, the response interval of the flexibility resources on the load and storage side is optimized based on the probabilistic trend, with the probability of the security boundary as the security constraint, and with… More >

Yue He¹, Yaxiong You¹, Zhian He¹, Haiying Lu¹, Lei Chen^2,*, Yuqi Jiang², Hongkun Chen²

Energy Engineering, Vol.121, No.10, pp. 2825-2849, 2024, DOI:10.32604/ee.2024.052371 - 11 September 2024

Abstract In recent times, the impact of typhoon disasters on integrated energy active distribution networks (IEADNs) has received increasing attention, particularly, in terms of effective cascading fault path prediction and enhanced fault recovery performance. In this study, we propose a modified ORNL-PSerc-Alaska (OPA) model based on optimal power flow (OPF) calculation to forecast IEADN cascading fault paths. We first established the topology and operational model of the IEADNs, and the typical fault scenario was chosen according to the component fault probability and information entropy. The modified OPA model consisted of two layers: An upper-layer model to More >

Ali S. Alghamdi^1,*, Mohamed A. Zohdy², Saad Aldoihi^3,4

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1339-1370, 2024, DOI:10.32604/cmes.2024.048839 - 20 May 2024

Abstract In the contemporary era, the global expansion of electrical grids is propelled by various renewable energy sources (RESs). Efficient integration of stochastic RESs and optimal power flow (OPF) management are critical for network optimization. This study introduces an innovative solution, the Gaussian Bare-Bones Levy Cheetah Optimizer (GBBLCO), addressing OPF challenges in power generation systems with stochastic RESs. The primary objective is to minimize the total operating costs of RESs, considering four functions: overall operating costs, voltage deviation management, emissions reduction, voltage stability index (VSI) and power loss mitigation. Additionally, a carbon tax is included in… More >

He Wang¹, Xiangsheng Xu¹, Guanye Shen², Bian Jing^1,*

Energy Engineering, Vol.120, No.10, pp. 2251-2272, 2023, DOI:10.32604/ee.2023.028965 - 28 September 2023

Abstract There are issues with flexible DC transmission system such as a lack of control freedom over power flow. In order to tackle these issues, a DC power flow controller (DCPFC) is incorporated into a multi-terminal, flexible DC power grid. In recent years, a multi-port DC power flow controller based on a modular multi-level converter has become a focal point of research due to its simple structure and robust scalability. This work proposes a model predictive control (MPC) strategy for multi-port interline DC power flow controllers in order to improve their steady-state dynamic performance. Initially, the… More >

Wei Zhang¹, Bin Ji², Ping He¹, Nanqin Wang¹, Yuwei Wang¹, Mengzhe Zhang^2,*

Energy Engineering, Vol.120, No.9, pp. 2177-2192, 2023, DOI:10.32604/ee.2023.026504 - 03 August 2023

Abstract Power flow calculation is the basis of power grid planning and many system analysis tasks require convergent power flow conditions. To address the unsolvable power flow problem caused by the reactive power imbalance, a method for adjusting reactive power flow convergence based on deep reinforcement learning is proposed. The deep reinforcement learning method takes switching parallel reactive compensation as the action space and sets the reward value based on the power flow convergence and reactive power adjustment. For the non-convergence power flow, the 500 kV nodes with reactive power compensation devices on the low-voltage side More >

P. Venkatesh^1,*, N. Visali²

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1001-1012, 2023, DOI:10.32604/iasc.2023.036268 - 29 April 2023

Abstract In the conventional technique, in the evaluation of the severity index, clustering and loading suffer from more iteration leading to more computational delay. Hence this research article identifies, a novel progression for fast predicting the severity of the line and clustering by incorporating machine learning aspects. The polynomial load modelling or ZIP (constant impedances (Z), Constant Current (I) and Constant active power (P)) is developed in the IEEE-14 and Indian 118 bus systems considered for analysis of power system security. The process of finding the severity of the line using a Hybrid Line Stability Ranking… More >

Qun Li, Ningyu Zhang^*, Jianhua Zhou, Xinyao Zhu, Peng Li

Energy Engineering, Vol.120, No.6, pp. 1485-1502, 2023, DOI:10.32604/ee.2023.022856 - 03 April 2023

Abstract The access of unified power flow controllers (UPFC) has changed the structure and operation mode of power grids all across the world, and it has brought severe challenges to the traditional real-time calculation of security correction based on traditional models. Considering the limitation of computational efficiency regarding complex, physical models, a data-driven power system security correction method with UPFC is, in this paper, proposed. Based on the complex mapping relationship between the operation state data and the security correction strategy, a two-stage deep neural network (DNN) learning framework is proposed, which divides the offline training… More >