Stability, Operation, and Control in Power Systems

A special issue of Electricity (ISSN 2673-4826).

Deadline for manuscript submissions: 30 March 2027 | Viewed by 2908

Special Issue Editors

School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China
Interests: study of stray currents; regenerative braking energy utilization; traction power supply system simulation
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Guest Editor
School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Interests: smart grid information and communication technology; multimodal power IoT technology; cyber–physical integration systems; smart power consumption; multi-dimensional load management
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Special Issue Information

Dear Colleagues,

With the rapid integration of renewable energy and smart grid technologies, modern power systems face unprecedented challenges in stability, operational efficiency, and control robustness. The traditional power system paradigm, which was designed for centralized generation and unidirectional power flow, is undergoing a profound transformation driven by the increased incorporation of variable renewable energy sources (VRESs), the proliferation of distributed energy resources (DERs), advanced metering infrastructure, and demand response programs. Furthermore, with the development of complex cyber–physical systems (CPSs), physical components, communication networks, and computational resources are deeply intertwined. This information–physical fusion has fundamentally transformed power system architectures, enabling real-time data exchange between generation, transmission, distribution, and consumption nodes—but simultaneously introducing unprecedented challenges in stability, operational efficiency, and control robustness.

Against this backdrop, artificial intelligence (AI) has emerged as a transformative force, redefining how power systems are monitored, analyzed, and controlled. Machine learning algorithms, deep neural networks, and reinforcement learning techniques are increasingly deployed to handle the massive data streams generated by smart meters, sensors, and IoT devices in cyber–physical power systems. These AI-driven solutions offer breakthrough capabilities in predicting renewable energy fluctuations, optimizing real-time power flow, detecting cyber–physical anomalies, and adapting control strategies to dynamic operating conditions.

This Special Issue invites original research addressing cutting-edge solutions for the following areas:

  • ‌Stability enhancement‌ in high-penetration renewable scenarios;
  • Cloud–edge–end collaboration for reliable power systems;
  • Sensing–Communication–Computing–Control Integration;
  • ‌AI-driven optimization‌ for grid operation and load balancing;
  • ‌Resilience strategies‌ against extreme events (e.g., cyberattacks, natural disasters);
  • ‌Decentralized control‌ of microgrids and distributed energy resources;
  • Machine learning and artificial intelligence applications in power system operations;
  • Robust control methods for dealing with uncertainties in renewable generation;
  • Market mechanisms and their impact on power system operation and control;
  • Power system planning considering stability and control requirements.

Dr. Wei Liu
Prof. Dr. Zhenyu Zhou
Guest Editors

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Keywords

  • transient stability analysis
  • renewable energy integration
  • smart grid automation
  • microgrid coordination
  • AI-based predictive control
  • voltage/frequency regulation
  • cyber–physical security
  • hybrid AC/DC systems

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Published Papers (3 papers)

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Research

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22 pages, 1478 KB  
Article
Dynamic Model of the European Power System for Wide-Area Monitoring and Control Applications
by Rossano Musca, Mariano Giuseppe Ippolito and Eleonora Riva Sanseverino
Electricity 2026, 7(2), 28; https://doi.org/10.3390/electricity7020028 - 1 Apr 2026
Viewed by 778
Abstract
The article presents the development of a large-scale dynamic model of the European power system, including all essential features for wide-area monitoring and control studies. The simulated system includes 3809 nodes, 7343 branches, 618 synchronous machines with 1854 controllers, and 1573 PMUs. The [...] Read more.
The article presents the development of a large-scale dynamic model of the European power system, including all essential features for wide-area monitoring and control studies. The simulated system includes 3809 nodes, 7343 branches, 618 synchronous machines with 1854 controllers, and 1573 PMUs. The system also integrates inverter-based resources, controlled in either grid-following or grid-forming mode. The model is developed in the phasor-based simulation domain and implemented in MATLAB/Simulink for computation according to a modelling approach that combines vectorized and elementwise operations. The model is publicly available and represents a fundamental tool for investigating transient phenomena and advanced control strategies at a wide-area level. As a demonstration of the possible use of the model, an innovative wide-area damping control is also applied. Numerical experiments are conducted under different configurations, investigating relevant inter-area oscillation phenomena in the European system and assessing the opportunity of the proposed wide-area damping control architectures. The main findings of the case study indicate a definite improvement in the dynamic performance of the system when a wide-area control is applied, leading to a sixfold increase in inter-area oscillation damping, with a reduction of about 80% in the energy involved during the system oscillations. Full article
(This article belongs to the Special Issue Stability, Operation, and Control in Power Systems)
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33 pages, 3915 KB  
Article
Edge Computing Architecture for Optimal Settings of Inverse Time Overcurrent Relays in Mesh Microgrids
by Gustavo Arteaga, John E. Candelo-Becerra, Jhon Montano, Javier Revelo-Fuelagán and Fredy E. Hoyos
Electricity 2026, 7(1), 14; https://doi.org/10.3390/electricity7010014 - 9 Feb 2026
Cited by 1 | Viewed by 814
Abstract
This paper presents a novel edge-computing-based architecture for optimal inverse time overcurrent relays installed to protect mesh microgrids (MGs) with distributed generation. The procedure employs graph theory to automate the detection of network changes, fault locations, and relay pairs in an MG. In [...] Read more.
This paper presents a novel edge-computing-based architecture for optimal inverse time overcurrent relays installed to protect mesh microgrids (MGs) with distributed generation. The procedure employs graph theory to automate the detection of network changes, fault locations, and relay pairs in an MG. In addition, an automated process obtains the initial protection settings based on the operating conditions of the MG. Furthermore, the Continuous Genetic Algorithm (CGA), Salp Swarm Algorithm (SSA), and Particle Swarm Optimization (PSO) were implemented to determine the optimal protection settings to obtain better coordination between primary and backup protection relays. These processes were implemented using PowerFactory 2024 Service Pack 5A and Python 3.13.1. The proposal was validated in 68 operating scenarios that considered the islanded and connected operation modes of the MG, charging and discharging cycles of electric vehicle stations, and the presence or absence of photovoltaic generation. The overcurrent protection relays were organized into 100 primary–backup relay pairs to ensure proper coordination and selectivity. The total miscoordination time (TMT) index was used to measure when all pairs of relays were coordinated, with a minimum time close to zero. The results of the graph theory show that all the meshes, fault locations, and relay pairs were identified in the MG. The approach successfully coordinated 100 relay pairs across 68 scenarios, demonstrating its scalability in complex real-world MGs. The automation process obtained an average TMT of 12.2%, while the optimization obtained a TMS of 91.6% with the CGA, and a TMT of 99% was obtained with the SSA and PSO, demonstrating the effectiveness of the optimization process in ensuring selectivity and appropriate fault clearing times. Full article
(This article belongs to the Special Issue Stability, Operation, and Control in Power Systems)
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Review

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22 pages, 2010 KB  
Review
Safety in the Operation of Electrical Networks: Inertia Compensation as a Measure of Frequency and Voltage Stability
by José Carvalho
Electricity 2026, 7(2), 40; https://doi.org/10.3390/electricity7020040 - 2 May 2026
Viewed by 369
Abstract
The main purpose of electrical transmission and distribution networks is to carry electrical energy from the places where it is produced to the places of consumption, where the energy is used. Electrical energy is produced in power plants by generating units, which convert [...] Read more.
The main purpose of electrical transmission and distribution networks is to carry electrical energy from the places where it is produced to the places of consumption, where the energy is used. Electrical energy is produced in power plants by generating units, which convert a form of primary energy into electrical energy. Primary energy comes from a number of sources, such as fossil fuels, nuclear energy, hydropower, wind, and solar. The carbon neutrality targets set by the European Union and several countries around the world have driven a transformation characterized by the gradual replacement of synchronous thermal generation based on fossil fuels with Renewable Energy Sources (RES), such as wind and solar. The energy transition, while necessary to achieve the established targets, introduces significant challenges to the stability of Electrical Power Systems (EPS) and electrical grids, since RES do not yet contribute to stability at levels comparable to the generating units of large thermal power plants, whether in terms of inertia, which has seen a notable reduction in recent years, or in voltage control or short-circuit power. This article presents and discusses solutions to mitigate the effect of this reduction in inertia in power plants using synchronous compensators and synthetic inertia emulation using battery storage. Full article
(This article belongs to the Special Issue Stability, Operation, and Control in Power Systems)
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