Advances in Enhancing Energy and Power System Stability and Control, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: closed (15 April 2025) | Viewed by 478

Special Issue Editors

Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Interests: complementary and coordinated dispatch technologies with multi-energy source structure; risk assessment in cyber-physical power systems; power system cascading failure and restoration control; computational intelligence and its application in smart grid; power system stability and control
Special Issues, Collections and Topics in MDPI journals
Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
Interests: trustworthy machine learning; data-driven methods in power systems; smart grids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of clean energy and power electronic equipment technology, energy and power systems will face unprecedented and profound changes. How to promote clean power generation technology, build a low-carbon clean energy system, and ensure energy security are significant tasks in modern power grid development. At the same time, the nonlinearity, uncertainty, time variability, and complexity of the system are constantly increasing, which not only puts forward higher requirements for the reliability and flexibility of system operation and control, but also brings greater challenges to the safety and stability of new energy and power systems for the future. Exploring and exploiting the corresponding security assessment model and advanced control strategy will effectively reduce the risks associated with a high share of clean energy and power electric equipment, further improving the stability and controllability of the energy and power systems.

The purpose of this Special Issue aims to highlight the novel and most recent advances in theory, modeling, and applications of energy and power system security assessment and control to better promote the construction and development of low-carbon clean energy and power systems. The Special Issue welcomes original articles that may focus on (but are not limited to) the following:

  1. Modeling analysis of energy and power system security assessment and control;
  2. Transient stability analysis of energy and power systems;
  3. Frequency stability analysis of energy and power systems;
  4. Voltage stability analysis of energy and power systems;
  5. Small-signal stability analysis of energy and power systems;
  6. Subsynchronous torsional oscillation analysis of energy and power systems;
  7. Resilience assessment of energy and power systems;
  8. Data-driven technology-based energy and power system security assessment and control;
  9. AI-based energy and power system stability analysis;
  10. Control and protection strategies for power electronic-based energy and power systems;
  11. Risk assessment and management of energy and power systems against extreme events;
  12. Stability-constrained optimal planning and operation of energy and power systems.

Dr. Libao Shi
Dr. Ren Wang
Guest Editors

Manuscript Submission Information

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Keywords

  • power system dynamics
  • risk assessment
  • clean energy
  • resilience assessment
  • AI methods
  • data-driven technology
  • modeling analysis

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Published Papers (1 paper)

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Research

19 pages, 5226 KiB  
Article
Day-Ahead Optimal Scheduling for a Full-Scale PV–Energy Storage Microgrid: From Simulation to Experimental Validation
by Zixuan Wang and Libao Shi
Electronics 2025, 14(8), 1509; https://doi.org/10.3390/electronics14081509 - 9 Apr 2025
Viewed by 241
Abstract
Microgrids facilitate the complementary and collaborative operation of various distributed energy resources. Implementing effective day-ahead scheduling strategies can significantly enhance the economic efficiency and operational stability of microgrid systems. In this study, the long short-term memory (LSTM) neural network is first employed to [...] Read more.
Microgrids facilitate the complementary and collaborative operation of various distributed energy resources. Implementing effective day-ahead scheduling strategies can significantly enhance the economic efficiency and operational stability of microgrid systems. In this study, the long short-term memory (LSTM) neural network is first employed to forecast photovoltaic (PV) power generation and load demand, using operational data from a full-scale microgrid system. Subsequently, an optimization model for a full-scale PV–energy storage microgrid is developed, integrating a PV power generation system, a battery energy storage system, and a specific industrial load. The model aims to minimize the total daily operating cost of the system while satisfying a set of system operational constraints, with particular emphasis on the safety requirements for grid exchange power. The formulated optimization problem is then transformed into a mixed-integer linear programming (MILP) model, which is solved using a computational solver to derive the day-ahead economic scheduling scheme. Finally, the proposed scheduling scheme is validated through field experiments conducted on the full-scale PV–energy storage microgrid system across various operational scenarios. By comparing the simulation results with the experimental outcomes, the effectiveness and practicality of the proposed day-ahead economic scheduling scheme for the microgrid are demonstrated. Full article
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