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Processes
Special Issues
Energy Storage Planning, Control, and Dispatch for Grid Dynamic Enhancement
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Energy Storage Planning, Control, and Dispatch for Grid Dynamic Enhancement

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 15 December 2025 | Viewed by 514

Special Issue Editors

Dr. Rui Wang

E-Mail Website
Guest Editor
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Interests: energy storage system; optimization problem; cooperative control
Special Issues, Collections and Topics in MDPI journals
Dr. Wentao Jiang

E-Mail Website
Guest Editor
School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: control of power electronics; DC microgrid; energy storage system

Special Issue Information

Dear Colleagues,

Energy storage as a technology capable of providing timely and safe power–energy output can effectively support the stable operation of novel power systems under normal conditions and enhance resilience under extreme scenarios. However, different types of energy storage systems affect system response speed and cost; different connection points alter system flow distribution, influencing network losses and voltage levels; and different energy storage control methods struggle to cope with the uncertainty of renewable energy output, which generates vast scenarios. How to rationally utilize energy storage technology to enhance grid dynamics is a pressing issue that needs to be addressed.

This Special Issue on "Energy Storage Planning, Control, and Dispatch for Grid Dynamic Enhancement" aims to introduce the latest planning, control, and dispatch technologies of energy storage systems to enhance grid dynamic performance. Topics include, but are not limited to, methods and/or application in the following areas:

  • New energy storage technologies, equipment, and applications;
  • Energy storage technologies and their applications in power grids and renewable energy stations;
  • Technologies for energy storage participation in voltage and frequency regulation of power grids;
  • Integrated source–grid–load–storage modeling and simulation technologies;
  • Integrated source–grid–load–storage planning, design, and operation technologies;
  • Integrated source–grid–load–storage coordinated control technologies.

Dr. Rui Wang
Dr. Wentao Jiang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • energy storage system
  • energy storage optimization and control
  • energy storage dispatch technologies
  • integrated source–grid–load–storage coordinated control
  • grid dynamic enhancement

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Published Papers (1 paper)

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Research

27 pages, 2165 KiB  
Article
Load Frequency Control via Multi-Agent Reinforcement Learning and Consistency Model for Diverse Demand-Side Flexible Resources
by Guangzheng Yu, Xiangshuai Li, Tiantian Chen and Jing Liu
Processes 2025, 13(6), 1752; https://doi.org/10.3390/pr13061752 - 2 Jun 2025
Viewed by 317
Abstract
With the high-proportion integration of renewable energy into the power grid, the fast-response capabilities of demand-side flexible resources (DSFRs), such as electric vehicles (EVs) and thermostatic loads, have become critical for frequency stability. However, the diverse dynamic characteristics of heterogeneous resources lead to [...] Read more.
With the high-proportion integration of renewable energy into the power grid, the fast-response capabilities of demand-side flexible resources (DSFRs), such as electric vehicles (EVs) and thermostatic loads, have become critical for frequency stability. However, the diverse dynamic characteristics of heterogeneous resources lead to high modeling complexity. Traditional reinforcement learning methods, which rely on neural networks to approximate value functions, often suffer from training instability and lack the effective quantification of resource regulation costs. To address these challenges, this paper proposes a multi-agent reinforcement learning frequency control method based on a Consistency Model (CM). This model incorporates power, energy, and first-order inertia characteristics to uniformly characterize the response delays and dynamic behaviors of EVs and air conditioners (ACs), providing a reduced-order analytical foundation for large-scale coordinated control. On this basis, a policy gradient controller is designed. By using projected gradient descent, it ensures that control actions satisfy physical boundaries. A reward function including state deviation penalties and regulation costs is constructed, dynamically adjusting penalty factors according to resource states to achieve priority configuration for frequency regulation. Simulations on the IEEE 39-node system demonstrate that the proposed method significantly outperforms traditional approaches in terms of frequency deviation, algorithm training efficiency, and frequency regulation economy. Full article
(This article belongs to the Special Issue Energy Storage Planning, Control, and Dispatch for Grid Dynamic Enhancement)
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