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Advances in Smart Grids and Microgrids: Distributed Generation and Energy...
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Advances in Smart Grids and Microgrids: Distributed Generation and Energy Storage Systems

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

Deadline for manuscript submissions: 10 November 2025 | Viewed by 859

Special Issue Editors

Dr. Yuzhou Zhou

E-Mail Website
Guest Editor
School of Automation, Faculty of Electronics and Information, Xi’an Jiaotong University, Xi’an 710049, China
Interests: energy and power system planning; operation optimization; smart grids; system optimization
Dr. Yizhou Zhou

E-Mail Website
Guest Editor
School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China
Interests: smart grids; integrated energy system; power system planning and operation
Special Issues, Collections and Topics in MDPI journals
Dr. Daogui Tang

E-Mail Website
Guest Editor
State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063, China
Interests: maritime power systems; integrated energy systems; cyber security; resilience optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Hang Shuai

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Clemson University, Charleston, SC 29634, USA
Interests: reinforcement learning for power systems; microgrid operation and control; bulk power system resilience

Special Issue Information

Dear Colleagues,

In recent years, new power systems have developed rapidly, with typical features including the large-scale grid connection of new energy sources and the rapid development of multi-modal smart grids and multi-energy microgrids. But, at the same time, development opportunities and challenges coexist. The rapid popularization of these heterogeneous facilities has brought new challenges to the safe and economic operation of the power energy system. New smart grids and multi-energy microgrids face a series of challenges in reasonable planning, safe operation, and market mechanism design. These challenges are also key and difficult issues of common concern to academia and industry.

This Special Issue entitled “Advances in Smart Grids and Microgrids: Distributed Generation and Energy Storage Systems” seeks high-quality works focusing on the latest novel planning, operation, and market on smart grids, especially the new-type microgrid. Topics include, but are not limited to, the following:

  • Planning of new-type smart grids and multi-energy microgrids, including the capacity planning of new energy, energy storage, lines, etc.
  • Optimization of smart grid and multi-energy microgrid operation considering uncertainty, including scheduling, automatic control systems, transient simulation, etc.
  • The design of new smart grid market mechanisms is explored with profit models, including power markets, reserve markets, carbon markets, etc.

Dr. Yuzhou Zhou
Dr. Yizhou Zhou
Dr. Daogui Tang
Dr. Hang Shuai
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

  • smart grids
  • multi-energy microgrids
  • operation optimization
  • planning and design
  • market designing

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

15 pages, 664 KiB  
Article
A Bio-Inspired Optimization Approach for Low-Carbon Dispatch in EV-Integrated Virtual Power Plants
by Renfei Gao, Kunze Song, Bijiang Zhu and Hongbo Zou
Processes 2025, 13(7), 1969; https://doi.org/10.3390/pr13071969 (registering DOI) - 21 Jun 2025
Abstract
With the increasing penetration of renewable energy and the large-scale integration of electric vehicles (EVs), the economic optimization dispatch of EV-integrated virtual power plants (VPPs) faces multiple uncertainties and challenges. This paper first proposes an optimized dispatching model for EV clusters to form [...] Read more.
With the increasing penetration of renewable energy and the large-scale integration of electric vehicles (EVs), the economic optimization dispatch of EV-integrated virtual power plants (VPPs) faces multiple uncertainties and challenges. This paper first proposes an optimized dispatching model for EV clusters to form large-scale coordinated regulation capabilities. Subsequently, considering diversified resources such as energy storage systems and photovoltaic (PV) generation within VPPs, a low-carbon economic optimization dispatching model is established to minimize the total system operation costs and polluted gas emissions. To address the limitations of traditional algorithms in solving high-dimensional, nonlinear dispatching problems, this paper introduces a plant root-inspired growth optimization algorithm. By simulating the nutrient-adaptive uptake mechanism and branching expansion strategy of plant roots, the algorithm achieves a balance between global optimization and local fine-grained search. Compared with the genetic algorithm, particle swarm optimization algorithm and bat algorithm, simulation results demonstrate that the proposed method can effectively enhance the low-carbon operational economy of VPPs with high PV, ESS, and EV penetration. The research findings provide theoretical support and practical references for optimal dispatch of multi-stakeholder VPPs. Full article
(This article belongs to the Special Issue Advances in Smart Grids and Microgrids: Distributed Generation and Energy Storage Systems)
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19 pages, 3558 KiB  
Article
A Dynamic Three-Dimensional Evaluation Framework for CCUS Deployment in Coal-Fired Power Plants
by Jiangtao Zhu, Tiankun Wang, Yongzheng Gu, Siyuan Liu, Zhiwei Xun, Dongpo Men and Bin Cai
Processes 2025, 13(6), 1911; https://doi.org/10.3390/pr13061911 - 16 Jun 2025
Viewed by 180
Abstract
Under the “dual-carbon” targets, the coal power industry faces significant challenges in low-carbon transition, with carbon capture, utilization, and storage (CCUS) technologies as a key solution for emission reduction and energy security. Existing evaluation methods lack comprehensive assessments of technical, economic, and environmental [...] Read more.
Under the “dual-carbon” targets, the coal power industry faces significant challenges in low-carbon transition, with carbon capture, utilization, and storage (CCUS) technologies as a key solution for emission reduction and energy security. Existing evaluation methods lack comprehensive assessments of technical, economic, and environmental synergies. This study proposes a dynamic three-dimensional framework integrating technical, economic, and emission indicators. By using Monte Carlo simulation and K-means clustering, the framework captures technology degradation and market fluctuations. Results show compression energy consumption averages of 0.37 ± 0.07 GJ/tCO2, with capture rates above 94%, increasing the variability by 35%. Lifecycle costs can be reduced by 24% at carbon prices of 80–100 USD/tCO2 with optimal subsidies. Emission costs peak alongside carbon prices above 430 USD/t, suggesting the need for tiered carbon pricing and CAPEX subsidies. A cluster analysis divides CCUS into high-capture-high-energy, balanced, and low-efficiency types, supporting differentiated policies such as tiered carbon pricing and phased subsidy withdrawal. This research offers actionable insights to balance economic viability and carbon neutrality goals. Full article
(This article belongs to the Special Issue Advances in Smart Grids and Microgrids: Distributed Generation and Energy Storage Systems)
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18 pages, 2815 KiB  
Article
Uncertainty-Aware Economic Dispatch of Integrated Energy Systems with Demand-Response and Carbon-Emission Costs
by Yuning Zhang, Jiawen Tian, Zhenglin Guo, Qiang Fu and Shi Jing
Processes 2025, 13(6), 1906; https://doi.org/10.3390/pr13061906 - 16 Jun 2025
Viewed by 114
Abstract
This study investigates the economic operation of integrated energy systems under uncertainty, aiming to boost operational efficiency and cost-effectiveness while reducing carbon emissions. Unlike existing methods that either ignore the demand response or treat uncertainties separately, we introduce a two-stage robust optimization scheduling [...] Read more.
This study investigates the economic operation of integrated energy systems under uncertainty, aiming to boost operational efficiency and cost-effectiveness while reducing carbon emissions. Unlike existing methods that either ignore the demand response or treat uncertainties separately, we introduce a two-stage robust optimization scheduling framework that simultaneously integrates demand-response mechanisms and carbon-emission costs. In Stage I, a preliminary dispatch is obtained for deterministic scenarios based on forecasted values of renewable outputs and load demands; in Stage II, the solution is refined against worst-case fluctuations in renewable output and load demand. A column-and-constraint generation algorithm facilitates efficient, iterative coordination between the two stages, resulting in an optimal and robust dispatch strategy. To validate our approach, we performed detailed numerical simulations on a standard benchmark for integrated energy systems commonly used in the literature. The results show that by accounting for multiple sources of uncertainty, the system’s energy cost fell from 7091.03 RMB to 6489.18 RMB—a saving of 8.49%—while the carbon emissions dropped from 6165.57 kg to 5732.54 kg, a reduction of 7.02%. Compared with conventional scenario-based dispatch methods, the proposed two-stage framework demonstrates superior adaptability and robustness in handling renewable generation and load uncertainties, providing strong technical backing and theoretical insights for the sustainable operation of integrated energy systems in uncertain environments. Full article
(This article belongs to the Special Issue Advances in Smart Grids and Microgrids: Distributed Generation and Energy Storage Systems)
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14 pages, 772 KiB  
Article
Load Frequency Control of Power Systems with an Energy Storage System Based on Safety Reinforcement Learning
by Song Gao, Yudun Li, Xiaodi Chen, Zhengtang Liang, Enren Liu, Kang Liu and Meng Zhang
Processes 2025, 13(6), 1897; https://doi.org/10.3390/pr13061897 - 16 Jun 2025
Viewed by 264
Abstract
Load frequency control (LFC) is a critical component in power systems that is employed to stabilize frequency fluctuations and ensure power quality. As energy storage systems (ESSs) are increasingly integrated into the grid, managing additional constraints has become more challenging. To address these [...] Read more.
Load frequency control (LFC) is a critical component in power systems that is employed to stabilize frequency fluctuations and ensure power quality. As energy storage systems (ESSs) are increasingly integrated into the grid, managing additional constraints has become more challenging. To address these challenges, this paper proposes a safety reinforcement learning-based approach that incorporates ESSs into the LFC framework. By formulating a constrained Markov decision process (CMDP), this approach overcomes the limitations of conventional Markov decision processes (MDPs) by explicitly handling system constraints. Furthermore, a long short-term memory (LSTM)-based cost prediction critic network is introduced to improve the accuracy of cost predictions, and a primal-dual deep deterministic policy gradient (PD-DDPG) algorithm is employed to solve the CMDP. Simulation results demonstrate significant improvements: a 58.2% faster settling time, a 72.5% reduction in peak frequency deviation, and a 68.2% lower mean absolute error while maintaining all operational constraints. Full article
(This article belongs to the Special Issue Advances in Smart Grids and Microgrids: Distributed Generation and Energy Storage Systems)
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