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

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 7856

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Special Issue Editors

Dr. Yuzhou Zhou

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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

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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

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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

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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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Published Papers (10 papers)

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Research

18 pages, 1386 KB

Open AccessArticle

Coordinated Control Strategy for Active–Reactive Power in High-Proportion Renewable Energy Distribution Networks with the Participation of Grid-Forming Energy Storage

by Yiqun Kang, Zhe Li, Li You, Xuan Cai, Bingyang Feng, Yuxuan Hu and Hongbo Zou

Processes 2025, 13(10), 3271; https://doi.org/10.3390/pr13103271 - 14 Oct 2025

Viewed by 235

Abstract

The high proportion of renewable energy connected to the grid has resulted in insufficient consumption capacity in distribution networks, while the construction of new-type power distribution systems has imposed higher reliability requirements. With its flexible power synchronization control capabilities, grid-forming energy storage systems possess the ability to both promote the consumption of distributed energy resources in new-type distribution networks and enhance their reliability. However, current control methods are still hindered by drawbacks such as high computational complexity and a singular optimization objective. To address this, this paper proposes an optimized strategy for unified active–reactive power coordinated control in high-proportion renewable energy distribution networks with the participation of multiple grid-forming energy storage systems. Firstly, to optimize the parameters of grid-forming energy storage systems more accurately, this paper employs an improved iterative self-organizing data analysis technique algorithm to generate typical scenarios consistent with the scheduling time scale. Quantile regression (QR) and Gaussian mixture model (GMM) clustering are utilized to generate typical scenarios for renewable energy output. Subsequently, considering operational constraints and equipment state constraints, a unified active–reactive power coordinated control model for the distribution network is established. Meanwhile, to ensure the optimality of the results, this paper adopts an improved northern goshawk optimization (NGO) algorithm to solve the model. Finally, the effectiveness and feasibility of the proposed method are validated and illustrated through an improved IEEE-33 bus test system tested on MATLAB 2024B. Through analysis, the proposed method can reduce the average voltage fluctuation by 6.72% and increase the renewable energy accommodation rate by up to 8.64%. 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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17 pages, 1465 KB

Open AccessArticle

Peer-to-Peer Energy Storage Capacity Sharing for Renewables: A Marginal Pricing-Based Flexibility Market for Distribution Networks

by Xiang Li, Tianqi Liu and Yikui Liu

Processes 2025, 13(10), 3143; https://doi.org/10.3390/pr13103143 - 30 Sep 2025

Viewed by 384

Abstract

The distributed renewable energy sources have been rapidly increasing in distribution networks, and some of them are configured with energy storage devices. Indeed, sharing surplus energy storage capacities for subsidizing the investment costs is economically attractive. Although such willingness is emerging, targeted trading mechanisms are less explored. Inspired by the electricity markets, this paper innovates a peer-to-peer energy storage flexibility market within distribution networks, which involves multiple vendors and customers, accompanied by a marginal pricing mechanism to enable the economic reallocation of surplus energy storage capacities in distribution systems. A small-scale market is first studied to show the proposed market mechanism and a larger-scale case is used to further demonstrate the scalability and effectiveness of the mechanism. Case studies set three distinct scenarios: markets with or without deficits and with carryover energy constraints. The numerical simulation validates its ability in reflecting the capacity supply–demand relationship, ensuring revenue adequacy and effectively improving economic efficiency. 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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15 pages, 1250 KB

Open AccessArticle

A GAN-and-Transformer-Assisted Scheduling Approach for Hydrogen-Based Multi-Energy Microgrid

by Yang Yang, Penghui Liu, Hao Ma, Zhao Tao, Zhongxiang Tang and Yuzhou Zhou

Processes 2025, 13(9), 2993; https://doi.org/10.3390/pr13092993 - 19 Sep 2025

Viewed by 365

Abstract

Against the backdrop of ever-increasing energy demand and growing awareness of environmental protection, the research and optimization of hydrogen-related multi-energy systems have become a key and hot issue due to their zero-carbon and clean characteristics. In the scheduling of such multi-energy systems, a typical problem is how to describe and deal with the uncertainties of multiple types of energy. Scenario-based methods and robust optimization methods are the two most widely used methods. The first one combines probability to describe uncertainties with typical scenarios, and the second one essentially selects the worst scenario in the uncertainty set to characterize uncertainties. The selection of these scenarios is essentially a trade-off between the economy and robustness of the solution. In this paper, to achieve a better balance between economy and robustness while avoiding the complex min-max structure in robust optimization, we leverage artificial intelligence (AI) technology to generate enough scenarios, from which economic scenarios and feasible scenarios are screened out. While applying a simple single-layer framework of scenario-based methods, it also achieves both economy and robustness. Specifically, first, a Transformer architecture is used to predict uncertainty realizations. Then, a Generative Adversarial Network (GAN) is employed to generate enough uncertainty scenarios satisfying the actual operation. Finally, based on the forecast data, the economic scenarios and feasible scenarios are sequentially screened out from the large number of generated scenarios, and a balance between economy and robustness is maintained. On this basis, a multi-energy collaborative optimization method is proposed for a hydrogen-based multi-energy microgrid with consideration of the coupling relationships between energy sources. The effectiveness of this method has been fully verified through numerical experiments. Data show that on the premise of ensuring scheduling feasibility, the economic cost of the proposed method is 0.67% higher than that of the method considering only economic scenarios. It not only has a certain degree of robustness but also possesses good economic performance. 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, 5007 KB

Open AccessArticle

A Study on the Key Factors Influencing Power Grid Outage Restoration Times: A Case Study of the Jiexi Area

by Jiajun Lin, Ruiyue Xie, Haobin Lin, Xingyuan Guo, Yudong Mao and Zhaosong Fang

Processes 2025, 13(9), 2708; https://doi.org/10.3390/pr13092708 - 25 Aug 2025

Viewed by 853

Abstract

In rural and mountainous regions, power supply reliability remains a persistent challenge due to structural vulnerabilities, data incompleteness, and limited automation. In this study, a data-driven methodology is leveraged, wherein a validated machine learning framework comprising Random Forest (RF), Lasso Regression, and Recursive Feature Elimination (RFE) is applied to analyze outage data. The machine learning models, validated on a held-out test set, demonstrated modest but positive predictive performance, confirming a quantifiable, non-random relationship between grid structure and restoration time. This validation provides a credible foundation for the subsequent feature importance analysis. Through a transparent, consensus-based analysis of these models, the most robust influencing factors were identified. The results reveal that key structural indicators related to network redundancy (e.g., Inter-Bus Loop Rate) and electrical stress (e.g., Peak Daily Load Current, Load Factor) are the most significant predictors of prolonged outages. Furthermore, statistical analyses confirm that increasing structural redundancy and regulating line loads can effectively reduce outage duration. These findings offer practical, data-driven guidance for prioritizing investments in rural grid planning and reinforcement. This study contributes to the broader application of machine learning in energy systems, particularly showcasing a robust methodology for identifying key drivers under data and resource 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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19 pages, 12556 KB

Open AccessArticle

Energy Management for Microgrids with Hybrid Hydrogen-Battery Storage: A Reinforcement Learning Framework Integrated Multi-Objective Dynamic Regulation

by Yi Zheng, Jinhua Jia and Dou An

Processes 2025, 13(8), 2558; https://doi.org/10.3390/pr13082558 - 13 Aug 2025

Cited by 1 | Viewed by 1694

Abstract

The integration of renewable energy resources (RES) into microgrids (MGs) poses significant challenges due to the intermittent nature of generation and the increasing complexity of multi-energy scheduling. To enhance operational flexibility and reliability, this paper proposes an intelligent energy management system (EMS) for MGs incorporating a hybrid hydrogen-battery energy storage system (HHB-ESS). The system model jointly considers the complementary characteristics of short-term and long-term storage technologies. Three conflicting objectives are defined: economic cost (EC), system response stability, and battery life loss (BLO). To address the challenges of multi-objective trade-offs and heterogeneous storage coordination, a novel deep-reinforcement-learning (DRL) algorithm, termed MOATD3, is developed based on a dynamic reward adjustment mechanism (DRAM). Simulation results under various operational scenarios demonstrate that the proposed method significantly outperforms baseline methods, achieving a maximum improvement of 31.4% in SRS and a reduction of 46.7% in BLO. 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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16 pages, 1511 KB

Open AccessArticle

A Network Partition-Based Optimal Reactive Power Allocation and Sizing Method in Active Distribution Network

by Deshu Gan, Huabao Ling, Zhijian Mao, Ran Gu, Kangxin Zhou and Keman Lin

Processes 2025, 13(8), 2524; https://doi.org/10.3390/pr13082524 - 11 Aug 2025

Cited by 1 | Viewed by 394

Abstract

To address the node voltage fluctuation and over-limit caused by the high penetration of distributed photovoltaic (PV) generation connected to distribution networks, this paper proposes a network partition-based optimal reactive power allocation and sizing method in the active distribution network (ADN). A network index incorporating network partition and critical node identification is introduced to obtain the optimal location for the reactive power compensation. A singular value entropy-based adaptive spectral clustering algorithm is applied to obtain the initial zones and obtain the critical nodes of each zone on the basis of the proposed network indexes. This method avoids the unreasonable scheme and enhances the efficiency and clarity of partitioning. The improved decimal coding method is proposed to improve the efficiency of the proposed method. A case study on the IEEE 33-node distribution system is carried out to verify the feasibility and effectiveness of the proposed method. The results show that compared with the conventional methods, the proposed method can effectively reduce voltage variations and control the voltage within the safe limit. 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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15 pages, 664 KB

Open AccessArticle

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 - 21 Jun 2025

Viewed by 584

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 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 KB

Open AccessArticle

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

Cited by 1 | Viewed by 572

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 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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17 pages, 2815 KB

Open AccessArticle

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 680

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 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 KB

Open AccessArticle

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

Cited by 3 | Viewed by 1512

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 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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