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Power System Optimization for Energy Storage: Methods and Applications

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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: 15 March 2026 | Viewed by 7386

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Special Issue Editors

Dr. Hejun Yang

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Guest Editor

School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
Interests: power system reliability; power system planning; energy storage system; demand response; quantum computing

Dr. Ning Yan

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Guest Editor

School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Interests: hybrid energy storage system; time-sharing regulation; low-carbon economic dispatch

Dr. Sen Tan

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Guest Editor

Energy Department, Aalborg University, 9220 Aalborg, Denmark
Interests: power management system; energy storage; security detection and cooperative control of microgrids; motor drive technologies
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Special Issue Information

Dear Colleagues,

With the increasing environmental problems in global economic development, renewable energy (e.g., wind and solar energy) is being developed as a clean and renewable alternative. Energy storage systems allow for flexible power adjustment and can effectively suppress the power system fluctuations caused by renewable energy’s stochasticity and intermittency. Aiming to address the differentiated demands of source–grid–load sides in power systems (such as peak shaving, frequency regulation, renewable energy consumption, etc.), energy storage is divided into source-, grid-, and user-side and is widely applied to different source–grid–load sides. So, energy storage’s application to power systems can efficiently promote high renewable energy consumption and improve the flexibility and reliability of power systems.

This Special Issue on “Power System Optimization for Energy Storage: Methods and Applications” seeks high-quality works focusing on optimization methods and applications for energy storage-integrated power systems. The topics include, but are not limited to, the following:

Energy storage system operation and control technology;
Economic dispatching for energy storage systems;
Modeling techniques for energy storage systems;
Energy storage planning theory and applications;
Co-optimization technology of multi-type energy storage systems;
Commercial modes and market mechanism for energy storage systems;
Energy storage system reliability assessment methods;
Energy storage system operation and maintenance technology;
Energy storage system safety and stability;
Energy storage application in distribution network planning.

Dr. Hejun Yang
Dr. Ning Yan
Dr. Sen Tan
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 250 words) can be sent to the Editorial Office for assessment.

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 system
methods
application
market and commercial mode
planning
assessment
operation and maintenance
dispatching and control
stability

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Published Papers (10 papers)

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Research

28 pages, 2740 KB

Open AccessArticle

Carbon Economic Dispatching for Active Distribution Networks via a Cyber–Physical System: A Demand-Side Carbon Penalty

by Jingfeng Zhao, Qi You, Yongbin Wang, Hong Xu, Huiping Guo, Lan Bai, Kunhua Liu, Zhenyu Liu and Ziqi Fan

Processes 2025, 13(11), 3749; https://doi.org/10.3390/pr13113749 - 20 Nov 2025

Viewed by 350

Abstract

To address the challenges of climate change mitigation and operational flexibility in active distribution networks (ADNs) amid high renewable energy penetration, this paper proposes a low-carbon economic dispatch framework integrating demand-side carbon regulation and cyber–physical system (CPS)-enabled shared energy storage. First, a consumer-side emission penalty mechanism is developed by fusing a carbon emission flow (CEF) model with price elasticity coefficients. This mechanism embeds carbon costs into end-user electricity pricing, guiding users to adjust consumption patterns (e.g., reducing usage during high-carbon-intensity periods) and shifting partial carbon responsibility to the demand side. Second, a CPS-based shared energy storage mechanism is constructed, featuring a three-layer architecture (physical layer, control decision layer, security layer) that aggregates distributed energy storage (DES) resources into a unified, schedulable pool. A cooperative, game-based profit-sharing strategy using Shapley values is adopted to allocate benefits based on each DES participant’s marginal contribution, ensuring fairness and motivating resource pooling. Finally, a unified mixed-integer linear programming (MILP) optimization model is formulated for ADNs, co-optimizing locational marginal prices, DES state-of-charge trajectories, and demand curtailment to minimize operational costs and carbon emissions simultaneously. Simulations on a modified IEEE 33-bus system demonstrate that the proposed framework reduces carbon emissions by 4.5–4.7% and renewable energy curtailment by 71.1–71.3% compared to traditional dispatch methods, while lowering system operational costs by 6.7–6.8%. The results confirm its effectiveness in enhancing ADN’s low-carbon performance, renewable energy integration, and economic efficiency. Full article

(This article belongs to the Special Issue Power System Optimization for Energy Storage: Methods and Applications)

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15 pages, 1998 KB

Open AccessArticle

A Hybrid GRU-MHSAM-ResNet Model for Short-Term Power Load Forecasting

by Xin Yang, Fan Zhou, Ran Xu, Yiwen Jiang and Hejun Yang

Processes 2025, 13(11), 3646; https://doi.org/10.3390/pr13113646 - 11 Nov 2025

Viewed by 527

Abstract

Reliable load forecasting is crucial for ensuring optimal dispatch, grid security, and cost efficiency. To address limitations in prediction accuracy and generalization, this paper proposes a hybrid model, GRU-MHSAM-ResNet, which integrates a gated recurrent unit (GRU), multi-head self-attention (MHSAM), and a residual network (ResNet)block. Firstly, GRU is employed as a deep temporal encoder to extract features from historical load data, offering a simpler structure than long short-term memory (LSTM). Then, the MHSAM is used to generate adaptive representations by weighting input features, thereby strengthening the key features. Finally, the features are processed by fully connected layers, while a ResNet block is added to mitigate gradient vanishing and explosion, thus improving prediction accuracy. The experimental results on actual load datasets from systems in China, Australia, and Malaysia demonstrate that the proposed GRU-MHSAM-ResNet model exhibits superior predictive accuracy to compared models, including the CBR model and the LSTM-ResNet model. On the three datasets, the proposed model achieved a mean absolute percentage error (MAPE) of 1.65% (China), 5.52% (Australia), and 1.57% (Malaysia), representing a significant improvement over the other models. Furthermore, in five repeated experiments on the Malaysian dataset, it exhibited lower error fluctuation and greater result stability compared to the benchmark LSTM-ResNet model. Therefore, the proposed model provides a new forecasting method for power system dispatch, exhibiting high accuracy and generalization ability. Full article

(This article belongs to the Special Issue Power System Optimization for Energy Storage: Methods and Applications)

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17 pages, 3076 KB

Open AccessArticle

Operational Flexibility Assessment of a Power System Considering Uncertainty of Flexible Resources Supported by Wind Turbines Under Load Shedding Operation

by Guifen Jiang, Jiayin Xu, Yuming Shen, Peiru Feng, Hao Yang, Xu Gui, Yipeng Cao, Mingcheng Chen, Ming Wei and Yinghao Ma

Processes 2025, 13(11), 3635; https://doi.org/10.3390/pr13113635 - 10 Nov 2025

Viewed by 305

Abstract

The high proportion of renewable energy introduces significant operation risks to the system’s flexibility balance due to its volatility and randomness. Traditional regulation methods struggle to meet the urgent demand for flexible resources. Utilizing wind turbines (WTs) under load shedding operation can provide additional reserve capacity, thereby reducing the risk of insufficient system flexibility. However, since wind speed and turbine output exhibit a cubic relationship, minor fluctuations in wind speed can lead to significant variations in output and reserve capacity. This increases the uncertainty in the supply of flexible resources from WTs, posing challenges to power system flexibility assessment. This paper investigates a method for assessing power system flexibility considering the uncertainty of flexible resources supported by WT under load shedding operation. Firstly, according to the flexibility supply control model of WT under shedding operation, the analytical relationship between output, flexible resources, and wind speed under a specific wind energy conversion coefficient is constructed; secondly, combined with the probabilistic model of wind speed based on the nonparametric kernel density estimation, the wind turbine flexible resource uncertainty model is constructed; thirdly, the Monte Carlo simulation is used to obtain the sampled wind speed data, and the operational flexibility assessment method of the power system considering the flexibility uncertainty of WT under load shedding operation is proposed. Finally, through case studies, the validity of the proposed model and method were verified. The analysis concludes that load shedding operation of WTs can enhance the system’s flexible resources to a certain extent but cannot provide stable bi-directional regulation capabilities. Full article

(This article belongs to the Special Issue Power System Optimization for Energy Storage: Methods and Applications)

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24 pages, 3293 KB

Open AccessArticle

Short-Term Forecasting of Photovoltaic Clusters Based on Spatiotemporal Graph Neural Networks

by Zhong Wang, Mao Yang, Yitao Li, Bo Wang, Zhao Wang and Zheng Wang

Processes 2025, 13(11), 3422; https://doi.org/10.3390/pr13113422 - 24 Oct 2025

Viewed by 557

Abstract

Driven by the dual-carbon goals, photovoltaic (PV) battery systems at renewable energy stations are increasingly clustered on the distribution side. The rapid expansion of these clusters, together with the pronounced uncertainty and spatio-temporal heterogeneity of PV generation, degrades battery utilization and forces conservative dispatch. To address this, we propose a “spatio-temporal clustering–deep estimation” framework for short-term interval forecasting of PV clusters. First, a graph is built from meteorological–geographical similarity and partitioned into sub-clusters by a self-supervised DAEGC. Second, an attention-based spatio-temporal graph convolutional network (ASTGCN) is trained independently for each sub-cluster to capture local dynamics; the individual forecasts are then aggregated to yield the cluster-wide point prediction. Finally, kernel density estimation (KDE) non-parametrically models the residuals, producing probabilistic power intervals for the entire cluster. At the 90% confidence level, the proposed framework improves PICP by 4.01% and reduces PINAW by 7.20% compared with the ASTGCN-KDE baseline without spatio-temporal clustering, demonstrating enhanced interval forecasting performance. Full article

(This article belongs to the Special Issue Power System Optimization for Energy Storage: Methods and Applications)

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20 pages, 748 KB

Open AccessArticle

A Grid-Based Scenario Delineation Method for Distribution Networks Based on Fuzzy Comprehensive Evaluation and SNN-DPC Clustering

by Liuzhu Zhu, Xin Yang, Xuli Wang, Fan Zhou, Zhi Guan and Hejun Yang

Processes 2025, 13(9), 2923; https://doi.org/10.3390/pr13092923 - 13 Sep 2025

Viewed by 476

Abstract

Aiming at the problems that the random probability characteristics of large-scale source and load resources lead to the ineffectiveness of deterministic planning methods, the standard grid structure is difficult to adapt to the demands of diversified scenarios. This paper proposes a grid-based scenario delineation method for distribution networks based on fuzzy comprehensive evaluation and SNN-DPC (density peak clustering based on shared-nearest-neighbors). First, analyze the response characteristics of various types of flexible resources, and establish a multi-dimensional comprehensive assessment index system that integrates operational characteristics and structural features. Second, the comprehensive weights of each index in the index layer are calculated based on the DEMATEL-ANP method and the CRITIC method, and the assessment value of the intermediate layer is calculated by the fuzzy comprehensive evaluation method. Finally, the assessment value of the intermediate layer is clustered based on the improved SNN-DPC algorithm, so as to classify the distribution grid scenarios. The results indicate that the proposed method can effectively and accurately classify distribution network scenarios. Full article

(This article belongs to the Special Issue Power System Optimization for Energy Storage: Methods and Applications)

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17 pages, 4596 KB

Open AccessArticle

Generative Adversarial Network-Based Detection and Defence of FDIAs: State Estimation for Battery Energy Storage Systems in DC Microgrids

by Hongru Wei, Minhong Zhu, Linting Guan and Tianqing Yuan

Processes 2025, 13(9), 2837; https://doi.org/10.3390/pr13092837 - 4 Sep 2025

Viewed by 791

Abstract

With the wide application of battery energy storage systems (BESSs) in DC microgrids, BESSs are facing increasingly severe cyber threats, among which, false data injection attacks (FDIAs) seriously undermine the accuracy of battery state estimation by tampering with sensor measurement data. To address this problem, this paper proposes an improved generative adversarial network (WGAN-GP)-based detection and defence method for FDIAs in battery energy storage systems. Firstly, a more perfect FDIA model is constructed based on the comprehensive consideration of the dual objectives of circumventing the bad data detection (BDD) system of microgrid and triggering the effective deviation of the system operating state quantity; subsequently, the WGAN-GP network architecture introducing the gradient penalty term is designed to achieve the efficient detection of the attack based on the anomalous scores output from the discriminator, and the generator reconstructs the tampered measurement data. Finally, the state prediction after repair is completed based on Gaussian process regression. The experimental results show that the proposed method achieves more than 92.9% detection accuracy in multiple attack modes, and the maximum reconstruction error is only 0.13547 V. The overall performance is significantly better than that of the traditional detection and restoration methods, and it provides an effective technical guarantee for the safe and stable operation of the battery energy storage system. Full article

(This article belongs to the Special Issue Power System Optimization for Energy Storage: Methods and Applications)

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26 pages, 3760 KB

Open AccessArticle

Energy Management for Integrated Energy System Based on Coordinated Optimization of Electric–Thermal Multi-Energy Retention and Reinforcement Learning

by Yan Cheng, Song Yang, Shumin Sun, Peng Yu and Jiawei Xing

Processes 2025, 13(9), 2693; https://doi.org/10.3390/pr13092693 - 24 Aug 2025

Cited by 1 | Viewed by 1150

Abstract

With the large-scale access to a large number of distributed electric and thermal flexible resources and multiple loads on the user side, the energy management of the integrated energy system (IES) has become an effective way for the efficient and low-carbon economic operation of energy systems. In order to explore a new mode of IES energy management with the participation of energy service providers (ESPs) and user clusters (UCs), this paper puts forward an energy management method for electric–thermal microgrids, considering the optimization of user energy consumption characteristics. Firstly, an energy management framework with multi-agent participation of ESP and user cluster is proposed, and a user energy preference model is established considering the user’s electricity and heat consumption preferences. Secondly, considering the operation benefit of ESP and user cluster, based on the reinforcement learning (RL) framework, an energy management model between ESPs and users is established, and a distributed solution algorithm combining Q-learning and quadratic programming is proposed. Finally, the IESs with different user scales and energy units are taken as the test system, and the optimal energy management strategy of the system, considering the user’s energy preference, is analyzed. The simulation results demonstrate that the energy management model proposed enhances the economic efficiency of IES operations and reduces emissions. In a test system with two UCs, the optimized system achieves a 5.05% reduction in carbon emissions. The RL-based distributed solution algorithm efficiently solves the energy management model for systems with varying UC scales, requiring only 6.55 s for systems with two UCs and 13.26 s for systems with six UCs. Full article

(This article belongs to the Special Issue Power System Optimization for Energy Storage: Methods and Applications)

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17 pages, 1451 KB

Open AccessArticle

Temporal–Spatial Acceleration Framework for Full-Year Operational Simulation of Power Systems with High Renewable Penetration

by Chen Wang, Zhiqiang Lu, Chunmiao Zhang, Mingyu Yan, Yirui Zhao and Yijia Zhou

Processes 2025, 13(8), 2502; https://doi.org/10.3390/pr13082502 - 8 Aug 2025

Viewed by 596

Abstract

With the rapid growth of renewable energy integration, power systems are facing increasing uncertainty and variability in operation. The intermittent and uncontrollable nature of wind and solar generation requires operational decisions to anticipate future fluctuations, creating strong temporal coupling across days. This leads to large-scale mixed-integer linear programming (MILP) with a large number of binary variables, which is computationally intensive—especially in year-long simulations. As a result, there is a growing need for efficient modeling approaches that can reduce complexity while preserving key temporal features. This paper proposes a temporal–spatial acceleration framework for long-term power system operation simulation. In the temporal dimension, a monthly K-means clustering algorithm is applied to reconstruct typical scenario days from 8760 h time series, preserving the characteristics of seasonal and intraday variability. In the spatial dimension, thermal units with similar characteristics are aggregated, and binary decision variables are relaxed into continuous variables, transforming the MILP into a tractable LP model, and thereby reducing computational burden. Case studies are performed based on the six-bus and the IEEE RTS-79 systems to validate the framework, being able to provide a practical solution for renewable-integrated power system planning and dispatch applications. Full article

(This article belongs to the Special Issue Power System Optimization for Energy Storage: Methods and Applications)

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18 pages, 2188 KB

Open AccessArticle

Cooperative Control Method Based on Two-Objective Co-Optimization for MMCs in HVDC Systems

by Jinli Lv, Jiankang Zhang, Yuan Zhi, Kangping Wang, Pengjiang Ge, Jun Zhang and Qiang Li

Processes 2025, 13(6), 1839; https://doi.org/10.3390/pr13061839 - 10 Jun 2025

Viewed by 482

Abstract

High-voltage direct current (HVDC) systems, with their advantages of large capacity, long distance, high efficiency, and low loss, are becoming the core support of new power systems. However, in conventional droop control, the fixed droop coefficient causes output power disproportionate to the available capacities among converters, as well as a relatively large deviation of DC voltage in HVDC systems. Therefore, in this paper, a two-objective optimization model for droop control is developed and then it is integrated to a cooperative control, which achieves the co-optimization of voltage deviation and power sharing among multiple converters. In the optimization model, there are two objectives, the minimization of voltage deviation and maximization of the capacity utilization rates of converters. Further, a cooperative control method based on the optimization model is proposed, where information on voltage and power in droop-controlled converters is acquired and the co-optimization of voltage deviation and power sharing is performed to obtain the optimal droop coefficients for these converters, which minimizes voltage deviation, and at the same time, makes power mismatches proportional to their available capacities among converters. Finally, a testbed is built in PSCAD/EMTDC and four cases are designed to verify the proposed method under different settings. The simulation results show that compared with conventional droop control, the voltage deviation is reduced by 71.74% and 67.67% under the cases that a converter is out of service and the three-phase ground fault of a converter occurs. Additionally, when large power fluctuations occur twice, the power mismatches are shared proportionally to their available capacities, which results in the capacity utilization rates of the droop-controlled converters increasing by 24.46% and 18.75%, respectively. Full article

(This article belongs to the Special Issue Power System Optimization for Energy Storage: Methods and Applications)

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19 pages, 6402 KB

Open AccessArticle

Modular Multilevel Converter-Based Hybrid Energy Storage System Integrating Supercapacitors and Batteries with Hybrid Synchronous Control Strategy

by Chuan Yuan, Jing Gou, Jiao You, Bo Li, Xinwei Du, Yifeng Fu, Weixuan Zhang, Xi Wang and Peng Shi

Processes 2025, 13(5), 1580; https://doi.org/10.3390/pr13051580 - 19 May 2025

Cited by 2 | Viewed by 1198

Abstract

This paper proposes a hybrid synchronization control modular multilevel converter-based hybrid energy storage system (HSC-MMC-HESS) that innovatively integrates battery units within MMC submodules (SMs) while connecting a supercapacitor (SC) to the DC bus. The configuration synergistically combines the high energy density of batteries with the high power density of SCs through distinct energy/power pathways. The operational principles and control architecture are systematically analyzed, incorporating a hybrid synchronization control (HSC) strategy to deliver system inertia, primary frequency regulation, fault-tolerant mode transition capabilities, and isolation control. A hierarchical control framework implements power distribution through filtering mechanisms and state-of-charge (SOC) balancing control for battery management. Hardware-in-the-loop experimental validation confirms the topology’s effectiveness in providing inertial support, enabling flexible operational mode switching and optimizing hybrid energy storage utilization. The demonstrated capabilities indicate strong application potential for medium-voltage distribution networks requiring dynamic grid support. Full article

(This article belongs to the Special Issue Power System Optimization for Energy Storage: Methods and Applications)

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