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26 pages, 6698 KB

Open AccessArticle

An Integrated Model of Microgrid Energy Storage Planning and Operation Considering Multi-Scenario Source–Load Timing Correlation

by Xinyuan Zhang, Xing Liu and Zhenbo Wei

Energies 2026, 19(9), 2241; https://doi.org/10.3390/en19092241 - 6 May 2026

Viewed by 366

Abstract

Scenario generation and reduction based on a single variable (e.g., photovoltaic power or load forecasting) is a mainstream approach in current power system planning. However, such methods often overlook the temporal correlation between source and load, which can compromise the credibility of the [...] Read more.

Scenario generation and reduction based on a single variable (e.g., photovoltaic power or load forecasting) is a mainstream approach in current power system planning. However, such methods often overlook the temporal correlation between source and load, which can compromise the credibility of the generated scenarios and lead to suboptimal planning outcomes. To address this issue, this paper proposes an integrated model for microgrid energy storage planning and operation that explicitly considers the joint distribution of source–load scenarios. First, a comprehensive similarity metric is developed by combining dynamic time warping (DTW) distance, slope distance, and source–load correlation distance. An improved K-medoids clustering algorithm is then employed to cluster the joint source–load time series, generating a set of typical scenarios that effectively preserve the coupling characteristics between photovoltaic generation and load demand. Subsequently, a bi-level optimization model is formulated, with energy storage capacity as the primary decision variable. The upper-level planning problem aims to maximize the return on investment (ROI) under energy storage investment constraints, determining the optimal capacity configuration. The lower-level operational problem maximizes the daily net revenue by optimizing the charging and discharging strategies of the energy storage system. Through iterative interaction between the two levels, the model achieves optimal coordination between investment decisions and economic dispatch. Case studies on a campus microgrid demonstrate that the proposed joint scenario generation method effectively captures the temporal correlation between source and load, enhancing both the credibility of the scenarios and the economic rationality of the integrated planning and operation framework. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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27 pages, 5866 KB

Open AccessArticle

Power System Risk Specified Operational Scenario Generation Based on Conditional Generative Adversarial Networks

by Bo Zhou, Yunyang Xu, Xinwei Sun, Congkai Huang and Yikui Liu

Energies 2026, 19(9), 2228; https://doi.org/10.3390/en19092228 - 5 May 2026

Viewed by 290

Abstract

The rapid growth of wind and solar energy poses new challenges to safe and reliable system operation. Effectively characterizing and generating high-risk wind and photovoltaic (PV) power output scenarios is therefore essential for system risk assessment and preventive dispatch and control. However, existing [...] Read more.

The rapid growth of wind and solar energy poses new challenges to safe and reliable system operation. Effectively characterizing and generating high-risk wind and photovoltaic (PV) power output scenarios is therefore essential for system risk assessment and preventive dispatch and control. However, existing scenario generation methods either rely on predefined probability distributions or focus narrowly on extreme output levels, failing to comprehensively reflect system-level operational risk induced by renewable energy. To this end, a power system optimal dispatch model and a flexibility indicator system mainly incorporating system ramping and transmission margins are established. Thereafter, analytic hierarchy process (AHP) and the entropy weight method (EWM) are used to fuse indicators into a quantitative operational risk index. Historical wind and PV scenarios are evaluated through the dispatch model to generate risk-labeled samples, based on which a conditional generative adversarial network (cGAN) is trained to produce wind and PV power output scenarios with specified risk levels. Case studies verify that the risk labels constructed can effectively guide the subsequent conditional generation model and scenarios corresponding to a given risk level can be effectively generated by the model. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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12 pages, 2903 KB

Open AccessArticle

Study on Coordination Failure Due to Mis-Operation and Failure to Operate of OCRs in DC Distribution System with Distributed Energy Resource

by Seung-Su Choi and Sung-Hun Lim

Energies 2026, 19(8), 1954; https://doi.org/10.3390/en19081954 - 17 Apr 2026

Viewed by 379

Abstract

DC distribution systems are increasingly utilized in data centers, electric vehicle charging infrastructures, and microgrids due to their superior power conversion efficiency compared to AC systems. In DC networks, the protection coordination of overcurrent relays (OCRs) is essential for selectively isolating faults and [...] Read more.

DC distribution systems are increasingly utilized in data centers, electric vehicle charging infrastructures, and microgrids due to their superior power conversion efficiency compared to AC systems. In DC networks, the protection coordination of overcurrent relays (OCRs) is essential for selectively isolating faults and maintaining operational stability. However, the integration of distributed energy resources (DERs), such as photovoltaics, introduces significant challenges by altering the magnitude and rate of change of fault currents. This study conducts a comprehensive analysis of various scenarios by varying both the fault location and the points of common coupling (PCC) for DER. The simulation results reveal that specific configurations lead to critical instances of protection mis-operation and failure to operate, which cause coordination failures and compromised coordination time intervals (CTIs). These findings demonstrate that conventional protection strategies may fail to ensure reliability in DER-integrated DC systems due to the dynamic nature of fault current characteristics. In this paper, these diverse scenarios and the resulting vulnerabilities in protection coordination were modeled and verified using PSCAD/EMTDC V5.0. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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29 pages, 3408 KB

Open AccessArticle

Research on a Low-Carbon Economic Dispatch Model and Control Strategy for Multi-Zone Hydrogen Hybrid Integrated Energy Systems

by Jie Li, Zhenbo Wei, Tianlei Zang, Chao Yang, Wenhui Niu and Danyu Wang

Energies 2026, 19(1), 140; https://doi.org/10.3390/en19010140 - 26 Dec 2025

Cited by 1 | Viewed by 431

Abstract

The electricity–hydrogen–electricity conversion chain offers an effective solution for integrating clean energy into the grid while addressing multiple grid control requirements. Moreover, multiregional, interconnected, and integrated energy systems (IESs) can significantly increase overall energy utilization efficiency and operational flexibility through spatiotemporal coordination among [...] Read more.

The electricity–hydrogen–electricity conversion chain offers an effective solution for integrating clean energy into the grid while addressing multiple grid control requirements. Moreover, multiregional, interconnected, and integrated energy systems (IESs) can significantly increase overall energy utilization efficiency and operational flexibility through spatiotemporal coordination among diverse energy sources. However, few researchers have considered these two aspects in a unified framework. To address this gap, a low-carbon economic dispatch model and control strategy for a multiregional hydrogen-blended IES are proposed in this work. The model is constructed based on a system architecture that incorporates electricity–hydrogen–electricity conversion links while accounting for source–load uncertainties and peak shaving requirements. We solve the resulting distributed nonconvex nonlinear optimization problem using the alternating direction method of multipliers (ADMM). Furthermore, we analyze how uncertainty factors and peak shaving needs affect the maximum allowable hydrogen blending ratio in the gas grid, as well as the corresponding dynamic blending strategy. Our findings demonstrate that the proposed multiregional hydrogen-blended integrated energy system, with dynamic hydrogen blending control, significantly enhances the capacity for clean energy integration and reduces carbon emissions by approximately 12.3%. The peak-shaving demand is addressed through a coordinated mechanism involving electrolyzers (ELs), gas turbines (GTs), and hydrogen fuel cells (HFCs). This coordinated mechanism enables hydrogen fuel cells to double their output during peak hours, while electrolyzers increase their power consumption by approximately 730 MW during off-peak hours. The proposed dispatch model employs conditional risk measures to quantify the impacts of uncertainty and uses economic coefficients to balance various cost components. This approach enables effective coordination among economic objectives, risk management, and system performance (including peak shaving capability), thereby improving the practical applicability of the model. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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24 pages, 3158 KB

Open AccessArticle

Ultra-Short-Term Multi-Step Photovoltaic Power Forecasting Based on Similarity-Based Daily Clustering

by Yongcheng Jin, Zhichao Sun, Dongliang Lv, Weicheng Gao, Fengze Liu and Qinghua Yu

Energies 2026, 19(1), 29; https://doi.org/10.3390/en19010029 - 20 Dec 2025

Cited by 1 | Viewed by 850

Abstract

Photovoltaic (PV) power generation is inherently intermittent and volatile, complicating power system operation and control. Accurate forecasting is crucial for proactive grid responses and optimal energy resource scheduling. This study proposes a novel hybrid forecasting model that achieves high-precision PV power forecasting by [...] Read more.

Photovoltaic (PV) power generation is inherently intermittent and volatile, complicating power system operation and control. Accurate forecasting is crucial for proactive grid responses and optimal energy resource scheduling. This study proposes a novel hybrid forecasting model that achieves high-precision PV power forecasting by integrating similar-day clustering, generating extreme weather samples, and optimizing the Bidirectional Temporal Convolutional Network (BiTCN) and Bidirectional Gated Recurrent Unit (BiGRU) model via the Animated Oat Optimization (AOO) algorithm. The proposed method outperforms other models in the three evaluation metrics of mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²). The innovations lie in the integration of similar-day clustering with deep learning and the application of AOO for hyperparameter optimization, which significantly enhances forecasting accuracy and robustness. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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24 pages, 2129 KB

Open AccessArticle

Low-Carbon Economic Dispatch Model for Virtual Power Plants Considering Multi-Type Load Demand Response

by Zhizhong Yan, Zhenbo Wei, Tianlei Zang and Jie Li

Energies 2025, 18(24), 6553; https://doi.org/10.3390/en18246553 - 15 Dec 2025

Cited by 2 | Viewed by 662 | Correction

Abstract

Maximizing the optimal scheduling capability of a virtual power plant (VPP) over its aggregated resources is crucial for increasing its revenue. However, the limited dispatchable resources in single-energy VPPs hinder maximum economic efficiency. To address this issue, in this paper, a multienergy virtual [...] Read more.

Maximizing the optimal scheduling capability of a virtual power plant (VPP) over its aggregated resources is crucial for increasing its revenue. However, the limited dispatchable resources in single-energy VPPs hinder maximum economic efficiency. To address this issue, in this paper, a multienergy virtual power plant (MEVPP), which aggregates distributed electrical, thermal, and demand-side flexible resources, is introduced. Furthermore, a low-carbon economic dispatch strategy model is proposed for the coordinated operation of the MEVPP with shared energy storage. First, an MEVPP model incorporating shared energy storage is constructed, with equipment modeling developed from both electrical and thermal dimensions. Second, a low-carbon dispatch strategy that incorporates multiple types of demand responses is formulated, accounting for the effects of electrical and thermal demand responses, as well as carbon emissions, on dispatch. The simulation results demonstrate that, compared with models that do not consider the multienergy demand response, the proposed model reduces system operating costs to 54.2% and system carbon emissions to 42%. Additionally, the MEVPP can leverage energy storage by charging during low-price periods and discharging during high-price periods, thereby enabling low-carbon and economically viable system operation. This study offers valuable insights for the optimized operation of MEVPP systems. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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26 pages, 1655 KB

Open AccessFeature PaperArticle

Topology and Reactive Power Co-Optimization for Condition-Aware Distribution Network Reconfiguration

by Arash Mohammadi Vaniar, Mohammad Mansouri and Mohsen Assadi

Energies 2025, 18(22), 6062; https://doi.org/10.3390/en18226062 - 20 Nov 2025

Cited by 2 | Viewed by 1190

Abstract

Distribution networks (DNs) now operate under tighter conditions due to rising penetration of renewables, active prosumers, and exposure to transmission-level contingencies. Distribution Network Reconfiguration (DNR) has proven effective for reducing losses, improving voltage profiles, and enhancing the resiliency of the grid. This paper [...] Read more.

Distribution networks (DNs) now operate under tighter conditions due to rising penetration of renewables, active prosumers, and exposure to transmission-level contingencies. Distribution Network Reconfiguration (DNR) has proven effective for reducing losses, improving voltage profiles, and enhancing the resiliency of the grid. This paper introduces a three-stage optimization strategy for DNR, combining topological reconfiguration with reactive power support. The first stage, Reconfiguration of Tie-Line Switches (RTLS), utilizes a Particle Swarm Optimization (PSO) algorithm augmented with a Depth-First Search (DFS) mechanism to identify optimal radial structures that minimize active power losses. Once a viable configuration is established, the process proceeds to the second stage, Shunt Capacitor Sizing (SCS), wherein PSO is again employed to determine optimal capacitor sizing across predefined bus locations. The third stage reexecutes the RTLS process using the updated reactive power profile to assess whether further improvements in loss reduction can be achieved. If a superior topology is discovered, it is adopted as the final configuration; otherwise, the SCS solution is retained. This iterative and feedback-based architecture ensures an effective balance between network efficiency and voltage stability using a heuristic approach. The proposed methodology is validated on the IEEE 33-bus and IEEE 123-bus benchmark systems, as well as a custom 7-bus test case. Comprehensive scenario-based analysis, including normal, heavily, and lightly loaded conditions and varying power factor (PF) cases (good and poor PF), confirms the robustness and effectiveness of the approach in achieving considerable loss minimization and voltage profile improvement. For instance, in heavy-load conditions, active-power losses dropped by 39% and 70% for 33-bus and 123-bus cases, respectively. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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18 pages, 2408 KB

Open AccessArticle

A Two-Stage Topology Identification Strategy for Low-Voltage Distribution Grids Based on Contrastive Learning

by Yang Lei, Fan Yang, Yanjun Feng, Wei Hu and Yinzhang Cheng

Energies 2025, 18(22), 5886; https://doi.org/10.3390/en18225886 - 8 Nov 2025

Cited by 1 | Viewed by 858

Abstract

An accurate topology of low-voltage distribution grids (LVDGs) serves as the foundation for advanced applications such as line loss analysis, fault location, and power supply planning. This paper proposes a two-stage topology identification strategy for LVDGs based on Contrastive Learning. Firstly, the Dynamic [...] Read more.

An accurate topology of low-voltage distribution grids (LVDGs) serves as the foundation for advanced applications such as line loss analysis, fault location, and power supply planning. This paper proposes a two-stage topology identification strategy for LVDGs based on Contrastive Learning. Firstly, the Dynamic Time Warping (DTW) algorithm is utilized to align the time series of measurement data and evaluate their similarity, yielding the DTW similarity coefficient of the sequences. The Prim algorithm is then employed to construct the initial topology framework. Secondly, aiming at the topology information obtained from the initial identification, an Unsupervised Graph Attention Network (Unsup-GAT) model is proposed to aggregate node features, enabling the learning of complex correlation patterns in unsupervised scenarios. Subsequently, a loss function paradigm that incorporates both InfoNCE loss and power imbalance loss is constructed for updating network parameters, thereby realizing the identification and correction of local connection errors in the topology. Finally, case studies are conducted on 7 LVDGs of different node scales in a certain region of China to verify the effectiveness of the proposed two-stage topology identification strategy. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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21 pages, 2130 KB

Open AccessArticle

Integrating High-DER-Penetrated Distribution Systems into Energy Market with Feasible Region and Accompanying Strategic Bidding

by Tianhui Zhao, Jingbo Zhao, Bingcheng Cen, Zhe Chen and Yongyong Jia

Energies 2025, 18(21), 5630; https://doi.org/10.3390/en18215630 - 27 Oct 2025

Viewed by 1003

Abstract

With the increasing penetration of distributed energy resources (DERs) in distribution networks, traditional passive distribution systems are evolving into active and flexible systems capable of participating in the transmission-level energy market. Integrating distribution networks into a transmission-centric market-clearing model introduces challenges, such as [...] Read more.

With the increasing penetration of distributed energy resources (DERs) in distribution networks, traditional passive distribution systems are evolving into active and flexible systems capable of participating in the transmission-level energy market. Integrating distribution networks into a transmission-centric market-clearing model introduces challenges, such as capturing internal operational constraints and reflecting the economic features of distribution systems. To this end, this paper proposes a market integration method for distribution networks based on a feasible region and an accompanying bidding strategic bidding method to enable their efficient participation in the transmission-level electricity market. With a two-stage adaptive robust optimization framework, the feasible region that preserves operational characteristics of the distribution system and ensures the satisfaction of operational constraints within the distribution system is first depicted. The feasible region appears as time-coupled box-shaped regions. On this basis, a strategic bidding method is proposed based on the nested segmentation of the feasible region, jointly considering power and reserve. With it, the bidding prices of energy and reserve can be prepared, and then, together with the feasible region, can be smoothly integrated into the transmission-level market model. Numerical case studies demonstrate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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36 pages, 5965 KB

Open AccessArticle

Multiple Stability Margin Indexes-Oriented Online Risk Evaluation and Adjustment of Power System Based on Digital Twin

by Bo Zhou, Yunyang Xu, Xinwei Sun, Xi Ye, Yuhong Wang, Huaqing Dai and Shilin Gao

Energies 2025, 18(18), 4804; https://doi.org/10.3390/en18184804 - 9 Sep 2025

Cited by 1 | Viewed by 1278

Abstract

To address the challenges of transient voltage stability in modern power systems with high renewables penetration, this paper proposes a multiple stability margin indexes-oriented online risk evaluation and adjustment framework based on a digital twin platform. The System Voltage Deviation Index (S [...] Read more.

To address the challenges of transient voltage stability in modern power systems with high renewables penetration, this paper proposes a multiple stability margin indexes-oriented online risk evaluation and adjustment framework based on a digital twin platform. The System Voltage Deviation Index (S_VDI) is first introduced as a quantitative metric to assess transient voltage stability from time-domain simulation results, capturing the system’s dynamic response under large disturbances. An arbitrary Polynomial Chaos (aPC) expansion combined with Sobol sensitivity analysis is then employed to model the nonlinear relationship between S_VDI and uncertain inputs such as wind power, photovoltaic output, and dynamic load variations, enabling accurate identification of key nodes influencing stability. Furthermore, an emergency control optimization model is developed that jointly considers voltage, frequency, and rotor angle stability margins, as well as the economic costs of load shedding, with a trajectory sensitivity-based local linearization technique applied to enhance computational efficiency. The proposed method is validated on a hybrid AC/DC test system (CSEE-VS), and results show that, compared with a traditional control strategy, the optimized approach reduces total load shedding from 322.59 MW to 191.40 MW, decreases economic cost from 229.18 to 178.11, and improves the transient rotor angle stability index from 0.31 to 0.34 and the transient frequency stability index from 0.3162 to 1.511, while maintaining acceptable voltage stability performance. These findings demonstrate that the proposed framework can accurately assess online operational risks, pinpoint vulnerable nodes, and generate cost-effective, stability-guaranteeing control strategies, showing strong potential for practical deployment in renewable-integrated power grids. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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17 pages, 1983 KB

Open AccessArticle

Two-Stage Transformer–Customer Relationship Identification Strategy for Low-Voltage Distribution Grid Using Physics-Guided Graph Attention Network

by Yang Lei, Fan Yang, Yanjun Feng, Wei Hu and Yinzhang Cheng

Energies 2025, 18(16), 4380; https://doi.org/10.3390/en18164380 - 17 Aug 2025

Cited by 2 | Viewed by 1269

Abstract

Accurate transformer–customer relationships are crucial for the efficient operation and high-quality service of the low-voltage distribution grid (LVDG). This paper proposes a novel two-stage transformer–customer relationship identification strategy for LVDG using physics-guided graph attention network (PGAT). First, considering both transient and steady-state voltage [...] Read more.

Accurate transformer–customer relationships are crucial for the efficient operation and high-quality service of the low-voltage distribution grid (LVDG). This paper proposes a novel two-stage transformer–customer relationship identification strategy for LVDG using physics-guided graph attention network (PGAT). First, considering both transient and steady-state voltage fluctuations, a modified piecewise aggregate approximation (MPAA) algorithm is developed to preprocess raw measurement data through compression and denoising while preserving key voltage correlation features. Second, electrical similarity among customers is explored using the Modified Piecewise Aggregate Approximation K-means (MPAA-K-means) algorithm, enabling preliminary identification of transformer–customer relationships. Then, a training paradigm based on PGAT is introduced to characterize node features constrained by grid topology and electrical properties, achieving refined identification of transformer–customer relationships. Finally, testing results on real LVDG demonstrate the effectiveness and accuracy of the proposed two-stage identification strategy, providing new insights for transformer–customer relationship identification. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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17 pages, 2975 KB

Open AccessArticle

A Topology Identification Strategy of Low-Voltage Distribution Grids Based on Feature-Enhanced Graph Attention Network

by Yang Lei, Fan Yang, Yanjun Feng, Wei Hu and Yinzhang Cheng

Energies 2025, 18(11), 2821; https://doi.org/10.3390/en18112821 - 29 May 2025

Cited by 6 | Viewed by 1541

Abstract

Accurate topological connectivity is critical for the safe operation and management of low-voltage distribution grids (LVDGs). However, due to the complexity of the structure and the lack of measurement equipment, obtaining and maintaining these topological connections has become a challenge. This paper proposes [...] Read more.

Accurate topological connectivity is critical for the safe operation and management of low-voltage distribution grids (LVDGs). However, due to the complexity of the structure and the lack of measurement equipment, obtaining and maintaining these topological connections has become a challenge. This paper proposes a topology identification strategy for LVDGs based on a feature-enhanced graph attention network (F-GAT). First, the topology of the LVDG is represented as a graph structure using measurement data collected from intelligent terminals, with a feature matrix encoding the basic information of each entity. Secondly, the meta-path form of the heterogeneous graph is designed according to the connection characteristics of the LVDG, and the walking sequence is enhanced using a heterogeneous skip-gram model to obtain an embedded representation of the structural characteristics of each node. Then, the F-GAT model is used to learn potential association patterns and structural information in the graph topology, achieving a joint low-dimensional representation of electrical attributes and graph semantics. Finally, case studies on five urban LVDGs in the Wuhan region are conducted to validate the effectiveness and practicality of the proposed F-GAT model. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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27 pages, 4119 KB

Open AccessArticle

Optimizing Automatic Voltage Control Collaborative Responses in Chain-Structured Cascade Hydroelectric Power Plants Using Sensitivity Analysis

by Li Zhang, Jie Yang, Jun Wang, Lening Wang, Haiming Niu, Xiaobing Liu, Simon X. Yang and Kun Yang

Energies 2025, 18(11), 2681; https://doi.org/10.3390/en18112681 - 22 May 2025

Cited by 1 | Viewed by 1326

Abstract

Southwestern China has abundant hydropower networks, wherein neighboring cascade hydropower stations within the same river basin are typically connected to the power system in a chain-structured configuration. However, when such chain-structured cascade hydroelectric power plants (CC-HPPs) participate in automatic voltage control (AVC), problems [...] Read more.

Southwestern China has abundant hydropower networks, wherein neighboring cascade hydropower stations within the same river basin are typically connected to the power system in a chain-structured configuration. However, when such chain-structured cascade hydroelectric power plants (CC-HPPs) participate in automatic voltage control (AVC), problems such as reactive power interactions among stations and unreasonable voltage gradients frequently arise. To address these issues, this study proposes an optimized multi-station coordinated response control strategy based on sensitivity analysis and hierarchical AVC. Firstly, based on the topology of the chain-structured hydropower sending-end network, a reactive power–voltage sensitivity matrix is constructed. Subsequently, a regional-voltage-coordinated regulation model is developed using sensitivity analysis, followed by the establishment of a mathematical model, solution algorithm, and operational procedure for multi-station AVC-coordinated response optimization. Finally, case studies based on the actual operational data of a CC-HPP network validate the effectiveness of the proposed strategy, and simulation results demonstrate that the approach reduces the interstation reactive power pulling up to 97.76% and improves the voltage gradient rationality by 16.67%. These results substantially improve grid stability and operational efficiency while establishing a more adaptable voltage control framework for large-scale hydropower integration. Furthermore, they provide a practical foundation for future advancements in multi-scenario hydropower regulation, enhanced coordination strategies, and predictive control capabilities within clean energy systems. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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20 pages, 920 KB

Open AccessArticle

Comprehensive Benefit Evaluation Analysis of Multi-Energy Complementary Off-Grid System Operation

by Yu Lei, Xiaobin Yan, Shenhao Yang, Yu Fan, Chao Ma, Qingsong Li, Yuanfeng Huang and Wei Yang

Energies 2025, 18(9), 2159; https://doi.org/10.3390/en18092159 - 23 Apr 2025

Cited by 1 | Viewed by 1073

Abstract

In the future, China’s demand for centralized industrial development in remote areas will gradually increase, but the operation evaluation analysis of off-grid systems applicable to the development of such areas has not yet matured, and it is an urgent challenge to improve the [...] Read more.

In the future, China’s demand for centralized industrial development in remote areas will gradually increase, but the operation evaluation analysis of off-grid systems applicable to the development of such areas has not yet matured, and it is an urgent challenge to improve the operation mechanism of off-grid systems and then conduct a comprehensive benefit evaluation of off-grid systems. First of all, this paper focuses on the problem that the existing dimensions of the benefit evaluation of multi-energy complementary off-grid systems are not refined and comprehensive enough, and takes into account their high safety and reliability requirements, as well as the potential impacts on local industries and people’s lives after their completion, and then constructs a more complete comprehensive benefit evaluation indicator system for multi-energy complementary off-grid systems. Secondly, the subjective and objective weighting method based on the combination of the AHP (analytic hierarchy process) and AEM (anti-entropy method) is used to assign weights to the evaluation indicators. Finally, based on the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) comprehensive evaluation method, a comprehensive benefit evaluation of a multi-energy complementary off-grid system under different operation schemes is conducted, and the example results show that the size of the relative closeness under different operation schemes has a maximum difference of 0.5592, which verifies that the proposed evaluation indicator system and the multilevel evaluation method can comprehensively evaluate and analyze the strengths and weaknesses of multi-energy complementary off-grid systems under different operation schemes, and provide theoretical guidance and decision-making support for the further promotion and construction of multi-energy complementary off-grid systems. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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22 pages, 14286 KB

Open AccessArticle

A Multi-Time Scale Optimal Scheduling Strategy for the Electro-Hydrogen Coupling System Based on the Modified TCN-PPO

by Dongsen Li, Kang Qian, Yiyue Xu, Jiangshan Zhou, Zhangfan Wang, Yufei Peng and Qiang Xing

Energies 2025, 18(8), 1926; https://doi.org/10.3390/en18081926 - 10 Apr 2025

Cited by 2 | Viewed by 1329

Abstract

The regional integrated energy system, centered on electro-hydrogen technology, serves as a crucial mechanism for advancing the utilization of a high proportion of renewable energy and achieving the low-carbon transition of the energy system. In this context, a multi-time scale optimization model for [...] Read more.

The regional integrated energy system, centered on electro-hydrogen technology, serves as a crucial mechanism for advancing the utilization of a high proportion of renewable energy and achieving the low-carbon transition of the energy system. In this context, a multi-time scale optimization model for distributed electro-hydrogen coupling systems is proposed, utilizing an enhanced deep reinforcement learning (DRL) method. Firstly, considering the comprehensive operation cost and real-time deviations, the optimization model of day-ahead and real-time multi-time scale electro-hydrogen coupling system is constructed. Secondly, A dynamic perception model of environmental information is established based on a time convolutional network (TCN) to achieve multi-time scale feature capture of the coupling system and to improve the ability of the agents to perceive the environment of the coupling system. Then, the proposed optimization model is transformed into the Markov decision process (MDP), and a modified Proximal Policy Optimization (PPO) algorithm is introduced to achieve optimal solutions. Finally, case studies are conducted to analyze the electro-hydrogen coupling system in a specific region. The case studies verify the effectiveness of deep reinforcement learning and the electro-hydrogen coupling system in new energy consumption. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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22 pages, 2496 KB

Open AccessArticle

Residential Electricity Demand Modelling: Validation of a Behavioural Agent-Based Approach

by Baxter L. M. Williams, R. J. Hooper, Daniel Gnoth and J. G. Chase

Energies 2025, 18(6), 1314; https://doi.org/10.3390/en18061314 - 7 Mar 2025

Cited by 15 | Viewed by 3069

Abstract

The targets for reducing greenhouse gas emissions, combined with increased electrification and the increased use of intermittent renewable energy sources, create significant challenges in matching supply and demand within distribution grid constraints. Demand response (DR) can shift electricity demand to align with constraints, [...] Read more.

The targets for reducing greenhouse gas emissions, combined with increased electrification and the increased use of intermittent renewable energy sources, create significant challenges in matching supply and demand within distribution grid constraints. Demand response (DR) can shift electricity demand to align with constraints, reducing peak loads and increasing the utilisation of renewable generation. In countries like Aotearoa (New Zealand), peak loads are driven primarily by the residential sector, which is a prime candidate for DR. However, traditional deterministic and stochastic models do not account for the important variability in behavioural-driven residential demand and thus cannot be used to design or optimise DR. This paper presents a behavioural agent-based model (ABM) of residential electricity demand, which is validated using real electricity demand data from residential distribution transformers owned by Powerco, an electricity distributor in Aotearoa (New Zealand). The model accurately predicts demand in three neighbourhoods and matches the changes caused by seasonal variation, as well as the effects of COVID-19 lockdowns. The Pearson correlation coefficients between the median modelled and real demand are above 0.8 in 83% of cases, and the total median energy use variation is typically within 1–4%. Thus, this model provides a robust platform for network planning, scenario analysis, and DR program design or optimisation. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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23 pages, 4365 KB

Open AccessArticle

Gas−Hydro Coordinated Peaking Considering Source-Load Uncertainty and Deep Peaking

by Chong Wu, Tong Xu, Shenhao Yang, Yong Zheng, Xiaobin Yan, Maoyu Mao, Ziyi Jiang and Qian Li

Energies 2025, 18(5), 1234; https://doi.org/10.3390/en18051234 - 3 Mar 2025

Viewed by 1288

Abstract

Considering the power demand in high-altitude special environmental areas and the peak-regulation issues in the power system caused by the uncertainties associated with wind and photovoltaic power as well as load, a gas–hydro coordinated peak-shaving method that considers source-load uncertainty is proposed. Firstly, [...] Read more.

Considering the power demand in high-altitude special environmental areas and the peak-regulation issues in the power system caused by the uncertainties associated with wind and photovoltaic power as well as load, a gas–hydro coordinated peak-shaving method that considers source-load uncertainty is proposed. Firstly, based on the regulation-related characteristics of hydropower and gas power, a gas−hydro coordinated operation mode is proposed. Secondly, the system operational risk caused by source-load uncertainty is quantified based on the Conditional Value-at-Risk theory. Then, the cost of deep peak shaving in connection with gas-fired power generation is estimated, and a gas−hydro coordinated peak-shaving model considering risk constraints and deep peak shaving is established. Finally, a specific example verifies that the proposed gas−hydro coordinated peak-regulation model can effectively improve the economy of the system. The total system profit increased by 36.03%, indicating that this method enhances the total system profit and achieves better peak-shaving effects. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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23 pages, 3699 KB

Open AccessArticle

Improved Multi-Objective Strategy Diversity Chaotic Particle Swarm Optimization of Ordered Charging Strategy for Electric Vehicles Considering User Behavior

by Shuyi Zhao, Chenshuo Ma and Zhiao Cao

Energies 2025, 18(3), 690; https://doi.org/10.3390/en18030690 - 2 Feb 2025

Cited by 3 | Viewed by 1783

Abstract

With the development of the EV industry, the number of EVs is increasing, and the random charging and discharging causes a great burden on the power grid. Meanwhile, the increasing electricity bills reduce user satisfaction. This article proposes an algorithm that considers user [...] Read more.

With the development of the EV industry, the number of EVs is increasing, and the random charging and discharging causes a great burden on the power grid. Meanwhile, the increasing electricity bills reduce user satisfaction. This article proposes an algorithm that considers user satisfaction to solve the charging and discharging scheduling problem of EVs. This article adds an objective function to quantify user satisfaction and addresses the issues of premature local optima and insufficient diversity in the MOPSO algorithm. Based on the performance of different particles, the algorithm assigns elite particle, general particle, and learning particle roles to the particles and assigns strategies for maintaining search, developing search, and learning search, respectively. In order to avoid falling into local optima, chaotic sequence perturbations are added during each iteration process avoiding premature falling into local optima. Finally, case studies are implemented and the comparison analysis is performed in terms of the use and benefit of each design feature of the algorithm. The results show that the proposed algorithm is capable of achieving up to 23% microgrid load reduction and up to 20% improvement in convergence speed compared to other algorithms. It is superior to other algorithms in solving the problem of orderly charging and discharging of electric vehicles and has strong usability and feasibility. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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21 pages, 2201 KB

Open AccessArticle

Ultra-Short-Term Distributed Photovoltaic Power Probabilistic Forecasting Method Based on Federated Learning and Joint Probability Distribution Modeling

by Yubo Wang, Chao Huo, Fei Xu, Libin Zheng and Ling Hao

Energies 2025, 18(1), 197; https://doi.org/10.3390/en18010197 - 5 Jan 2025

Cited by 9 | Viewed by 2487

Abstract

The accurate probabilistic forecasting of ultra-short-term power generation from distributed photovoltaic (DPV) systems is of great significance for optimizing electricity markets and managing energy on the user side. Existing methods regarding cluster information sharing tend to easily trigger issues of data privacy leakage [...] Read more.

The accurate probabilistic forecasting of ultra-short-term power generation from distributed photovoltaic (DPV) systems is of great significance for optimizing electricity markets and managing energy on the user side. Existing methods regarding cluster information sharing tend to easily trigger issues of data privacy leakage during information sharing, or they suffer from insufficient information sharing while protecting data privacy, leading to suboptimal forecasting performance. To address these issues, this paper proposes a privacy-preserving deep federated learning method for the probabilistic forecasting of ultra-short-term power generation from DPV systems. Firstly, a collaborative feature federated learning framework is established. For the central server, information sharing among clients is realized through the interaction of global models and features while avoiding the direct interaction of raw data to ensure the security of client data privacy. For local clients, a Transformer autoencoder is used as the forecasting model to extract local temporal features, which are combined with global features to form spatiotemporal correlation features, thereby deeply exploring the spatiotemporal correlations between different power stations and improving the accuracy of forecasting. Subsequently, a joint probability distribution model of forecasting values and errors is constructed, and the distribution patterns of errors are finely studied based on the dependencies between data to enhance the accuracy of probabilistic forecasting. Finally, the effectiveness of the proposed method was validated through real datasets. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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19 pages, 3882 KB

Open AccessArticle

Research on Thyristor Reverse Recovery Behavior in High-Voltage Direct Current Transmission Converter Valves and Its Application in Integrated Protection Systems

by Cao Wen, Liang Song, Yu Huang, Dong Peng, Peng Zhang, Jianquan Liao, Longjie Yang and Shilin Gao

Energies 2024, 17(24), 6472; https://doi.org/10.3390/en17246472 - 23 Dec 2024

Cited by 3 | Viewed by 2461

Abstract

The performance of converter valves is essential for the reliability and efficiency of high-voltage direct current (HVDC) transmission systems. Converter valves consist of multiple thyristor levels, each requiring regular testing to ensure proper functionality. Protective triggering tests play a crucial role in evaluating [...] Read more.

The performance of converter valves is essential for the reliability and efficiency of high-voltage direct current (HVDC) transmission systems. Converter valves consist of multiple thyristor levels, each requiring regular testing to ensure proper functionality. Protective triggering tests play a crucial role in evaluating the safety and performance of these thyristors during maintenance. This study introduces a high-power experimental setup designed to investigate the effects of varying current levels and thermal stresses on the reverse recovery behavior of thyristors—a key performance indicator. Results indicate that the reverse recovery time increases rapidly with higher current levels before reaching a saturation point. Additionally, prolonged exposure to high temperatures significantly reduces both the storage time and the amount of charge recovered during the reverse recovery process. These findings enable the optimization of protective test settings, thereby enhancing the effectiveness of the Thyristor Control Unit (TCU) in protecting converter valves. Improved testing methodologies derived from this research contribute to more reliable maintenance practices and increased overall stability of HVDC transmission systems. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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