Global Energy Interconnection最新文献

Electricity‑carbon price dual‑response strategy for power systems considering renewable energy uncertainty 考虑可再生能源不确定性的电力系统的电力-碳价格双响应策略

IF 3.9

Global Energy Interconnection Pub Date : 2026-08-01 Epub Date: 2026-06-26 DOI: 10.1016/j.gloei.2026.06.002

Huaze Yan, Guangzheng Yu, Zhenlong Zhang, Yunpeng Xiao, Huiqing Lv, Xiaoqi Xie

{"title":"Electricity‑carbon price dual‑response strategy for power systems considering renewable energy uncertainty","authors":"Huaze Yan, Guangzheng Yu, Zhenlong Zhang, Yunpeng Xiao, Huiqing Lv, Xiaoqi Xie","doi":"10.1016/j.gloei.2026.06.002","DOIUrl":"10.1016/j.gloei.2026.06.002","url":null,"abstract":"<div><div>To address the challenges of renewable energy uncertainty to power system security and low-carbon transition, this paper proposes an optimal dispatch strategy with dual response to electricity and carbon pricing. Targeting the temporal and spatial variations in carbon intensity caused by renewable fluctuations and the underutilized carbon reduction potential on the load side, a cross-temporal updating mechanism for nodal dynamic carbon potential is developed. This mechanism enables dynamic and accurate allocation of carbon emission responsibilities between generation and load. To enhance the quality of renewable output scenario generation, an Advanced Temporal-Spatial Generative Adversarial Network (ATS-GAN) is employed, and a data-driven ambiguity uncertainty set for forecast errors is constructed using the Wasserstein distance. Building on this, a distributionally robust optimization model is formulated, incorporating nodal dual response to electricity and carbon signals. Case studies on a modified IEEE 30-node system show that the proposed strategy effectively reduces carbon emissions, facilitates renewable integration, and improves system economic efficiency. Compared to conventional stochastic and robust optimization methods, it achieves a better trade-off between conservatism and computational performance.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"9 4","pages":"Pages 793-809"},"PeriodicalIF":3.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148822769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TF-LiteNet: a time–frequency fusion lightweight network for multi-source load and renewable forecasting in virtual power plants TF-LiteNet：用于虚拟电厂多源负荷和可再生能源预测的时频融合轻量级网络

IF 3.9

Global Energy Interconnection Pub Date : 2026-08-01 Epub Date: 2026-04-16 DOI: 10.1016/j.gloei.2025.12.006

Zixu Zhu, Di Chen, Linjun Wei, Zhongxing Wu

{"title":"TF-LiteNet: a time–frequency fusion lightweight network for multi-source load and renewable forecasting in virtual power plants","authors":"Zixu Zhu, Di Chen, Linjun Wei, Zhongxing Wu","doi":"10.1016/j.gloei.2025.12.006","DOIUrl":"10.1016/j.gloei.2025.12.006","url":null,"abstract":"<div><div>With the rapid integration of renewable and distributed energy resources, Virtual Power Plants (VPPs) have emerged as a key paradigm for the future power system. By aggregating heterogeneous resources for coordinated scheduling and market participation, the operation of VPPs relies heavily on accurate forecasting of load and renewable outputs. However, traditional statistical models fail to capture nonlinear characteristics of multi-source time series, while single-domain approaches in either time or frequency remain limited in accuracy and robustness. To address these challenges, this study focuses on the joint forecasting of load, photovoltaic (PV), and wind power time series for VPPs, where accurate multi-source prediction is essential for reliable scheduling and market participation. We proposes <em>TF-LiteNet</em>, a time–frequency fusion deep learning framework tailored for multi-source forecasting in VPPs. The framework incorporates a parallel time-domain encoder and frequency-domain encoder to extract long-term dependencies, local dynamics, and non-stationary components at different frequency bands. A bilinear interaction module with energy-aware fusion enables deep integration of temporal and spectral features, while a closed-form readout layer enhances efficiency and stability. Experiments on the real-world dataset demonstrate that the proposed method consistently outperforms state-of-the-art models in forecasting load, photovoltaic, and wind generation. The results confirm the framework’s effectiveness across both short- and long-horizon tasks, providing reliable predictive support for VPP scheduling and market participation.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"9 4","pages":"Pages 697-711"},"PeriodicalIF":3.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148822736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Time-Scale dispatch optimization strategy for rural distribution networks considering seasonal load characteristics of agricultural facilities 考虑农业设施季节性负荷特征的农村配电网多时间尺度调度优化策略

IF 3.9

Global Energy Interconnection Pub Date : 2026-08-01 Epub Date: 2026-05-27 DOI: 10.1016/j.gloei.2026.05.002

Enyu Jiang, Wenjing Li, Yuqiang Wang, Kai Guo, Cuicui Wu, Kailong Jiang, Shunfu Lin

{"title":"Multi-Time-Scale dispatch optimization strategy for rural distribution networks considering seasonal load characteristics of agricultural facilities","authors":"Enyu Jiang, Wenjing Li, Yuqiang Wang, Kai Guo, Cuicui Wu, Kailong Jiang, Shunfu Lin","doi":"10.1016/j.gloei.2026.05.002","DOIUrl":"10.1016/j.gloei.2026.05.002","url":null,"abstract":"<div><div>With the large-scale integration of distributed energy into rural distribution networks, the mismatch between the seasonal power consumption of traditional agricultural facilities and renewable energy output triggers security risks like over-limit operation and voltage fluctuation. To this end, a coordinated optimal dispatch strategy for distribution network-agricultural park considering the shiftable characteristics of agricultural facility loads is proposed. Its core innovation is a seasonally adaptive bi-level dispatch framework: in busy farming seasons, load shifting is incentivized to coordinate with stationary energy storage for peak shaving and valley filling; in slack seasons, idle electric tractors are innovatively used as mobile energy storage for bidirectional demand response. A Stackelberg game model with dynamic compensation price is established. Simulation results based on real data and IEEE 33-bus system verify the strategy’s economic and secure performance for high-penetration renewable energy rural grids.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"9 4","pages":"Pages 855-876"},"PeriodicalIF":3.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148822782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimized control strategies for BLDC motor in sustainable energy systems: enhancing stability and performance through intelligent controllers, filtering techniques and internal model control 可持续能源系统中无刷直流电机的优化控制策略：通过智能控制器、滤波技术和内模控制增强稳定性和性能

IF 3.9

Global Energy Interconnection Pub Date : 2026-08-01 Epub Date: 2025-12-31 DOI: 10.1016/j.gloei.2025.09.004

Appalabathula Venkatesh, Shankar Nalinakshan, S. Phani Kumar, S.S. Kiran, K. Akash

{"title":"Optimized control strategies for BLDC motor in sustainable energy systems: enhancing stability and performance through intelligent controllers, filtering techniques and internal model control","authors":"Appalabathula Venkatesh, Shankar Nalinakshan, S. Phani Kumar, S.S. Kiran, K. Akash","doi":"10.1016/j.gloei.2025.09.004","DOIUrl":"10.1016/j.gloei.2025.09.004","url":null,"abstract":"<div><div>The challenge of achieving reliable and efficient control of Brushless DC Motors (BLDCMs) in electric and hybrid vehicles, where system nonlinearities and dynamic disturbances hinder traditional control methods, is analyzed in this article. It compares conventional and intelligent control techniques through both time- and frequency- domain analyses. Metrics such as speed, torque, back Electromotive Force (EMF), and phase currents are evaluated under different conditions. Although standard PID and PI controllers are common, their tuning becomes complicated by nonlinear behaviors and limited flexibility. The study explores advanced methods like Sliding Mode Control (SMC), Fuzzy Logic Control (FLC), and hybrid strategies that combine FLC and SMC with PI control to enhance transient and steady-state performance. Filtering techniques like the Extended Kalman Filter (EKF) and Particle Filter (PF) are used to improve state estimation and noise reduction, with PF outperforming EKF by approximately 30%. The Internal Model Control (IMC), including its two-degree-of-freedom (2-DoF) variant, is also analyzed for its ability to enhance gain margin and stability in non-minimum phase systems. These hybrid control schemes offer better disturbance rejection, faster settling times, and greater robustness, showing promise for increasing the reliability of BLDCM-driven sustainable transportation systems.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"9 4","pages":"Pages 877-896"},"PeriodicalIF":3.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148822783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physics-informed neural networks for battery smart modeling and state estimation with electrochemical mechanisms and data-driven intelligence 基于电化学机制和数据驱动智能的电池智能建模和状态估计的物理信息神经网络

IF 3.9

Global Energy Interconnection Pub Date : 2026-08-01 Epub Date: 2026-07-21 DOI: 10.1016/j.gloei.2026.07.001

Kailong Liu, Weiming Li, Qiao Peng, Chunhui Zhao, Hongwei Zhou, Bin Duan, Xiangjun Li, Chenghui Zhang

{"title":"Physics-informed neural networks for battery smart modeling and state estimation with electrochemical mechanisms and data-driven intelligence","authors":"Kailong Liu, Weiming Li, Qiao Peng, Chunhui Zhao, Hongwei Zhou, Bin Duan, Xiangjun Li, Chenghui Zhang","doi":"10.1016/j.gloei.2026.07.001","DOIUrl":"10.1016/j.gloei.2026.07.001","url":null,"abstract":"<div><div>Physics-informed neural networks (PINNs) have emerged as a promising framework that bridges physics-based battery models and data-driven learning. This review systematically summarizes recent progress in PINN-based battery modeling and state estimation. First, the theoretical foundations of PINNs are analyzed, including physical constraint embedding, loss function formulation, boundary condition treatment, and training stability. Second, battery modeling approaches that can be integrated with PINNs are reviewed, including electrochemical models, electro-thermal coupled models, parameter identification and uncertainty analysis frameworks, and cross-scale or cross-chemistry modeling strategies. Third, applications of PINNs in battery state estimation are reviewed, covering key tasks such as state of charge, state of health, degradation prediction, and the inference of internal states including temperature and ion concentration fields. Through comparative analysis, the advantages of PINNs in improving prediction accuracy, interpretability, and data efficiency are highlighted. The challenges related to model generalization, training stability, and computational cost are also discussed. Through comparative analysis, this review indicates that PINNs provide an effective bridge between physics-based battery models and data-driven learning by improving prediction accuracy, physical consistency, and data efficiency under limited measurement conditions. Nevertheless, challenges related to training stability, computational cost, and cross-condition generalization still restrict practical deployment. Future research is expected to focus on lightweight and transferable PINN frameworks, multi-physics integration, uncertainty-aware learning, and real-time battery management applications for next-generation intelligent energy storage systems.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"9 4","pages":"Pages 639-663"},"PeriodicalIF":3.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148822734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-configurational energy storage management in a residential nanogrid under constraints of renewable energy resources and demand uncertainties 可再生能源和需求不确定性约束下的住宅纳米电网多配置储能管理

IF 3.9

Global Energy Interconnection Pub Date : 2026-08-01 Epub Date: 2026-06-01 DOI: 10.1016/j.gloei.2026.02.004

Ahmed Tijjani Dahiru, Chee Wei Tan, Kwan Yiew Lau, Chuen Ling Toh, Razman Ayop, Oladimeji Ibrahim, Chin Kim Gan, Sarmid Shakir Abdulsattar

{"title":"Multi-configurational energy storage management in a residential nanogrid under constraints of renewable energy resources and demand uncertainties","authors":"Ahmed Tijjani Dahiru, Chee Wei Tan, Kwan Yiew Lau, Chuen Ling Toh, Razman Ayop, Oladimeji Ibrahim, Chin Kim Gan, Sarmid Shakir Abdulsattar","doi":"10.1016/j.gloei.2026.02.004","DOIUrl":"10.1016/j.gloei.2026.02.004","url":null,"abstract":"<div><div>Renewable energy resource dynamics contribute to system complexities in addition to reliability issues in modern power grids. Energy storage systems such as batteries are used to address the reliability issues. Energy storage is also considered for improved economy in modern systems such as microgrids. Batteries are used for storage in modern systems due to low acquisition cost, large-scale commercial availability and high storage densities. However, the competitive advantages of batteries render other storage facilities underutilized. These include significant parking time that electric vehicles spend and long periods of energy storage retention in hydrogen fuel cells. This paper considers the technoeconomic advantages of various energy storage facilities in establishing energy storage management strategies using batteries, electric vehicles and hydrogen fuel cells. The proposed energy storage management is configured in a combined and substituted mode of storage configurations for a residential nanogrid. The objective is to exploit potentials of other storage facilities for energy tradeoffs between the nanogrid and the main grid for low energy cost and improved energy utilization. Results indicate an hourly average energy availability range of 24–115 kWh in battery storage, 19–75 kWh in electric vehicle and 5–45 kWh in hydrogen storage configurations. The three substituted configurations had an energy cost range of 0.1604–0.2005 $/kWh. The combined storage configuration projected a 110–250 kW daily energy availability and energy cost of 0.3609 $/kWh. It is suggested that dynamic energy pricing can be suitable for such uncertain renewable energy and customer demands scenarios in future investigations.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"9 4","pages":"Pages 676-696"},"PeriodicalIF":3.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148822735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimization of charging-discharging strategies and EV battery swapping recommendation algorithm considering aggregation of battery swapping stations 考虑换电池站聚集的电动汽车充放电策略优化及换电池推荐算法

IF 3.9

Global Energy Interconnection Pub Date : 2026-08-01 Epub Date: 2026-04-08 DOI: 10.1016/j.gloei.2026.01.004

Wenke Xu, Jiawei Chen, Xianglu Liu, Xi Chen

{"title":"Optimization of charging-discharging strategies and EV battery swapping recommendation algorithm considering aggregation of battery swapping stations","authors":"Wenke Xu, Jiawei Chen, Xianglu Liu, Xi Chen","doi":"10.1016/j.gloei.2026.01.004","DOIUrl":"10.1016/j.gloei.2026.01.004","url":null,"abstract":"<div><div>Electric Vehicles (EVs) play a crucial role in carbon emission reduction and energy structure adjustment. The EV battery swapping mode, characterized by short energy replenishment time and strong grid interaction capabilities, has developed rapidly in recent years. Research on EV battery swapping recommendations and optimization of charging/discharging strategies for swapping stations has gradually become a hotspot, but existing studies focus on optimizing single issues without considering the deep coupling relationship between swapping recommendations and station operations under an aggregated system. For the aggregation of battery swapping station (BSS), this paper proposes a multi-time-scale scheduling model. On a large time scale, the model aims to minimize the operational costs of the BSSs by optimizing charging/discharging strategies and battery swapping demand allocation. On a small time scale, Lyapunov optimization is employed to recommend suitable swapping stations and driving routes for EV users in real time. Additionally, considering scenarios where EVs reject system recommendations, the model achieves real-time adjustments through rolling optimization by promptly updating the status of the swapping station cluster, thereby improving the economic efficiency of the aggregated system while meeting battery swapping demands. Based on the real operational data of NIO’s BSSs, the case study analysis shows that under the proposed strategy, the operational cost of the aggregated battery swap station system is reduced by 37.11%.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"9 4","pages":"Pages 664-675"},"PeriodicalIF":3.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148822733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stability analysis and parameter optimization method of multiple grid-forming converter system with VSG and droop control 带VSG和下垂控制的多并网转炉系统稳定性分析及参数优化方法

IF 3.9

Global Energy Interconnection Pub Date : 2026-08-01 Epub Date: 2026-04-16 DOI: 10.1016/j.gloei.2026.01.005

Huan Liu, Haijiao Wang, Hui Huang, Mingyuan Zhang, Xing Zhang

{"title":"Stability analysis and parameter optimization method of multiple grid-forming converter system with VSG and droop control","authors":"Huan Liu, Haijiao Wang, Hui Huang, Mingyuan Zhang, Xing Zhang","doi":"10.1016/j.gloei.2026.01.005","DOIUrl":"10.1016/j.gloei.2026.01.005","url":null,"abstract":"<div><div>In this article, a stability analysis and parameter optimization method is proposed for multiple grid-forming (GFM) converter system. Firstly, a general small-signal state-space model is developed with modular method. Then, the comparative analyses of the stability characteristics and transient response are carried out for the VSG/Droop heterogeneous system and homogeneous control system. Meanwhile, the stable regions of the outer-loop control coefficients are studied, including the droop, virtual inertia coefficients and voltage integral coefficients. The impact of the proportion of VSG to droop-controlled converters on system stability is also investigated. Furthermore, a universal parameter optimization method is proposed for diverse multiple GFM converter systems. Critical parameters that govern stability are first identified via comprehensive parametric sensitivity analysis, after which the particle swarm optimization (PSO) algorithm is utilized for optimization. The objective function comprehensively incorporates the influence of all eigenvalues. The effectiveness of the proposed method is ultimately validated through simulations.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"9 4","pages":"Pages 828-841"},"PeriodicalIF":3.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148822780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive frequency regulation control strategy for flywheel energy storage coupled thermal power units based on optimal variational mode decomposition 基于最优变分模态分解的飞轮储能耦合火电机组自适应调频控制策略

IF 3.9

Global Energy Interconnection Pub Date : 2026-08-01 Epub Date: 2026-02-17 DOI: 10.1016/j.gloei.2026.02.001

Ping Zhang, Changsheng Jiao, Mingzhi Li, Pengyang Ma, Jiabao Zhang

{"title":"Adaptive frequency regulation control strategy for flywheel energy storage coupled thermal power units based on optimal variational mode decomposition","authors":"Ping Zhang, Changsheng Jiao, Mingzhi Li, Pengyang Ma, Jiabao Zhang","doi":"10.1016/j.gloei.2026.02.001","DOIUrl":"10.1016/j.gloei.2026.02.001","url":null,"abstract":"<div><div>As the proportion of renewable energy sources such as wind and solar power connected to the grid continues to increase, maintaining grid frequency stability faces severe challenges. Traditional thermal power units exhibit insufficient frequency regulation capabilities, necessitating the integration of rapid response resources like flywheel energy storage. This paper investigates the frequency regulation models of flywheels and thermal power units, proposes an improved frequency regulation command decomposition method, and designs an adaptive integrated control strategy for grid frequency characteristics and SOC (State of Charge) under various frequency regulation scenarios. This enables the flywheel-thermal power hybrid system to respond flexibly to grid frequency demands. Simulation results demonstrate that under step load conditions, this strategy reduces the maximum frequency deviation by 36.66% and shortens the steady-state regulation time by 33.44%. Under continuous load conditions, the standard deviation of frequency deviation decreases by over 5.03%, with significantly reduced output fluctuations in the thermal power unit. The proposed strategy not only effectively enhances system frequency stability but also alleviates the frequency regulation burden on thermal power units while ensuring the flywheel’s SOC remains within a safe range.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"9 4","pages":"Pages 730-742"},"PeriodicalIF":3.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148822765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Economic dispatch optimization of off-grid wind solar complementary hydrogen synthesis ammonia system based on reverse chaos sparrow search algorithm 基于逆混沌麻雀搜索算法的离网风能太阳能互补氢合成氨系统经济调度优化

IF 3.9

Global Energy Interconnection Pub Date : 2026-08-01 Epub Date: 2026-07-17 DOI: 10.1016/j.gloei.2026.06.004

Jianyu Yu, Huisheng Wang, Shiqi Yin, Hengli Quan, Biao Li, Xianhai Liu, Guohui Zhang, Zaijiang Lin, Wang Lu, Qingzhu Wan, Tianyi Zhang

{"title":"Economic dispatch optimization of off-grid wind solar complementary hydrogen synthesis ammonia system based on reverse chaos sparrow search algorithm","authors":"Jianyu Yu, Huisheng Wang, Shiqi Yin, Hengli Quan, Biao Li, Xianhai Liu, Guohui Zhang, Zaijiang Lin, Wang Lu, Qingzhu Wan, Tianyi Zhang","doi":"10.1016/j.gloei.2026.06.004","DOIUrl":"10.1016/j.gloei.2026.06.004","url":null,"abstract":"<div><div>Off-grid wind-solar hydrogen and ammonia synthesis systems offer a promising route to reduce the grid impact of variable renewable generation and improve its utilization. However, the inherent intermittency and volatility of wind and solar power pose significant challenges to the stable and efficient operation of electrolysis and ammonia synthesis units. To address these issues, this study focuses on load-side ammonia supply and develops an economic dispatch model that integrates battery energy storage and hydrogen storage within an off-grid hybrid system. The model is solved using the reverse chaos sparrow search algorithm (RCSSA), and its performance is benchmarked against the original sparrow search algorithm (SSA) as well as other conventional algorithms. Case study results demonstrate that, compared with the original SSA, RCSSA reduces operating costs by approximately 7%, substantially decreases the number of iterations, and accelerates convergence. By coordinating energy and hydrogen storage, the proposed scheduling strategy ensures a reliable ammonia supply while minimizing overall system costs.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"9 4","pages":"Pages 779-792"},"PeriodicalIF":3.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148822768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0