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Dynamic Boundary Optimization via IDBO-VMD:A Novel Power Allocation Strategy for Hybrid Energy Storage with Enhanced Grid Stability 认领 引用 被引量：1
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作者 Zujun Ding Qi Xiang +10 位作者 Chengyi Li Mengyu Ma Chutong Zhang Xinfa Gu Jiaming Shi Hui Huang Aoyun Xia Wenjie Wang Wan Chen Ziluo Yu Jie Ji 《Energy Engineering》 EI 2026年第1期527-552,共26页
In order to address environmental pollution and resource depletion caused by traditional power generation,this paper proposes an adaptive iterative dynamic-balance optimization algorithm that integrates the Improved D... In order to address environmental pollution and resource depletion caused by traditional power generation,this paper proposes an adaptive iterative dynamic-balance optimization algorithm that integrates the Improved Dung Beetle Optimizer(IDBO)with VariationalMode Decomposition(VMD).The IDBO-VMD method is designed to enhance the accuracy and efficiency of wind-speed time-series decomposition and to effectively smooth photovoltaic power fluctuations.This study innovatively improves the traditional variational mode decomposition(VMD)algorithm,and significantly improves the accuracy and adaptive ability of signal decomposition by IDBO selfoptimization of key parameters K and a.On this basis,Fourier transform technology is used to define the boundary point between high frequency and low frequency signals,and a targeted energy distribution strategy is proposed:high frequency fluctuations are allocated to supercapacitors to quickly respond to transient power fluctuations;Lowfrequency components are distributed to lead-carbon batteries,optimizing long-term energy storage and scheduling efficiency.This strategy effectively improves the response speed and stability of the energy storage system.The experimental results demonstrate that the IDBO-VMD algorithm markedly outperforms traditional methods in both decomposition accuracy and computational efficiency.Specifically,it effectively reduces the charge–discharge frequency of the battery,prolongs battery life,and optimizes the operating ranges of the state-of-charge(SOC)for both leadcarbon batteries and supercapacitors.In addition,the energy management strategy based on the algorithm not only improves the overall energy utilization efficiency of the system,but also shows excellent performance in the dynamic management and intelligent scheduling of renewable energy generation. 展开更多
关键词 Energy efficiency hybrid energy storage system intelligent algorithm power fluctuation mitigation renewable energy
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Advanced Predictive Analytics for Green Energy Systems: An IPSS System Perspective 认领 引用 被引量：1
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作者 Lei Shen Chutong Zhang +4 位作者 Yuwei Ge Shanyun Gu Qiang Gao Wei Li Jie Ji 《Energy Engineering》 EI 2025年第4期1581-1602,共22页
The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent ... The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent Power Stability and Scheduling(IPSS)System,which is designed to enhance the safety,stability,and economic efficiency of power systems,particularly those integrated with green energy sources.The IPSS System is distinguished by its integration of a CNN-Transformer predictive model,which leverages the strengths of Convolutional Neural Networks(CNN)for local feature extraction and Transformer architecture for global dependency modeling,offering significant potential in power safety diagnostics.TheIPSS System optimizes the economic and stability objectives of the power grid through an improved Zebra Algorithm,which aims tominimize operational costs and grid instability.Theperformance of the predictive model is comprehensively evaluated using key metrics such as Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and Coefficient of Determination(R2).Experimental results demonstrate the superiority of the CNN-Transformer model,with the lowest RMSE and MAE values of 0.0063 and 0.00421,respectively,on the training set,and an R2 value approaching 1,at 0.99635,indicating minimal prediction error and strong data interpretability.On the test set,the model maintains its excellence with the lowest RMSE and MAE values of 0.009 and 0.00673,respectively,and an R2 value of 0.97233.The IPSS System outperforms other models in terms of prediction accuracy and explanatory power and validates its effectiveness in economic and stability analysis through comparative studies with other optimization algorithms.The system’s efficacy is further supported by experimental results,highlighting the proposed scheme’s capability to reduce operational costs and enhance system stability,making it a valuable contribution to the field of green energy systems. 展开更多
关键词 Advanced predictive analytics green energy systems IPSS system CNN-transformer predictivemodel economic and stability optimization improved zebra algorithm
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Smart Grid Peak Shaving with Energy Storage:Integrated Load Forecasting and Cost-Benefit Optimization 认领 引用
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作者 Cong Zhang Chutong Zhang +4 位作者 Lei Shen Renwei Guo Wan Chen Hui Huang Jie Ji 《Energy Engineering》 EI 2025年第5期2077-2097,共21页
This paper presents a solution for energy storage system capacity configuration and renewable energy integration in smart grids using a multi-disciplinary optimization method.The solution involves a hybrid prediction ... This paper presents a solution for energy storage system capacity configuration and renewable energy integration in smart grids using a multi-disciplinary optimization method.The solution involves a hybrid prediction framework based on an improved grey regression neural network(IGRNN),which combines grey prediction,an improved BP neural network,and multiple linear regression with a dynamic weight allocation mechanism to enhance prediction accuracy.Additionally,an improved cuckoo search(ICS)algorithm is designed to empower the neural network model,incorporating a gamma distribution disturbance factor and adaptive inertia weight to balance global exploration and local exploitation,achieving a 40%faster convergence rate.A multi-objective snake optimization algorithm is also developed to optimize economic cost,grid stability,and energy utilization efficiency using energy storage capacity as the decision variable.The experimental results,based on a 937-day load dataset from a chemical park in Jiangsu Province,show that the IGRNN model has better prediction accuracy than traditional models,with an RMSE of 11.1361,an MAE of 8.264,and an R2 of 96.90%.The optimized energy storage system stabilizes the daily load curve at 800 kW,reduces the peak-valley difference by 62%,and decreases grid regulation pressure by 58.3%.This research provides theoretical and practical support for energy storage planning in high renewable energy proportion grids.Future work will focus on integrating weather data and dynamic optimization strategies under policy constraints to improve system applicability in real-world scenarios. 展开更多
关键词 Predictive models capacity allocation cost-benefit analysis multi-objective optimization
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