2022 34th Chinese Control and Decision Conference (CCDC), Journal Year: 2024, Volume and Issue: unknown, P. 2940 - 2945
Published: May 25, 2024
Language: Английский
Energy, Journal Year: 2024, Volume and Issue: 300, P. 131513 - 131513
Published: May 9, 2024
Language: Английский
Citations
5
World Electric Vehicle Journal, Journal Year: 2024, Volume and Issue: 15(9), P. 398 - 398
Published: Sept. 2, 2024
With the increasing share of renewable energy generation and integration large-scale electric vehicles (EVs) into grid, reasonable charging discharging scheduling is essential for stable operation power grid. Therefore, this paper proposes a bi-objective optimal strategy microgrids based on participation in vehicle-to-grid technology (V2G) mode. Firstly, system structure participating schedule was established. Secondly, optimization model formulated, considering load mean square error user cost. A heuristic method employed to handle constraints related balance equipment output. Then, Monte Carlo simulate vehicle loads facilitate reduction scenario scenes. Finally, solved using an improved multi-objective barebones particle swarm algorithm. The simulation results show that proposed has lower cost (CNY 11,032.4) (12.84 × 105 kW2) than comparison experiment, which ensures economic microgrid.
Language: Английский
Citations
3
Energy Conversion and Economics, Journal Year: 2025, Volume and Issue: unknown
Published: April 15, 2025
Abstract Multiple energy storage systems (ESSs) often face imbalances in charging–discharging operations, as well the uncertainties of practical scenarios and influencing factors. To address these challenges, this study proposes a user‐side cloud (CES) model with active participation operator. This CES incorporates adjustable time‐of‐use (TOU) electricity pricing state‐of‐charge (SOC) management. In configuration process, net load scenario generation reduction is performed first. Subsequently, demand response implemented based on updated TOU pricing. imbalance ESSs, an improved multiobjective particle swarm optimization employed, followed by access verification multi‐ESS aggregation. dispatch two‐stage interval adopted. Specifically, day‐ahead scheduling determines SOC limit interval, intra‐day achieves rolling to determine exact duration. ensures that cycles are controllable, orderly, efficient. Ultimately, fair settlement method optimal various fees within “cloud” proposed, ensuring sustainable revenue growth for all types users. A case demonstrates proposed methods can achieve multifaceted value management enhance socioeconomics ESS projects.
Language: Английский
Citations
0
PLoS ONE, Journal Year: 2025, Volume and Issue: 20(4), P. e0320486 - e0320486
Published: April 17, 2025
The research on the value evaluation system of power-to-hydrogen (P2H) equipment configuration in integrated energy systems is great for optimizing resource allocation, improving utilization efficiency, and promoting clean technology development. However, there no comprehensive evaluating P2H systems. Therefore, a multi-dimensional proposed to realize thorough with different capacity configurations system. Initially, mathematical model considering flexibility benefit, new consumption economic environmental benefit established maximize benefits brought by system, solved using an improved backbone particle swarm optimization (IBBPSO) algorithm; subsequently, based analytic hierarchy process (AHP) -entropy weight method constructed, compared analyzed when optimal. experimental results show that IBBPSO algorithm exhibits better performance solving model. Compared PSO, IBBPSO, GWO, WOA algorithms, it improves 9.8%, 11.09%, 33.57%, 17.7%, respectively. optimal solution achieved configured 50 MW.
Language: Английский
Citations
0
Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 251, P. 123983 - 123983
Published: April 16, 2024
Language: Английский
Citations
2
Electronics, Journal Year: 2024, Volume and Issue: 13(19), P. 3841 - 3841
Published: Sept. 28, 2024
In the digital age, personalized recommendation systems have become crucial for information dissemination and user experience. While traditional focus on accuracy, they often overlook diversity, novelty, serendipity. This study introduces an innovative system model, Time-based Outlier Aware Recommender (TOAR), designed to address challenges of content homogenization bubbles in recommendations. TOAR integrates Neural Matrix Factorization (NeuMF), Bidirectional Long Short-Term Memory Networks (Bi-LSTM), Mean Shift clustering enhance diversity. The model analyzes temporal dynamics behavior facilitates cross-domain knowledge exchange through feature sharing transfer learning mechanisms. By incorporating attention mechanism unsupervised clustering, effectively captures important time-series ensures Experimental results a news dataset demonstrate TOAR’s superior performance across multiple metrics, including AUC, precision, NDCG, compared deep learning-based models. research provides foundation developing more intelligent services that balance accuracy with
Language: Английский
Citations
1
Intelligent Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 200452 - 200452
Published: Oct. 1, 2024
Language: Английский
Citations
1
2022 34th Chinese Control and Decision Conference (CCDC), Journal Year: 2024, Volume and Issue: unknown, P. 2940 - 2945
Published: May 25, 2024
Language: Английский
Citations
0