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Forecasting and Power System Scheduling Based on Uncertainty Modeling and Optimization Strategies

Published: 31 July 2024 Publication History

Abstract

This study proposes an innovative framework for wind energy forecasting and power system scheduling. Leveraging advanced statistical and machine learning models, coupled with uncertainty modeling using Monte Carlo simulations, the approach aims to enhance accuracy in wind energy predictions. Optimization strategies, encompassing multi-objective decision-making and real-time scheduling policies, are introduced to address variability and uncertainty in power systems. Diversified wind farm layouts and energy storage integration further mitigate uncertainty. The paper emphasizes the significance of a robust monitoring system with feedback mechanisms. Case studies illustrate the framework's practical application, demonstrating its efficacy in optimizing power systems amidst uncertain wind conditions.

References

[1]
Fan H, Wang C, Liu L, Review of uncertainty modeling for optimal operation of integrated energy system [J]. Frontiers in energy research, 2022, 9: 641337.
[2]
Oh E, Wang H. Reinforcement-learning-based energy storage system operation strategies to manage wind power forecast uncertainty [J]. IEEE Access, 2020, 8: 20965-20976.
[3]
Shaw W J, Berg L K, Cline J, The second wind forecast improvement project (wfip2): general overview [J]. Bulletin of the American Meteorological Society, 2019, 100(9): 1687-1699.
[4]
Yang B, Zhong L, Wang J, State-of-the-art one-stop handbook on wind forecasting technologies: An overview of classifications, methodologies, and analysis [J]. Journal of Cleaner Production, 2021, 283: 124628.
[5]
Wu Z, Luo G, Yang Z, A comprehensive review on deep learning approaches in wind forecasting applications [J]. CAAI Transactions on Intelligence Technology, 2022, 7(2): 129-143.
[6]
Deng X, Lv T. Power system planning with increasing variable renewable energy: A review of optimization models [J]. Journal of Cleaner Production, 2020, 246: 118962.
[7]
Liu B, Wang Y. Energy system optimization under uncertainties: A comprehensive review [J]. Towards sustainable chemical processes, 2020: 149-170.
[8]
Hong Y Y, Apolinario G F D G. Uncertainty in unit commitment in power systems: A review of models, methods, and applications [J]. Energies, 2021, 14(20): 6658.
[9]
Sanjari M J, Karami H. Optimal control strategy of battery-integrated energy system considering load demand uncertainty [J]. Energy, 2020, 210: 118525.
[10]
Kaffash M, Ceusters G, Deconinck G. Interval optimization to schedule a multi-energy system with data-driven PV uncertainty representation [J]. Energies, 2021, 14(10): 2739.
[11]
Moradmand A, Dorostian M, Shafai B. Energy scheduling for residential distributed energy resources with uncertainties using model-based predictive control [J]. International Journal of Electrical Power & Energy Systems, 2021, 132: 107074.

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  1. Forecasting and Power System Scheduling Based on Uncertainty Modeling and Optimization Strategies

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      PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
      January 2024
      969 pages
      ISBN:9798400716638
      DOI:10.1145/3674225
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      New York, NY, United States

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      Published: 31 July 2024

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