Multistage robust optimization for the day-ahead scheduling of hybrid thermal-hydro-wind-solar systems

Zhong, Zhiming; Fan, Neng; Wu, Lei

doi:10.1007/s10898-023-01328-2

J Glob Optim

2023

DOI: 10.1007/s10898-023-01328-2

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Multistage robust optimization for the day-ahead scheduling of hybrid thermal-hydro-wind-solar systems

Zhiming Zhong,

Neng Fan,

Lei Wu

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2024

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Cited by 3 publications

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Multistage Stochastic optimization for mid-term integrated generation and maintenance scheduling of cascaded hydroelectric system with renewable energy uncertainty

Zhong,

Fan,

2024

European Journal of Operational Research

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Multistage Stochastic optimization for mid-term integrated generation and maintenance scheduling of cascaded hydroelectric system with renewable energy uncertainty

Zhong,

Fan,

2024

European Journal of Operational Research

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A multi-time scale grid scheduling optimization method considering demand-side resources

Jiang,

Wang,

et al. 2024

J. Phys.: Conf. Ser.

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This research introduces a pioneering strategy for optimizing power grid scheduling across various time scales, addressing a previously unmet need in the field of energy regulation. Initially, we amassed and categorized tunable resources on the demand side into three distinct temporal categories—long-term, short-term, and immediate—reflecting their respective response attributes. This taxonomy informed the development of a cohesive scheduling framework, encompassing pre-emptive, daily, and instantaneous sub-models, designed to facilitate precise control over grid operations. The deployment of this framework has led to marked enhancements in both the efficiency and dependability of power grid management. The merits of this methodology were exemplified through its application to the IEEE33 network model, showcasing its proficiency in the acquisition of optimized scheduling parameters. This multi-tiered modeling and optimization technique offers an innovative pathway for the dynamic and effective management of smart grids.

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Model for sustainable carbon emission reduction energy development and smart grid technology strategy

Xiang,

Chen,

Chen

et al. 2024

AIMSE

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<p>In the context of sustainable energy development to reduce carbon emissions, the application of new energy sources and smart grid technologies in power systems is becoming more widespread. However, current research results on power system technology strategies for carbon emission reduction are not satisfactory. To address this problem, a model for optimal power system operation and scheduling based on the prediction error mechanism and synthetic fuel technology is proposed. The model used the carbon trading mechanism to further reduce carbon emissions and the carnivorous plant algorithm to optimize the scheduling strategy. The results indicate that the model demonstrates significant advantages in terms of carbon emission, total operating cost, prediction accuracy, and energy utilization efficiency, respectively, at 60.8 kg, 2517.5 yuan, 96.5%, and 90.2%, indicating that it utilizes energy more fully and helps to enhance the overall energy efficiency of the system. The calculation time of the optimized power system was only 12.5 s, the stability was as high as 98.7%, and the satisfaction rate was 95.6% in terms of user satisfaction. Compared to other contemporary designs, the proposed model can successfully reduce the system's carbon emissions while increasing energy efficiency. The model has positive implications for smart grid and sustainable development.</p>

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Multistage robust optimization for the day-ahead scheduling of hybrid thermal-hydro-wind-solar systems

Cited by 3 publications

References 47 publications

Multistage Stochastic optimization for mid-term integrated generation and maintenance scheduling of cascaded hydroelectric system with renewable energy uncertainty

Multistage Stochastic optimization for mid-term integrated generation and maintenance scheduling of cascaded hydroelectric system with renewable energy uncertainty

A multi-time scale grid scheduling optimization method considering demand-side resources

Model for sustainable carbon emission reduction energy development and smart grid technology strategy

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