Overview and applications of Robust optimization in the avant-garde energy grid infrastructure: A systematic review

Rahim, Sahar; Wang, Zhen; Ju, Ping

doi:10.1016/j.apenergy.2022.119140

Cited by 23 publications

(5 citation statements)

References 92 publications

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“…As a result, due to the change of one of the existing data, many of the applied restrictions may be violated and lead to non-optimization of the obtained answer [ 32 ]. As a result, the main goal is to provide a method that is resistant to this data uncertainty and provides an optimal response [ 33 ]. In recent years, robust optimization has been widely studied from the view of uncertainty management [ 34 ].…”

Section: Concepts Under Discussionmentioning

confidence: 99%

Optimal demand response aggregation in wholesale electricity markets: Comparative analysis of polyhedral; ellipsoidal and box methods for modeling uncertainties

Nojavan,

Hagh,

Taghizad-Tavana

et al. 2024

Heliyon

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Section: Concepts Under Discussionmentioning

confidence: 99%

Optimal demand response aggregation in wholesale electricity markets: Comparative analysis of polyhedral; ellipsoidal and box methods for modeling uncertainties

Nojavan,

Hagh,

Taghizad-Tavana

et al. 2024

Heliyon

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“…Security risks are formulated by chance constraints. Compared with the multi-scenario analysis 23 and robust optimization method 24 , chance-constrained programming can probabilistically characterise the uncertainties in FDNs, and allow a specified degree of constraint violations 25 , thus enabling a trade-off between investment costs and operational security. Next, uncertainty quantification is performed to model the random variables as inputs involved in the chance constraints 26 .…”

Section: A Probabilistic Framework For Fdn Planningmentioning

confidence: 99%

Multi-resource dynamic coordinated planning of flexible distribution network

Wang,

Ji,

et al. 2024

Nat Commun

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The flexible distribution network presents a promising architecture to accommodate highly integrated distributed generators and increasing loads in an efficient and cost-effective way. The distribution network is characterised by flexible interconnections and expansions based on soft open points, which enables it to dispatch power flow over the entire system with enhanced controllability and compatibility. Herein, we propose a multi-resource dynamic coordinated planning method of flexible distribution network that allows allocation strategies to be determined over a long-term planning period. Additionally, we establish a probabilistic framework to address source-load uncertainties, which mitigates the security risks of voltage violations and line overloads. A practical distribution network is adopted for flexible upgrading based on soft open points, and its cost benefits are evaluated and compared with that of traditional planning approaches. By adjusting the acceptable violation probability in chance constraints, a trade-off between investment efficiency and operational security can be realised.

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“…In Tunisia, the predominant electrical energy source is crude oil, except for certain countries relying on renewable sources [4]. Investing in renewables, specifically solar power, reduces reliance on fossil fuels, like oil, and enhances the country's energy independence [5][6][7][8]. Furthermore, embracing renewable energies, particularly solar power, contributes to mitigating CO2 emissions, offering both economic and environmental advantages [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Hybrid PVP/Battery/Fuel Cell Wireless Charging Station Using High-Frequency Optimized Inverter Technology for Electric Vehicles

Baccouche,

Chehab,

Ben Salah

et al. 2024

Preprint

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The design and integration of intelligent energy management systems in hybrid Electric Vehicle (EV) charging stations, leveraging Industry 4.0 and renewable energy sources, is crucial for advancing sustainability, efficiency, and technological development. This paper introduces a pioneering hybrid EV charging station that employs hybrid power sources, combining photovoltaic panels, a battery, and a fuel cell, each operating at 240V and 14A. Utilizing wireless power transfer technology, the system comprises five integral blocks: a power source, a boost converter, a High-Frequency (HF) inverter, transfer coils, and EV batteries. The optimization process involves two key stages: i) maximum power point tracking optimization of the boost converter, ensuring maximum power generation by varying the duty cycle between 10% and 90%, and ii) the HF phase employing a class-ϕ2 inverter at 30 MHz, synchronized with the resonant frequency of wireless power transfer coils. Control is executed through a digital signal processor card utilizing zero-voltage switching for efficient operations. This design enhances sustainability in EV charging solutions by optimizing power transfer through the use of hybrid sources.

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Overview and applications of Robust optimization in the avant-garde energy grid infrastructure: A systematic review

Cited by 23 publications

References 92 publications

Optimal demand response aggregation in wholesale electricity markets: Comparative analysis of polyhedral; ellipsoidal and box methods for modeling uncertainties

Optimal demand response aggregation in wholesale electricity markets: Comparative analysis of polyhedral; ellipsoidal and box methods for modeling uncertainties

Multi-resource dynamic coordinated planning of flexible distribution network

Hybrid PVP/Battery/Fuel Cell Wireless Charging Station Using High-Frequency Optimized Inverter Technology for Electric Vehicles

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