Applying the modified TLBO algorithm to solve the unit commitment problem

Khazaei, Peyman; Dabbaghjamanesh, Morteza; Kalantarzadeh, Ali; Mousavi, Hasan

doi:10.1109/wac.2016.7583026

Cited by 16 publications

(4 citation statements)

References 24 publications

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“…There are a number of research work on operation optimization ordinary power plants regarding optimization of unit commitment as can be found in (Zheng, Wang and Liu, 2015;Khazaei et al, 2016). Also, researches considering unit commitment optimization of a power system including renewable sources are present in the literature as in (Lorca and Sun, 2017;Sun et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Experimental and Numerical Research of Micro Wind Turbine for Low Wind Speeds

Fike¹,

Pezdevšek²,

Predin³

et al. 2019

7th Symposium on Applied Electromagnetics SAEM`18: Conference Proceedings

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The International Symposium on Applied Electromagnetics is aimed at the presentation of research work results in various areas of Applied Electromagnetics. The Symposium is devoted to exchanging ideas, presenting achievements and results, providing the forum for researchers and academics. Open and friendly atmosphere, which our conferences are known for, encourages especially young researchers to present their achievements and ideas, even at an early stage of their research work. The Symposium continues the tradition of joint conferences, organised in previous years by the organisers from Macedonia, Poland, Slovenia and Hungary. The symposium is open for participants from all over the world. This year's symposium took place between 17th and 20th June 2018 in Podčetrtek, Slovenia.We would like to thank all the participants for their participation at the International Symposium on Applied Electromagnetics and wish them a successful academic career.

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Section: Discussionmentioning

confidence: 99%

Experimental and Numerical Research of Micro Wind Turbine for Low Wind Speeds

Fike¹,

Pezdevšek²,

Predin³

et al. 2019

7th Symposium on Applied Electromagnetics SAEM`18: Conference Proceedings

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show abstract

“…where, the definition of the parameters in (4) and (5) are similar to those of (2) electronic transformers to improve the efficiency and solution range of the modulation index [25]- [39]. In addition, for distributed generations (DGs) that generate DC voltages, a DC/AC converter is required to connect to grid or loads.…”

Section: Applying the Proposed Methods In Single-phase Structuresmentioning

confidence: 99%

Non-equal DC link Voltages in a Cascaded H-Bridge with a Selective Harmonic Mitigation-PWM Technique Based on the Fundamental Switching Frequency

Moeini¹,

Iman‐Eini²,

Najjar³

2017

Journal of Power Electronics

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In this paper, the Selective Harmonic Mitigation-PWM (SHM-PWM) method is used in single-phase and three-phase Cascaded H-Bridge (CHB) inverters in order to fulfill different power quality standards such as EN 50160, CIGRE WG 36-05, IEC 61000-3-6 and IEC 61000-2-12. Non-equal DC link voltages are used to increase the degrees of freedom for the proposed SHM-PWM technique. In addition, it will be shown that the obtained solutions become continuous and without sudden changes. As a result, the look-up tables can be significantly reduced. The proposed three-phase modulation method can mitigate up to the 50th harmonic from the output voltage, while each switch has just one switching in a fundamental period. In other words, the switching frequency of the power switches are limited to 50 Hz, which is the lowest switching frequency that can be achieved in the multilevel converters, when the optimal selective harmonic mitigation method is employed. In single-phase mode, the proposed method can successfully mitigate harmonics up to the 50th, where the switching frequency is 150 Hz. Finally, the validity of the proposed method is verified by simulations and experiments on a 9-level CHB inverter.

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“…Smart cities are aimed to optimize communication and energy management systems to enhance sustainability, economic growth, and quality of life of urban residents [21,22]. This literature review aims to evaluate the proposed hybrid machine learning and the modified teaching learning-based optimization English algorithm for smart city communication and energy management through a case study of the test system [23,24]. The algorithm combines machine learning techniques and the teaching learning-based optimization algorithm, which optimizes the energy consumption and communication systems by adjusting their parameters [25].…”

Section: Literature Analysismentioning

confidence: 99%

“…Several studies have shown the effectiveness of machine learning algorithms and optimization algorithms in addressing smart city challenges. For instance, references [23,24] proposed a deep learning-based energy forecasting model to predict short-term load forecasting for buildings in smart cities. The proposed model achieved a high level of accuracy in predicting energy consumption, demonstrating the effectiveness of deep learning algorithms in addressing smart city energy management challenges.…”

Section: Literature Analysismentioning

confidence: 99%

Hybrid Machine Learning and Modified Teaching Learning-Based English Optimization Algorithm for Smart City Communication

2023

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This paper introduces a hybrid algorithm that combines machine learning and modified teaching learning-based optimization (TLBO) for enhancing smart city communication and energy management. The primary objective is to optimize the modified systems, which face challenges due to their high population density. The proposed algorithm integrates the strengths of machine learning techniques, more specifically, the long short-term memory (LSTM) technique, with teaching learning-based optimization algorithms. To achieve optimization, the algorithm learns from historical data on energy consumption and communication patterns specific to the modeled system. By leveraging these insights, it can predict future energy consumption and communication patterns accurately. Additionally, the algorithm incorporates a modified teaching learning-based optimization approach inspired by the teaching and learning process in classrooms. It adjusts the system parameters based on feedback received from the system, thereby optimizing both energy consumption and communication systems. The effectiveness of the proposed algorithm is evaluated through a case study conducted on the test system, where historical data on energy consumption and communication patterns are analyzed. The results demonstrate that the algorithm efficiently optimizes the communication and energy management systems, leading to substantial energy savings and improved communication efficiency within the test system. In conclusion, this study presents a hybrid machine learning and modified teaching learning-based optimization algorithm that effectively addresses the communication and energy management challenges in the test system. Moreover, this algorithm holds the potential for application in various smart city domains beyond the test system. The findings of this research contribute to the advancement of smart city technologies and offer valuable insights into reducing energy consumption in densely populated urban areas.

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Applying the modified TLBO algorithm to solve the unit commitment problem

Cited by 16 publications

References 24 publications

Experimental and Numerical Research of Micro Wind Turbine for Low Wind Speeds

Experimental and Numerical Research of Micro Wind Turbine for Low Wind Speeds

Non-equal DC link Voltages in a Cascaded H-Bridge with a Selective Harmonic Mitigation-PWM Technique Based on the Fundamental Switching Frequency

Hybrid Machine Learning and Modified Teaching Learning-Based English Optimization Algorithm for Smart City Communication

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