Multi-objective distributed generation hierarchical optimal planning in distribution network: Improved beluga whale optimization algorithm

Li, Ling-Ling; Fan, Xing-Da; Wu, Kuo-Jui; Sethanan, Kanchana; Tseng, Ming-Lang

doi:10.1016/j.eswa.2023.121406

Cited by 32 publications

(3 citation statements)

References 44 publications

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“…Ref. [36] proposed a highperformance improved beluga whale optimization algorithm to optimize the DG multiobjective hierarchical optimization planning model. The proposed method effectively solved the nonlinear problem of the model.…”

Section: Enhanced and Improved Beluga Whale Optimization Extreme Lear...mentioning

confidence: 99%

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High-Accuracy Photovoltaic Power Prediction under Varying Meteorological Conditions: Enhanced and Improved Beluga Whale Optimization Extreme Learning Machine

Du,

Peng,

et al. 2024

Energies

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Accurate photovoltaic (PV) power prediction plays a crucial role in promoting energy structure transformation and reducing greenhouse gas emissions. This study aims to improve the accuracy of PV power generation prediction. Extreme learning machine (ELM) was used as the core model, and enhanced and improved beluga whale optimization (EIBWO) was proposed to optimize the internal parameters of ELM, thereby improving its prediction accuracy for PV power generation. Firstly, this study introduced the chaotic mapping strategy, sine dynamic adaptive factor, and disturbance strategy to beluga whale optimization, and EIBWO was proposed with high convergence accuracy and strong optimization ability. It was verified through standard testing functions that EIBWO performed better than comparative algorithms. Secondly, EIBWO was used to optimize the internal parameters of ELM and establish a PV power prediction model based on enhanced and improved beluga whale optimization algorithm–optimization extreme learning machine (EIBWO-ELM). Finally, the measured data of the PV output were used for verification, and the results show that the PV power prediction results of EIBWO-ELM were more accurate regardless of whether it was cloudy or sunny. The R2 of EIBWO-ELM exceeded 0.99, highlighting its efficient ability to adapt to PV power generation. The prediction accuracy of EIBWO-ELM is better than that of comparative models. Compared with existing models, EIBWO-ELM significantly improves the predictive reliability and economic benefits of PV power generation. This study not only provides a technological foundation for the optimization of intelligent energy systems but also contributes to the sustainable development of clean energy.

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Section: Enhanced and Improved Beluga Whale Optimization Extreme Lear...mentioning

confidence: 99%

“…The relative error between the two at each sample point is represented by a data statistical histogram. The calculation equation for relative error δ is shown in Equation (36).…”

Section: Case 1-sunny Daysmentioning

confidence: 99%

High-Accuracy Photovoltaic Power Prediction under Varying Meteorological Conditions: Enhanced and Improved Beluga Whale Optimization Extreme Learning Machine

Du,

Peng,

et al. 2024

Energies

Self Cite

View full text Add to dashboard Cite

show abstract

“…Intelligent algorithms have been widely used in solving this problem in recent years due to their excellent performance. Li et al [25] adopted an improved beluga whale optimization algorithm to realize the location and position planning of distributed generation. In order to deal with the non-convex, non-linear dynamic economic emission dispatch problem, an enhanced moth-flame optimization algorithm was proposed to reduce pollutant emissions and improve the economics of system operation [26].…”

Section: Literature Reviewmentioning

confidence: 99%

Microgrid Operation Optimization Method Considering Power-to-Gas Equipment: An Improved Gazelle Optimization Algorithm

Wu,

Wen

et al. 2024

Symmetry

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With the increasingly prominent defects of traditional fossil energy, large-scale renewable energy access to power grids has become a trend. In this study, a microgrid operation optimization method, including power-to-gas equipment and a hybrid energy storage system, is proposed. Firstly, this study constructs a microgrid system structure including P2G equipment and a hybrid energy storage system of electricity and hydrogen. Secondly, aiming at minimizing the system operation cost and carbon emission penalty cost, a low-carbon economic operation optimization model of the proposed microgrid is established. Then, this study proposes a microgrid optimization method based on an improved gazelle optimization algorithm to symmetrically improve economic and environmental performance. Finally, the practicability and superiority of the above model and optimization method are verified using a real microgrid case. The results show that considering the participation of P2G equipment and a hybrid energy storage system in the optimal operation, the carbon emission of the microgrid is reduced to 33.56% of the microgrid considering only a single electric energy storage system, and the comprehensive operation cost is reduced by 6.54%. This study is conducive to promoting the development of clean energy and symmetrically enhancing the economic and environmental performance of the microgrid.

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Solving the cost minimization problem of optimal reactive power dispatch in a renewable energy integrated distribution system using rock hyraxes swarm optimization

Das,

Roy,

Mandal

2024

Electr Eng

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Multi-objective distributed generation hierarchical optimal planning in distribution network: Improved beluga whale optimization algorithm

Cited by 32 publications

References 44 publications

High-Accuracy Photovoltaic Power Prediction under Varying Meteorological Conditions: Enhanced and Improved Beluga Whale Optimization Extreme Learning Machine

High-Accuracy Photovoltaic Power Prediction under Varying Meteorological Conditions: Enhanced and Improved Beluga Whale Optimization Extreme Learning Machine

Microgrid Operation Optimization Method Considering Power-to-Gas Equipment: An Improved Gazelle Optimization Algorithm

Solving the cost minimization problem of optimal reactive power dispatch in a renewable energy integrated distribution system using rock hyraxes swarm optimization

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