An Enhanced Multi-Objective Optimizer for Stochastic Generation Optimization in Islanded Renewable Energy Microgrids

Lakhina, Upasana; Badruddin, Nasreen; Elamvazuthi, Irraivan; Jangra, Ajay; Huy, Truong Hoang Bao; Guerrero, Josep M.

doi:10.3390/math11092079

Cited by 6 publications

(2 citation statements)

References 63 publications

(51 reference statements)

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“…Multiobjective Optimization: Multiobjective optimization is used in MGs to optimize multiple conflicting objectives simultaneously. The optimization problem is formulated as a multiobjective function, and the solution space is searched to identify the optimal trade-off between the objectives [113][114][115].…”

Section: Multiobjective Optimizationmentioning

confidence: 99%

Digital Transformation of Microgrids: A Review of Design, Operation, Optimization, and Cybersecurity

Irmak

Kabalcı

2023

Energies

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This paper provides a comprehensive review of the future digitalization of microgrids to meet the increasing energy demand. It begins with an overview of the background of microgrids, including their components and configurations, control and management strategies, and optimization techniques. It then discusses the key digital technologies that can be used to improve the performance of microgrids, including distributed energy resources management systems, the Internet of Things, big data analytics, blockchain technology, artificial intelligence, digital twin technology, cloud computing, and augmented reality. The paper also highlights the importance of cybersecurity in microgrids, identifying the potential security vulnerabilities and threats to microgrid cybersecurity, as well as strategies for addressing these challenges. Finally, the paper discusses the barriers and challenges regarding the digitalization of microgrids, including technical complexity, high implementation costs, regulatory barriers, data privacy and security concerns, lack of standardization, interoperability issues, limited technical expertise, and integration with the main grid. Overall, this paper demonstrates the significant potential for digital technologies to transform the future of microgrids. By leveraging advanced technologies and implementing effective cybersecurity measures, microgrids can become more efficient, reliable, and resilient, enabling them to meet the growing demand for energy and contribute to a sustainable energy future.

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Section: Multiobjective Optimizationmentioning

confidence: 99%

Digital Transformation of Microgrids: A Review of Design, Operation, Optimization, and Cybersecurity

Irmak

Kabalcı

2023

Energies

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“…In recent years, complex data analysis has become an essential research field due to the increasing and diversified data generated by various sources [1], such as traffic logistics, commercial transactions, financial investment, and healthcare services [2,3]. One of the most significant and prevalent challenges in analyzing complex data include the multiobjective optimization problems (MOPs) [4], as optimizing one objective often leads to the deterioration of others. Traditional optimization methods, such as gradient-based methods, can only optimize one objective at a time, which may not be able to address MOPs effectively.…”

Section: Introductionmentioning

confidence: 99%

A Synergistic MOEA Algorithm with GANs for Complex Data Analysis

Qian,

Xu,

Chen

et al. 2024

Mathematics

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The multi-objective evolutionary algorithm optimization (MOEA) is a challenging but critical approach for tackling complex data analysis problems. However, prevailing MOEAs often rely on single strategies to obtain optimal solutions, leading to concerns such as premature convergence and insufficient population diversity, particularly in high-dimensional data scenarios. In this paper, we propose a novel adversarial population generation algorithm, APG-SMOEA, which synergistically combines the benefits of MOEAs and Generative Adversarial Networks (GANs) to address these limitations. In order to balance the efficiency and quality of offspring selection, we introduce an adaptive population entropy strategy, which includes control parameters based on population entropy and a learning pool for storing and retrieving optimal solutions. Additionally, we attempt to alleviate the training complexity and model collapse problems common in GANs with APG-SMOEA. Experimental results on benchmarks demonstrate that the proposed algorithm is superior to the existing algorithms in terms of solution quality and diversity of low-dimensional or high-dimensional complex data.

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An Energy Management System for Multi-Microgrid system considering uncertainties using multi-objective multi-verse optimization

Aeggegn,

Nyakoe,

Wekesa

2025

Energy Reports

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An Enhanced Multi-Objective Optimizer for Stochastic Generation Optimization in Islanded Renewable Energy Microgrids

Cited by 6 publications

References 63 publications

Digital Transformation of Microgrids: A Review of Design, Operation, Optimization, and Cybersecurity

Digital Transformation of Microgrids: A Review of Design, Operation, Optimization, and Cybersecurity

A Synergistic MOEA Algorithm with GANs for Complex Data Analysis

An Energy Management System for Multi-Microgrid system considering uncertainties using multi-objective multi-verse optimization

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