An Intelligent Two-Stage Energy Dispatch Management System for Hybrid Power Plants: Impact of Machine Learning Deployment

Shibl, Mostafa M.; Ismail, Loay; Massoud, Ahmed M.

doi:10.1109/access.2023.3243097

Cited by 11 publications

(1 citation statement)

References 23 publications

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“…Agrawal et al [126] developed a two-tier optimization approach to improve the operational efficiency of HRESs, while Hashemi and Zarif [127] introduced a two-phase method to manage reactive power in power systems. Shibl et al [128] introduced a dual-phase energy dispatch management framework for HPPs, integrating machine learning techniques.…”

Section: Control Strategiesmentioning

confidence: 99%

Current Status, Sizing Methodologies, Optimization Techniques, and Energy Management and Control Strategies for Co-Located Utility-Scale Wind–Solar-Based Hybrid Power Plants: A Review

Bade,

Meenakshisundaram,

Tomomewo

2024

Eng

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The integration of renewable energy sources, such as wind and solar, into co-located hybrid power plants (HPPs) has gained significant attention as an innovative solution to address the intermittency and variability inherent in renewable systems among plant developers because of advancements in technology, economies of scale, and government policies. However, it is essential to examine different challenges and aspects during the development of a major work on large-scale hybrid plants. This includes the need for optimization, sizing, energy management, and a control strategy. Hence, this research offers a thorough examination of the present state of co-located utility-scale wind–solar-based HPPs, with a specific emphasis on the problems related to their sizing, optimization, and energy management and control strategies. The authors developed a review approach that includes compiling a database of articles, formulating inclusion and exclusion criteria, and conducting comprehensive analyses. This review highlights the limited number of peer-reviewed studies on utility-scale HPPs, indicating the need for further research, particularly in comparative studies. The integration of machine learning, artificial intelligence, and advanced optimization algorithms for real-time decision-making is highlighted as a potential avenue for addressing complex energy management challenges. The insights provided in this manuscript will be valuable for researchers aiming to further explore HPPs, contributing to the development of a cleaner, economically viable, efficient, and reliable power system.

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Section: Control Strategiesmentioning

confidence: 99%