A survey on multi-criterion decision parameters, integration layout, storage technologies, sizing methodologies and control strategies for integrated renewable energy system

Mishra, Shubhangi; Saini, Gaurav; Saha, Saikat; Chauhan, Anurag; Kumar, Anuj; Maity, Santanu

doi:10.1016/j.seta.2022.102246

Cited by 19 publications

(8 citation statements)

References 96 publications

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“…As the use of RE becomes more prevalent, AI will play an increasingly significant role in ensuring that these systems operate efficiently and reliably. 138 However, several challenges must be overcome before these models can be widely adopted in RE, and the most important of these is the lack of high-quality data. AI algorithms require large amounts of data to train the model and allow it to make precise predictions, but RE systems frequently generate sparse and irregular data, making it challenging to train AI models.…”

Section: Benefits and Challenges Of Ai Application In Renewablementioning

confidence: 99%

“…These algorithms can help to reduce the cost of RE systems, while increasing their reliability and cost-effectiveness. As the use of RE becomes more prevalent, AI will play an increasingly significant role in ensuring that these systems operate efficiently and reliably . However, several challenges must be overcome before these models can be widely adopted in RE, and the most important of these is the lack of high-quality data.…”

Section: Introduction To Ai and Its Applications In Renewable Energymentioning

confidence: 99%

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Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects

Nguyen,

Tarełko,

Sharma

et al. 2024

Energy Fuels

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Modern machine learning (ML) techniques are making inroads in every aspect of renewable energy for optimization and model prediction. The effective utilization of ML techniques for the development and scaling up of renewable energy systems needs a high degree of accountability. However, most of the ML approaches currently in use are termed black box since their work is difficult to comprehend. Explainable artificial intelligence (XAI) is an attractive option to solve the issue of poor interoperability in black-box methods. This review investigates the relationship between renewable energy (RE) and XAI. It emphasizes the potential advantages of XAI in improving the performance and efficacy of RE systems. It is realized that although the integration of XAI with RE has enormous potential to alter how energy is produced and consumed, possible hazards and barriers remain to be overcome, particularly concerning transparency, accountability, and fairness. Thus, extensive research is required to address the societal and ethical implications of using XAI in RE and to create standardized data sets and evaluation metrics. In summary, this paper shows the potential, perspectives, opportunities, and challenges of XAI application to RE system management and operation aiming to target the efficient energy-use goals for a more sustainable and trustworthy future.

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Section: Benefits and Challenges Of Ai Application In Renewablementioning

confidence: 99%

Section: Introduction To Ai and Its Applications In Renewable Energymentioning

confidence: 99%

Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects

Nguyen,

Tarełko,

Sharma

et al. 2024

Energy Fuels

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“…During excess generation, solar power is used to pump water to higher elevation storage, and when the demand surges or sunlight is scarce, the water is released to generate hydroelectricity, as [26] describes. Design optimization of this hybrid system thus demands an intricate approach of impedance matching [32] [33] with computational models [23] [5] assessing the performance across diverse scenarios [21][7] to determine an optimal set-up for harmonizing energy generation and consumption [12] [27]. It goes in line with the research by [28], emphasizing the need to methodically analyze solar array sizing, pump and turbine capacities, and reservoir volume.…”

Section: Literature Reviewmentioning

confidence: 99%

Design Optimization of a Hybrid Solar PV Panel and Pumped Hydro Energy Supply System

Abdul-Azeez Momodu and Ameze Big-Alabo

2024

JCMPS

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Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.

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“…It connects rechargeable batteries with electrolytic capacitors. It can take and distribute charges considerably quicker than batteries and withstand many more charge and discharge cycles than rechargeable batteries [44]. It stores 10 to 100 times more energy per unit mass volume than electrolytic capacitors.…”

Section: Supercapacitor and Ultra-super Capacitormentioning

confidence: 99%

Multi-Objective Optimization Algorithms for a Hybrid AC/DC Microgrid Using RES: A Comprehensive Review

et al. 2023

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Optimization methods for a hybrid microgrid system that integrated renewable energy sources (RES) and supplies reliable power to remote areas, were considered in order to overcome the intermittent nature of RESs. The hybrid AC/DC microgrid system was constructed with a solar photovoltaic system, wind turbine, battery storage, converter, and diesel generator. There is a steady increase in the utilization of hybrid renewable energy sources with hybrid AC/DC microgrids; consequently, it is necessary to solve optimization techniques. Therefore, the present study proposed utilizing multi-objective optimization methods using evolutionary algorithms. In this context, a few papers were reviewed regarding multi-objective optimization to determine the capacity and optimal design of a hybrid AC/DC microgrid with RESs. Here, the optimal system consisted of the minimum cost of energy, minimum net present cost, low operating cost, low carbon emissions and a high renewable fraction. These were determined by using multi-objective optimization (MOO) algorithms. The sizing optimization of the hybrid AC/DC microgrid was based on the multi-objective grey wolf optimizer (MOGWO) and multi-objective particle swarm optimization (MOPSO). Similarly, multi-objective optimization with different evolutionary algorithms (MOGA, MOGOA etc.) reduces energy cost and net present cost, and increases the reliability of islanded hybrid microgrid systems.

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A survey on multi-criterion decision parameters, integration layout, storage technologies, sizing methodologies and control strategies for integrated renewable energy system

Cited by 19 publications

References 96 publications

Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects

Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects

Design Optimization of a Hybrid Solar PV Panel and Pumped Hydro Energy Supply System

Multi-Objective Optimization Algorithms for a Hybrid AC/DC Microgrid Using RES: A Comprehensive Review

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