Comparison of different optimization techniques applied to optimal operation of energy storage systems in standalone and grid-connected direct current microgrids

Montano, Jhon; Guzmán-Rodríguez, Juan Pablo; Palomeque, Jose Mena; González-Montoya, Daniel

doi:10.1016/j.est.2024.112708

Journal of Energy Storage

2024

DOI: 10.1016/j.est.2024.112708

|View full text |Cite

Comparison of different optimization techniques applied to optimal operation of energy storage systems in standalone and grid-connected direct current microgrids

Jhon Montano,

Juan Pablo Guzmán-Rodríguez,

Jose Mena Palomeque

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Techno-economic approach for energy management system: Multi-objective optimization algorithms for energy storage in standalone and grid-connected DC microgrids

Montano,

Guzmán,

Garzón

et al. 2024

Journal of Energy Storage

View full text Add to dashboard Cite

Techno-economic approach for energy management system: Multi-objective optimization algorithms for energy storage in standalone and grid-connected DC microgrids

Montano,

Guzmán,

Garzón

et al. 2024

Journal of Energy Storage

View full text Add to dashboard Cite

Machine Learning Models for Solar Power Generation Forecasting in Microgrid Application Implications for Smart Cities

Suanpang,

Jamjuntr

2024

Sustainability

View full text Add to dashboard Cite

In the context of escalating concerns about environmental sustainability in smart cities, solar power and other renewable energy sources have emerged as pivotal players in the global effort to curtail greenhouse gas emissions and combat climate change. The precise prediction of solar power generation holds a critical role in the seamless integration and effective management of renewable energy systems within microgrids. This research delves into a comparative analysis of two machine learning models, specifically the Light Gradient Boosting Machine (LGBM) and K Nearest Neighbors (KNN), with the objective of forecasting solar power generation in microgrid applications. The study meticulously evaluates these models’ accuracy, reliability, training times, and memory usage, providing detailed experimental insights into optimizing solar energy utilization and driving environmental sustainability forward. The comparison between the LGBM and KNN models reveals significant performance differences. The LGBM model demonstrates superior accuracy with an R-squared of 0.84 compared to KNN’s 0.77, along with lower Root Mean Squared Error (RMSE: 5.77 vs. 6.93) and Mean Absolute Error (MAE: 3.93 vs. 4.34). However, the LGBM model requires longer training times (120 s vs. 90 s) and higher memory usage (500 MB vs. 300 MB). Despite these computational differences, the LGBM model exhibits stability across diverse time frames and seasons, showing robustness in handling outliers. These findings underscore its suitability for microgrid applications, offering enhanced energy management strategies crucial for advancing environmental sustainability. This research provides essential insights into sustainable practices and lays the foundation for a cleaner energy future, emphasizing the importance of accurate solar power forecasting in microgrid planning and operation.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Comparison of different optimization techniques applied to optimal operation of energy storage systems in standalone and grid-connected direct current microgrids

Cited by 2 publications

References 54 publications

Techno-economic approach for energy management system: Multi-objective optimization algorithms for energy storage in standalone and grid-connected DC microgrids

Techno-economic approach for energy management system: Multi-objective optimization algorithms for energy storage in standalone and grid-connected DC microgrids

Machine Learning Models for Solar Power Generation Forecasting in Microgrid Application Implications for Smart Cities

Contact Info

Product

Resources

About