Sizing PV and BESS for Grid-Connected Microgrid Resilience: A Data-Driven Hybrid Optimization Approach
This article presents a comprehensive data-driven approach on enhancing grid-connected microgrid grid resilience through advanced forecasting and optimization techniques in the context of power outages. Power outages pose significant challenges to modern societies, affecting various sectors such as...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/21/7300 |
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| author | Mahtab Murshed Manohar Chamana Konrad Erich Kork Schmitt Suhas Pol Olatunji Adeyanju Stephen Bayne |
| author_facet | Mahtab Murshed Manohar Chamana Konrad Erich Kork Schmitt Suhas Pol Olatunji Adeyanju Stephen Bayne |
| author_sort | Mahtab Murshed |
| collection | DOAJ |
| description | This article presents a comprehensive data-driven approach on enhancing grid-connected microgrid grid resilience through advanced forecasting and optimization techniques in the context of power outages. Power outages pose significant challenges to modern societies, affecting various sectors such as industries, households, and critical infrastructures. The research combines statistical analysis, machine-learning algorithms, and optimization methods to address this issue to develop a holistic approach for predicting and mitigating power outage events. The proposed methodology involves the use of Monte Carlo simulations in MATLAB for future outage prediction, training a Long Short-Term Memory (LSTM) network for forecasting solar irradiance and load profiles with a dataset spanning from 2009 to 2018, and a hybrid LSTM-Particle Swarm Optimization (PSO) model to improve accuracy. Furthermore, the role of battery state of charge (SoC) in enhancing system resilience is explored. The study also assesses the techno-economic advantages of a grid-tied microgrid integrated with solar panels and batteries over conventional grid systems. The proposed methodology and optimization process demonstrate their versatility and applicability to a wide range of microgrid design scenarios comprising solar PV and battery energy storage systems (BESS), making them a valuable resource for enhancing grid resilience and economic efficiency across diverse settings. The results highlight the potential of the proposed approach in strengthening grid resilience by improving autonomy, reducing downtime by 25%, and fostering sustainable energy utilization by 82%. |
| first_indexed | 2024-03-11T11:30:46Z |
| format | Article |
| id | doaj.art-74e520f292c8458d982119eae806677a |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T11:30:46Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-74e520f292c8458d982119eae806677a2023-11-10T15:02:07ZengMDPI AGEnergies1996-10732023-10-011621730010.3390/en16217300Sizing PV and BESS for Grid-Connected Microgrid Resilience: A Data-Driven Hybrid Optimization ApproachMahtab Murshed0Manohar Chamana1Konrad Erich Kork Schmitt2Suhas Pol3Olatunji Adeyanju4Stephen Bayne5Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USARenewable Energy Program, Texas Tech University, Lubbock, TX 79409, USADepartment of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USARenewable Energy Program, Texas Tech University, Lubbock, TX 79409, USARenewable Energy Program, Texas Tech University, Lubbock, TX 79409, USADepartment of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USAThis article presents a comprehensive data-driven approach on enhancing grid-connected microgrid grid resilience through advanced forecasting and optimization techniques in the context of power outages. Power outages pose significant challenges to modern societies, affecting various sectors such as industries, households, and critical infrastructures. The research combines statistical analysis, machine-learning algorithms, and optimization methods to address this issue to develop a holistic approach for predicting and mitigating power outage events. The proposed methodology involves the use of Monte Carlo simulations in MATLAB for future outage prediction, training a Long Short-Term Memory (LSTM) network for forecasting solar irradiance and load profiles with a dataset spanning from 2009 to 2018, and a hybrid LSTM-Particle Swarm Optimization (PSO) model to improve accuracy. Furthermore, the role of battery state of charge (SoC) in enhancing system resilience is explored. The study also assesses the techno-economic advantages of a grid-tied microgrid integrated with solar panels and batteries over conventional grid systems. The proposed methodology and optimization process demonstrate their versatility and applicability to a wide range of microgrid design scenarios comprising solar PV and battery energy storage systems (BESS), making them a valuable resource for enhancing grid resilience and economic efficiency across diverse settings. The results highlight the potential of the proposed approach in strengthening grid resilience by improving autonomy, reducing downtime by 25%, and fostering sustainable energy utilization by 82%.https://www.mdpi.com/1996-1073/16/21/7300microgridhybrid LSTM-PSO modelmachine learningMonte Carlooptimizationpower outage |
| spellingShingle | Mahtab Murshed Manohar Chamana Konrad Erich Kork Schmitt Suhas Pol Olatunji Adeyanju Stephen Bayne Sizing PV and BESS for Grid-Connected Microgrid Resilience: A Data-Driven Hybrid Optimization Approach Energies microgrid hybrid LSTM-PSO model machine learning Monte Carlo optimization power outage |
| title | Sizing PV and BESS for Grid-Connected Microgrid Resilience: A Data-Driven Hybrid Optimization Approach |
| title_full | Sizing PV and BESS for Grid-Connected Microgrid Resilience: A Data-Driven Hybrid Optimization Approach |
| title_fullStr | Sizing PV and BESS for Grid-Connected Microgrid Resilience: A Data-Driven Hybrid Optimization Approach |
| title_full_unstemmed | Sizing PV and BESS for Grid-Connected Microgrid Resilience: A Data-Driven Hybrid Optimization Approach |
| title_short | Sizing PV and BESS for Grid-Connected Microgrid Resilience: A Data-Driven Hybrid Optimization Approach |
| title_sort | sizing pv and bess for grid connected microgrid resilience a data driven hybrid optimization approach |
| topic | microgrid hybrid LSTM-PSO model machine learning Monte Carlo optimization power outage |
| url | https://www.mdpi.com/1996-1073/16/21/7300 |
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