Design, control, reliability, economic and energy management of microgrid: A review
•Assessment of Optimum design and modeling of the microgrid system.•Energy management of the microgrid system.•Analysis of the control system and reliability of the microgrid system.•Application of the recent trends such as artificial intelligence, data analysis and Blockchain in the field of micro...
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Published in | e-Prime Vol. 5; p. 100239 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Elsevier Ltd
01.09.2023
Elsevier |
Subjects | |
Online Access | Get full text |
ISSN | 2772-6711 2772-6711 |
DOI | 10.1016/j.prime.2023.100239 |
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Abstract | •Assessment of Optimum design and modeling of the microgrid system.•Energy management of the microgrid system.•Analysis of the control system and reliability of the microgrid system.•Application of the recent trends such as artificial intelligence, data analysis and Blockchain in the field of micro grid system.
Currently, fossil fuels still dominate as the primary energy source, accounting for around 80% of global output. However, the COVID-19 pandemic has severely affected various industries, including the electric power sector. Consequently, the energy industry has recognized the importance of renewable energy sources in addressing environmental concerns and establishing a sustainable future. Recently, microgrid systems based on renewable energy have emerged as crucial players in reducing pollution and promoting environmental sustainability. This paper offers a comprehensive assessment of microgrid systems, starting with the optimal design of the microgrid and extending to the evaluation of its control system. Through a detailed analysis of existing literature and case studies, the review identifies several key findings. Firstly, effective design and control strategies are crucial for optimizing the operation of microgrid's and maximizing their economic and energy management potential. Secondly, the integration of renewable energy sources and energy storage systems can significantly enhance the reliability and resilience of microgrid's. Thirdly, advanced control techniques and optimization algorithms play a vital role in achieving optimal energy management, cost reduction, and efficient load scheduling within microgrid systems. Furthermore, the paper explores energy management, reliability assessment, and economic analysis within the microgrid context. Lastly, the paper highlights the application of cutting-edge trends like artificial intelligence, data analysis, and blockchain in the field of microgrid systems. The numerical results showcase the enhanced reliability and resilience of microgrid's with the integration of energy storage systems, reducing the frequency and duration of power outages by 30% compared to traditional grids. |
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AbstractList | •Assessment of Optimum design and modeling of the microgrid system.•Energy management of the microgrid system.•Analysis of the control system and reliability of the microgrid system.•Application of the recent trends such as artificial intelligence, data analysis and Blockchain in the field of micro grid system.
Currently, fossil fuels still dominate as the primary energy source, accounting for around 80% of global output. However, the COVID-19 pandemic has severely affected various industries, including the electric power sector. Consequently, the energy industry has recognized the importance of renewable energy sources in addressing environmental concerns and establishing a sustainable future. Recently, microgrid systems based on renewable energy have emerged as crucial players in reducing pollution and promoting environmental sustainability. This paper offers a comprehensive assessment of microgrid systems, starting with the optimal design of the microgrid and extending to the evaluation of its control system. Through a detailed analysis of existing literature and case studies, the review identifies several key findings. Firstly, effective design and control strategies are crucial for optimizing the operation of microgrid's and maximizing their economic and energy management potential. Secondly, the integration of renewable energy sources and energy storage systems can significantly enhance the reliability and resilience of microgrid's. Thirdly, advanced control techniques and optimization algorithms play a vital role in achieving optimal energy management, cost reduction, and efficient load scheduling within microgrid systems. Furthermore, the paper explores energy management, reliability assessment, and economic analysis within the microgrid context. Lastly, the paper highlights the application of cutting-edge trends like artificial intelligence, data analysis, and blockchain in the field of microgrid systems. The numerical results showcase the enhanced reliability and resilience of microgrid's with the integration of energy storage systems, reducing the frequency and duration of power outages by 30% compared to traditional grids. Currently, fossil fuels still dominate as the primary energy source, accounting for around 80% of global output. However, the COVID-19 pandemic has severely affected various industries, including the electric power sector. Consequently, the energy industry has recognized the importance of renewable energy sources in addressing environmental concerns and establishing a sustainable future. Recently, microgrid systems based on renewable energy have emerged as crucial players in reducing pollution and promoting environmental sustainability. This paper offers a comprehensive assessment of microgrid systems, starting with the optimal design of the microgrid and extending to the evaluation of its control system. Through a detailed analysis of existing literature and case studies, the review identifies several key findings. Firstly, effective design and control strategies are crucial for optimizing the operation of microgrid's and maximizing their economic and energy management potential. Secondly, the integration of renewable energy sources and energy storage systems can significantly enhance the reliability and resilience of microgrid's. Thirdly, advanced control techniques and optimization algorithms play a vital role in achieving optimal energy management, cost reduction, and efficient load scheduling within microgrid systems. Furthermore, the paper explores energy management, reliability assessment, and economic analysis within the microgrid context. Lastly, the paper highlights the application of cutting-edge trends like artificial intelligence, data analysis, and blockchain in the field of microgrid systems. The numerical results showcase the enhanced reliability and resilience of microgrid's with the integration of energy storage systems, reducing the frequency and duration of power outages by 30% compared to traditional grids. |
ArticleNumber | 100239 |
Author | Khare, Vikas Chaturvedi, Pradyumn |
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Keywords | Data analysis Solar system DC microgrid AC microgrid Battery storage Artificial intelligence Wind system |
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34 Javed (10.1016/j.prime.2023.100239_bib0022) 2019; 176 Wei (10.1016/j.prime.2023.100239_bib0150) 2017 Alice (10.1016/j.prime.2023.100239_bib0080) 2021; 300 Amiri (10.1016/j.prime.2023.100239_bib0089) 2018; 158 Caliano (10.1016/j.prime.2023.100239_bib0075) 2022; 32 Srinivasarathnam (10.1016/j.prime.2023.100239_bib0058) 2019 Leeuwen (10.1016/j.prime.2023.100239_bib0153) 2020; 263 Zhu (10.1016/j.prime.2023.100239_bib0033) 2019 Sanjeev (10.1016/j.prime.2023.100239_bib0044) 2018; 9 Bukar (10.1016/j.prime.2023.100239_bib0019) 2019; 188 Pham (10.1016/j.prime.2023.100239_bib0122) 2020 Li (10.1016/j.prime.2023.100239_bib0039) 2018; 232 Shi (10.1016/j.prime.2023.100239_bib0025) 2021; 7 Song (10.1016/j.prime.2023.100239_bib0047) 2018; 9 Perez (10.1016/j.prime.2023.100239_bib0009) 2022; 8 Dong (10.1016/j.prime.2023.100239_bib0073) 2023; 277 Deepanraj (10.1016/j.prime.2023.100239_bib0102) 2022; 36 Prasad (10.1016/j.prime.2023.100239_bib0069) 2022; 8 Khare (10.1016/j.prime.2023.100239_bib0007) 2016; 41 Danish (10.1016/j.prime.2023.100239_bib0030) 2019 Wang (10.1016/j.prime.2023.100239_bib0038) 2023; 9 Roy (10.1016/j.prime.2023.100239_bib0093) 2021 Qian (10.1016/j.prime.2023.100239_bib0055) 2020; 6 Munisamy (10.1016/j.prime.2023.100239_bib0156) 2022; 50 Dhundhara (10.1016/j.prime.2023.100239_bib0109) 2018; 177 Jiang (10.1016/j.prime.2023.100239_bib0070) 2022; 8 Sonnenberg (10.1016/j.prime.2023.100239_bib0031) 2018 Li (10.1016/j.prime.2023.100239_bib0040) 2019; 66 Dey (10.1016/j.prime.2023.100239_bib0097) 2022; 46 Mallesham (10.1016/j.prime.2023.100239_bib0149) 2012 Binu (10.1016/j.prime.2023.100239_bib0050) 2022; 204 Babaiahgari (10.1016/j.prime.2023.100239_bib0057) 2019; 113 Liu (10.1016/j.prime.2023.100239_bib0068) 2020; 35 Tsao (10.1016/j.prime.2023.100239_bib0152) 2021; 163 Garg (10.1016/j.prime.2023.100239_bib0036) 2020; 31 Majji (10.1016/j.prime.2023.100239_bib0048) 2022; 54 Hussain (10.1016/j.prime.2023.100239_bib0142) 2020; 13 Mason (10.1016/j.prime.2023.100239_bib0092) 2017; 270 Rangu (10.1016/j.prime.2023.100239_bib0148) 2020; 44 Prakash (10.1016/j.prime.2023.100239_bib0095) 2021 Opathella (10.1016/j.prime.2023.100239_bib0032) 2018 Sun (10.1016/j.prime.2023.100239_bib0091) 2022; 305 Tobajas (10.1016/j.prime.2023.100239_bib0053) 2022; 306 Beheshtaein (10.1016/j.prime.2023.100239_bib0145) 2019; 1 Egbue (10.1016/j.prime.2023.100239_bib0026) 2020; 33 Ni (10.1016/j.prime.2023.100239_bib0062) 2021; 128 Adefarati (10.1016/j.prime.2023.100239_bib0132) 2019; 236 Sackey (10.1016/j.prime.2023.100239_bib0112) 2023; 19 Khare (10.1016/j.prime.2023.100239_bib0003) 2022 Ren (10.1016/j.prime.2023.100239_bib0043) 2019; 34 Abeleira (10.1016/j.prime.2023.100239_bib0079) 2022; 324 Luna-Rubio (10.1016/j.prime.2023.100239_bib0018) 2012; 86 Veilleux (10.1016/j.prime.2023.100239_bib0108) 2020; 54 Sharma (10.1016/j.prime.2023.100239_bib0010) 2022; 13 Vaka (10.1016/j.prime.2023.100239_bib0016) 2020; 273 Kavitha (10.1016/j.prime.2023.100239_bib0082) 2022; 244 Kumar (10.1016/j.prime.2023.100239_bib0146) 2017; 71 Trivedi (10.1016/j.prime.2023.100239_bib0135) 2022; 8 Padhi (10.1016/j.prime.2023.100239_bib0087) 2020; 101 Yeh (10.1016/j.prime.2023.100239_bib0116) 2022; 8 Alamir (10.1016/j.prime.2023.100239_bib0101) 2023; 214 Short (10.1016/j.prime.2023.100239_bib0046) 2007; 22 Li (10.1016/j.prime.2023.100239_bib0037) 2022; 56 Erdocia (10.1016/j.prime.2023.100239_bib0066) 2023; 144 Tribioli (10.1016/j.prime.2023.100239_bib0110) 2019; 179 Siti (10.1016/j.prime.2023.100239_bib0056) 2022; 52 Yamashita (10.1016/j.prime.2023.100239_bib0139) 2020; 118 Bayat (10.1016/j.prime.2023.100239_bib0067) 2022; 143 Gunasekaran (10.1016/j.prime.2023.100239_bib0118) 2018; 8 Khare (10.1016/j.prime.2023.100239_bib0002) 2022 Nasir (10.1016/j.prime.2023.100239_bib0120) 2017; 9 Zhu (10.1016/j.prime.2023.100239_bib0027) 2020; 116 Yin (10.1016/j.prime.2023.100239_bib0086) 2020 Lu (10.1016/j.prime.2023.100239_bib0085) 2017; 165 Lake (10.1016/j.prime.2023.100239_bib0012) 2018; 172 Sayed (10.1016/j.prime.2023.100239_bib0123) 2019; 12 Yang (10.1016/j.prime.2023.100239_bib0049) 2022; 315 Bayati (10.1016/j.prime.2023.100239_bib0144) 2018; 1 Gugulothu (10.1016/j.prime.2023.100239_bib0074) 2023; 57 Rahmani (10.1016/j.prime.2023.100239_bib0115) 2023; 214 Rodriguez (10.1016/j.prime.2023.100239_bib0100) 2023; 335 Menon (10.1016/j.prime.2023.100239_bib0063) 2014; 39 Zhao (10.1016/j.prime.2023.100239_bib0034) 2021 Marqusee (10.1016/j.prime.2023.100239_bib0126) 2021; 285 Zehra (10.1016/j.prime.2023.100239_bib0134) 2022; 239 Shahkamrani (10.1016/j.prime.2023.100239_bib0090) 2021; 307 Khare (10.1016/j.prime.2023.100239_bib0006) 2013; 27 Gopalan (10.1016/j.prime.2023.100239_bib0141) 2014; 32 Jenssen (10.1016/j.prime.2023.100239_bib0020) 2014; 61 Yang (10.1016/j.prime.2023.100239_bib0131) 2018; 228 Mehdi (10.1016/j.prime.2023.100239_bib0136) 2023; 58 Bazmohammadi (10.1016/j.prime.2023.100239_bib0035) 2022; 10 Sitharthan (10.1016/j.prime.2023.100239_bib0155) 2023; 106 Sandelic (10.1016/j.prime.2023.100239_bib0128) 2022; 159 Khare (10.1016/j.prime.2023.100239_bib0001) 2022 Jian (10.1016/j.prime.2023.100239_bib0151) 2022; 55 Tabar (10.1016/j.prime.2023.100239_bib0084) 2018; 203 Kumar (10.1016/j.prime.2023.100239_bib0096) 2023; 61 Sinha (10.1016/j.prime.2023.100239_bib0071) 2021; 128 Nasir (10.1016/j.prime.2023.100239_bib0119) 2020; 8 Polimeni (10.1016/j.prime.2023.100239_bib0061) 2021; 2 Rosero (10.1016/j.prime.2023.100239_bib0099) 2023; 334 |
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