Economic scheduling and dispatching of distributed generators considering uncertainties in modified 33-bus and modified 69-bus system under different microgrid regions

This paper presents a comprehensive framework for the economic scheduling and dispatching of Distributed Generators (DGs) in modified 33-bus and 69-bus systems across multi-microgrid regions. The framework introduces two key techniques: a novel dispatch strategy for optimizing the charging and disch...

Full description

Autores:
Mavuri, Sri Suresh
Nakka, Jayaram
Tipo de recurso:
Article of journal
Fecha de publicación:
2024
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/13530
Acceso en línea:
https://doi.org/10.32397/tesea.vol5.n2.570
Palabra clave:
Index of Energy Reliability (IER)
Distribution Generation (DG)
BAT optimization
Jaya algorithm
p-ELECTRE
Rights
openAccess
License
Sri Suresh Mavuri, Jayaram Nakka - 2024
id UTB2_102890d55b0c7e246feb8e49847f3ac8
oai_identifier_str oai:repositorio.utb.edu.co:20.500.12585/13530
network_acronym_str UTB2
network_name_str Repositorio Institucional UTB
repository_id_str
dc.title.spa.fl_str_mv Economic scheduling and dispatching of distributed generators considering uncertainties in modified 33-bus and modified 69-bus system under different microgrid regions
dc.title.translated.spa.fl_str_mv Economic scheduling and dispatching of distributed generators considering uncertainties in modified 33-bus and modified 69-bus system under different microgrid regions
title Economic scheduling and dispatching of distributed generators considering uncertainties in modified 33-bus and modified 69-bus system under different microgrid regions
spellingShingle Economic scheduling and dispatching of distributed generators considering uncertainties in modified 33-bus and modified 69-bus system under different microgrid regions
Index of Energy Reliability (IER)
Distribution Generation (DG)
BAT optimization
Jaya algorithm
p-ELECTRE
title_short Economic scheduling and dispatching of distributed generators considering uncertainties in modified 33-bus and modified 69-bus system under different microgrid regions
title_full Economic scheduling and dispatching of distributed generators considering uncertainties in modified 33-bus and modified 69-bus system under different microgrid regions
title_fullStr Economic scheduling and dispatching of distributed generators considering uncertainties in modified 33-bus and modified 69-bus system under different microgrid regions
title_full_unstemmed Economic scheduling and dispatching of distributed generators considering uncertainties in modified 33-bus and modified 69-bus system under different microgrid regions
title_sort Economic scheduling and dispatching of distributed generators considering uncertainties in modified 33-bus and modified 69-bus system under different microgrid regions
dc.creator.fl_str_mv Mavuri, Sri Suresh
Nakka, Jayaram
dc.contributor.author.eng.fl_str_mv Mavuri, Sri Suresh
Nakka, Jayaram
dc.subject.eng.fl_str_mv Index of Energy Reliability (IER)
Distribution Generation (DG)
BAT optimization
Jaya algorithm
p-ELECTRE
topic Index of Energy Reliability (IER)
Distribution Generation (DG)
BAT optimization
Jaya algorithm
p-ELECTRE
description This paper presents a comprehensive framework for the economic scheduling and dispatching of Distributed Generators (DGs) in modified 33-bus and 69-bus systems across multi-microgrid regions. The framework introduces two key techniques: a novel dispatch strategy for optimizing the charging and discharging of Electric Vehicle (EV) batteries, and a robust power dispatch method for islanded distribution systems. The EV dispatch strategy uses a multi-criteria decision analysis method, Probabilistic Elimination and Choice Expressing Reality (p-ELECTRE), to maximize profits for EV owners while meeting power system requirements. This strategy is tested on fleets of 100 and 200 EVs with random travel plans within the modified 33-bus and 69-bus systems, and employs the BAT Optimization Algorithm (BOA) for optimal power dispatch. The second technique addresses the power dispatch in islanded systems by sectionalizing them into self-supplied microgrids, aiming to minimize operational costs, system losses, and voltage deviation using the Jaya algorithm. Additionally, a multi-objective cost-effective emission dispatch is evaluated using Whale Optimization Algorithm (WOA), showing superior performance over Differential Evolution (DE), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO). Comparative analysis highlights the scalability and adaptability of the proposed approach, making it a valuable tool for efficient microgrid management. Simulation results confirm significant improvements in cost savings, system reliability, and operational efficiency under various uncertainty scenarios.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-12-24 00:00:00
dc.date.available.none.fl_str_mv 2024-12-24 00:00:00
dc.date.issued.none.fl_str_mv 2024-12-24
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.eng.fl_str_mv info:eu-repo/semantics/article
dc.type.coar.eng.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.local.eng.fl_str_mv Journal article
dc.type.content.eng.fl_str_mv Text
dc.type.version.eng.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.coarversion.eng.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
format http://purl.org/coar/resource_type/c_6501
status_str publishedVersion
dc.identifier.url.none.fl_str_mv https://doi.org/10.32397/tesea.vol5.n2.570
dc.identifier.doi.none.fl_str_mv 10.32397/tesea.vol5.n2.570
dc.identifier.eissn.none.fl_str_mv 2745-0120
url https://doi.org/10.32397/tesea.vol5.n2.570
identifier_str_mv 10.32397/tesea.vol5.n2.570
2745-0120
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.references.eng.fl_str_mv Moslem Uddin, M.F. Romlie, M.F. Abdullah, ChiaKwang Tan, Gm Shafiullah, and A.H.A. Bakar. A novel peak shaving algorithm for islanded microgrid using battery energy storage system. Energy, 196:117084, April 2020. [2] Richard Wang, Shu-Chien Hsu, Saina Zheng, Jieh-Haur Chen, and Xuran Ivan Li. Renewable energy microgrids: Economic evaluation and decision making for government policies to contribute to affordable and clean energy. Applied Energy, 274:115287, September 2020. [3] Peng Kou, Deliang Liang, and Lin Gao. Distributed empc of multiple microgrids for coordinated stochastic energy management. Applied Energy, 185:939–952, January 2017. [4] Xiao Xu, Weihao Hu, Di Cao, Qi Huang, Cong Chen, and Zhe Chen. Optimized sizing of a standalone pv-wind-hydropower station with pumped-storage installation hybrid energy system. Renewable Energy, 147:1418–1431, March 2020. [5] Seyedmohsen Hosseini and Md Sarder. Development of a bayesian network model for optimal site selection of electric vehicle charging station. International Journal of Electrical Power Energy Systems, 105:110–122, February 2019. [6] Md. Alam, Abdullah Almehizia, Fahad Al-Ismail, Md. Hossain, Muhammad Islam, Md. Shafiullah, and Aasim Ullah. Frequency stabilization of ac microgrid clusters: An efficient fractional order supercapacitor controller approach. Energies, 15(14):5179, July 2022. [7] Jun Hou, Ziyou Song, Heath Hofmann, and Jing Sun. Adaptive model predictive control for hybrid energy storage energy management in all-electric ship microgrids. Energy Conversion and Management, 198:111929, October 2019. [8] Halil Cimen, Nurettin Cetinkaya, Juan C. Vasquez, and Josep M. Guerrero. A microgrid energy management system based on non-intrusive load monitoring via multitask learning. IEEE Transactions on Smart Grid, 12(2):977–987, March 2021. [9] Seyed Ehsan Ahmadi, Delnia Sadeghi, Mousa Marzband, Abdullah Abusorrah, and Khaled Sedraoui. Decentralized bi-level stochastic optimization approach for multi-agent multi-energy networked micro-grids with multi-energy storage technologies. Energy, 245:123223, April 2022. [10] Sanjeev Pannala, Niloy Patari, Anurag K. Srivastava, and Narayana Prasad Padhy. Effective control and management scheme for isolated and grid connected dc microgrid. IEEE Transactions on Industry Applications, 56(6):6767–6780, November 2020. [11] N Kumar, S Dahiya, and KP Singh Parmar. Multi-objective economic emission dispatch optimization strategy considering battery energy storage system in islanded microgrid. Journal of Operation and Automation in Power Engineering, 12(4):296–311, 2024. [12] M Ben Belgacem, B Gassara, and A Fakhfakh. Design and implementation of multi-source and multi-consumer energy sharing system in collaborative smart microgrid installation. Journal of Operation and Automation in Power Engineering, 10(3):189–199, 2022. [13] Claudia Battistelli, Yashodhan P. Agalgaonkar, and Bikash C. Pal. Probabilistic dispatch of remote hybrid microgrids including battery storage and load management. IEEE Transactions on Smart Grid, 8(3):1305–1317, May 2017. [14] Jiayong Li, Mohammad E. Khodayar, JianhuiWang, and Bin Zhou. Data-driven distributionally robust co-optimization of p2p energy trading and network operation for interconnected microgrids. IEEE Transactions on Smart Grid, 12(6):5172–5184, November 2021. [15] Akhtar Hussain, Ji-Hye Lee, and Hak-Man Kim. An optimal energy management strategy for thermally networked microgrids in grid-connected mode. International Journal of Smart Home, 10(3):239–258, March 2016. [16] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation, 6(2):182–197, April 2002. [17] Yun Liu, Hoay Beng Gooi, Yuanzheng Li, Huanhai Xin, and Jian Ye. A secure distributed transactive energy management scheme for multiple interconnected microgrids considering misbehaviors. IEEE Transactions on Smart Grid, 10(6):5975–5986, November 2019. [18] B. Papari, C. S. Edrington, and T. Vu. Stochastic operation of interconnected microgrids. July 2017. [19] Joydeep Mitra, Mallikarjuna R. Vallem, and Chanan Singh. Optimal deployment of distributed generation using a reliability criterion. IEEE Transactions on Industry Applications, 52(3):1989–1997, May 2016. [20] Ch. Shyamala. Design and development of anti-lock braking system for electric vehicle. INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT, 06(05), May 2022. [21] M. Fathi and H. Bevrani. Statistical cooperative power dispatching in interconnected microgrids. IEEE Transactions on Sustainable Energy, 4(3):586–593, July 2013. [22] R. Rao. Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, page 19–34, January 2016. [23] W. Xiao, M.G.J. Lind, W.G. Dunford, and A. Capel. Real-time identification of optimal operating points in photovoltaic power systems. IEEE Transactions on Industrial Electronics, 53(4):1017–1026, June 2006. [24] Bishwajit Dey, Shyamal Krishna Roy, and Biplab Bhattacharyya. Solving multi-objective economic emission dispatch of a renewable integrated microgrid using latest bio-inspired algorithms. Engineering Science and Technology, an International Journal, 22(1):55–66, 2019. [25] Bishwajit Dey, Biplab Bhattacharyya, Apoorv Srivastava, and Kumar Shivam. Solving energy management of renewable integrated microgrid systems using crow search algorithm. Soft Computing, 24(14):10433–10454, November 2019.
dc.relation.ispartofjournal.eng.fl_str_mv Transactions on Energy Systems and Engineering Applications
dc.relation.citationvolume.eng.fl_str_mv 5
dc.relation.citationstartpage.none.fl_str_mv 1
dc.relation.citationendpage.none.fl_str_mv 22
dc.relation.bitstream.none.fl_str_mv https://revistas.utb.edu.co/tesea/article/download/570/404
dc.relation.citationedition.eng.fl_str_mv Núm. 2 , Año 2024 : Transactions on Energy Systems and Engineering Applications
dc.relation.citationissue.eng.fl_str_mv 2
dc.rights.eng.fl_str_mv Sri Suresh Mavuri, Jayaram Nakka - 2024
dc.rights.uri.eng.fl_str_mv https://creativecommons.org/licenses/by/4.0
dc.rights.accessrights.eng.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.creativecommons.eng.fl_str_mv This work is licensed under a Creative Commons Attribution 4.0 International License.
dc.rights.coar.eng.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv Sri Suresh Mavuri, Jayaram Nakka - 2024
https://creativecommons.org/licenses/by/4.0
This work is licensed under a Creative Commons Attribution 4.0 International License.
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.eng.fl_str_mv application/pdf
dc.publisher.eng.fl_str_mv Universidad Tecnológica de Bolívar
dc.source.eng.fl_str_mv https://revistas.utb.edu.co/tesea/article/view/570
institution Universidad Tecnológica de Bolívar
repository.name.fl_str_mv Repositorio Digital Universidad Tecnológica de Bolívar
repository.mail.fl_str_mv bdigital@metabiblioteca.com
_version_ 1858228384886161408
spelling Mavuri, Sri SureshNakka, Jayaram2024-12-24 00:00:002024-12-24 00:00:002024-12-24This paper presents a comprehensive framework for the economic scheduling and dispatching of Distributed Generators (DGs) in modified 33-bus and 69-bus systems across multi-microgrid regions. The framework introduces two key techniques: a novel dispatch strategy for optimizing the charging and discharging of Electric Vehicle (EV) batteries, and a robust power dispatch method for islanded distribution systems. The EV dispatch strategy uses a multi-criteria decision analysis method, Probabilistic Elimination and Choice Expressing Reality (p-ELECTRE), to maximize profits for EV owners while meeting power system requirements. This strategy is tested on fleets of 100 and 200 EVs with random travel plans within the modified 33-bus and 69-bus systems, and employs the BAT Optimization Algorithm (BOA) for optimal power dispatch. The second technique addresses the power dispatch in islanded systems by sectionalizing them into self-supplied microgrids, aiming to minimize operational costs, system losses, and voltage deviation using the Jaya algorithm. Additionally, a multi-objective cost-effective emission dispatch is evaluated using Whale Optimization Algorithm (WOA), showing superior performance over Differential Evolution (DE), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO). Comparative analysis highlights the scalability and adaptability of the proposed approach, making it a valuable tool for efficient microgrid management. Simulation results confirm significant improvements in cost savings, system reliability, and operational efficiency under various uncertainty scenarios.application/pdfengUniversidad Tecnológica de BolívarSri Suresh Mavuri, Jayaram Nakka - 2024https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.utb.edu.co/tesea/article/view/570Index of Energy Reliability (IER)Distribution Generation (DG)BAT optimizationJaya algorithmp-ELECTREEconomic scheduling and dispatching of distributed generators considering uncertainties in modified 33-bus and modified 69-bus system under different microgrid regionsEconomic scheduling and dispatching of distributed generators considering uncertainties in modified 33-bus and modified 69-bus system under different microgrid regionsArtículo de revistainfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Journal articleTextinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85https://doi.org/10.32397/tesea.vol5.n2.57010.32397/tesea.vol5.n2.5702745-0120Moslem Uddin, M.F. Romlie, M.F. Abdullah, ChiaKwang Tan, Gm Shafiullah, and A.H.A. Bakar. A novel peak shaving algorithm for islanded microgrid using battery energy storage system. Energy, 196:117084, April 2020. [2] Richard Wang, Shu-Chien Hsu, Saina Zheng, Jieh-Haur Chen, and Xuran Ivan Li. Renewable energy microgrids: Economic evaluation and decision making for government policies to contribute to affordable and clean energy. Applied Energy, 274:115287, September 2020. [3] Peng Kou, Deliang Liang, and Lin Gao. Distributed empc of multiple microgrids for coordinated stochastic energy management. Applied Energy, 185:939–952, January 2017. [4] Xiao Xu, Weihao Hu, Di Cao, Qi Huang, Cong Chen, and Zhe Chen. Optimized sizing of a standalone pv-wind-hydropower station with pumped-storage installation hybrid energy system. Renewable Energy, 147:1418–1431, March 2020. [5] Seyedmohsen Hosseini and Md Sarder. Development of a bayesian network model for optimal site selection of electric vehicle charging station. International Journal of Electrical Power Energy Systems, 105:110–122, February 2019. [6] Md. Alam, Abdullah Almehizia, Fahad Al-Ismail, Md. Hossain, Muhammad Islam, Md. Shafiullah, and Aasim Ullah. Frequency stabilization of ac microgrid clusters: An efficient fractional order supercapacitor controller approach. Energies, 15(14):5179, July 2022. [7] Jun Hou, Ziyou Song, Heath Hofmann, and Jing Sun. Adaptive model predictive control for hybrid energy storage energy management in all-electric ship microgrids. Energy Conversion and Management, 198:111929, October 2019. [8] Halil Cimen, Nurettin Cetinkaya, Juan C. Vasquez, and Josep M. Guerrero. A microgrid energy management system based on non-intrusive load monitoring via multitask learning. IEEE Transactions on Smart Grid, 12(2):977–987, March 2021. [9] Seyed Ehsan Ahmadi, Delnia Sadeghi, Mousa Marzband, Abdullah Abusorrah, and Khaled Sedraoui. Decentralized bi-level stochastic optimization approach for multi-agent multi-energy networked micro-grids with multi-energy storage technologies. Energy, 245:123223, April 2022. [10] Sanjeev Pannala, Niloy Patari, Anurag K. Srivastava, and Narayana Prasad Padhy. Effective control and management scheme for isolated and grid connected dc microgrid. IEEE Transactions on Industry Applications, 56(6):6767–6780, November 2020. [11] N Kumar, S Dahiya, and KP Singh Parmar. Multi-objective economic emission dispatch optimization strategy considering battery energy storage system in islanded microgrid. Journal of Operation and Automation in Power Engineering, 12(4):296–311, 2024. [12] M Ben Belgacem, B Gassara, and A Fakhfakh. Design and implementation of multi-source and multi-consumer energy sharing system in collaborative smart microgrid installation. Journal of Operation and Automation in Power Engineering, 10(3):189–199, 2022. [13] Claudia Battistelli, Yashodhan P. Agalgaonkar, and Bikash C. Pal. Probabilistic dispatch of remote hybrid microgrids including battery storage and load management. IEEE Transactions on Smart Grid, 8(3):1305–1317, May 2017. [14] Jiayong Li, Mohammad E. Khodayar, JianhuiWang, and Bin Zhou. Data-driven distributionally robust co-optimization of p2p energy trading and network operation for interconnected microgrids. IEEE Transactions on Smart Grid, 12(6):5172–5184, November 2021. [15] Akhtar Hussain, Ji-Hye Lee, and Hak-Man Kim. An optimal energy management strategy for thermally networked microgrids in grid-connected mode. International Journal of Smart Home, 10(3):239–258, March 2016. [16] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation, 6(2):182–197, April 2002. [17] Yun Liu, Hoay Beng Gooi, Yuanzheng Li, Huanhai Xin, and Jian Ye. A secure distributed transactive energy management scheme for multiple interconnected microgrids considering misbehaviors. IEEE Transactions on Smart Grid, 10(6):5975–5986, November 2019. [18] B. Papari, C. S. Edrington, and T. Vu. Stochastic operation of interconnected microgrids. July 2017. [19] Joydeep Mitra, Mallikarjuna R. Vallem, and Chanan Singh. Optimal deployment of distributed generation using a reliability criterion. IEEE Transactions on Industry Applications, 52(3):1989–1997, May 2016. [20] Ch. Shyamala. Design and development of anti-lock braking system for electric vehicle. INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT, 06(05), May 2022. [21] M. Fathi and H. Bevrani. Statistical cooperative power dispatching in interconnected microgrids. IEEE Transactions on Sustainable Energy, 4(3):586–593, July 2013. [22] R. Rao. Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, page 19–34, January 2016. [23] W. Xiao, M.G.J. Lind, W.G. Dunford, and A. Capel. Real-time identification of optimal operating points in photovoltaic power systems. IEEE Transactions on Industrial Electronics, 53(4):1017–1026, June 2006. [24] Bishwajit Dey, Shyamal Krishna Roy, and Biplab Bhattacharyya. Solving multi-objective economic emission dispatch of a renewable integrated microgrid using latest bio-inspired algorithms. Engineering Science and Technology, an International Journal, 22(1):55–66, 2019. [25] Bishwajit Dey, Biplab Bhattacharyya, Apoorv Srivastava, and Kumar Shivam. Solving energy management of renewable integrated microgrid systems using crow search algorithm. Soft Computing, 24(14):10433–10454, November 2019.Transactions on Energy Systems and Engineering Applications5122https://revistas.utb.edu.co/tesea/article/download/570/404Núm. 2 , Año 2024 : Transactions on Energy Systems and Engineering Applications220.500.12585/13530oai:repositorio.utb.edu.co:20.500.12585/135302025-09-16 09:15:12.624https://creativecommons.org/licenses/by/4.0Sri Suresh Mavuri, Jayaram Nakka - 2024metadata.onlyhttps://repositorio.utb.edu.coRepositorio Digital Universidad Tecnológica de Bolívarbdigital@metabiblioteca.com

Publicaciones similares