Optimal power dispatch in microgrids using mixed-integer linear programming
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Renata Rodrigues Lautert
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Cláudio Adriano C. Cambambi
Abstract
As greenhouse gases emissions continue to rise, society is actively seeking methods to reduce them. Microgrids (MGs), which predominantly consist of renewable energy sources, play a significant role in achieving this objective. This paper proposes an optimized methodology for power dispatch in MGs using mixed-integer linear programming (MILP). The MGs include photovoltaic systems, wind turbines, biogas (BG) generators, battery energy storage systems (BESS), electric vehicles (EV), and loads. The model features an objective function focused on cost minimization, power balance, and the necessary limits and constraints for the system’s safe operation. Real-time pricing is employed for energy transactions between the MGs and the main grid. The results demonstrate a cost-efficient operation for the proposed system comprising two MGs and the main grid. During periods of negative power balance, the demand was met by discharging the BESS, EV’s battery, or purchasing energy from the grid. The BESS was charged when energy prices were low and discharged during peak demand periods and high energy prices. The intermittent nature of renewable sources necessitates an efficient management system to ensure reliable operation. Additionally, storage systems help mitigate the variability in generation. The BG generator was another crucial component for power supply due to its flexibility. Integrating these components into the system improved reliability and ensured a secure and balanced operation.
Zusammenfassung
Da die Treibhausgasemissionen weiter ansteigen, sucht die Gesellschaft aktiv nach Methoden, um sie zu reduzieren. Microgrids (MGs), die überwiegend aus erneuerbaren Energiequellen bestehen, spielen eine wichtige Rolle bei der Erreichung dieses Ziels. In diesem Beitrag wird eine optimierte Methodik für die Energieverteilung in MGs unter Verwendung der gemischt-ganzzahligen linearen Programmierung (MILP) vorgeschlagen. Zu den MGs gehören Photovoltaikanlagen, Windturbinen, Biogasanlagen (BG), Batteriespeichersysteme (BESS), Elektrofahrzeuge (EV) und Lasten. Das Modell verfügt über eine Zielfunktion, die sich auf die Kostenminimierung, die Leistungsbilanz und die notwendigen Grenzen und Einschränkungen für den sicheren Betrieb des Systems konzentriert. Für die Energietransaktionen zwischen den MGs und dem Hauptnetz werden Echtzeitpreise verwendet. Die Ergebnisse zeigen einen kosteneffizienten Betrieb für das vorgeschlagene System, das aus zwei MGs und dem Hauptnetz besteht. In Zeiten negativer Leistungsbilanz wurde der Bedarf durch Entladung des BESS, der EV-Batterie oder durch den Kauf von Energie aus dem Netz gedeckt. Der BESS wurde geladen, wenn die Energiepreise niedrig waren und entladen, wenn der Bedarf am höchsten und die Energiepreise hoch waren. Die inkonstante Energieerzeugung der erneuerbaren Energiequellen erfordert ein effizientes Managementsystem, um einen zuverlässigen Betrieb zu gewährleisten. Darüber hinaus tragen Speichersysteme dazu bei, die Schwankungen in der Stromerzeugung abzumildern. Die Biogasanlage stellt aufgrund ihrer Flexibilität eine entscheidende Komponente in der Stromversorgung dar. Durch die Integration dieser Komponenten in das System wurde die Zuverlässigkeit verbessert und ein sicherer und ausgewogener Betrieb gewährleistet.
About the authors
Renata Rodrigues Lautert received her B.Sc. degree (2017) in Control and Automation Engineering, and the M.Sc. degree (2020) in Electrical Engineering both from the Federal University of Technology – Parana (UTFPR), Curitiba, Brazil. Currently, she is pursuing a Ph.D. degree in a cotutelle program at Federal University of Santa Maria (UFSM), Santa Maria, Brazil, and Otto-von-Guericke University (OVGU), Magdeburg, Germany. Her research interests include renewable energies, power systems, microgrids, distributed generation, and energy storage.
Cláudio Adriano Correia Cambambi received a Bachelor’s degree in Energy Engineering from UNILAB (2018) and a Master’s degree in Electrical Engineering from UNIFEI (2020), both in Brazil. He is currently pursuing a Ph.D. in Electrical Engineering at UFSM and is a certified Energy and Electrical Installations Technician (2011) from the Polytechnic Institute of Cacuaco (Angola). His areas of interest include distributed energy resources, microgrids, Virtual Power Plant (VPP), artificial intelligence, renewable energy, energy efficiency, and the mitigation of energy poverty in rural African communities.
Mauro dos Santos Ortiz received the B.Sc. degree and the M.Sc. degree in electrical engineering from the Federal University of Santa Maria (UFSM), Santa Maria, Brazil, in 2017 and 2019, respectively. Undergraduate in Teacher Education for Professional Education by UFSM in 2019. He is currently pursuing a Ph.D. degree in electrical engineering at UFSM with cotutelle at the Otto von Guericke University Magdeburg (OVGU), Magdeburg, Germany. Since 2023, he has been a Research Assistant with the Chair of Electric Power Networks and Renewable Energy, OVGU. His research interests include energy quality, integration of renewable energies and electromobility.
Martin Wolter received his diploma in electrical engineering in 2006, his Dr.-Ing degree in 2008 and his venia legendi in 2012 all from Leibniz University Hannover. From 2011 to 2015 he worked at 50Hertz Transmission GmbH in system operation concept development. Since 2015, he has been head of the chair of Electric Networks and Renewable Energy at Otto-von-Guericke University Magdeburg. His research topics are power system modelling and simulation, system security and system operation as well as power system dynamics.
Luciane Neves Canha, Doctor in Electrical Engineering (2004), Full Professor at the Federal University of Santa Maria (UFSM), PQ-1D Researcher at the National Research Council of Brazil and IEEE Senior Member. In 2021 she was included among the 21 most influential women in mobility in the world by Vulog. She has experience in Electrical Engineering, with an emphasis on power systems, energy transition, renewable energy sources, smart grids, energy storage, microgrids, electrical mobility and distributed generation.
Acknowledgments
Not applicable.
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Research ethics: Not applicable.
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Author contributions: Renata R. Lautert: conceptualization, methodology, data collection, data analysis, writing; Cláudio A. C. Cambambi: conceptualization, methodology, data analysis, review; Mauro S. Ortiz: conceptualization, writing, review; Martin Wolter: review, supervision; Luciane N. Canha: conceptualization, review, supervision. The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: The authors state no conflict of interest.
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Research funding: The present work has been done with the support of CNPq, the Brazilian National Council for Scientific and Technological Development, process CNPq PQ 1-D 311339/2022-0. The authors acknowledge the technical and financial support of the National Institute of Science and Technology on Distributed Generation Power Systems (INCT-GD), Higher Level Personnel Training Coordination (CAPES—no 23038.000776/2017-54), Foundation for Research of the State of Rio Grande do Sul (FAPERGS—no 17/2551-0000517-1), and Federal University of Santa Maria (UFSM), Brazilian Institutions. The authors thank CAPES Foundation (“Coordenação de Aperfeiçoamento de Pessoal de Nível Superior”)—Brazil (CAPES/PROEX)—Financial code 001. The authors also appreciate the support from Otto-von-Guericke University Magdeburg (OvGU), Germany.
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Data availability: Not applicable.
References
[1] H. Ritchie, P. Rosado, and M. Roser, “CO2 and greenhouse gas emissions,” OurWorldInData.org, 2024. Available at: https://ourworldindata.org/co2-and-greenhouse-gas-emissions.Search in Google Scholar
[2] IRENA, International Renewable Energy Agency, World Energy Transitions Outlook 2022: 1.5 °C Pathway – Executive Summary, Abu Dhabi, International Renewable Energy Agency, 2022.Search in Google Scholar
[3] A. Hirsch, Y. Parag, and J. Guerrero, “Microgrids: a review of technologies, key drivers, and outstanding issues,” Renew. Sustain. Energy Rev., vol. 90, no. 1, pp. 402–411, 2018. https://doi.org/10.1016/j.rser.2018.03.040.Search in Google Scholar
[4] D. T. Ton and M. A. Smith, “The U.S. department of energy’s microgrid initiative,” Electr. J., vol. 25, no. 8, pp. 84–94, 2012. https://doi.org/10.1016/j.tej.2012.09.013.Search in Google Scholar
[5] A. P. Medeiros, L. N. Canha, V. J. Garcia, and C. A. S. Rangel, “A matheuristic for active flexibility management of battery energy storage systems in the context of local flexibility markets,” J. Energy Storage, vol. 95, no. 1, 2024, Art. no. 112625, https://doi.org/10.1016/j.est.2024.112625.Search in Google Scholar
[6] S. F. Contreras, C. A. Cortes, and J. M. A. Myrzik, “Opti-mal microgrid planning for enhancing ancillary service provision,” J. Mod. Power Syst. Clean Energy, vol. 7, no. 4, pp. 862–875, 2019. https://doi.org/10.1007/s40565-019-0528-3.Search in Google Scholar
[7] J. L. Martinez-Ramos, et al.., “Provision of ancillary ser-vices by a smart microgrid: an OPF approach,” in 2018 International Conference on Smart Energy Systems and Technologies (SEST) Sevilla, 2018.10.1109/SEST.2018.8495883Search in Google Scholar
[8] Z. Huang, Z. Guo, P. Ma, M. Wang, Y. Long, and M. Zhang, “Economic-environmental scheduling of microgrid considering V2G-enabled electric vehicles integration,” Sustain. Energy, Grids Netw., vol. 32, no. 1, 2022, Art. no. 100872, https://doi.org/10.1016/j.segan.2022.100872.Search in Google Scholar
[9] X. Zhang, Z. Wang, and Z. Lu, “Multi-objective load dispatch for microgrid with electric vehicles using modified gravitational search and particle swarm optimization algorithm,” Appl. Energy, vol. 306, no. 1, 2022, Art. no. 118018, https://doi.org/10.1016/j.apenergy.2021.118018.Search in Google Scholar
[10] A. Jagan, P. K. Ray, B. P. Behera, and G. Panda, “A fuzzy-logic-based smart power management strategy for reliability enhancement of energy storage system in a hybrid AC-DC microgrid with EV charging station,” Int. J. Emerg. Electr. Power Syst., vol. 25, no. 3, pp. 405–419, 2023. https://doi.org/10.1515/ijeeps-2023-0128.Search in Google Scholar
[11] V. Z. Gjorgievski, S. Cundeva, and G. E. Georghiou, “Social arrangements, technical designs and impacts of energy communities: a review,” Renew. Energy, vol. 169, no. 1, pp. 1138–1156, 2021. https://doi.org/10.1016/j.renene.2021.01.078.Search in Google Scholar
[12] T. M. M. Guibentif and F. Vuille, “Prospects and barriers for microgrids in Switzerland,” Energy Strat. Rev., vol. 39, no. 1, 2022, Art. no. 100776. https://doi.org/10.1016/j.esr.2021.100776.Search in Google Scholar
[13] J. Hu, Y. Shan, K. W. Cheng, and S. Islam, “Overview of power converter control in microgrids – challenges, advances, and future trends,” IEEE Trans. Power Electron., vol. 37, no. 8, pp. 9907–9922, 2022. https://doi.org/10.1109/TPEL.2022.3159828.Search in Google Scholar
[14] K. I. Ibekwe, et al.., “Microgrid systems in U.S. energy infrastructure: a comprehensive review: exploring decentralized energy solutions, their benefits, and challenges in regional implementation,” World J. Adv. Res. Rev., vol. 21, no. 1, pp. 973–987, 2024. https://doi.org/10.30574/wjarr.2024.21.1.0112.Search in Google Scholar
[15] M. F. Zia, M. Benbouzid, E. Elbouchikhi, S. M. Muyeen, K. Techato, and J. M. Guerrero, “Microgrid transactive energy: review, architectures, distributed ledger technologies, and market analysis,” IEEE Access, vol. 8, no. 1, pp. 19410–19432, 2020. https://doi.org/10.1109/ACCESS.2020.2968402.Search in Google Scholar
[16] G. S. Thirunavukkarasu, M. Seyedmahmoudian, E. Jamei, B. Horan, S. Mekhilef, and A. Stojcevski, “Role of optimization techniques in microgrid energy management systems – a review,” Energy Strat. Rev., vol. 43, no. 1, 2022, Art. no. 100899. https://doi.org/10.1016/j.esr.2022.100899.Search in Google Scholar
[17] A. R. Battula, S. Vuddanti, and S. R. Salkuti, “Review of energy management system approaches in microgrids,” Energies, vol. 14, no. 17, p. 5459, 2021. https://doi.org/10.3390/en14175459.Search in Google Scholar
[18] G. Papazoglou and P. Biskas, “Review and comparison of genetic algorithm and particle swarm optimization in the optimal power flow problem,” Energies, vol. 16, no. 3, p. 1152, 2023. https://doi.org/10.3390/en16031152.Search in Google Scholar
[19] X. S. Yang, Engineering Optimization: An Introduction with Metaheuristic Applications, United Kingdom, John Wiley & Sons, 2010.10.1002/9780470640425Search in Google Scholar
[20] R. R. Lautert, C. A. C. Cambambi, C. A. S. Rangel, L. N. Canha, M. Wolter, and M. S. Ortiz, “Modeling and optimization of transactive energy management for microgrids,” in Dresdener Kreis, 2024, to appear.Search in Google Scholar
[21] F. A. Kassab, B. Celik, F. Locment, M. Sechilariu, S. Liaquat, and T. M. Hansen, “Optimal sizing and energy management of a microgrid: a joint MILP approach for minimization of energy cost and carbon emission,” Renew. Energy, vol. 224, no. 1, 2024, Art. no. 120186. https://doi.org/10.1016/j.renene.2024.120186.Search in Google Scholar
[22] A. Nurkanović, et al.., “Optimization-based primary and secondary control of microgrids,” Automatisierungstechnik, vol. 68, no. 12, pp. 1044–1058, 2020. https://doi.org/10.1515/auto-2020-0088.Search in Google Scholar
[23] R. R. Lautert, W. D. S. Brignol, L. N. Canha, O. M. Adeyanju, and V. J. Garcia, “A flexible-reliable operation model of storage and distributed generation in a biogas power plant,” Energies, vol. 15, no. 9, p. 3154, 2022. https://doi.org/10.3390/en15093154.Search in Google Scholar
[24] P. Faria and Z. Vale, “Demand response in electrical energy supply: an optimal real time pricing approach,” Energy, vol. 36, no. 8, pp. 5374–5384, 2011. https://doi.org/10.1016/j.energy.2011.06.049.Search in Google Scholar
[25] K. Milis, H. Peremans, and S. Van Passel, “The impact of policy on microgrid economics: a review,” Renew. Sustain. Energy Rev., vol. 81, no. 1, pp. 3111–3119, 2018. https://doi.org/10.1016/j.rser.2017.08.091.Search in Google Scholar
[26] T. Achterberg, R. E. Bixby, Z. Gu, E. Rothberg, and D. Weninger, “Presolve reductions in mixed integer programming,” INFORMS J. Comput., vol. 32, no. 2, pp. 473–506, 2020. https://doi.org/10.1287/ijoc.2018.0857.Search in Google Scholar
[27] J. O. Lee and Y. S. Kim, “Novel battery degradation cost formulation for optimal scheduling of battery energy storage systems,” Int. J. Electr. Power Energy Syst., vol. 137, no. 1, 2022, Art. no. 107795. https://doi.org/10.1016/j.ijepes.2021.107795.Search in Google Scholar
[28] C. A. C. Cambambi, L. N. Canha, M. Sperandio, C. A. S. Rangel, and I. Milani, “Energy exchange optimization among multiple geolocated microgrids: a coalition formation approach for cost reduction,” SSRN, 2024, https://doi.org/10.2139/ssrn.4870166.Search in Google Scholar
[29] G. Rancilio, M. Merlo, A. Lucas, E. Kotsakis, and M. Delfanti, “BESS modeling: investigating the role of auxiliary system consumption in efficiency derating,” in 2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), Sorrento, Italy, 2020, pp. 189–194.10.1109/SPEEDAM48782.2020.9161875Search in Google Scholar
[30] C. A. Sepúlveda Rangel, L. N. Canha, M. Sperandio, and V. Miranda, “Mixed-integer stochastic evaluation of battery energy storage system integration strategies in distribution systems,” IET Gener. Transm. Distrib., vol. 16, no. 4, pp. 641–655, 2022. https://doi.org/10.1049/gtd2.12316.Search in Google Scholar
[31] Z. Xiaohui, G. Wenbo, and Z. Jiaqing, “Decentralized economic dispatching of multi-micro grid considering wind power and photovoltaic output uncertainty,” IEEE Access, vol. 9, no. 1, pp. 104093–104103, 2021. https://doi.org/10.1109/ACCESS.2021.3099401.Search in Google Scholar
[32] M. Gholami, S. M. Muyeen, and S. Lin, “Optimizing microgrid efficiency: coordinating commercial and residential demand patterns with shared battery energy storage,” J. Energy Storage, vol. 88, no. 1, 2024, Art. no. 111485. https://doi.org/10.1016/j.est.2024.111485.Search in Google Scholar
© 2024 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Editorial
- Resilient energy supply in times of crises and transition
- Methods
- Batteries for fast frequency containment response: market impacts on outage dynamics
- Synthesis of speed controllers by the polynomial equations method for an unstable electromechanical object
- Optimal power dispatch in microgrids using mixed-integer linear programming
- Review on peak detect and hold circuits and their applicability in partial discharge detection
- Applications
- Analysis of the degree of correlation of spatial distribution of electricity theft and exogenous variables: case study of Florianopolis, Brazil
- Linear transverse flux generator for wave energy conversion: design optimization and analysis
- Improving quality of electricity generated by grid-tied inverters in solar power plants in low generated power mode using frequency adaptive PWM
- Survey
- Regional marketing mechanisms for industrial energy flexibility enabled by service-oriented IT platforms
Articles in the same Issue
- Frontmatter
- Editorial
- Resilient energy supply in times of crises and transition
- Methods
- Batteries for fast frequency containment response: market impacts on outage dynamics
- Synthesis of speed controllers by the polynomial equations method for an unstable electromechanical object
- Optimal power dispatch in microgrids using mixed-integer linear programming
- Review on peak detect and hold circuits and their applicability in partial discharge detection
- Applications
- Analysis of the degree of correlation of spatial distribution of electricity theft and exogenous variables: case study of Florianopolis, Brazil
- Linear transverse flux generator for wave energy conversion: design optimization and analysis
- Improving quality of electricity generated by grid-tied inverters in solar power plants in low generated power mode using frequency adaptive PWM
- Survey
- Regional marketing mechanisms for industrial energy flexibility enabled by service-oriented IT platforms
