Review of Microgrid Energy Management Techniques on Virtual Power Plant System

Yıl 2023, Cilt: 2 Sayı: 2, 381 - 388, 27.12.2023

Obed Nelson Onsomu , Bülent Yeşilata

Öz

The growing energy supply and demand are slowly changing the nature of power transmission and distribution, and the application of virtual power plant (VPP) has already gained traction in countries like Sweden, Germany, and Belgium. The dynamic nature of the VPP platform to connect multiple microgrids within the same geographical location, and to some degree, large-scale nationwide energy resources make it a state-of-the-art technological innovation. The platform has been applied for distributed energy resources (DERs) and dispatchable generation units such as combined heat and power (CHP) to monitor and control energy production and consumption, which also includes the integration of renewable energy sources (RES) into the energy mix. The energy management system (EMS) is one of the function modules in the control system of the VPP and can regulate energy stored and discharged from the energy storage system (ESS), generally, microgrids are known to connect regions far away from the main grid and can operate on islanding mode or on-grid, and they largely facilitate electrification of remote areas as energy production is done onsite. Therefore, in this review control strategies for energy management systems are analyzed and compared, e.g., Multi-Agent System (MAS), and Model Predictive Control (MPC), i.e., chance-constrained optimization, with the main emphasis being on minimizing costs and facilitating micro-grid stability through economical dispatch of energy generational units.

Anahtar Kelimeler

Virtual Power Plant, Energy Management System, Multi-Agent system, Model Predictive Control, Chance-Constrained Optimization.

Kaynakça

• Chang, W., Dong, W., Zhao, L., & Yang, Q. (2020). Model Predictive Control based Energy Collaborative Optimization Management for Energy Storage System of Virtual Power Plant. 2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), 112–115. doi: https://doi.org/10.1109/DCABES50732.2020.00037
• Chouhan, S., Ghorbani, J., Inan, H., Feliachi, A., & Choudhry, M. A. (2013). Smart MAS restoration for distribution system with Microgrids. 2013 IEEE Power & Energy Society General Meeting, 1–5. doi: https://doi.org/10.1109/PESMG.2013.6672945
• Fernández, F. J. V., Segura Manzano, F., Andújar Márquez, J. M., & Calderón Godoy, A. J. (2020). Extended Model Predictive Controller to Develop Energy Management Systems in Renewable Source-Based Smart Microgrids with Hydrogen as Backup. Theoretical Foundation and Case Study. Sustainability, 12(21), 8969. doi: https://doi.org/10.3390/su12218969
• Grosso, J. M., Maestre, J. M., Ocampo-Martinez, C., & Puig, V. (2014). On the Assessment of TreeBased and Chance-Constrained Predictive Control Approaches applied to Drinking Water Networks. IFAC Proceedings Volumes, 47(3), 6240–6245. doi: https://doi.org/10.3182/20140824-6-ZA-1003.01648
• Luo, S., Hu, C., Zhang, Y., Ma, R., & Meng, L. (2017). Multi-agent systems using model predictive control for coordinative optimization control of microgrid. 2017 20th International Conference on Electrical Machines and Systems (ICEMS), 1–5. doi: https://doi.org/10.1109/ICEMS.2017.8056293
• Omarov, B., & Altayeva, A. (2018). Towards Intelligent IoT Smart City platform Based on OneM2M Guideline: Smart Grid Case Study. 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), 701–704. doi: https://doi.org/10.1109/BigComp.2018.00130 Parisio, A., Rikos, E., Tzamalis, G., & Glielmo, L. (2014). Use of model predictive control for experimental microgrid optimization. Applied Energy, 115, 37–46. doi: https://doi.org/10.1016/j.apenergy.2013.10.027
• Parisio, A., Wiezorek, C., Kyntaja, T., Elo, J., Strunz, K., & Johansson, K. H. (2017). Cooperative MPC-Based Energy Management for Networked Microgrids. IEEE Transactions on Smart Grid, 8(6), 3066–3074. doi: https://doi.org/10.1109/TSG.2017.2726941
• Raju, L., Appaswamy, K., Vengatraman, J., & Morais, A. A. (2016). Advanced energy management in virtual power plant using multi agent system. 2016 3rd International Conference on Electrical Energy Systems (ICEES), 133–138. doi: https://doi.org/10.1109/ICEES.2016.7510630
• Valverde, L., Rosa, F., Del Real, A. J., Arce, A., & Bordons, C. (2013). Modeling, simulation and experimental set-up of a renewable hydrogen-based domestic microgrid. International Journal of Hydrogen Energy, 38(27), 11672–11684. doi: https://doi.org/10.1016/j.ijhydene.2013.06.113
• Velarde, P., Valverde, L., Maestre, J. M., Ocampo-Martinez, C., & Bordons, C. (2017). On the comparison of stochastic model predictive control strategies applied to a hydrogen-based microgrid. Journal of Power Sources, 343, 161–173. doi: https://doi.org/10.1016/j.jpowsour.2017.01.015