<figure><img src="image_23.png" /><figcaption>Figure 19. Frequency spectrum, (a) inverter output current, (b) inverter output voltage, (c) load voltage.</figcaption></figure>

## 5. Conclusions and Perspectives

In this paper, an energy management system, based on a generalized predictive control approach, was introduced for being deployed in the secondary and the tertiary control layers. In fact, the secondary layer focused on power regulation and energy demand/response balance. An optimization function was defined, for this level, by considering a GPC strategy. State-space equations are presented for the controllable micro-grid system's entities depending on the constraint, which is defined in the tertiary control layer. Therefore, the tertiary control focused only on power dispatch and economic aspects. Within this context, the electricity cost is used as a constraint to control the batteries charge/discharge. Indeed, the electricity price is integrated as a cost function to be minimized while respecting the constraint defined by the GPC model. The controller manages the power between the RESs, the batteries, and the utility grid depending on the operational constraints of the EM strategy deployed using the GPC model. The powerful of the GPC model for the EM system was shown by minimizing the electricity bill. For a given scenario, the deployed control strategy manages the energy flow in the deployed MG system depending on the electricity price variability during the day. The main objective is to ensure the power to the consumer with a minimum price by integrating predictive control methods that considers multiple objective function and constraint. While the battery charge/discharge current is controlled depending on the defined constraint with the possibility to charge the battery storage system from the utility grid when the electricity price is minimal. It is worth noting that actual controllers of the power converter manage the energy flow based on real-time measurement (e.g., current, voltage, power), however, it is possible to control the power exchange using predictive control offering the possibility to integrate multi-objective functions and constraints. Today, it is possible to connect actual inverters with the Internet in order to store, process, and analyze measured data. These latter could be used to train machine learning algorithms and to generate the control input parameters offering more accuracy for the predictive control. In future works, a power
converter will be deployed based on the GPC controller for energy management in our actual MG system.

**Author Contributions:** Conceptualization, A.E., M.B. and R.O.; data curation, A.E.; methodology, A.E., R.O. and M.B.; software, A.E. and R.O.; supervision, R.O., M.B., N.E.k. and K.Z.-D.; validation, R.O. and M.B.; writing—original draft, A.E.; writing—review and editing, A.E., M.B., N.E.k. and K.Z.-D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is supported by IRESEN-Innov-Project (2020–2022), under the HOLSYS project program. It is also partially supported by MIGRID project (Grant 5-398, 2017–2019), which is funded by USAID under the PEER program.

**Data Availability Statement:** Data sharing is not applicable to this article.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders and the project participants had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

## References

1. Elmouatamid, A.; NaitMalek, Y.; Ouladsine, R.; Bakhouya, M.; Elkamoun, N.; Khaidar, M.; Zine-Dine, K. A Micro-grid system infrastructure for efficient energy management in smart buildings. In *Submitted to ATSPES'1 (Advanced Technologies for Solar Photovoltaics Energy Systems)*; Springer: Cham, Switzerland, 2020.

2. Li, J.; Murphy, E.; Winnick, J.; Kohl, P.A. Studies on the cycle life of commercial lithium-ion batteries during rapid charge-discharge cycling. *J. Power Sources* **2001**, *102*, 294–301. [CrossRef]

3. Elmouatamid, A. MAPCAST: An adaptive control approach using predictive analytics for energy balance in micro-grid systems. *Int. J. Renew. Energy Res.* **2020**, *10*, 945–954.

4. Elmouatamid, A.; NaitMalek, Y.; Bakhouya, M.; Ouladsine, R.; Elkamoun, N.; Zine-Dine, K.; Khaidar, M. An energy management platform for micro-grid systems using internet of things and big-data technologies. *Proc. Inst. Mech. Eng. Part I J. Syst. Control. Eng.* **2019**, *233*, 904–917. [CrossRef]

5. Elmouatamid, A.; Ouladsine, R.; Bakhouya, M.; Zine-Dine, K.; Khaidar, M. A Control Strategy Based on Power Forecasting for Micro-Grid Systems. In *Proceedings of the 2019 IEEE International Smart Cities Conference (ISC2)*, Casablanca, Morocco, 14–17 October 2019; pp. 735–740.

6. Serale, G.; Fiorentini, M.; Capozzoli, A.; Bernardini, D.; Bemporad, A. Model predictive control (MPC) for enhancing building and HVAC system energy efficiency: Problem formulation, applications and opportunities. *Energies* **2018**, *11*, 631. [CrossRef]

7. Elkhoukhi, H.; NaitMalek, Y.; Bakhouya, M.; Berouine, A.; Kharbouch, A.; Lachhab, F.; Essaaidi, M. A platform architecture for occupancy detection using stream processing and machine learning approaches. *Concurr. Comput. Pract. Exp.* **2020**, *32*, e5651. [CrossRef]

8. NaitMalek, Y.; Najib, M.; Bakhouya, M.; Essaaidi, M. On the Use of Machine Learning for State-of-Charge Forecasting in Electric Vehicles. In *Proceedings of the 2019 IEEE International Smart Cities Conference (ISC2)*, Casablanca, Morocco, 14–17 October 2019; pp. 408–413.

9. Elmouatamid, A.; Ouladsine, R.; Bakhouya, M.; El Kamoun, N.; Khaidar, M.; Zine-Dine, K. Review of control and energy management approaches in micro-grid systems. *Energies* **2020**, *14*, 168. [CrossRef]

10. Hu, J.; Zhu, J.; Guerrero, J.M. Model Predictive Control of Smart Microgrids. In *Proceedings of the 2014 17th International Conference on Electrical Machines and Systems (ICEMS)*, Hangzhou, China, 22–25 October 2014; pp. 2815–2820.

11. Dehghani-Pilehvarani, A.; Markou, A.; Ferrarini, L. A hierarchical distributed predictive control approach for microgrids energy management. *Sustain. Cities Soc.* **2019**, *48*, 101536.

12. Marzband, M.; Yousefnejad, E.; Sumper, A.; Domínguez-García, J.L. Real time experimental implementation of optimum energy management system in standalone microgrid by using multi-layer ant colony optimization. *Int. J. Electr. Power Energy Syst.* **2016**, *75*, 265–274. [CrossRef]

13. Elmouatamid, A.; Ouladsine, R.; Bakhouya, M.; El Kamoun, N.; Zine-Dine, K.; Khaidar, M. A Model Predictive Control Approach for Energy Management in Micro-Grid Systems. In *Proceedings of the 2019 International Conference on Smart Energy Systems and Technologies (SEST)*, Porto, Portugal, 9–11 September 2019; pp. 1–6.

14. Hosseinzadeh, M.; Salmasi, F.R. Robust optimal power management system for a hybrid AC/DC micro-grid. *IEEE Trans. Sustain. Energy* **2015**, *6*, 675–687. [CrossRef]

15. Luna, A.C.; Diaz, N.L.; Graells, M.; Vasquez, J.C.; Guerrero, J.M. Mixed-integer-linear-programming-based energy management system for hybrid PV-wind-battery microgrids: Modeling, design, and experimental verification. *IEEE Trans. Power Electron.* **2017**, *32*, 2769–2783. [CrossRef]

16. Vergara, P.P.; López, J.C.; da Silva, L.C.; Rider, M.J. Security-constrained optimal energy management system for three-phase residential microgrids. *Electr. Power Syst. Res.* **2017**, *146*, 371–382. [CrossRef]
converter will be deployed based on the GPC controller for energy management in our actual MG system.

**Author Contributions:** Conceptualization, A.E., M.B. and R.O.; data curation, A.E.; methodology, A.E., R.O. and M.B.; software, A.E. and R.O.; supervision, R.O., M.B., N.E.k. and K.Z.-D.; validation, R.O. and M.B.; writing—original draft, A.E.; writing—review and editing, A.E., M.B., N.E.k. and K.Z.-D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is supported by IRESEN-Innov-Project (2020–2022), under the HOLSYS project program. It is also partially supported by MIGRID project (Grant 5-398, 2017–2019), which is funded by USAID under the PEER program.

**Data Availability Statement:** Data sharing is not applicable to this article.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders and the project participants had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

## References

1. Elmouatamid, A.; NaitMalek, Y.; Ouladsine, R.; Bakhouya, M.; Elkamoun, N.; Khaidar, M.; Zine-Dine, K. A Micro-grid system infrastructure for efficient energy management in smart buildings. In *Submitted to ATSPES'1 (Advanced Technologies for Solar Photovoltaics Energy Systems)*; Springer: Cham, Switzerland, 2020.

2. Li, J.; Murphy, E.; Winnick, J.; Kohl, P.A. Studies on the cycle life of commercial lithium-ion batteries during rapid charge-discharge cycling. *J. Power Sources* **2001**, *102*, 294–301. [CrossRef]

3. Elmouatamid, A. MAPCAST: An adaptive control approach using predictive analytics for energy balance in micro-grid systems. *Int. J. Renew. Energy Res.* **2020**, *10*, 945–954.

4. Elmouatamid, A.; NaitMalek, Y.; Bakhouya, M.; Ouladsine, R.; Elkamoun, N.; Zine-Dine, K.; Khaidar, M. An energy management platform for micro-grid systems using internet of things and big-data technologies. *Proc. Inst. Mech. Eng. Part I J. Syst. Control. Eng.* **2019**, *233*, 904–917. [CrossRef]

5. Elmouatamid, A.; Ouladsine, R.; Bakhouya, M.; Zine-Dine, K.; Khaidar, M. A Control Strategy Based on Power Forecasting for Micro-Grid Systems. In *Proceedings of the 2019 IEEE International Smart Cities Conference (ISC2)*, Casablanca, Morocco, 14–17 October 2019; pp. 735–740.

6. Serale, G.; Fiorentini, M.; Capozzoli, A.; Bernardini, D.; Bemporad, A. Model predictive control (MPC) for enhancing building and HVAC system energy efficiency: Problem formulation, applications and opportunities. *Energies* **2018**, *11*, 631. [CrossRef]

7. Elkhoukhi, H.; NaitMalek, Y.; Bakhouya, M.; Berouine, A.; Kharbouch, A.; Lachhab, F.; Essaaidi, M. A platform architecture for occupancy detection using stream processing and machine learning approaches. *Concurr. Comput. Pract. Exp.* **2020**, *32*, e5651. [CrossRef]

8. NaitMalek, Y.; Najib, M.; Bakhouya, M.; Essaaidi, M. On the Use of Machine Learning for State-of-Charge Forecasting in Electric Vehicles. In *Proceedings of the 2019 IEEE International Smart Cities Conference (ISC2)*, Casablanca, Morocco, 14–17 October 2019; pp. 408–413.

9. Elmouatamid, A.; Ouladsine, R.; Bakhouya, M.; El Kamoun, N.; Khaidar, M.; Zine-Dine, K. Review of control and energy management approaches in micro-grid systems. *Energies* **2020**, *14*, 168. [CrossRef]

10. Hu, J.; Zhu, J.; Guerrero, J.M. Model Predictive Control of Smart Microgrids. In *Proceedings of the 2014 17th International Conference on Electrical Machines and Systems (ICEMS)*, Hangzhou, China, 22–25 October 2014; pp. 2815–2820.

11. Dehghani-Pilehvarani, A.; Markou, A.; Ferrarini, L. A hierarchical distributed predictive control approach for microgrids energy management. *Sustain. Cities Soc.* **2019**, *48*, 101536.

12. Marzband, M.; Yousefnejad, E.; Sumper, A.; Domínguez-García, J.L. Real time experimental implementation of optimum energy management system in standalone microgrid by using multi-layer ant colony optimization. *Int. J. Electr. Power Energy Syst.* **2016**, *75*, 265–274. [CrossRef]

13. Elmouatamid, A.; Ouladsine, R.; Bakhouya, M.; El Kamoun, N.; Zine-Dine, K.; Khaidar, M. A Model Predictive Control Approach for Energy Management in Micro-Grid Systems. In *Proceedings of the 2019 International Conference on Smart Energy Systems and Technologies (SEST)*, Porto, Portugal, 9–11 September 2019; pp. 1–6.

14. Hosseinzadeh, M.; Salmasi, F.R. Robust optimal power management system for a hybrid AC/DC micro-grid. *IEEE Trans. Sustain. Energy* **2015**, *6*, 675–687. [CrossRef]

15. Luna, A.C.; Diaz, N.L.; Graells, M.; Vasquez, J.C.; Guerrero, J.M. Mixed-integer-linear-programming-based energy management system for hybrid PV-wind-battery microgrids: Modeling, design, and experimental verification. *IEEE Trans. Power Electron.* **2017**, *32*, 2769–2783. [CrossRef]

16. Vergara, P.P.; López, J.C.; da Silva, L.C.; Rider, M.J. Security-constrained optimal energy management system for three-phase residential microgrids. *Electr. Power Syst. Res.* **2017**, *146*, 371–382. [CrossRef]
17. Xiang, Y.; Liu, J.; Liu, Y. Robust energy management of microgrid with uncertain renewable generation and load. *IEEE Trans. Smart Grid* **2015**, *7*, 1034–1043. [CrossRef]

18. Berouine, A.; Ouladsine, R.; Bakhouya, M.; Essaaidi, M. Towards a real-time predictive management approach of indoor air quality in energy-efficient buildings. *Energies* **2020**, *13*, 3246. [CrossRef]

19. Buyak, N.A.; Deshko, V.I.; Sukhodub, I.O. Buildings energy use and human thermal comfort according to energy and exergy approach. *Energy Build.* **2017**, *146*, 172–181. [CrossRef]

20. Rahmani-Andebili, M.; Shen, H. Cooperative Distributed Energy Scheduling for Smart Homes Applying Stochastic Model Predictive Control. In Proceedings of the 2017 IEEE International Conference on Communications (ICC), Paris, France, 21–25 May 2017; pp. 1–6.

21. Hemmati, R.; Saboori, H. Stochastic optimal battery storage sizing and scheduling in home energy management systems equipped with solar photovoltaic panels. *Energy Build.* **2017**, *152*, 290–300. [CrossRef]

22. Bordons, C.; Teno, G.; Marquez, J.J.; Ridao, M.A. Effect of the Integration of Disturbances Prediction in Energy Management Systems for Microgrids. In Proceedings of the 2019 International Conference on Smart Energy Systems and Technologies (SEST), Porto, Portugal, 9–11 September 2019; pp. 1–6.

23. Petrollese, M. Optimal Generation Scheduling for Renewable Microgrids Using Hydrogen Storage Systems. Ph.D. Thesis, The University of Cagliari, Cagliari, Italy, 2015.

24. Negenborn, R.R.; Houwing, M.; De Schutter, B.; Hellendoorn, J. Model Predictive Control for Residential Energy Resources Using a Mixed-Logical Dynamic Model. In Proceedings of the 2009 International Conference on Networking, Sensing and Control, Okayama, Japan, 26–29 March 2009; pp. 702–707.

25. Mendes, P.R.; Isorna, L.V.; Bordons, C.; Normey-Rico, J.E. Energy management of an experimental microgrid coupled to a V2G system. *J. Power Sources* **2016**, *327*, 702–713. [CrossRef]

26. Garcia-Torres, F.; Vilaplana, D.G.; Bordons, C.; Roncero-Sanchez, P.; Ridao, M.A. Optimal management of microgrids with external agents including battery/fuel cell electric vehicles. *IEEE Trans. Smart Grid* **2018**, *10*, 4299–4308. [CrossRef]

27. Chen, H.; Chen, J.; Lu, H.; Yan, C.; Liu, Z. A modified MPC-based optimal strategy of power management for fuel cell hybrid vehicles. *IEEE/ASME Trans. Mechatron.* **2020**, *25*, 2009–2018. [CrossRef]

28. Parisio, A.; Rikos, E.; Tzamalis, G.; Glielmo, L. Use of model predictive control for experimental microgrid optimization. *Appl. Energy* **2014**, *115*, 37–46. [CrossRef]

29. Bozchalui, M.C.; Hashmi, S.A.; Hassen, H.; Canizares, C.A.; Bhattacharya, K. Optimal operation of residential energy hubs in smart grids. *IEEE Trans. Smart Grid* **2012**, *3*, 1755–1766. [CrossRef]

30. Garcia-Torres, F.; Vazquez, S.; Moreno-Garcia, I.M.; Gil-de-Castro, A.; Roncero-Sanchez, P.; Moreno-Munoz, A. Microgrids power quality enhancement using model predictive control. *Electronics* **2021**, *10*, 328. [CrossRef]

31. Bordons, C.; Montero, C. Basic principles of MPC for power converters: Bridging the gap between theory and practice. *IEEE Ind. Electron. Mag.* **2015**, *9*, 31–43. [CrossRef]

32. Linder, A.; Kennel, R. Model Predictive Control for Electrical Drives. In Proceedings of the 2005 IEEE 36th Power Electronics Specialists Conference, Dresden, Germany, 16 June 2005; pp. 1793–1799.

33. Rodriguez, J.; Pontt, J.; Silva, C.A.; Correa, P.; Lezana, P.; Cortés, P.; Ammann, U. Predictive current control of a voltage source inverter. *IEEE Trans. Ind. Electron.* **2007**, *54*, 495–503. [CrossRef]

34. Gregor, R.; Barrero, F.; Toral, S.; Arahal, M.R.; Prieto, J.; Durán, M.J. Enhanced Predictive Current Control Method for the Asymmetrical Dual—Three Phase Induction Machine. In Proceedings of the 2009 IEEE International Electric Machines and Drives Conference, Miami, FL, USA, 3–6 May 2009; pp. 265–272.

35. Cortes, P.; Wilson, A.; Kouro, S.; Rodriguez, J.; Abu-Rub, H. Model predictive control of multilevel cascaded H-bridge inverters. *IEEE Trans. Ind. Electron.* **2010**, *57*, 2691–2699. [CrossRef]

36. Lezana, P.; Aguilera, R.; Quevedo, D.E. Model predictive control of an asymmetric flying capacitor converter. *IEEE Trans. Ind. Electron.* **2008**, *56*, 1839–1846. [CrossRef]

37. Vargas, R.; Ammann, U.; Hudoffsky, B.; Rodriguez, J.; Wheeler, P. Predictive torque control of an induction machine fed by a matrix converter with reactive input power control. *IEEE Trans. Power Electron.* **2010**, *25*, 1426–1438. [CrossRef]

38. Correa, P.; Rodríguez, J.; Rivera, M.; Espinoza, J.R.; Kolar, J.W. Predictive control of an indirect matrix converter. *IEEE Trans. Ind. Electron.* **2009**, *56*, 1847–1853. [CrossRef]

39. Vazquez, S.; Leon, J.I.; Franquelo, L.G.; Rodriguez, J.; Young, H.A.; Marquez, A.; Zanchetta, P. Model predictive control: A review of its applications in power electronics. *IEEE Ind. Electron. Mag.* **2014**, *8*, 16–31. [CrossRef]

40. Fontenot, H.; Dong, B. Modeling and control of building-integrated microgrids for optimal energy management—A review. *Appl. Energy* **2019**, *254*, 113689. [CrossRef]

41. Villalón, A.; Rivera, M.; Salgueiro, Y.; Muñoz, J.; Dragičević, T.; Blaabjerg, F. Predictive control for microgrid applications: A review study. *Energies* **2020**, *13*, 2454. [CrossRef]

42. Hu, J.; Shan, Y.; Guerrero, J.M.; Ioinovici, A.; Chan, K.W.; Rodriguez, J. Model predictive control of microgrids—An overview. *Renew. Sustain. Energy Rev.* **2021**, *136*, 110422. [CrossRef]

43. Jayachandran, M.; Ravi, G. Decentralized model predictive hierarchical control strategy for islanded AC microgrids. *Electr. Power Syst. Res.* **2019**, *170*, 92–100. [CrossRef]
17. Xiang, Y.; Liu, J.; Liu, Y. Robust energy management of microgrid with uncertain renewable generation and load. *IEEE Trans. Smart Grid* **2015**, *7*, 1034–1043. [CrossRef]

18. Berouine, A.; Ouladsine, R.; Bakhouya, M.; Essaaidi, M. Towards a real-time predictive management approach of indoor air quality in energy-efficient buildings. *Energies* **2020**, *13*, 3246. [CrossRef]

19. Buyak, N.A.; Deshko, V.I.; Sukhodub, I.O. Buildings energy use and human thermal comfort according to energy and exergy approach. *Energy Build.* **2017**, *146*, 172–181. [CrossRef]

20. Rahmani-Andebili, M.; Shen, H. Cooperative Distributed Energy Scheduling for Smart Homes Applying Stochastic Model Predictive Control. In *Proceedings of the 2017 IEEE International Conference on Communications (ICC)*, Paris, France, 21–25 May 2017; pp. 1–6.

21. Hemmati, R.; Saboori, H. Stochastic optimal battery storage sizing and scheduling in home energy management systems equipped with solar photovoltaic panels. *Energy Build.* **2017**, *152*, 290–300. [CrossRef]

22. Bordons, C.; Teno, G.; Marquez, J.J.; Ridao, M.A. Effect of the Integration of Disturbances Prediction in Energy Management Systems for Microgrids. In *Proceedings of the 2019 International Conference on Smart Energy Systems and Technologies (SEST)*, Porto, Portugal, 9–11 September 2019; pp. 1–6.

23. Petrollese, M. Optimal Generation Scheduling for Renewable Microgrids Using Hydrogen Storage Systems. Ph.D. Thesis, The University of Cagliari, Cagliari, Italy, 2015.

24. Negenborn, R.R.; Houwing, M.; De Schutter, B.; Hellendoorn, J. Model Predictive Control for Residential Energy Resources Using a Mixed-Logical Dynamic Model. In *Proceedings of the 2009 International Conference on Networking, Sensing and Control, Okayama, Japan*, 26–29 March 2009; pp. 702–707.

25. Mendes, P.R.; Isorna, L.V.; Bordons, C.; Normey-Rico, J.E. Energy management of an experimental microgrid coupled to a V2G system. *J. Power Sources* **2016**, *327*, 702–713. [CrossRef]

26. Garcia-Torres, F.; Vilaplana, D.G.; Bordons, C.; Roncero-Sanchez, P.; Ridao, M.A. Optimal management of microgrids with external agents including battery/fuel cell electric vehicles. *IEEE Trans. Smart Grid* **2018**, *10*, 4299–4308. [CrossRef]

27. Chen, H.; Chen, J.; Lu, H.; Yan, C.; Liu, Z. A modified MPC-based optimal strategy of power management for fuel cell hybrid vehicles. *IEEE/ASME Trans. Mechatron.* **2020**, *25*, 2009–2018. [CrossRef]

28. Parisio, A.; Rikos, E.; Tzamalis, G.; Glielmo, L. Use of model predictive control for experimental microgrid optimization. *Appl. Energy* **2014**, *115*, 37–46. [CrossRef]

29. Bozchalui, M.C.; Hashmi, S.A.; Hassen, H.; Canizares, C.A.; Bhattacharya, K. Optimal operation of residential energy hubs in smart grids. *IEEE Trans. Smart Grid* **2012**, *3*, 1755–1766. [CrossRef]

30. Garcia-Torres, F.; Vazquez, S.; Moreno-Garcia, I.M.; Gil-de-Castro, A.; Roncero-Sanchez, P.; Moreno-Munoz, A. Microgrids power quality enhancement using model predictive control. *Electronics* **2021**, *10*, 328. [CrossRef]

31. Bordons, C.; Montero, C. Basic principles of MPC for power converters: Bridging the gap between theory and practice. *IEEE Ind. Electron. Mag.* **2015**, *9*, 31–43. [CrossRef]

32. Linder, A.; Kennel, R. Model Predictive Control for Electrical Drives. In *Proceedings of the 2005 IEEE 36th Power Electronics Specialists Conference, Dresden, Germany*, 16 June 2005; pp. 1793–1799.

33. Rodriguez, J.; Pontt, J.; Silva, C.A.; Correa, P.; Lezana, P.; Cortés, P.; Ammann, U. Predictive current control of a voltage source inverter. *IEEE Trans. Ind. Electron.* **2007**, *54*, 495–503. [CrossRef]

34. Gregor, R.; Barrero, F.; Toral, S.; Arahal, M.R.; Prieto, J.; Durán, M.J. Enhanced Predictive Current Control Method for the Asymmetrical Dual—Three Phase Induction Machine. In *Proceedings of the 2009 IEEE International Electric Machines and Drives Conference, Miami, FL, USA*, 3–6 May 2009; pp. 265–272.

35. Cortes, P.; Wilson, A.; Kouro, S.; Rodriguez, J.; Abu-Rub, H. Model predictive control of multilevel cascaded H-bridge inverters. *IEEE Trans. Ind. Electron.* **2010**, *57*, 2691–2699. [CrossRef]

36. Lezana, P.; Aguilera, R.; Quevedo, D.E. Model predictive control of an asymmetric flying capacitor converter. *IEEE Trans. Ind. Electron.* **2008**, *56*, 1839–1846. [CrossRef]

37. Vargas, R.; Ammann, U.; Hudoffsky, B.; Rodriguez, J.; Wheeler, P. Predictive torque control of an induction machine fed by a matrix converter with reactive input power control. *IEEE Trans. Power Electron.* **2010**, *25*, 1426–1438. [CrossRef]

38. Correa, P.; Rodríguez, J.; Rivera, M.; Espinoza, J.R.; Kolar, J.W. Predictive control of an indirect matrix converter. *IEEE Trans. Ind. Electron.* **2009**, *56*, 1847–1853. [CrossRef]

39. Vazquez, S.; Leon, J.I.; Franquelo, L.G.; Rodriguez, J.; Young, H.A.; Marquez, A.; Zanchetta, P. Model predictive control: A review of its applications in power electronics. *IEEE Ind. Electron. Mag.* **2014**, *8*, 16–31. [CrossRef]

40. Fontenot, H.; Dong, B. Modeling and control of building-integrated microgrids for optimal energy management—A review. *Appl. Energy* **2019**, *254*, 113689. [CrossRef]

41. Villalón, A.; Rivera, M.; Salgueiro, Y.; Muñoz, J.; Dragičević, T.; Blaabjerg, F. Predictive control for microgrid applications: A review study. *Energies* **2020**, *13*, 2454. [CrossRef]

42. Hu, J.; Shan, Y.; Guerrero, J.M.; Ioinovici, A.; Chan, K.W.; Rodriguez, J. Model predictive control of microgrids—An overview. *Renew. Sustain. Energy Rev.* **2021**, *136*, 110422. [CrossRef]

43. Jayachandran, M.; Ravi, G. Decentralized model predictive hierarchical control strategy for islanded AC microgrids. *Electr. Power Syst. Res.* **2019**, *170*, 92–100. [CrossRef]
44. Tedesco, F.; Mariam, L.; Basu, M.; Casavola, A.; Conlon, M.F. Economic model predictive control-based strategies for cost-effective supervision of community microgrids considering battery lifetime. *IEEE J. Emerg. Sel. Top. Power Electron.* **2015**, *3*, 1067–1077.
[CrossRef]

45. Drgoňa, J.; Arroyo, J.; Figueroa, I.C.; Blum, D.; Arendt, K.; Kim, D.; Helsen, L. All you need to know about model predictive control for buildings. *Annu. Rev. Control.* **2020**, *50*, 190–232. [CrossRef]

46. Ławryńczuk, M. *Computationally Efficient Model Predictive Control Algorithms*; Springer: Berlin, Germany, 2014.

47. Lv, J.; Wang, X.; Wang, G.; Song, Y. Research on control strategy of isolated DC microgrid based on SOC of energy storage system. *Electronics* **2021**, *10*, 834. [CrossRef]

48. Grigg, C.; Wong, P.; Albrecht, P.; Allan, R.; Bhavaraju, M.; Billinton, R.; Li, W. The IEEE reliability test system-1996. A report prepared by the reliability test system task force of the application of probability methods subcommittee. *IEEE Trans. Power Syst.* **1999**, *14*, 1010–1020. [CrossRef]

49. Clarke, D.W.; Mohtadi, C. Properties of generalized predictive control. *Automatica* **1989**, *25*, 859–875. [CrossRef]

50. Yi, X.; Zhang, J.; Wang, Z.; Li, T.; Zheng, Y. Deep Distributed Fusion Network for Air Quality Prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK, 19–23 August 2018; pp. 965–973. [CrossRef]

51. Bruninx, K.; Delarue, E. A statistical description of the error on wind power forecasts for probabilistic reserve sizing. *IEEE Trans. Sustain. Energy* **2014**, *5*, 995–1002. [CrossRef]

52. Camacho, E.F.; Alba, C.B. *Model Predictive Control*; Springer Science & Business Media: London, UK, 2013.

53. Vazquez, S.; Rodriguez, J.; Rivera, M.; Franquelo, L.G.; Norambuena, M. Model predictive control for power converters and drives: Advances and trends. *IEEE Trans. Ind. Electron.* **2016**, *64*, 935–947. [CrossRef]

54. Cortes, P.; Rodriguez, J.; Silva, C.; Flores, A. Delay compensation in model predictive current control of a three-phase inverter. *IEEE Trans. Ind. Electron.* **2011**, *59*, 1323–1325. [CrossRef]

55. Larrinaga, S.A.; Vidal, M.A.R.; Oyarbide, E.; Apraiz, J.R.T. Predictive control strategy for DC/AC converters based on direct power control. *IEEE Trans. Ind. Electron.* **2007**, *54*, 1261–1271. [CrossRef]

56. IEEE Standard Association. *Standard for Interconnection and Interoperability of Distributed Energy Resources with Associated Electric Power Systems Interfaces*; IEEE Standard Association: Piscataway, NJ, USA, 2018; pp. 1547–2018. [CrossRef]
44. Tedesco, F.; Mariam, L.; Basu, M.; Casavola, A.; Conlon, M.F. Economic model predictive control-based strategies for cost-effective supervision of community microgrids considering battery lifetime. *IEEE J. Emerg. Sel. Top. Power Electron.* **2015**, *3*, 1067–1077.
[CrossRef]

45. Drgoňa, J.; Arroyo, J.; Figueroa, I.C.; Blum, D.; Arendt, K.; Kim, D.; Helsen, L. All you need to know about model predictive control for buildings. *Annu. Rev. Control.* **2020**, *50*, 190–232. [CrossRef]

46. Ławryńczuk, M. *Computationally Efficient Model Predictive Control Algorithms*; Springer: Berlin, Germany, 2014.

47. Lv, J.; Wang, X.; Wang, G.; Song, Y. Research on control strategy of isolated DC microgrid based on SOC of energy storage system. *Electronics* **2021**, *10*, 834. [CrossRef]

48. Grigg, C.; Wong, P.; Albrecht, P.; Allan, R.; Bhavaraju, M.; Billinton, R.; Li, W. The IEEE reliability test system-1996. A report prepared by the reliability test system task force of the application of probability methods subcommittee. *IEEE Trans. Power Syst.* **1999**, *14*, 1010–1020. [CrossRef]

49. Clarke, D.W.; Mohtadi, C. Properties of generalized predictive control. *Automatica* **1989**, *25*, 859–875. [CrossRef]

50. Yi, X.; Zhang, J.; Wang, Z.; Li, T.; Zheng, Y. Deep Distributed Fusion Network for Air Quality Prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK, 19–23 August 2018; pp. 965–973. [CrossRef]

51. Bruninx, K.; Delarue, E. A statistical description of the error on wind power forecasts for probabilistic reserve sizing. *IEEE Trans. Sustain. Energy* **2014**, *5*, 995–1002. [CrossRef]

52. Camacho, E.F.; Alba, C.B. *Model Predictive Control*; Springer Science & Business Media: London, UK, 2013.

53. Vazquez, S.; Rodriguez, J.; Rivera, M.; Franquelo, L.G.; Norambuena, M. Model predictive control for power converters and drives: Advances and trends. *IEEE Trans. Ind. Electron.* **2016**, *64*, 935–947. [CrossRef]

54. Cortes, P.; Rodriguez, J.; Silva, C.; Flores, A. Delay compensation in model predictive current control of a three-phase inverter. *IEEE Trans. Ind. Electron.* **2011**, *59*, 1323–1325. [CrossRef]

55. Larrinaga, S.A.; Vidal, M.A.R.; Oyarbide, E.; Apraiz, J.R.T. Predictive control strategy for DC/AC converters based on direct power control. *IEEE Trans. Ind. Electron.* **2007**, *54*, 1261–1271. [CrossRef]

56. IEEE Standard Association. *Standard for Interconnection and Interoperability of Distributed Energy Resources with Associated Electric Power Systems Interfaces*; IEEE Standard Association: Piscataway, NJ, USA, 2018; pp. 1547–2018. [CrossRef]