Current - Issue

1. H. A. Muqeet et al., “Sustainable Solutions for Advanced Energy Management System of Campus Microgrids: Model Opportunities and
Future Challenges,” Sensors, vol. 22, no. 6, p. 2345, Jan. 2022, doi: https://doi.org/10.3390/s22062345.
2. K. Lalbiakhlua, S. Deb, Ksh. R. Singh, S. Datta, H. A. Shuaibu, and T. S. Ustun, “Deep learning based solar forecasting for optimal PV
BESS sizing in ultra fast charging stations,” Scientific Reports, vol. 15, no. 1, Sep. 2025, doi: https://doi.org/10.1038/s41598-025-17408-
0.
3. L. Bin et al., “Scheduling and Sizing of Campus Microgrid Considering Demand Response and Economic Analysis,” Sensors, vol. 22, no.
16, p. 6150, Aug. 2022, doi: https://doi.org/10.3390/s22166150.
4. G. Talluri, Gabriele Maria Lozito, F. Grasso, Carlos Iturrino Garcia, and A. Luchetta, “Optimal Battery Energy Storage System Scheduling
within Renewable Energy Communities,” Energies (Basel), vol. 14, no. 24, pp. 8480–8480, Dec. 2021, doi:
https://doi.org/10.3390/en14248480.
5. A. Theja and M. Pal, “An Explainable Equity-Aware P2P Energy Trading Framework for Socio-Economically Diverse Microgrid,”
arXiv.org, 2025. https://arxiv.org/abs/2507.18738
6. T. O. Pocola et al., “Optimal sizing and control of a grid-connected battery in a stacked revenue model including an energy community,”
Applied Energy, vol. 397, p. 126122, Jun. 2025, doi: https://doi.org/10.1016/j.apenergy.2025.126122.
7. T. J. J. van den Boom and B. De Schutter, “Modeling and control of switching max-plus-linear systems with random and deterministic
switching,” Discrete Event Dynamic Systems, vol. 22, no. 3, pp. 293–332, Dec. 2011, doi: https://doi.org/10.1007/s10626-011-0123-x.
8. D. Watari et al., “Multi-time scale energy management framework for smart PV systems mixing fast and slow dynamics,” Applied Energy,
vol. 289, pp. 116671–116671, May 2021, doi: https://doi.org/10.1016/j.apenergy.2021.116671.
9. D. Thomas and E. Kotsakis, “Energy Management and Optimal Power Scheduling in a Smart Building under Uncertainty,” Smart Cities -
Their Framework and Applications, Oct. 2021, doi: https://doi.org/10.5772/intechopen.94989.
10. P. Wenzelburger and F. Allgöwer, “Model Predictive Control for Flexible Job Shop Scheduling in Industry 4.0,” Applied Sciences, vol.
11, no. 17, p. 8145, Sep. 2021, doi: https://doi.org/10.3390/app11178145.
11. S. A. Farghal, M. A. Tantawy, and A. E. El-Alfy, “Impact of Solar Thermal Power Plants on Economy and Reliability of Utility System,”
IEEE Transactions on Energy Conversion, vol. EC-2, no. 2, pp. 208–214, Jun. 1987, doi: https://doi.org/10.1109/tec.1987.4765831.
12. S. A. Farghal, M. A. Tantawy, and A. E. El-Alfy, “Optimum Design of Stand Alone Solar Thermal Power System with Reliability
Constraint,” IEEE Transactions on Energy Conversion, vol. EC-2, no. 2, pp. 215–221, Jun. 1987, doi:
https://doi.org/10.1109/tec.1987.4765832.
13. Y. Yang, S. Bremner, C. Menictas, and M. Kay, “Battery energy storage system size determination in renewable energy systems: A
review,” Renewable and Sustainable Energy Reviews, vol. 91, pp. 109–125, Aug. 2018, doi: https://doi.org/10.1016/j.rser.2018.03.047.
14. Y. Yang, S. Bremner, C. Menictas, and M. Kay, “Modelling and optimal energy management for battery energy storage systems in
renewable energy systems: A review,” Renewable and Sustainable Energy Reviews, vol. 167, Art. no. 112671, Oct. 2022, doi:
10.1016/j.rser.2022.112671.
15. K. B. Gassi and M. Baysal, “Analysis of a linear programming-based decision-making model for microgrid energy management systems
with renewable sources,” International Journal of Energy Research, vol. 46, no. 6, pp. 7495–7518, 2022, doi: 10.1002/er.7656.
16. A. Shufian and N. Mohammad, “Modeling and analysis of cost-effective energy management for integrated microgrids,” Cleaner
Engineering and Technology, vol. 8, Art. no. 100508, Jun. 2022, doi: 10.1016/j.clet.2022.100508.17. E. F. Orumwense and K. Abo-Al-Ez, “Internet of Things for smart energy systems: A review on its applications, challenges and future
trends,” AIMS Electronics and Electrical Engineering, vol. 7, no. 1, pp. 50–74, 2023, doi: 10.3934/electreng.2023004.
18. L. O. Aghenta and M. T. Iqbal, “Development of an IoT based open source SCADA system for PV system monitoring,” in Proc. 2019
IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), Edmonton, AB, Canada, 2019, pp. 1–4, doi:
10.1109/CCECE.2019.8861827.
19. M. R. B. Khan, J. Pasupuleti, J. Al-Fattah, and M. Tahmasebi, “Energy management system for PV-battery microgrid based on model
predictive control,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 15, no. 1, pp. 20–26, 2019, doi:
10.11591/ijeecs.v15.i1.pp20-26.
20. J. Hu, Y. Xu, K. W. Cheng, and J. M. Guerrero, “A model predictive control strategy of PV-Battery microgrid under variable power
generations and load conditions,” Applied Energy, vol. 221, pp. 195–203, Jul. 2018, doi: 10.1016/j.apenergy.2018.03.085.
21. J. Hu, Y. Shan, J. M. Guerrero, A. Ioinovici, K. W. Chan, and J. Rodriguez, “Model predictive control of microgrids—An overview,”
Renewable and Sustainable Energy Reviews, vol. 136, Art. no. 110422, Feb. 2021, doi: 10.1016/j.rser.2020.110422.
22. P. Horrillo-Quintero, J. F. Reynaud, and C. Bordons, “Model predictive control of a microgrid with energy-stored quasi-Z-source cascaded
H-bridge multilevel inverter,” Applied Energy, vol. 346, Art. no. 121390, 2023, doi: 10.1016/j.apenergy.2023.121390.
23. C. A. Hans, P. Sopasakis, A. Bemporad, J. Raisch, and C. Reincke-Collon, “Scenario-based model predictive operation control of islanded
microgrids,” in Proc. 54th IEEE Conference on Decision and Control (CDC), Osaka, Japan, 2015, pp. 3272–3277.
24. Y. Lin, L. Li, J. Zhang, and J. Wang, “A scenario-based stochastic model predictive control approach for microgrid operation at an
Australian cotton farm under uncertainties,” International Journal of Electrical Power & Energy Systems, vol. 159, Art. no. 110025, 2024,
doi: 10.1016/j.ijepes.2024.110025.