Optimizing Microgrid Performance: A Data-Driven Approach with IoT Integration


  • Raafat K. Oubida


Microgrid Performance, Management Strategies, Technology, Iot Integration, Data-Driven, Microgrid Infrastructure.


The essential goal of this exploration is to work on the efficiency of microgrids by using a data-driven system that is expanded by the joining of Internet of Things (IoT) technology integration. Microgrids, which are decentralized energy systems, are a fundamental part during the time spent upgrading energy resilience, bringing down fossil fuel byproducts, and further developing admittance to power. By and by, there are serious issues associated with expanding the efficiency and reliability of microgrid tasks. These difficulties are brought about by the multifaceted cooperations that happen between the a large number and the dynamic outer impacts. Inside the extent of this examination, we offer a remarkable system that utilizes data-driven procedures to evaluate, reproduce, and improve the performance of microgrids. We plan predictive models to conjecture energy utilization, further develop energy generation and conveyance, and lift system constancy by using real-time data gathered from Internet of Things (IoT)- empowered sensors and devices implanted inside the design of the microgrid. The consolidation of Internet of Things technology makes it conceivable to consistently screen and work the resources of a microgrid, which thus pursues it more straightforward to settle on proactive decisions and execute adaptive management strategies. Through the use of case studies and simulated tests, we illustrate the efficacy of our method in terms of promoting energy sustainability, lowering operational costs, and improving the performance of microgrids. The findings of this study provide a significant contribution to the development of intelligent energy systems and lay the groundwork for future advances in the optimization and administration of microgrid electricity networks.


Download data is not yet available.


Abdulgalil, M, Khalid, M & Alismail, F 2019, ‘Optimal Sizing of Battery Energy Storage for a Grid-Connected Microgrid Subjected to Wind Uncertainties’, Energies, vol. 12, no. 12, p. 2412.

Akorede, M. F., Hizam, H., & Pouresmaeil, E. (2010). Distributed energy resources and benefits to the environment. Renewable and Sustainable Energy Reviews, 14, 724–734.

Akram, U, Khalid, M & Shafiq, S 2018, ‘Optimal sizing of a wind/solar/battery hybrid grid-connected microgrid system’, IET Renewable Power Generation, vol. 12, no. 1, pp. 72-80.

Ashabani, M, Gooi, H & Guerrero, J 2020, ‘Designing high-order power-source synchronous current converters for islanded and gridconnected microgrids’.

Awal, M, Yu, H, Tu, H, Lukic, S & Husain, I 2020, ‘Hierarchical Control for Virtual Oscillator Based Grid-Connected and Islanded Microgrids’, IEEE Transactions on Power Electronics, vol. 35, no. 1, pp. 988-1001.

Chen, H, Yang, C, Heidari, A & Zhao, X 2020, ‘An efficient double adaptive random spare reinforced whale optimization algorithm’, Expert Systems with Applications, vol. 154, p. 113018.

Danesh, S, Farnoosh, R & Razzaghnia, T 2016, ‘Fuzzy nonparametric regression based on an adaptive neuro-fuzzy inference system’, Neurocomputing, vol. 173, pp. 1450-1460.

Dey, B & Bhattacharyya, B 2018, ‘Dynamic cost analysis of a grid connected microgrid using neighborhood based differential evolution technique’, International Transactions on Electrical Energy Systems, vol. 29, no. 1, p. e2665.

Han, X, Zhang, H, Yu, X & Wang, L 2020, ‘Economic evaluation of grid-connected micro-grid system with photovoltaic and energy storage under different investment and financing models.

Hirsch, A., Parag, Y., & Guerrero, J. (2018). Microgrids: A review of technologies, key drivers and outstanding issues. Renewable and Sustainable Energy Reviews, 90, 402–411.

Jiang, H, Ning, S & Ge, Q 2019, ‘Multi-Objective Optimal Dispatching of Microgrid with Large-Scale Electric Vehicles’, IEEE Access, vol. 7, pp. 145880-145888.

Julien, M., Mazhelis, O., Su, X., & Tarkoma, S. (2016). A gap analysis of internet-of-things platforms. Computers & Communications, 89, 5–16.

Jumani, T, Mustafa, M, Rasid, M, Mirjat, N, Baloch, M & Salisu, S 2019, ‘Optimal Power Flow Controller for Grid-Connected Microgrids using Grasshopper Optimization Algorithm’, Electronics, vol. 8, no. 1, pp. 111.

Mishra, M & Rout, P 2018, ‘Fast discrete s-transform and extreme learning machine-based approach to islanding detection in gridconnected distributed generation’, Energy Systems, vol. 10, no. 3, pp. 757-789.

Naggar, A. H., Saleh, G. A., Omar, M. A., Haredy, A. M., & Derayea, S. M. (2020). Square-wave adsorptive anodic stripping voltammetric determination of antidiabetic drug linagliptin in pharmaceutical formulations and biological fluids using a pencil graphite electrode. Analytical Sciences, 36(9), 1031-1038.

Pippia, T, Sijs, J & De Schutter, B 2019, ‘A Single-Level Rule-Based Model Predictive Control Approach for Energy Management of GridConnected Microgrids’, IEEE Transactions on Control Systems Technology, pp. 1-13.

Roy, K., Islam, M. S., Das, S., & Biswas, S. (2019). Utilization of Ant Lion Optimizer Algorithm Based Recurrent Neural Networks for Energy Scheduling in Microgrids. IEEE Access, 7, 89489-89502.

Sahoo, P. K., Memaran, S., Xin, Y., Balicas, L., & Gutiérrez, H. R. (2018). One-pot growth of two-dimensional lateral heterostructures via sequential edge-epitaxy. Nature, 553(7686), 63-67.

Xing, X, Xie, L & Meng, H 2019, ‘Cooperative energy management optimization based on distributed MPC in grid-connected microgrids community’, International Journal of Electrical Power & Energy Systems, vol. 107, pp. 186-199.

Zolfaghari, M, Abedi, M & Gharehpetian, G 2019, ‘Power Flow Control of Interconnected AC–DC Microgrids in Grid-Connected Hybrid Microgrids Using Modified UIPC’, IEEE Transactions on Smart Grid, vol. 10, no. 6, pp. 6298-6307.




How to Cite

K. Oubida, R. . (2024). Optimizing Microgrid Performance: A Data-Driven Approach with IoT Integration. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1085–1094. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5508



Research Article