Modernized Planning of Smart Grid Based on Distributed Power Generations and Energy Storage Systems Using Soft Computing Methods
Arul Rajagopalan,
Dhivya Swaminathan,
Meshal Alharbi,
Sudhakar Sengan,
Oscar Danilo Montoya,
Walid El-Shafai,
Mostafa M. Fouda,
Moustafa H. Aly
Affiliations
Arul Rajagopalan
School of Electrical Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India
Dhivya Swaminathan
School of Electrical Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India
Meshal Alharbi
Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
Sudhakar Sengan
Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli 627152, Tamil Nadu, India
Oscar Danilo Montoya
Grupo de Compatibilidad e Interferencia Electromagnética (GCEM), Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 110231, Colombia
Walid El-Shafai
Security Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh 11586, Saudi Arabia
Mostafa M. Fouda
Department of Electrical and Computer Engineering, College of Science and Engineering, Idaho State University, Pocatello, ID 83209, USA
Moustafa H. Aly
Electronics and Communications Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria P.O. Box 1029, Egypt
The modest objective is to check the integrated effect of energy storage systems (ESSs) and distributed generations (DGs) and compare the optimization of the size and location of ESS and DG to explore its challenges for smart grids (SGs) modernization. The research enlisted different algorithms for cost-effectiveness, security, voltage control, and less power losses. From this perspective, optimization of the distribution network’s energy storage and capacity are being performed using a variety of methods, including the particle swarm, ant-lion optimization, genetic, and flower pollination algorithms. The experimental outcomes demonstrate the effectiveness of these techniques in lowering distribution network operating costs and controlling system load fluctuations. The efficiency and dependability of the distribution network (DN) are both maximized by these strategies.
