Advanced Supply-Demand Optimization in Renewable-Powered Microgrids

¹ Department of Electronics and Communication, New Horizon College of Engineering, Bangalore, India.
² Lovely Professional University, Phagwara, India.
³ Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, India.
⁴ Lloyd Law College, Plot No. 11, Knowledge Park II, Greater Noida, Uttar Pradesh 201310
⁵ Lovely Hilla University College, Babylon, Iraq
⁶ Department of Electrical and Electronics Engineering, Vardhaman College of Engineering, Hyderabad, Telangana, India
⁷ Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Selangor, Malaysia

^* Corresponding Author: aravindake@gmail.com

Abstract

As natural disasters become more frequent and severe threatening modern power grids, increasing the resilience of electric supply systems has grown increasingly important. Several approaches for assessing power system resilience have been proposed in the literature but no effective methods have been developed to incorporate advanced technologies, renewable energy, cybersecurity measures and cost effective frameworks. This paper presents a comprehensive review of state of the art methodologies for assessing resilience frameworks and measures taking into account pre and post event phases in order to learn from their preparedness and their ability to recover. This study also provides opportunities to apply machine learning, AI, and big data analytics to resilience planning, identifies the role of microgrids, policy frameworks, and renewable energy integration, and categorizes all existing techniques into characteristics and shortcomings. Moreover, the paper discusses emerging challenges such as the increasing need for enhanced cybersecurity in power networks and the ways in which social and environmental impacts of resilience measures can potentially influence water and power supply. The work aims to provide a holistic understanding of power distribution system resilience, to present future research directions, and to help develop efficient methods to strengthen grid resilience to improve restoration following disruptions.