Smart grid electrical systems are integral to the efficient functioning of our modern society, providing reliable and sustainable energy distribution. However, these systems are becoming increasingly complex and interdependent, making them vulnerable to natural disasters, cyber-attacks, and equipment failures. Thus, it is crucial to enhance the resilience of these systems to ensure uninterrupted power supply and minimum impact on the overall grid operation. One approach to achieving this is optimization algorithms, which can help identify potential vulnerabilities, optimize network performance, and facilitate quick restoration during a disruption. These algorithms analyze vast amounts of data from various sources, including real- time sensor data, historical data, and grid topology, to identify the most critical components and develop strategies to mitigate potential risks. Optimization algorithms can support decision-making by providing insights into grid operational and maintenance strategies. Considering different scenarios and constraints, they can help identify the most cost-effective solutions for power generation, transmission, and distribution. This can result in improved operational efficiency and reduced maintenance costs. Incorporating machine learning techniques into these optimization algorithms can enable the system to continuously learn and adapt to changing conditions and improve its resilience. This can lead to more robust and reliable power systems capable of withstanding unexpected events and recovering quickly. Enhancing the resilience of smart grid electrical systems through optimization algorithms is a promising approach to ensure the sustainability and reliability of our energy infrastructure.

Target Audience
The target audience would be professionals and researchers in electrical engineering and innovative grid systems. This may include engineers, scientists, and academics involved in designing, developing, and maintaining smart grid electrical systems. Other potential target audiences include policymakers and government agencies involved in energy and sustainability. They would be interested in understanding how optimization algorithms can help enhance the
resilience of electrical grids and improve their efficiency. Utility companies and professionals working for electricity providers may also be interested in this topic as it directly pertains to their work and the optimization of their systems. The target audience could also extend to students andeducators in electrical engineering and energy systems. The topic could be relevant for classroom discussions, research projects, or individuals looking to further their knowledge in this field. The target audience for this topic is quite broad and may encompass various professionals, academics, and students interested in the advancements and improvements in smart grid technology.

List of Topics to be covered
1. Demand response and load management in smart grids
2. Predictive maintenance and fault detection in smart grid systems
3. Cyber security in smart grids
4. Distributed energy resources integration and control
5. Optimal power flow algorithms and solutions
6. Micro grid optimization
7. Renewable energy forecasting and management
8. Intelligent energy storage and grid storage optimization
9. Real-time energy pricing and market design
10. Big data analytics for smart grid optimization
11. Advanced metering infrastructure and smart meter data analytics
12. Virtual power plants and demand-side management
13. Grid modernization and distribution system optimization
14. Smart grid communication and control systems
15. Energy efficiency optimization and eco-design
16. Multi-agent systems and coordination in smart grid operation
17. Resilient and robust control in smart grid systems
18. Dynamic pricing and demand response management
19. Hybrid energy systems and integrated resource planning
20. Optimization of renewable energy integration and grid stability.

Submission Guidelines
We invite original research articles, innovative reviews, and perspectives covering the topics above. Authors are requested to follow the journal’s guidelines and formatting. All submissions will undergo a thorough peer-review process to ensure high-quality articles for publication.

Important Deadline
> Deadline for Abstract Submission: April 30, 2024
> Full Paper Submission: May 31, 2024
> Notification of Acceptance: June 15, 2025
> Publication: July 15, 2024

Guest Editors
> Jaganathan Logeshwaran, Sri Eshwar College of Engineering, Coimbatore-641202,
India ( | Google Scholar:
> T. Kiruthiga, Vetri Vinayaha College of Engineering and Technology, Trichy, Tamil
Nadu - 621215, India ( | Google Scholar: