Special Issue(24-08):Smart Grid Technologies for Demand Response and Grid Resilience | International Journal of Automation and Smart Technology

Smart Grid Technologies for Demand Response and Grid Resilience

Smart grids with demand response. DR models were developed in order to preserve the supply and demand trade-off in load management systems. One of the key tactics used by power companies to reduce customer energy use during peak hours is demand response (DR). In order to better match the supply and demand of power in real time, reduce costs, and preserve the stability and dependability of the grid, smart grids integrate digital technology, sensors, and software. Demand response is the process of persuading consumers to change their electricity use to periods when there is more availability of electricity or when there is less demand overall. This is usually done through price adjustments or other financial incentives. Resilience to network interruptions is referred to as grid resilience. Furthermore, a self-healing smart grid is one that can recover swiftly on its own without assistance from humans. In a Smart Grid (SG), Demand Side Management (DSM) refers to the intelligent management of load appliances. Customers can save money using DSM programs because they lower their electricity rates, minimise peak demand from the utility, and increase load factor. The two types of networks used by smart grids for communication are WAN and HAN.

The smart metre and household appliances are connected via HAN. A home area network can be based on a variety of technologies, including Bluetooth, Wireless Ethernet, Wired Ethernet, and Zigbee. Demand response enhances the efficiency and dependability of the grid by assisting utilities in managing demand and optimising energy delivery. To reduce the impact on the business, GridPoint offers tailored load curtailment along with automatic demand response. Being paid to remain in a hotel while your flight is overbooked is another way to compare demand response to that. Instead of renting a new aircraft, the airline would prefer to pay for your hotel stay. In a similar vein, the grid operator would prefer to compensate you for using less than to construct a new power plant. Because its planners, designers, and operators anticipate, prepare for, and react to changing grid circumstances, a resilient power grid can withstand, respond to, and recover quickly from significant power outages. Demand response gives users the chance to actively participate in the functioning of the electrical grid by adjusting or lowering their peak-time electricity use in response to time-based pricing or other types of monetary incentives.

A smart grid will collect data on prices and grid conditions, precisely limit electricity power to the residential level, network small-scale distributed energy generation and storage devices, communicate information about operating status and needs, and transition the grid from a centralised to a collaborative network. By balancing surges in electricity usage, V2G helps lessen grid overload during peak hours. For instance, to increase grid stability and optimise the advantages of renewable energy, V2G can transfer energy, that is, unused battery capacity back into the power grid from the battery of an electric car. The WAN connects numerous NANs and LDCs and operates over large geographic areas, acting as the backbone of the smart grid communication infrastructure. We encourage submissions from a variety of fields and viewpoints, such as but not limited to: Smart Grid Technologies for Demand Response and Grid Resilience.

Potential topics include but are not limited to the following:

In an effort to render the grid resilient to natural calamities, demand response is used to restore distribution services.
An investigation of smart grid restoration to improve the resilience of cyber-physical systems.

Innovative green energy solutions for a resilient and sustainable grid of the future.
Microgrids to improve the resilience of the electrical grid under harsh circumstances.
Combining emergency and preventive actions to improve the resilience of the electricity grid.
Machine learning-based secure and robust demand side management engine for Internet of Things smart grid.
Demand response management strategy for smart grid systems that is both scalable and resilient.
Cyber-constrained optimal power flow model for enhancing smart grid resilience.
Demand response program impact study on dynamic clustered distribution systems resilience.
Adaptive demand and renewable energy system operations together in a smart grid.
Enhancement of the distribution systems' resilience through demand response and operational resources.
An unusual smart grid resilient demand response system based on microgrids.

uest Editor Information:

Dr. Emmanuel Kweinor Tetteh

Assistant Professor,

Durban University of Technology, Durban, South Africa

Email-ID: [email protected], [email protected]

Google Scholar: https://scholar.google.co.uk/citations?user=gyl5umYAAAAJ&hl=en

Official Website: https://www.dut.ac.za/dr-emmanuel-kweinor-tetteh/

Dr. Ifeanyi Michael Smarte Anekwe

School of Chemical and Metallurgical Engineering,

University of the Witwatersrand, Johannesburg, South Africa.

Email-ID: [email protected]

Google Scholar: https://scholar.google.com/citations?user=XD0RansAAAAJ&hl=en