A real-time management and evolutionary optimization scheme for a secure and flexible smart grid towards sustainable energy

• In this paper, Genetic Algorithm is used to handle the reactive power management.
• Goal of short-term energy resource management is to minimize the operational costs.
• RMOSFS: sustainable energy detection, degree of power saving and power optimization.
• Consists of a location centric hybrid architecture and collaboration algorithm.

Sustainable energy is the energy production without compromising the energy production for the future generations. The existing power grid model does not provide real-time information of transmission devices, security during emergency events, and frequency and voltage control. The proposed scheme consists of location-centric hybrid system architecture for the coordinated processing among proximate devices. The neighboring devices follow a collaboration algorithm to handle faulty and incomplete information. The proposed scheme also consists of distributed algorithm for the maintenance of the local state of the smart grid and a real-time accessibility control of transmission devices. The security of the system can be guaranteed by reconfiguration through power-electronics and switches. An embedded intelligence is inserted into the power-electronics to facilitate the reconfiguration of the system, and thereby ensuring security. A generalized optimization formulation determines the optimum location of the transmission devices. In this paper, Genetic Algorithm (GA) is used to handle the reactive power management. The use of GA decreases execution time of resource scheduling. This method performs better than the existing power grid models in terms of fault detection, degree of power saving due to power optimization, memory usage, consumer-GW (gateway) communication overhead, consumer computational overhead, and critical time.