Prof.M.H Yaghmaee Moghaddam
Full Professor at the Computer Engineering Department, Ferdowsi University of Mashhad (FUM)
Cloud Based Demand Response Architecture for the Future Smart Grid: As Electric energy can not be easily stored, the utility companies prefer to use Demand Response (DR) programs to make a balance between the power generation and the power consumption. For this purpose, different techniques such as time-based pricing and incentive programs are widely used. Demand response which is one of ten top research topics in the smart grid can also be leveraged to shift the power usage from peak hours to the non-peak hours where the price of energy is low. In this case, to find the optimal solution, having a global view of the power network is too important. It has been proven that distributed demand response techniques, suffer from some critical problems. Recently cloud computing has received attention for smart grid applications. Most smart grid applications need reliable and efficient communications. This can be met by utilizing the cloud computing based on software-defined infrastructure. Cloud computing brings some opportunities for smart grid applications. Flexible resources and services shared in the network, parallel processing, and omnipresent access are some features of cloud computing that are desirable for smart grid applications. In this talk, a cloud-based demand response program that schedules the power consumption by customers in different regions is introduced. The cloud-based demand response utilizes the processing and storage resources of two-tier cloud computing consisting of the “Smart Edge” and the “Core cloud”, to develop an optimal demand side management. In this architecture, customers are classified into different regions. Each region is controlled by a “Smart Edge” cloud to provide cloud computing resources at the edge of the network precisely to meet low latency requirements as well as to reduce the volume of traffic that needs to traverse the network backbone. Cloud-based nature of the proposed architecture reduces the cost of computation which makes it easier for the future smart grid.