4.7 Article

Demand Response Application as a Service: An SDN-Based Management Framework

期刊

IEEE TRANSACTIONS ON SMART GRID
卷 13, 期 3, 页码 1952-1966

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2021.3139004

关键词

Cloud computing; Communication networks; Servers; Quality of service; Switches; Delays; Reliability; Two-tier cloud computing; DR softwarization; software-defined networking; demand response automation server (DRAS); flow management; OpenFlow switches

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This paper proposes an SDN-enabled framework for DR flow management to address the communication challenges in Demand Response. The framework utilizes two-tier cloud computing and NFV technology to seamlessly manage energy and data traffic. Experimental results show that it achieves low delay, high throughput, and effectively balances energy and data on the entire DR network.
With an increase in the utilization of appliances, meeting the energy demand of consumers by traditional power grids is an important issue. The success of Demand Response (DR) depends conclusively on real-time data communication between the consumers and the suppliers. Hence, a scalable and programmable communication network is required to handle the data generated. We prove that the problem of DR global load balancing includes energy and data constraints is NP-hard. So, a dynamic and self-configurable network technology known as Software-defined Networking (SDN) can be an efficient solution. In order to handle DR communication challenges, an SDN-enabled framework for DR flow management is designed in this paper. This framework is based on two-tier cloud computing and manages energy and data traffic seamlessly. We also equip this framework with Network Functions Virtualization (NFV) technology. The proposed framework is implemented on a practical testbed, which includes Open vSwitch, Floodlight controller, and OpenStack. Its performance is appraised by comprehensive experiments and scenarios. Based on the results, it achieves low delay, a high throughput, and improves Peak to Average Ratio (PAR) by balancing the energy and data on the entire DR network.

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