DROpS: A demand response optimization scheme in SDN-enabled smart energy ecosystem

With an exponential increase in the utilization of intellective appliances, meeting the energy demand of consumers by traditional power grids is a significant challenge. In integration, the amalgamation of electric vehicles, industrial Internet-of-Things (IoT), and smart communities with power grids has escalated the global energy demand. Consequently, the desideratum for a reliable energy supply and resilient energy ecosystem has incentivized the evolution of Smart Grids (SG). Such intelligent grids are equipped with autonomous controllers and advanced technologies like advanced metering infrastructure, smart sensors, and accounting management software. However, the existing demand response management and energy supply techniques are lagging behind in meeting the desired objectives in the SG ecosystem. In order to handle these challenges, a Demand Replication Optimization Scheme (DROpS) for the smart energy ecosystem is designed in this paper. In addition, a Multi-Leader Multi-Follower Stackelberg game is formulated in this paper to operate with the proposed scheme. However, the success of DROpS depends heavily on real-time communication between the consumers and the suppliers. Hence, dynamic and scalable network architecture is required to handle the seamless data generated by a sizably voluminous number of connected sensors, devices, and smart appliances deployed in the SG. For the successful operation of the architecture, a Software-Defined Networking (SDN)-enabled control scheme for flow management is additionally developed. In the proposed scheme, the SG ecosystem is divided into multiple zones such that the dedicated virtual SDN controllers are deployed for network resource utilization in an optimized manner. The proposed scheme is evaluated using a real smart home test bed and data traces from the Haryana power grid. The effectiveness of the proposed scheme is demonstrated in terms of significant gains observed for load variation and latency. (C) 2018 Elsevier Inc. All rights reserved.