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Priority-Based Residential Energy Management With Collaborative Edge and Cloud Computing
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DOI：10.1109/TII.2019.2933631.png)
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Residential energy management (REM) is an important way to encourage users to reduce or shift energy demand with dynamic pricing. It could significantly affect the supply-demand relationship between electricity service providers (ESPs) and users, reduce energy cost and consumption, and contribute to sustainable development. To improve latency and processing performance, a three-tier edge-cloud collaborative REM (ECCREM) architecture is presented. In consideration of matching the architecture, a two-stage energy management mechanism is proposed with system reliability and resource utilization requirements taken into account. At the first stage, the interaction between real-time pricing and energy demand is modeled by a Stackelberg and Lyapunov-based pricing and energy demand joint optimization (SLPEDO) algorithm. At the second stage, two procedures, i.e., energy scheduling between a cloud tier and an access tier, and energy scheduling between an access tier and an infrastructure tier, are implemented. A priority-based demand ratio sequentially scheduling strategy is proposed to address energy scheduling in these two procedures, respectively. Simulation results show that compared with the existing demand ratio-based scheduling and equally scheduling strategies, the proposed strategy can improve overall satisfaction of users by up to 20%. In addition, energy cost can be reduced and demand fluctuation relieved.
Keyword:
Computer architecture
Energy management
Cloud computing
Pricing
Job shop scheduling
Servers
Edge computing
Collaborative edge and cloud computing
load priority
residential energy management
smart grid
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