Decentralized Energy Demand Regulation in Smart Homes

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Presentation transcript:

Decentralized Energy Demand Regulation in Smart Homes Authors: S. N. Akshay Uttama Nambi, R. Venkatesha Prasad, Antonio R. Lua Source: IEEE Transactions on Green Communications and Networking, Vol. 1, pp. 372 – 380, Sept. 2017 Speaker: Kai-Fan Chien Date: 2019/01/17

Outline Introduction Related Work System Model Day-Ahead Demand Scheduling Algorithm Experimental Evaluation Conclusions

Introduction What is Smart Grid/Smart Homes. Demand regulation (DR) The challenges of demand regulation.

Related Work Many DR programs have been proposed.

System Model(1/2) ModCO

System Model(2/2) Energy disaggregation Modified CO (ModCO) Combinatorial Optimization (CO) Factorial Hidden Markov Model (FHMM) Modified CO (ModCO)

Day-Ahead Demand Scheduling Algorithm(1/5) Flexibility coefficient Sensitivity coefficient Dependency coefficient

Day-Ahead Demand Scheduling Algorithm(2/5) Flexibility coefficient Sensitivity coefficient Dependency coefficient

Day-Ahead Demand Scheduling Algorithm(3/5) Schedule Filtering

Day-Ahead Demand Scheduling Algorithm(4/5) Schedule Selection Schedule Enhancement

Day-Ahead Demand Scheduling Algorithm(5/5)

Experimental Evaluation(1/2) Datasets DRED REDD Results CO Modified CO FHMM

Experimental Evaluation(2/2) Demand scheduling

Conclusions We presented a decentralized algorithm to derive optimal day-ahead schedules using consumer preferences and appliance usage patterns. The proposed algorithm was empirically evaluated across multiple datasets such as DRED and REDD Cost savings of up to 25% and 30% can be achieved in DRED and REDD for monthly electricity consumption.