Multi-Agent Optimization for Residential Demand Response under Real-Time Pricing

Zhanle Wang, Raman Paranjape, Zhikun Chen and Kai Zeng
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Zhanle Wang: Faculty of Engineering and Applied Science, University of Regina, Regina, S4S 0A2, Canada
Raman Paranjape: Faculty of Engineering and Applied Science, University of Regina, Regina, S4S 0A2, Canada
Zhikun Chen: Department of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, China
Kai Zeng: Department of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, China

Energies, 2019, vol. 12, issue 15, 1-15

Abstract: Demand response (DR) programs encourage consumers to adapt the time of using electricity based on certain factors, such as cost of electricity, renewable energy availability, and ancillary request. It is one of the most economical methods to improve power system stability and energy efficiency. Residential electricity consumption occupies approximately one-third of global electricity usage and has great potential in DR applications. In this study, we propose a multi-agent optimization approach to incorporate residential DR flexibility into the power system and electricity market. The agents collectively optimize their own interests; meanwhile, the global optimal solution is achieved. The agent perceives its environment, predicts electricity consumption, and forecasts electricity price, based on which it takes intelligent actions to minimize electrical energy cost and time delay of using household appliances. The decision-making action is formulated into a convex program (CP) model. A distributed heuristic algorithm is developed to solve the proposed multi-agent optimization model. Case studies and numerical analysis show promising results with low variation of the aggregated load profile and reduction of electrical energy cost. The proposed approaches can be utilized to investigate various emerging technologies and DR strategies.

Keywords: demand response; multi-agent optimization; convex program; electric vehicle; heuristic algorithm; real-time pricing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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