Smart residential distribution energy management system with integration of demand response and Aggregator

In this paper, we present an energy management system with the help of aggregator and demand response program (DR). We have considered a smart residential distribution system consisting of residential aggregator, heterogenous consumers and one common charging station (CCS) for EVs. The role of aggregator is to communicate and to control the power flow between electric grid and consumers. The power flow for EVs is G2V and V2G. For energy management, smart home appliances, battery storage systems, and EVs are considered as flexible loads. The objective of a residential aggregator is to minimize the amount of power imported from the grid, thereby, maximizing the net profit. Reliability and affordability are key concerns for consumers hence, consumers objective is to reduce their electricity bills without reducing their comfort level and the objective of a common charging station (CCS) is to reduce the total cost of charging and discharging all-electric vehicles. To satisfy these objectives, a multi-objective problem is formulated and solved as Mixed Integer Linear Programming (MILP). The real-time pricing (RTP) scheme is implemented as a price-based demand response (DR) program. The main purpose of this study to show a combination of aggregator and demand response program can reduce the total cost of electricity and improve the overall efficiency and viable economic frameworks for the smart residential distribution system. Furthermore, study results suggest that smart EV charging/discharging is a viable option for balancing demand and supply over a shorter time. Also, will improve the flexibility of the electric grid.

Automated summary

In this paper, an energy management system using an aggregator and demand response program (DR) is proposed. The system includes a smart residential distribution system with a residential aggregator, heterogeneous consumers, and a common charging station (CCS) for EVs. The objectives are to minimize imported power from the grid, reduce electricity bills for consumers, and lower the total cost of charging and discharging EVs. The problem is solved using a multi-objective mixed integer linear programming (MILP) approach, with the implementation of a real-time pricing (RTP) scheme as a price-based DR program. The study shows that the combination of aggregator and demand response program can reduce the total cost of electricity, improve overall efficiency, and provide viable economic frameworks for the smart residential distribution system. Additionally, smart EV charging/discharging is found to be a feasible option for balancing demand and supply and improving the flexibility of the electric grid.