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Keywords

demand response, electric vehicle, air conditioning load, user satisfaction, multiobjective optimization

Abstract

In view of the fact that the current community load is high and the load peak and offpeak difference is increasing in summer, community load is regulated by invoking demand response resources. This paper mainly studies the controllable power load (resident, commerce) scheduling strategy in the community energy platform. Focusing on the analysis of two types of loads, electric vehicles and air conditioning, this paper constructs a multiobjective load optimization dispatch model with the lowest electricity cost and the smallest difference between community load peaks and valleys under the satisfaction of user comfort level. For the resident inverter air conditioning and commercial central air conditioning, temperature control and periodic stopping control are adopted, respectively. Moreover, orderly charging mode is adopted for the electric vehicle. The improved particle swarm optimization algorithm is used to solve the model. Simulation results show that reasonable control of the electric vehicle, air conditioning load and energy storage can evidently reduce the user electricity costs and improve community load curve characteristics.

DOI

10.19781/j.issn.1673-9140.2021.03.009

First Page

76

Last Page

83

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