Electric vehicle real-time scheduling optimization method considering demand response willingness classification

Abstract

The invention discloses an electric vehicle real-time scheduling optimization method considering demand response willingness grading. The method comprises the following steps: classifying electric vehicle users, and establishing power constraints and electric quantity constraints of various electric vehicle users according to the electric vehicle users; determining the scheduling potential of each time period; calculating the difference between the total predicted load in each time period and the scheduling target power as a scheduling demand; determining a hierarchical scheduling scheme according to the scheduling demand and the scheduling potential of each electric vehicle user group; establishing a real-time optimization target, enabling the predicted power to be close to the target power as much as possible so as to achieve a peak clipping effect, and solving the charging power of the electric vehicle by adopting a particle swarm algorithm; and establishing a subsidy mechanism, and calculating the subsidy and repayment of the electric vehicle aggregator by the power grid company and the subsidy and repayment of the electric vehicle user by the electric vehicle aggregator. According to the method, a good load peak clipping effect can be realized on the basis of considering the willingness of the user to participate in the demand response, and the power grid safety can be effectively improved.

Description

technical field [0001] The invention belongs to the technical field of power system demand response, and in particular relates to a real-time scheduling optimization method for electric vehicles considering the classification of demand response willingness. Background technique [0002] As the number of electric vehicles connected to the grid and the continuous increase in battery capacity have brought a huge operational burden to the power system, it is necessary to allow electric vehicles to participate in distribution network scheduling through orderly charging to reduce load pressure and eliminate the risk of grid overload. At present, the methods for studying the optimization problem of electric vehicle charging power are divided into day-ahead global optimization and real-time local optimization. The day-ahead optimized scheduling instruction is based on the day-ahead data. Since it is difficult to know the vehicle network access time and charging demand, day-ahead for...

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Problems solved by technology

Users who have signed contracts with electric vehicle aggregators need to participate in the response when there is dispatching demand on the grid side, while uncontracted users (ordinary users) with high travel demand flexibility are rarely considered in real-time dispatching. The responsiveness of this part of electric vehicles Serious waste, so the existing technology cannot make full use of the dispatching potential of electric vehicles (Zhang Bingxu, Xu Gang. Rolling time-domain optimization of grid-connected electric vehicles considering demand differences [J]. Automation of Electric Power Systems, 2020,44(13):106 -114.)

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