Electric vehicle demand response regulation method based on Stackelberg game theory

Abstract

The invention discloses an electric vehicle demand response regulation method based on the Stackelberg game theory. A price is made a day in advance through a polymerization agent and adjustable load resources are appointed to a user, and the user properly adjusts electric vehicle charging time and power according to the price of electricity. The user reserves a part of interruptible and transferable load resources in the process of adjustment, and the benefit maximization of the user and use loss minimization are realized by the adjustment. Under the premise of ensuring the benefit maximization of a lower level user, the polymerization agent polymerizes the load resources reserved by a user electric vehicle to participate in the competition with other polymerization agents. Finally, while the benefit (mainly satisfaction, namely utility) maximization of the user is realized, the benefit maximization of the polymerization agent is realized.

Description

technical field [0001] The invention relates to an electric vehicle demand response control strategy based on Stackelberg game theory. The aggregator publishes the electricity price and the load resource capacity expected to be reserved one day in advance. After the user knows the electricity price, the user builds the user's electric vehicle charging strategy and user load regulation strategy model through the user benefit analysis model, reserves some adjustable load resources from the electric vehicle charging process, and submits the relevant information of the load resource. The aggregator integrates the user-side adjustable load resources according to the load information, and then properly adjusts the next day's electricity price again. Finally, on the basis of continuously revising the electricity price, the resource capacity of the load is close to the capacity value expected by the aggregator. Finally, the aggregator goes to bid with other aggregators. It involves ...

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Problems solved by technology

(3) Establish an optimization model for the minimum network loss of the distribution network, and use the method of iteratively correcting the node voltage to optimize the solution, but does not consider the configuration of reactive power compensation equipment and reactive power optimization

(4) A power demand side management strategy with the goal of power grid load-shaving and valley-filling was proposed, and a multi-objective function for the most economical charging cost at the user level and grid load-shaving and valley-filling was established. Based on the time-of-use electricity price, the differential evolution algorithm was used to optimize the solution , and use the fuzzy clustering algorithm to divide the time period of the time-of-use electricity price, but the shortcoming is that there is no research on the pricing of the time-of-use electricity price

However, none of these cannot take into account the interests of users and aggregators at the same time, and ensure the satisfaction of electric vehicle users

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