Optimal Planning And Incentive Design Of Public Parking Lots Under Decision-Dependent Uncertainties

As an important energy-saving and emission-reducing tool,electric vehicles have become an important part of a sustainable society in the future.Large-scale electric vehicle access poses a huge challenge to the power system,and the energy storage characteristics of the electric vehicle charging load also provide favorable conditions for the reliability and economic operation of the large power grid.Under such circumstances,it is a good idea to plan to deploy appropriate public charging facilities to control the electric vehicle access to the power system.At the same time,electric vehicle users have a wide range of uncertainties.Therefore,it is of great practical significance to study the electric vehicle planning method that reasonably considers the decision-dependent dependence on uncertain factors.Firstly,this paper analyzes the development status of electric vehicle parking lot planning at home and abroad,summarizes the basic knowledge of two-stage stochastic programming,describes the most common planning methods considering uncertain factors,and summarizes the two-stage stochastic programming.The research status of the algorithm is finally proposed.The problem proposed in this paper is solved by two-stage stochastic programming considering the decision-dependent uncertainty.Secondly,this paper describes the exogenous uncertainty of electric vehicles using a probability density function for a single electric vehicle.At the same time,the concept of virtual power plant is proposed to simulate the influence of electric vehicle orderly charging on the power distribution system.The endogenous uncertainty of electric vehicles,that is,the investment decision at the current stage may have a significant impact on the temporal and spatial distribution of electric vehicle charging load in the future.A model for describing the uncertainty of decision-making in electric vehicles is proposed.In this paper,according to the reflection-reaction paradigm,a model of regret mechanism is proposed to describe the probability distribution of electric vehicle users’ behavior under different decision-making and contract incentives.In addition,this paper describes the utility function of quantifying regrets in detail,and finally uses cluster analysis to generate uncertainty scenarios,and completes the modeling of the entire decision-dependent uncertainty.Based on the above-mentioned uncertainty modeling and statistical scenario modeling,this paper takes all the electric vehicle integrators in the distribution network to obtain the maximum profit as the objective function,formulates all planning decisions and incentive contract design,and establishes decision-based dependence.Deterministic electric car parking lot planning model.Finally,in order to solve the proposed model,a solution to the two-stage random programming is proposed.The improved IEEE 12-bus system is used to simulate and study the model,and the effectiveness of the proposed model is verified.Research shows that the proposed method can better solve the problem of decision-dependent uncertainty planning.The model can more realistically simulate the charging demand,provide electric vehicle integrators with more effective investment solutions for electric vehicle parking lots,and also explain the optimal location and volume problem of electric vehicle parking lots and the adoption of electric vehicle integrators.The incentive mechanism is closely related.