Research On Optimization Of Electric Logistics Fleet Driven By Energy-transportation-information Integration

With the electrification of the urban logistics transportation system in recent years,electric logistics fleets under the background of energy-transportation integration can provide important demand-side flexible resource for the power system.At the same time,with the development and application of the Internet of Things technology,the logistics-transportation system is beginning to use cloudbased platforms for fleet management.This has led to new coupling and correlation characteristics of the operational decision-making of electric logistics fleets in multiple dimensions such as energy,transportation,and information.In order to further explore the flexible regulation potential of the growing electric logistics fleet in terms of charging load regulation,this paper focuses on the optimization of the operation of electric logistics fleets driven by the integration of energy,transportation,and information.The paper analyzes the multi-dimensional coupling characteristics of the operational decision-making of electric logistics fleets in the networked scenario,proposes dispatching and charging control strategies for large-scale electric logistics fleets,and establishes a coordinated pricing model for charging operators that takes into account the value of fleet operational information.The specific research contents are as follows:Specifically,the paper first analyzes the multi-dimensional coupling characteristics of the operational decision-making of urban electric logistics fleets in an intelligent networked scenario,and constructs a model for the management and charging control of electric logistics fleets that considers logistics distribution,route planning,and charging control.It then summarizes and analyzes the multidimensional coupling decision-making characteristics of electric logistics fleet operation in a cloud platform centralized dispatch scenario,and proposes an optimization and dispatch model for electric logistics fleets in an intelligent networked scenario as the basis for subsequent research.Next,the paper proposes an optimization and dispatch method for large-scale electric logistics fleets in the networked scenario.Based on the Markov decision process,a model for optimizing the dispatch and charging control of large-scale electric logistics fleets is constructed,taking into account the multi-dimensional coupling decision-making characteristics of logistics fleets,relevant constraints and optimization objectives.A deep reinforcement learning-based optimization and dispatch strategy for large-scale electric logistics fleets is proposed,taking into account charging cost spatiotemporal characteristics,distribution quality constraints,and large-scale vehicle coordination scheduling,and the effectiveness of the proposed strategy is verified by the case study.Finally,from the perspective of energy-transportation-information integration,the paper constructs a framework for evaluating and quantitatively analyzing the value of transportation information in the energy-transportation integration operation scenario using electric logistics fleets as an example,and establishes a charging operator pricing model that takes into account the application of fleet operational information value.Firstly,the paper proposes an information interaction framework between electric logistics fleets and charging operators for operational optimization in an intelligent networked scenario,and analyzes the potential value of fleet operational information and information introduction methods.Secondly,a charging operator pricing strategy based on multi-agent deep reinforcement learning is proposed for the application of fleet operational information,which reflects the value of fleet operational information in charging operator pricing decision optimization,thereby increasing the revenue of the charging operator,and the effectiveness of the proposed strategy is verified through case analysis.