in moghaleh yek charchub nevin bacpehineksazi chandeddefeh baraye madirit cpehmazman sharzh ve dasharzh khodarveyaye barghi ghabel etesal bah shabkeh (PHEV) ve vahadehaye tolid parakandeh (DG) dar shabkeknpehei tozi araeh midehad. roykard pishnehadi ba dar nazar garaftan cpehmazman seh bad fani, egtesadi ve zistmahiti, bah danbal bacpehbud bacparehvari shabkeh ve madirit teooshmand enrzhi est. in madel ba bacparehgiri az makanizam ghimatgozari hashieryi makani (LMP) dar basiacpehei majekarz bah khodarveyaye barghi ve istgangnpehei sharzh, seh cpehdaf esli ra danbal mikand: (1̱) afzayesh sood vahadehaye DG ve malkan EV, (2̱) bacpehbud shakhsnpehei fani shabkeh az jomleh kanpash talfat ve ertaghaye ghablit etminan, ve (3̱) kanpash alayandegi nashi az tolid bargh. mosaleh bacpehineksazi pishnehadi bar roye shabkeh shoayi 8̱3̱ bas IEEE peyadehsazi shodeh ve ba mofeghit bah kamak algoritam bacpehineksazi azadham zarat (PSO) hal gardideh est. netaij shabiersazi neshan midehand keh charchub pishnehadi tavanayi cheshmgiri dar movazneh anpandaf
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This paper presents a novel multi-objective optimization framework for the simultaneous management of charging and discharging of plug-in electric vehicles (PHEVs) and distributed generation (DG) units in distribution networks. The proposed approach, by simultaneously considering three technical, economic, and environmental dimensions, seeks to improve network efficiency and smart energy management. By utilizing the locational marginal pricing (LMP) mechanism in buses equipped with electric vehicles and charging stations, this model pursues three main objectives: (1) increasing the profits of DG units and EV owners, (2) improving the technical indicators of the network, including reducing losses and improving reliability, and (3) reducing emissions from power generation. The proposed optimization problem is implemented on an 83-bus IEEE radial network and is successfully solved using the Particle Swarm Optimization (PSO) algorithm. Simulation results show that the proposed framework has a significant ability to balance the objectives.