Distribution Locational Marginal Pricing for Optimal Electric Vehicle Charging Management

By Ruoyang Li, Qiuwei Wu, Shmuel S. Oren. IEEE Transactions on Power Systems, 2014

387 citations recorded by the source corpus.

This paper presents an integrated distribution locational marginal pricing (DLMP) method designed to alleviate congestion induced by electric vehicle (EV) loads in future power systems. In the proposed approach, the distribution system operator (DSO) determines distribution locational marginal prices (DLMPs) by solving the social welfare optimization of the electric distribution system which considers EV aggregators as price takers in the local DSO market and demand price elasticity. Nonlinear optimization has been used to solve the social welfare optimization problem in order to obtain the DLMPs. The efficacy of the proposed approach was demonstrated by using the bus 4 distribution system of the Roy Billinton Test System (RBTS) and Danish driving data. The case study results show that the integrated DLMP methodology can successfully alleviate the congestion caused by EV loads. It is also shown that the socially optimal charging schedule can be implemented through a decentralized mechanism where loads respond autonomously to the posted DLMPs by maximizing their individual net surplus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/62c31556466b34379490222a6537714f7d8738bf

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Citations in the corpus

Designing Decentralized Markets for Distribution System Flexibility (2019, background): “Optimization approaches have been studied for coordinating flexible energy resources in distribution networks and microgrids [7]–[10].”
Constrained EV Charging Scheduling Based on Safe Deep Reinforcement Learning (2020, background): “In this study, we assume that the EV user is a price-taker [18], [19] and the charging action does not affect the electricity price.”
Distribution Locational Marginal Pricing Through Quadratic Programming for Congestion Management in Distribution Networks (2015, methodology): “Decentralized Congestion Management Through DLMP and DT According to [7] and [16], the procedure of using the DLMP and DT concepts to solve the congestion problem in a decentralized manner can be summarized as follows.”
Novel Linearized Power Flow and Linearized OPF Models for Active Distribution Networks With Application in Distribution LMP (2018, methodology): “In [11], an integrated DLMP method is designed to alleviate congestion introduced by electric vehicle (EV) loads in future power systems.”
Low-Carbon Operation of Multiple Energy Systems Based on Energy-Carbon Integrated Prices (2020, methodology): “The DC power flow model is widely used and considered to be sufficient for LMP calculation [24]–[26].”
Distribution Locational Marginal Pricing (DLMP) for Congestion Management and Voltage Support (2018, background): “In [14], [15], DLMP is proposed to alleviate congestions in future distribution networks with high penetrations of EVs.”
Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects (2016, neutral): “It is discussed that compared with linear programming based DLMP formulation [128], QP based formulation ensures convergence to the DLMP.”
Local flexibility markets: Literature review on concepts, models and clearing methods (2020, methodology): “The dynamic tariff (DT) methods were used in [62], [104]-[106] to solve congestion problems for distribution systems by motivating aggregators to shift the flexible demands from peak hours at specific nodes.”