Distributional Locational Marginal Pricing Based Optimization for Electric Vehicle Charging Management

TL;DR

This paper introduces a distributional locational marginal pricing (DLMP) model to optimize electric vehicle charging, reduce grid congestion, and promote fair pricing in smart grids with increasing EV adoption.

Contribution

It proposes a novel DLMP-based pricing framework for distribution systems, specifically tailored to manage EV charging and improve grid efficiency.

Findings

DLMP effectively alleviates grid congestion.

Optimal EV charging schedules are developed using DLMP.

The model promotes fair and efficient electricity pricing.

Abstract

Electric power generation, transmission, and distribution systems are attracting a large amount of interest from researchers with the development of the smart grid technologies. A smart grid aims at effective control and conditioning of the distribution of electricity. Pricing signal based distribution system are seen as one of the novel ways to achieve control in a smart grid. In our work, we propose to use a pricing signal modeled after the locational marginal price in the transmission system to locally provide price data to the users. The formulation and implementation of the distributional locational marginal price (DLMP) are achieved to develop a fair pricing model. The work is further practically implemented to a grid with Electric Vehicles in addition to the conventional load. The increasing popularity of EVs because of their ability to reduce greenhouse gas (GHG) emissions will pose a greater challenge in terms of demand on the power grid. We propose to use the DLMP modeling to alleviate congestion in the power grid and develop an optimal charging schedule for EVs.