Electric Vehicle Charging Strategy in Smart Grids with Distributed Generation

Bruinsma, Gustavo; Carati, Emerson Giovani; Piveta, Marcelo; Salvatti, Gabriel A.; Rech, Cassiano

doi:10.1109/sepoc54972.2022.9976416

Cited by 4 publications

(1 citation statement)

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“…The coordination method refers to optimal charging/discharging of EVs in recognition of renewable fluctuations to alleviate uncertainties of EV driving patterns and renewable generation. The coordination can be based on market objective [1][2][3][4] or to address operational issues [5][6][7]. The market objective is to take advantage of renewable arbitrage and provide revenues for renewable DGs and EVs.…”

Section: Introductionmentioning

confidence: 99%

Synergistic Integration of EVs and Renewable DGs in Distribution Micro-Grids

Ghofrani

2024

Sustainability

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This paper proposes a multi-objective optimization framework for safe, reliable, and economic integration of electric vehicles (EVs) and renewable distributed generators (DGs) in distribution micro-grids. EV and DG coordination optimization with the use of vehicle-to-grid (V2G) technology along with system reconfiguration optimization is developed to provide collective revenues and address integrational complications that may occur by additional system loading due to EV charging and EV-DG energy exchanges. A Genetic Algorithm (GA) optimizes the EV charging/discharging in synergies with renewable DGs to maximize benefits that can be captured by their collaborative participation in electricity market and through renewable energy arbitrage. The developed EV charging/discharging optimization is implemented in a real 134-bus distribution network and is evaluated for its potential operational implications, namely, increased system losses. A system reconfiguration is then proposed to reduce the system losses by optimizing the flow of power through switching on/off the connections within the micro-grid and/or with other distribution systems. Simulation results demonstrate the efficiency of the proposed method in not only providing collective revenues, but also in enhancing the system operation by reducing the losses of the distribution grid. The collective benefits proposed by the developed optimization and validated by the simulation results facilitate transitioning to clean and eco-friendly sources of energy for generation and transportation, which in turn leads to more sustainable development of societies and communities.

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