Demand responsive charging strategy of electric vehicles to mitigate the volatility of renewable energy sources

Gong, Lili; Cao, Wu; Liu, Kangli; Yu, Yue; Zhao, Jianfeng

doi:10.1016/j.renene.2020.04.061

Cited by 53 publications

(42 citation statements)

References 27 publications

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“…With the advanced technology of battery storages, the battery systems have been widely deployed in the renewable supported multi-energy systems [24]. The main functions of battery storages include the mitigation on renewable intermittence [25,26], load leveling through peak shaving and valley filling [27,28], power stability of micro-grid [29,30], economic savings with energy shifting [31,32], together with battery optimisation [33,34] and grid-responsive energy flexibility [35]. Phan et al [32] optimized the battery storage capacity for the operating cost savings of a grid-connected building energy system.…”

Section: Literature Reviewmentioning

confidence: 99%

An energy paradigm transition framework from negative towards positive district energy sharing networks—Battery cycling aging, advanced battery management strategies, flexible vehicles-to-buildings interactions, uncertainty and sensitivity analysis

2021

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Section: Literature Reviewmentioning

confidence: 99%

An energy paradigm transition framework from negative towards positive district energy sharing networks—Battery cycling aging, advanced battery management strategies, flexible vehicles-to-buildings interactions, uncertainty and sensitivity analysis

2021

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“…Supplying FCS from the existing energy system imposes an extra burden on the electrical grid. Moreover, stochastic connections to charge EVs cause overloading at peak demand, creating instability and, ultimately, blackouts [7].…”

Section: Introductionmentioning

confidence: 99%

“…RES are considered as the main element in any de-carbonization strategy [7]. Due to the stochastic nature of RES, their large-scale penetration at the supply end of the existing energy system can stress network performance by reducing power quality, causing voltage deviations and increasing power losses [4].…”

Section: Introductionmentioning

confidence: 99%

“…One study [4] used an improved version of the same tools for the optimal allocation and sizing of RES and EV charging stations, considering voltage fluctuations and EV charging costs among other factors. GA-PSO optimization tools were used as well [7] to determine Charging Station (CS) locations and capacities when considering distributed RES, EV users' satisfaction degree and distribution network technical constraints.…”

mentioning

confidence: 99%

“…The study by Gong et al [7] proposed an uncertainty model using Set Pair Analysis (SPA) for predicting RES outputs. Considering EVs as mobile energy storage units, the study evaluated a smart charging strategy for reducing RES fluctuations.…”

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confidence: 99%

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A Case Study in Qatar for Optimal Energy Management of an Autonomous Electric Vehicle Fast Charging Station with Multiple Renewable Energy and Storage Systems

Wahedi

Biçer

2020

Energies

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E-Mobility deployment has attained increased interest during recent years in various countries all over the world. This interest has focused mainly on reducing the reliance on fossil fuel-based means of transportation and decreasing the harmful emissions produced from this sector. To secure the electricity required to satisfy Electric Vehicles’ (EVs’) charging needs without expanding or overloading the existing electricity infrastructure, stand-alone charging stations powered by renewable sources are considered as a reasonable solution. This paper investigates the simulation of the optimal energy management of a proposed grid-independent, multi-generation, fast-charging station in the State of Qatar, which comprises hybrid wind, solar and biofuel systems along with ammonia, hydrogen and battery storage units. The study aims to assess the optimal sizing of the solar, wind and biofuel units to be incorporated in the design along with the optimal ammonia, hydrogen and battery storage capacities to fulfill the daily EV demand in an uninterruptable manner. The main objective is to fast-charge a minimum of 50 EVs daily, while the constraints are the intermittent and volatile nature of renewable energy sources, the stochastic nature of EV demand, local meteorological conditions and land space limitations. The results show that the selection of a 468 kWp concentrated photovoltaic thermal plant, 250 kW-rated wind turbine, 10 kW biodiesel power generator unit and 595 kWh battery storage system, along with the on-site production of hydrogen and ammonia, to generate 200 kW power via fuel cells can achieve the desired target, with a total halt of on-site hydrogen and ammonia production during October and November and 50% reduction during December.

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S2NA‐GEO method–based charging strategy of electric vehicles to mitigate the volatility of renewable energy sources

Ilango¹,

Rajesh

Shajin

2021

Int Trans Electr Energ Syst

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In this manuscript, an efficient hybrid strategy is proposed to mitigate the negative impact of RES output fluctuations and smart charging method of electric vehicles (EVs). The proposed hybrid system is the joint implementation of Spike Neural Network Learning Algorithm (S2NA) algorithm and Golden Eagle Optimizer (GEO) algorithm; hence, it is known as S2NA-GEO strategy. An innovative uncertainty mode of renewable energy sources (RESs) based upon S2NA-GEO strategy is proposed, which can avert the difficult parameter collection and formula derivation. Price-based mode is accepted as EVs are considered responsive loads, which also take into account the spatial-temporal uncertainty of electric vehicles. Here, two fluctuation indexes are determined as quantitative evaluation of the volatility of RESs, then the charging cost is adopted as a guideline for the satisfaction of electric vehicle charging. The main purpose of the proposed system is diminishing

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Demand responsive charging strategy of electric vehicles to mitigate the volatility of renewable energy sources

Cited by 53 publications

References 27 publications

An energy paradigm transition framework from negative towards positive district energy sharing networks—Battery cycling aging, advanced battery management strategies, flexible vehicles-to-buildings interactions, uncertainty and sensitivity analysis

An energy paradigm transition framework from negative towards positive district energy sharing networks—Battery cycling aging, advanced battery management strategies, flexible vehicles-to-buildings interactions, uncertainty and sensitivity analysis

A Case Study in Qatar for Optimal Energy Management of an Autonomous Electric Vehicle Fast Charging Station with Multiple Renewable Energy and Storage Systems

S2NA‐GEO method–based charging strategy of electric vehicles to mitigate the volatility of renewable energy sources

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