Optimal battery cycling strategies in workplaces with electric vehicle chargers, energy storage systems and renewable energy generation

Toosi, Hooman Ekhteraei; Merabet, Adel; Al‐Durra, Ahmed; Swingler, Andrew

doi:10.1049/rpg2.12376

Cited by 9 publications

(2 citation statements)

References 48 publications

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“…The departure time is another important parameter to simulate the EVCS and consideration of its uncertainties [46]. Several probability distributions have been introduced for the departure times of EVCSs.…”

Section: Clustering-based Modelling Of Evcssmentioning

confidence: 99%

Optimized allocation of microgrids’ distributed generations and electric vehicle charging stations considering system uncertainties by clustering algorithms

Yaghoubi‐Nia,

Hashemi‐Dezaki,

Niasar

2024

IET Renewable Power Gen

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The reliability‐oriented optimized sizing and placement of electric vehicle (EV) charging stations (EVCSs) has received less attention. In addition, the literature review shows that a research gap exists regarding a clustering‐based method to optimize the allocation of DGs and EVCSs, considering the system uncertainties. This article tries to fill such a knowledge gap by proposing a new clustering‐based method to optimize the allocation of DGs and EVs simultaneously, considering the uncertainties of EV behaviours and stochastic behaviours of renewable DGs. Developing a new stochastic model for EVs using the clustering algorithm is one of the essential contributions. The uncertain parameters, e.g. EV charging loads based on EV owners’ behaviours (arrival time, departure time, and driving distance) and renewable DGs, would be clustered. The proposed method could solve the execution time challenges of Monte Carlo simulation‐based approaches to concern the stochastic behaviours of smart grids. The simultaneously reliability‐oriented optimal allocation of EVCSs, DGs, and protection equipment, using the proposed clustering‐based algorithm is another main contribution. The IEEE 33‐bus test system is studied to examine the introduced method. Simulation results imply that a 1.45% accuracy improvement could be obtained compared to available analytical ones, while its execution time is appropriate.

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Section: Clustering-based Modelling Of Evcssmentioning

confidence: 99%

Optimized allocation of microgrids’ distributed generations and electric vehicle charging stations considering system uncertainties by clustering algorithms

Yaghoubi‐Nia,

Hashemi‐Dezaki,

Niasar

2024

IET Renewable Power Gen

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show abstract

“…In [45], the rain‐flow algorithm is used to estimate the BES degradation cost; however, the factors affecting the BES calendar aging are not considered. A novel energy management strategy for commercial buildings is provided in [46] to minimise the operation cost. In this strategy, the BES degradation cost is calculated based on the DOD at each cycle; however, the incomplete cycles and the BES calendar aging are not integrated into the proposed strategy.…”

Section: Introductionmentioning

confidence: 99%

Predictive energy management strategy for battery energy storage considering battery degradation cost

Amini

Nazari

Hosseinian

2022

IET Renewable Power Gen

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This study proposes a novel predictive energy management strategy to integrate the battery energy storage (BES) degradation cost into the BES scheduling problem and address the uncertainty in the energy management problem. As the first step, the factors affecting the BES calendar aging and cycle aging are linearly modelled. Furthermore, a linear algorithm is provided to calculate the BES cycle aging due to the BES complete and incomplete cycles. Subsequently, a novel approach to estimating the BES degradation cost function according to the BES specifications and degradation process is presented. Finally, taking into account the BES degradation cost model, the proposed predictive energy management strategy framework is implemented on an integrated photovoltaic and BES system to evaluate the applicability and efficiency of the proposed scheme in integrating the BES degradation cost in the energy management problem. The numerical simulation results indicate that integrating the BES degradation cost into the energy management problem significantly affects the BES charge/discharge strategy. Moreover, comparing the proposed predictive energy management strategy with a simple one, it is verified that the provided approach could decrease the BES capacity fade and degradation cost by 5.06% and 4.67%, respectively, and increase the photovoltaic farm profit by 1.10%.

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A novel control approach to improve the stability of hybrid AC/DC microgrids

Khosravi¹,

Baghbanzadeh

Oubelaid

et al. 2023

Applied Energy

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Optimal battery cycling strategies in workplaces with electric vehicle chargers, energy storage systems and renewable energy generation

Cited by 9 publications

References 48 publications

Optimized allocation of microgrids’ distributed generations and electric vehicle charging stations considering system uncertainties by clustering algorithms

Optimized allocation of microgrids’ distributed generations and electric vehicle charging stations considering system uncertainties by clustering algorithms

Predictive energy management strategy for battery energy storage considering battery degradation cost

A novel control approach to improve the stability of hybrid AC/DC microgrids

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