Sustainable Energy System Planning for an Industrial Zone by Integrating Electric Vehicles as Energy Storage

Noorollahi, Younes; Golshanfard, Aminabbas; Aligholian, Armin; Mohammadi‐Ivatloo, Behnam; Nielsen, Steffen; Hajinezhad, Ahmad

doi:10.1016/j.est.2020.101553

Cited by 59 publications

(15 citation statements)

References 33 publications

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“…Concerning the technologies of exploration and exploitation of fossil resources and current consumption, we will soon see fossil fuels' end. The completion of fossil sources is an alert to find alternative sources and use them 1,2 …”

Section: Introductionmentioning

confidence: 99%

Distributed wind and solar power for grid sustainability and emission reduction

Noorollahi

Kalantari

Saifoddin

et al. 2021

Env Prog and Sustain Energy

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This article intends to maximize the amount of solar and wind power produced while minimizing the tested network's annual energy loss. It also wants to improve the voltage profile and reduce the dependence on the upstream network. Two types of distributed generation sources, that is, wind DGs and solar DGs, are investigated. In this article, the network loads vary according to the local consumption pattern. This research shows that the optimal utilizing of solar and wind distributed generation sources causes energy loss to decrease by about 43% and 94%, respectively. Besides, the grid will receive 1109.49 GWh of energy from the upstream power grid without distributed generation sources. In contrast, solar and wind generation sources receive only 801.47 and 125.84 GWh, respectively. Additionally, in solar and wind scenarios, micro‐grid sends 71.62 and 102.42 GWh energy to the upstream network. Also, by calculating the emission reduction, it was observed that by installing 465.45 MW of the wind turbine units in the area under consideration, the country's CO2 equivalent is reduced by 754,586 tons, and the country's primary energy is reduced by 263,916 TOE.

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Section: Introductionmentioning

confidence: 99%

Distributed wind and solar power for grid sustainability and emission reduction

Noorollahi

Kalantari

Saifoddin

et al. 2021

Env Prog and Sustain Energy

Self Cite

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“…There are various methods to predict the load profile of EVs. Such methods forecast the EVs' load using historical driving/traffic data for fifty-five electric taxis in Beijing [163] and an Industrial zone in Iran [164]. In another study [165], the Monte Carlo simulation has been adopted to forecast the EVs' load by considering the EV types, charging mode, charging period, daily mileage, and charging power, whereas the number of EVs was forecasted by using the Bass model.…”

Section: Forecasting the Electric Vehicles' Charging Loadmentioning

confidence: 99%

A comprehensive review on electric vehicles smart charging: Solutions, strategies, technologies, and challenges

Sadeghian

Oshnoei

Mohammadi‐Ivatloo

et al. 2022

Journal of Energy Storage

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162

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“…A N U S C R I P T off between exergy efficiency and operational cost. Noorollahi et al [15] investigated the introduction of electric vehicles as the energy storage for optimal design of energy systems in an industrial zone. Wu et al [16] proposed a biomass polygeneration integrated energy system optimisation model and investigated the feasibility to various case regions, where the vital factor of the split ratios was further evaluated.…”

Section: A C C E P T E D Mmentioning

confidence: 99%