2021
DOI: 10.1109/jsyst.2020.3009695
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A Battery Energy Storage Sizing Method for Parking Lot Equipped With EV Chargers

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Cited by 9 publications
(3 citation statements)
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“…In [8], the authors propose an algorithm for parking lot management which is used for optimal scheduling of electric vehicles considering supply from solar generation with the objective of minimizing the grid power import during peak pricing periods. In [9], the authors propose a more formal approach for estimating the size of a stationary BSS for PLs in a constrained grid while considering EV travel patterns, charging needs, and the driver behavior of EVs. The BSS sizing and PL charging optimization is performed in a two-step process, with BSS sizing using the region reduction method and PL charging optimization based on nonlinear programming.…”
Section: Literature Reviewmentioning
confidence: 99%
“…In [8], the authors propose an algorithm for parking lot management which is used for optimal scheduling of electric vehicles considering supply from solar generation with the objective of minimizing the grid power import during peak pricing periods. In [9], the authors propose a more formal approach for estimating the size of a stationary BSS for PLs in a constrained grid while considering EV travel patterns, charging needs, and the driver behavior of EVs. The BSS sizing and PL charging optimization is performed in a two-step process, with BSS sizing using the region reduction method and PL charging optimization based on nonlinear programming.…”
Section: Literature Reviewmentioning
confidence: 99%
“…In (10), θ is regarded as a parameter, and equality constraints are equivalently expressed via two opposite inequalities. Suppose the unit capacity cost of power electronics converters, battery array, and transmission line is c p , c e , and c l ,…”
Section: Feasible Set Of Capacity Parametersmentioning
confidence: 99%
“…Ref. [10] developed an optimization method to size the battery energy storage in electric vehicle parking lots. The uncertainty of charging demand was estimated by investigating the driving patterns and behaviors, such as the probability distributions of arrival/departure time and driving distances.…”
mentioning
confidence: 99%