An economic analysis model for the energy storage system applied to a distribution substation

Leou, Rong‐Ceng

doi:10.1016/j.ijepes.2011.09.016

Cited by 65 publications

(41 citation statements)

References 15 publications

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“…Considering losses of transformer, power convertor system (PCS), battery, and pumping, the VRB efficiency is assumed about 75% [35]. Since the specific energy and energy density are low, VRB is suitable for small and medium scale applications.…”

Section: Bess Modelingmentioning

confidence: 99%

“…As shown in inequalities of (4) the maximum power and energy capacity of BESS should be considered. Table 2 shows the economic parameters of the battery used [35]. …”

Section: Bess Constraintsmentioning

confidence: 99%

“…A solution method that combines the genetic algorithm with linear programing method (GALP) is proposed in this paper to find the optimal solution for number, placement, sizing, and scheduling of BESS [35]. In [35], GA is used to transform the cost function to an LP that can be solved by Simplex Method.…”

Section: Optimal Management Approachmentioning

confidence: 99%

“…In [35], GA is used to transform the cost function to an LP that can be solved by Simplex Method. GA and LP are run only in MATLAB.…”

Section: Optimal Management Approachmentioning

confidence: 99%

“…2, too. Assumed hourly energy price [47] and transmission access fee [35] are shown in 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 According to (6)- (8), the benefits are maximized when BESS charges in midnight because energy price, emission rate, and transmission access fee are lower than noon and sundown (see Figs. 2 and 3).…”

mentioning

confidence: 99%

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Optimal placement, sizing, and daily charge/discharge of battery energy storage in low voltage distribution network with high photovoltaic penetration

et al. 2018

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Early version, also known as pre-print Link to publication from Aalborg University Citation for published version (APA): Jannesar, M. R., Sedighi, A., Savaghebi, M., & Guerrero, J. M. (2018). Optimal placement, sizing, and daily charge/discharge of battery energy storage in low voltage distribution network with high photovoltaic penetration. Applied Energy, 226, 957-966. https://doi.org/10.1016/j.apenergy.2018.06.036 General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.? Users may download and print one copy of any publication from the public portal for the purpose of private study or research. ? You may not further distribute the material or use it for any profit-making activity or commercial gain ? You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us at vbn@aub.aau.dk providing details, and we will remove access to the work immediately and investigate your claim. can mitigate these disadvantages and as a result, improve the system operation.For this purpose, battery energy storage system is charged when production of photovoltaic is more than consumers' demands and discharged when consumers' demands are increased. Since the price of battery energy storage system is high, economic, environmental, and technical objectives should be considered together for its placement and sizing. In this paper, optimal placement, sizing, and daily (24 hours) charge/discharge of battery energy storage system are performed based on a cost function that includes energy arbitrage, environmental emission, energy losses, transmission access fee, as well as capital and maintenance costs of battery energy storage system. All simulations are carried out in DIgSILENT and MATLAB linked together. Results show that by using the proposed approach, overvoltage 2 and energy losses are decreased, reverse power flow is prevented, environmental emission is reduced, and economic profit is maximized.

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Section: Bess Modelingmentioning

confidence: 99%

“…As shown in inequalities of (4) the maximum power and energy capacity of BESS should be considered. Table 2 shows the economic parameters of the battery used [35]. …”

Section: Bess Constraintsmentioning

confidence: 99%