Battery Energy Storage System Applied to Wind Power System Based On Z-Source Inverter Connected to Grid

2016 ◽  
Vol 14 (9) ◽  
pp. 4035-4042 ◽  
Author(s):  
Michael Andres Hernandez Navas ◽  
Fredy Lozada G. ◽  
Jose Luis Azcue Puma ◽  
Jose A. Torrico A. ◽  
Alfeu J. Sguarezi Filho
Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 522
Author(s):  
Rajitha Udawalpola ◽  
Taisuke Masuta ◽  
Taisei Yoshioka ◽  
Kohei Takahashi ◽  
Hideaki Ohtake

Power imbalances such as power shortfalls and photovoltaic (PV) curtailments have become a major problem in conventional power systems due to the introduction of renewable energy sources. There can be large power shortfalls and PV curtailments because of PV forecasting errors. These imbalances might increase when installed PV capacity increases. This study proposes a new scheduling method to reduce power shortfalls and PV curtailments in a PV integrated large power system with a battery energy storage system (BESS). The model of the Kanto area, which is about 30% of Japan’s power usage with 60 GW grid capacity, is used in simulations. The effect of large PV power integration of 50 GW and 100 GW together with large BESS capacity of 100 GWh and 200 GWh has been studied. Mixed integer linear programming technique is used to calculate generator unit commitment and BESS charging and discharging schedules. The simulation results are shown for two months with high and low solar irradiance, which include days with large PV over forecast and under forecast errors. The results reveal that the proposed method eliminates power shortfalls by 100% with the BESS and reduce the PV curtailments by 69.5% and 95.2% for the months with high and low solar irradiance, respectively, when 200 GWh BESS and 100 GW PV power generation are installed.


Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3065 ◽  
Author(s):  
Monika Sandelic ◽  
Daniel-Ioan Stroe ◽  
Florin Iov

This paper focuses on the sizing of a battery energy storage system providing frequency containment reserves in a power system with a large wind power penetration level. A three-stage sizing methodology including the different aspect of battery energy storage system performance is proposed. The first stage includes time-domain simulations, investigating battery energy storage system dynamic response and its capability of providing frequency reserves. The second stage involves lifetime investigation. An economic assessment of the battery unit is carried out by performing the last stage. The main outcome of the proposed methodology is to choose the suitable battery energy storage system size for providing frequency containment reserve from augmented wind power plants while fulfilling relevant evaluation criteria imposed for each stage.


2019 ◽  
Vol 2019 (18) ◽  
pp. 5028-5032
Author(s):  
Pranda Prasanta Gupta ◽  
Prerna Jain ◽  
Suman Sharma ◽  
Kailash Chand Sharma ◽  
Rohit Bhakar

2018 ◽  
Vol 8 (10) ◽  
pp. 1957 ◽  
Author(s):  
Xin Jiang ◽  
Guoliang Nan ◽  
Hao Liu ◽  
Zhimin Guo ◽  
Qingshan Zeng ◽  
...  

An optimal sizing model of the battery energy storage system (BESS) for large-scale wind farm adapting to the scheduling plan is proposed in this paper. Based on the analysis of the variability and uncertainty of wind output, the cost of auxiliary services of systems that are eased by BESS is quantized and the constraints of BESS accounting for the effect of wind power on system dispatching are proposed. Aiming to maximum the benefits of wind-storage union system, an optimal capacity model considering BESS investment costs, wind curtailment saving, and auxiliary services compensation is established. What’s more, the effect of irregular charge/discharge process on the life cycle of BESS is considered into the optimal model by introducing an equivalent loss of the cycle life. Finally, based on the typical data of a systems, results show that auxiliary services compensation can encourage wind farm configuration BESS effectively. Various sensitivity analyses are performed to assess the effect of the auxiliary services compensation, on-grid price of wind power, investment cost of BESS, cycle life of BESS, and wind uncertainty reserve level of BESS on this optimal capacity.


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