An efficient ramp rate and state of charge control for PV-battery system capacity firming

Author(s):  
Amit Kumar Bhattacharjee ◽  
Issa Batarseh ◽  
Haibing Hu ◽  
Nasser Kutkut
2019 ◽  
Vol 11 (12) ◽  
pp. 3471 ◽  
Author(s):  
Elshurafa ◽  
Aldubyan

In this paper, we quantify the economic and environmental implications of operating a standalone photovoltaic-battery system (PVB) while varying the battery’s minimum allowable state of charge (MSOC), the load profile, and simultaneously incorporating ambient temperature effects in hot climates. To that end, Saudi Arabia has been chosen for this case study. Over a project lifetime of 25 years, we find that, contrary to the widely accepted norm of 50% being a reasonable MSOC, a lower MSOC can bestow economic benefits. For example, a MSOC of 20% results in a lower number of batteries required throughout the lifetime of the project—while still meeting demand. For a village of 1000 homes, this translates to a saving of $47 million in net present value. Further, incorporating temperature effects results in deducing more realistic costs that are 125% higher than the ideal scenario (i.e., when temperature is not modeled). This difference stems from underestimating the actual number of batteries needed throughout the project lifetime. Compared to a diesel-powered microgrid, and for a village of 1000 homes, a PVB would, on an annual basis, avoid emitting 5000 tons of carbon and avoid burning 2 million liters of diesel.


2014 ◽  
Vol 953-954 ◽  
pp. 790-795
Author(s):  
Yuan Bin Yu ◽  
Zhou Cai ◽  
Kai Peng ◽  
Wen Qiang Lv

Accurate battery state of charge is the prerequisite and precondition for optimal control of hybrid vehicles. This article will be based on the established dynamic model of battery, estimate the battery state of charge in real time. Firstly, analysis the application limitations of Kalman filtering algorithm estimates battery state of charge. Secondly, for some uncertain parameters contained in the model of battery system, paper proposes a parameter line identification extended Kalman filter algorithm to estimate the battery state of charge. Finally, experimental verification algorithm dynamic conditions in the battery state of charge estimation accuracy and effectiveness.


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