State of Charge (SoC) Estimation of Battery Energy Storage System (BESS) Using Artificial Neural Network (ANN) Based on IoT- Enabled Embedded System

2021 ◽  
Author(s):  
Putu Handre Kertha Utama ◽  
Hilda Hamdah Husniyyah ◽  
Irsyad Nashirul Haq ◽  
Justin Pradipta ◽  
Edi Leksono
Keyword(s):  
Neural Network ◽  
Embedded System ◽  
Storage System ◽  
Energy Storage System ◽  
State Of Charge ◽  
Battery Energy Storage System ◽  
Battery Energy Storage ◽  
Soc Estimation ◽  
Artificial Neural Network Ann ◽  
Battery Energy

scholarly journals Battery Energy Storage System for Aggregated Inertia-Droop Control and a Novel Frequency Dependent State-of-Charge Recovery

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2003 ◽  
Author(s):  
Ujjwal Datta ◽  
Akhtar Kalam ◽  
Juan Shi
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Storage System ◽  
Energy Storage System ◽  
State Of Charge ◽  
Battery Energy Storage System ◽  
Frequency Dependent ◽  
Battery Energy Storage ◽  
Battery Energy ◽  
Dependent State

To deal with the technical challenges of renewable energy penetration, this paper focuses on improving the grid voltage and frequency responses in a hybrid renewable energy source integrated power system following load and generation contingency events. A consolidated methodology is proposed to employ a battery energy storage system (BESS) to contribute to voltage regulation through droop-type control and frequency regulation by assimilated inertia emulation (IE) and droop-type control. In addition, a novel frequency-dependent state-of-charge (SOC) recovery (FDSR) is presented to regulate BESS power consumption within the FDSR constraints and recharge the battery during idle periods whenever needed. The efficacy of the proposed BESS controller is demonstrated in an IEEE-9 bus system with a 22.5% photovoltaics (PV) and wind penetration level. The simulation results obtained manifest the satisfactory performance of the proposed controller in regulating simultaneous voltage and frequency in terms of lower rate of change of frequency and better frequency nadir. Furthermore, the proposed FDSR demonstrates its superiority at the time of SOC recovery compared to the conventional approach.

scholarly journals A Mixed Receding Horizon Control Strategy for Battery Energy Storage System Scheduling in a Hybrid PV and Wind Power Plant with Different Forecast Techniques

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2326 ◽  
Author(s):  
Yuqing Yang ◽  
Stephen Bremner ◽  
Chris Menictas ◽  
Merlinde Kay
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Storage System ◽  
Energy Storage System ◽  
Receding Horizon Control ◽  
Wind Power Plant ◽  
Battery Energy Storage System ◽  
Battery Energy Storage ◽  
Battery Energy ◽  
The Cost

This paper presents a mixed receding horizon control (RHC) strategy for the optimal scheduling of a battery energy storage system (BESS) in a hybrid PV and wind power plant while satisfying multiple operational constraints. The overall optimisation problem was reformulated as a mixed-integer linear programming (MILP) problem, aimed at minimising the total operating cost of the entire system. The cost function of this MILP is composed of the profits of selling electricity, the cost of purchasing ancillary services for undersupply and oversupply, and the operation and maintenance cost of each component. To investigate the impacts of day-ahead and hour-ahead forecasting for battery optimisation, four forecasting methods, including persistence, Elman neural network, wavelet neural network and autoregressive integrated moving average (ARIMA), were applied for both day-ahead and hour-ahead forecasting. Numerical simulations demonstrated the significant increased efficiency of the proposed mixed RHC strategy, which improved the total operation profit by almost 29% in one year, in contrast to the day-ahead RHC strategy. Moreover, the simulation results also verified the significance of using more accurate forecasting techniques, where ARIMA can reduce the total operation cost by almost 5% during the whole year operation when compared to the persistence method as the benchmark.

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