scholarly journals Short-term load forecasting using an Artificial Neural Network for Battery Energy Storage System

2016 ◽  
pp. 345-348
Author(s):  
Hyang-A Park ◽  
Suel-Ki Kim ◽  
Jong-yul Kim ◽  
Jin-Wook Kim ◽  
Kyeng-Hee Cho ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Storage System ◽  
Load Forecasting ◽  
Energy Storage System ◽  
Short Term ◽  
Battery Energy Storage System ◽  
Battery Energy Storage ◽  
Short Term Load Forecasting ◽  
Battery Energy

scholarly journals Artificial Neural Network Control of Battery Energy Storage System to Damp-Out Inter-Area Oscillations in Power Systems

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3372 ◽  
Author(s):  
Heung-Jae Lee ◽  
Seong-Su Jhang ◽  
Won-Kun Yu ◽  
Jung-Hyun Oh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Storage ◽  
Storage System ◽  
Energy Storage System ◽  
Battery Energy Storage System ◽  
Heavy Load ◽  
Battery Energy Storage ◽  
Battery Energy ◽  
Load Conditions

This paper proposed an ANN (Artificial Neural Network) controller to damp out inter-area oscillation of a power system using BESS (Battery Energy Storage System). The conventional lead-lag controller-based PSSs (Power System Stabilizer) have been designed using linear models usually linearized at heavy load conditions. This paper proposes a non-linear ANN based BESS controller as the ANN can emulate nonlinear dynamics. To prove the performance of this nonlinear PSS, two linear PSS are introduced at first which are linearized at the heavy load and light load conditions, respectively. It is then verified that each controller can damp out inter-area oscillations at its own condition but not satisfactorily at the other condition. Finally, an ANN controller, that learned the dynamics of these two controllers, is proposed. Case studies are performed using PSCAD/EMTDC and MATLAB. As a result, the proposed ANN PSS shows a promising robust nonlinear performance.

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.

scholarly journals Operation of Battery Energy Storage System in Demand Side using Local Load Forecasting

2010 ◽  
Vol 130 (11) ◽  
pp. 995-1001 ◽  
Author(s):  
Yusuke Hida ◽  
Ryuichi Yokoyama ◽  
Jun Shimizukawa ◽  
Kenji Iba ◽  
Kouji Tanaka ◽  
...  
Keyword(s):  
Energy Storage ◽  
Storage System ◽  
Load Forecasting ◽  
Energy Storage System ◽  
Local Load ◽  
Demand Side ◽  
Battery Energy Storage System ◽  
Battery Energy Storage ◽  
Battery Energy
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