An optimal and real-time control strategy for energy storage system

2016 ◽  
pp. 257-261
Author(s):  
L Yang ◽  
Y Gao ◽  
Y Niu ◽  
F Zhang ◽  
T Du
Keyword(s):  
Energy Storage ◽  
Real Time ◽  
Control Strategy ◽  
Storage System ◽  
Energy Storage System ◽  
Real Time Control ◽  
Time Control

scholarly journals Practical Operation Strategies for Energy Storage System under Uncertainty

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1098 ◽  
Author(s):  
Minsoo Kim ◽  
Kangsan Kim ◽  
Hyungeun Choi ◽  
Seonjeong Lee ◽  
Hongseok Kim
Keyword(s):  
Real Time ◽  
Peak Power ◽  
Storage System ◽  
Peak Load ◽  
Load Forecasting ◽  
Energy Storage System ◽  
Real Time Control ◽  
Time Control ◽  
Forecasting Error ◽  
Marginal Power

Recent advances in battery technologies have reduced the financial burden of using the energy storage system (ESS) for customers. Peak cut, one of the benefits of using ESS, can be achieved through proper charging/discharging scheduling of ESS. However, peak cut is sensitive to load-forecasting error, and even a small forecasting error may result in the failure of peak cut. In this paper, we propose a two-phase approach of day-ahead optimization and real-time control for minimizing the total cost that comes from time-of-use (TOU), peak load, and battery degradation. In day-ahead optimization, we propose to use an internalized pricing to manage peak load in addition to the cost from TOU. The proposed method can be implemented by using dynamic programming, which also has an advantage of accommodating the state-dependent battery degradation cost. Then in real-time control, we propose a concept of marginal power to alleviate the performance loss incurred from load-forecasting error and mimic the offline optimal battery scheduling by learning from load-forecasting error. By exploiting the marginal power, real-time ESS charging/discharging power gets close to the offline optimal battery scheduling. Case studies show that under load-forecasting uncertainty, the peak power using the proposed method is only 22.4% higher than the offline optimal peak power, while the day-ahead optimization has 76.8% higher peak power than the offline optimal power. In terms of profit, the proposed method achieves 77.0% of the offline optimal profit while the day-ahead method only earns 19.6% of the offline optimal profit, which shows the substantial improvement of the proposed method.

scholarly journals Real-Time Control Strategy of Fuel Cell and Battery System for Electric Hybrid Boat Application

2021 ◽  
Vol 13 (16) ◽  
pp. 8693
Author(s):  
Ahmed Al Amerl ◽  
Ismail Oukkacha ◽  
Mamadou Baïlo Camara ◽  
Brayima Dakyo
Keyword(s):  
Fuel Cell ◽  
Real Time ◽  
Control Strategy ◽  
Storage System ◽  
Effective Control ◽  
Real Time Control ◽  
Loop Control ◽  
Time Control ◽  
Hybrid Electric ◽  
Varying Demand

In this paper, an effective control strategy is proposed to manage energy distribution from fuel cells and batteries for hybrid electric boat applications. The main objectives of this real-time control are to obtain fast current tracking for the batteries’ system, the DC bus voltage stability by using a fuel cell, and energy load distribution for a hybrid electric boat under varying demand conditions. The proposed control strategy is based on a combination of frequency approach and current/voltage control of interleaved boost converters to reduce the hydrogen consumption by the fuel cell and improve the quality of energy transfer. The frequency approach was dedicated to managing the DC power-sharing between the load, the fuel cell, and the batteries’ storage system by extracting the power references. The closed loop control system utilized to control the energy is based on the DC/DC converters. The performance evaluation of the proposed control strategy has been tested through a real-time experimental test bench based on a dSPACE board (DS1104).

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