scholarly journals Typical Daily Power Curve Mining for Energy Storage Systems under Smoothing Power Fluctuation Scenarios

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xiyun Yang ◽  
Jie Ren ◽  
Xiangjun Li ◽  
Hang Zhang
Keyword(s):  
Genetic Algorithm ◽  
Energy Storage ◽  
Probability Distribution ◽  
Storage System ◽  
Capacity Allocation ◽  
Power Curve ◽  
Power Fluctuation ◽  
Quantum Genetic Algorithm ◽  
Weather Patterns ◽  
Sample Time

Under the application scenario of smoothing photovoltaic (PV) power fluctuation, a novel typical daily power curve mining method is developed for a battery energy storage system (BESS) that utilizes the power probability distribution and Bloch spherical quantum genetic algorithm. The charging/discharging of BESS is analyzed by applying fuzzy-c means clustering techniques. In the mining approach, at any sample time, those distribution intervals containing concentrated power points are individually located by using probability distribution information and Bloch spherical quantum genetic algorithm. Character power for the specified interval can also be determined using Bloch spherical quantum genetic algorithm. Next, a roulette principal is employed, to determine one value from the character power data as a typical value of the mined power curve at the sample time. By connecting the typical power at each sample time, the typical daily power curve for BESS is achieved. Based on typical power curve, decision-maker can master the important operating parameters of BESS and analyze optimal capacity allocation. By error evaluation indexes between the mined typical daily power curve and power curve under different weather patterns, the simulation results verify that the mined power curve can address the operating power of the BESS under different weather patterns.

scholarly journals A Capacity Configuration Control Strategy to Alleviate Power Fluctuation of Hybrid Energy Storage System Based on Improved Particle Swarm Optimization

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 642 ◽  
Author(s):  
Tiezhou Wu ◽  
Xiao Shi ◽  
Li Liao ◽  
Chuanjian Zhou ◽  
Hang Zhou ◽  
...  
Keyword(s):  
Energy Storage ◽  
Objective Function ◽  
Control Strategy ◽  
Storage System ◽  
Particle Swarm ◽  
Energy Storage System ◽  
Power Fluctuation ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Hybrid Energy Storage System

In view of optimizing the configuration of each unit’s capacity for energy storage in the microgrid system, in order to ensure that the planned energy storage capacity can meet the reasonable operation of the microgrid’s control strategy, the power fluctuations during the grid-connected operation of the microgrid are considered in the planning and The economic benefit of hybrid energy storage is quantified. A multi-objective function aiming at minimizing the power fluctuation on the DC bus in the microgrid and optimizing the capacity ratio of each energy storage system in the hybrid energy storage system (HESS) is established. The improved particle swarm algorithm (PSO) is used to solve the objective function, and the solution is applied to the microgrid experimental platform. By comparing the power fluctuations of the battery and the supercapacitor in the HESS, the power distribution is directly reflected. Comparing with the traditional mixed energy storage control strategy, it shows that the optimized hybrid energy storage control strategy can save 4.3% of the cost compared with the traditional hybrid energy storage control strategy, and the performance of the power fluctuation of the renewable energy is also improved. It proves that the proposed capacity configuration of the HESS has certain theoretical significance and practical application value.

Application of integrated energy storage system in wind power fluctuation mitigation

2020 ◽  
Vol 32 ◽  
pp. 101835
Author(s):  
Yushu Sun ◽  
Wei Pei ◽  
Dongqiang Jia ◽  
Genming Zhang ◽  
Heng Wang ◽  
...  
Keyword(s):  
Energy Storage ◽  
Wind Power ◽  
Storage System ◽  
Energy Storage System ◽  
Power Fluctuation

scholarly journals Incorporating State-of-Charge Balancing into the Control of Energy Storage Systems for Smoothing Renewable Intermittency

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1190 ◽  
Author(s):  
Jae Shim ◽  
Heejin Kim ◽  
Kyeon Hur
Keyword(s):  
Energy Storage ◽  
Storage System ◽  
Energy Storage System ◽  
State Of Charge ◽  
Effective Control ◽  
Power Fluctuation ◽  
Energy Feedback ◽  
Time Control ◽  
Power Smoothing ◽  
Specific Frequency

This paper proposes an effective control methodology for the Energy Storage System (ESS), compensating for renewable energy intermittency. By connecting generation variability and the preset service range of the State of Charge (SOC), this methodology successfully secures the desired SOC range while smoothing out power fluctuations. Adaptive to grid conditions, it can adjust response time (control bandwidth) of the ESS via energy feedback coefficients subject to the ESS capacity and its SOC range. This flexibility facilitates the process of developing ESS operation and planning strategies. Mathematical analysis proves that the proposed method controls the ESS to perform best for specific frequency bands associated with power fluctuation. Time-domain simulation studies along with power-spectrum analysis using PSCAD and MATLAB demonstrate the excellent power-smoothing performance to the power grid.

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