scholarly journals Retraction: “Characteristic analysis of operation curve of energy storage system considering typical weather conditions to suppress photovoltaic power fluctuation” [J. Renewable Sustainable Energy 10, 063502 (2018)]

2021 ◽  
Vol 13 (4) ◽  
pp. 049901
Author(s):  
Huiqing Shi ◽  
Ruolan Wang
Keyword(s):  
Energy Storage ◽  
Sustainable Energy ◽  
Storage System ◽  
Weather Conditions ◽  
Energy Storage System ◽  
Characteristic Analysis ◽  
Power Fluctuation ◽  
Photovoltaic Power

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 Effect of Prediction Error of Machine Learning Schemes on Photovoltaic Power Trading Based on Energy Storage Systems

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1249 ◽  
Author(s):  
Kuk Bae ◽  
Han Jang ◽  
Bang Jung ◽  
Dan Sung
Keyword(s):  
Machine Learning ◽  
Energy Storage ◽  
Output Power ◽  
Storage System ◽  
Weather Conditions ◽  
Energy Storage System ◽  
Energy Markets ◽  
Prediction Errors ◽  
Power Prediction ◽  
Power Trading

Photovoltaic (PV) output power inherently exhibits an intermittent property depending on the variation of weather conditions. Since PV power producers may be charged to large penalties in forthcoming energy markets due to the uncertainty of PV power generation, they need a more accurate PV power prediction scheme in energy market operation. In this paper, we characterize the effect of PV power prediction errors on energy storage system (ESS)-based PV power trading in energy markets. First, we analyze the prediction accuracy of two machine learning (ML) schemes for the PV output power and estimate their error distributions. We propose an efficient ESS management scheme for charging and discharging operation of ESS in order to reduce the deviations between the day-ahead (DA) and real-time (RT) dispatch in energy markets. In addition, we estimate the capacity of ESSs, which can absorb the prediction errors and then compare the PV power producer’s profit according to ML-based prediction schemes with/without ESS. In case of ML-based prediction schemes with ESS, the ANN and SVM schemes yield a decrease in the deviation penalty by up to 87% and 74%, respectively, compared with the profit of those schemes without ESS.

scholarly journals Multi-Objective Sizing of Hybrid Energy Storage System for Large-Scale Photovoltaic Power Generation System

2019 ◽  
Vol 11 (19) ◽  
pp. 5441 ◽  
Author(s):  
Chao Ma ◽  
Sen Dong ◽  
Jijian Lian ◽  
Xiulan Pang
Keyword(s):  
Power Generation ◽  
Energy Storage ◽  
Large Scale ◽  
Yellow River ◽  
Storage System ◽  
Weather Conditions ◽  
Energy Storage System ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Power Stations

Hybrid energy storage systems (HESS) are an effective way to improve the output stability for a large-scale photovoltaic (PV) power generation systems. This paper presents a sizing method for HESS-equipped large-scale centralized PV power stations. The method consists of two parts: determining the power capacity by a statistical method considering the effects of multiple weather conditions and calculating the optimal energy capacity by employing a mathematical model. The method fully considers the characteristics of PV output and multiple kinds of energy storage combinations. Additionally, a pre-storage strategy that can further improve stability of output is proposed. All of the above methods were verified through a case study application to an 850 MW centralized PV power station in the upstream of the Yellow river. The optimal hybrid energy storage combination and its optimization results were obtained by this method. The results show that the optimal capacity configuration can significantly improve the stability of PV output and the pre-storage strategy can further improve the target output satisfaction rate by 8.28%.

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