Monitoring and Control for Hundreds Megawatt Scale Battery Energy Storage Station Based on Multi-Agent: Methodology and System Design

Li, Yue; Li, Xiangjun; Jia, Xianglei; Ma, Rui; Dong, Hui

doi:10.1109/iicspi.2018.8690406

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…Approach-IV comes from the reference (Wang et al, 2021). Approach-V comes from the reference (Li et al, 2018).…”

Section: Results and Analysis Of Dynamic Groupingmentioning

confidence: 99%

Power allocation method of battery energy storage system considering state balance in smoothing photovoltaic power fluctuations

Zhang¹,

Hou²,

Diao³

et al. 2022

Front. Energy Res.

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Aiming at the imbalances of SOC (state of charge, SOC) and SOH (state of health, SOH) for battery energy storage system (BESS) in smoothing photovoltaic power fluctuations, a power allocation method of BESS is proposed. Firstly, the hierarchical structure of the power allocation method is given, including acquisition of the grid-connected photovoltaic power reference and double-layer power allocation strategy for BESS. Secondly, a swing door trend algorithm optimized by the improved Aquila optimizer is devised and used to deal with the raw photovoltaic power to obtain the grid-connected signal. Next, the dynamic grouping principle considering the SOC and SOH of battery units is devised, and the double-layer power allocation method of BESS is designed in terms of the dynamic grouping results of battery units. Finally, the proposed power allocation method is simulated through actual data, and the results compared with other methods show that it can smooth photovoltaic power fluctuations more effectively, and the SOC and SOH can be balanced more quickly and accurately, which verifies its effectiveness.

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“…Approach-IV comes from the reference (Wang et al, 2021). Approach-V comes from the reference (Li et al, 2018).…”

Section: Results and Analysis Of Dynamic Groupingmentioning

confidence: 99%

Power allocation method of battery energy storage system considering state balance in smoothing photovoltaic power fluctuations

Zhang¹,

Hou²,

Diao³

et al. 2022

Front. Energy Res.

View full text Add to dashboard Cite

show abstract

“…With the application of artificial intelligence technology, 11,12 some deep learning algorithms are favored in the field of new energy 13,14 . Indifferent to other algorithms, deep learning algorithms can efficiently map certain types of information or data and henceforth, therefore, generate acceptable true and good predictions by accessing and leveraging the relationships and hidden designs and patterns in the information or data set.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the nonlinear fitting characteristics of neural networks, many researchers apply neural network algorithms to analyze historical data in short-term and ultra-short-term forecasting, and then combine other algorithms to design composite algorithms for WPF. [6][7][8][9][10] With the application of artificial intelligence technology, 11,12 some deep learning algorithms are favored in the field of new energy. 13,14 Indifferent to other algorithms, deep learning algorithms can efficiently map certain types of information or data and henceforth, therefore, generate acceptable true and good predictions by accessing and leveraging the relationships and hidden designs and patterns in the information or data set.…”

mentioning

confidence: 99%

A new wind power forecasting algorithm based on long short‐term memory neural network

Feng

Xiang

et al. 2021

Int Trans Electr Energ Syst

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Wind power is one of the most large-scale new energy sources. But wind power instability will affect power grid safety, which is in a great need for wind power forecasting algorithms. To accurately predict wind power and reduce power grid fluctuations, it proposes a new wind power forecasting (WPF) algorithm based on long short-term memory (LSTM) neural network using wind farm real operation data. First, the wind farm power data are de-averaged and divided into two different sets in order to meet the requirements of the algorithm. Then, structure of the LSTM neural network is designed and hyperparameters are adjusted to improve accuracy of forecasting. Finally, the definite LSTM neural network is used for forecasting the power data in time series to derive the power forecasting value, which is reduced and evaluated according to the original size. The results show that compared with other forecasting methods, in short-term WPF at different time scales, this algorithm has smaller errors in power forecasting results, which is also suitable for longterm WPF. K E Y W O R D Sdeep learning, long short-term memory neural network, time series forecasting, wind power forecasting | INTRODUCTIONWind energy has received widespread attention in recent years. The advantages of wind power lie in its low cost and low pollution. 1 But wind power has large fluctuations and will greatly impact the grid when integrated into the grid. Countermeasures can be taken based on WPF. In general, a comprehensive study of the local wind energy should be done before the wind farm is installed. 2 WPF can estimate the future output power of wind turbine. By the forecast period, there are two forecasting: short-term and long-term. Short-term forecasting generally lasts less than a few months, while long-term forecasting generally lasts more than 1 year. Short-term forecasting can be used for downtime maintenance of wind turbine,

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Short-Term Wind Power Forecasting Based on Two-Stage Feature Selection

Jin

Niu

Han

et al. 2023

2023 6th International Conference on Energy, Electrical and Power Engineering (CEEPE)

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Monitoring and Control for Hundreds Megawatt Scale Battery Energy Storage Station Based on Multi-Agent: Methodology and System Design

Cited by 4 publications

References 4 publications

Power allocation method of battery energy storage system considering state balance in smoothing photovoltaic power fluctuations

Power allocation method of battery energy storage system considering state balance in smoothing photovoltaic power fluctuations

A new wind power forecasting algorithm based on long short‐term memory neural network

Short-Term Wind Power Forecasting Based on Two-Stage Feature Selection

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