Reinforcement learning for fluctuation reduction of wind power with energy storage

Yang, Zhen; Ma, Xiaoteng; Xia, Li; Zhao, Qianchuan; Guan, Xiaohong

doi:10.1016/j.rico.2021.100030

Cited by 7 publications

(5 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Taking the terminal current ( 33) into (34), heat generation can be obtained. To build a simplified lump thermal model, it is assumed that the battery shell temperature and internal temperature are both uniform, and heat generation is uniformly distributed within the battery.…”

Section: Electrothermal Model Of Essmentioning

confidence: 99%

“…where T in and T sh are battery internal and shell temperature, respectively; T amb is the ambient temperature; C 1 , C 2 are the battery internal and shell thermal capacity, respectively; Q is the generated power in (34); k 1 is the heat conduction coefficient between the battery internal and the shell, and k 2 the heat conduction coefficient between the battery shell and the ambience. Discretizing ( 35) and ( 36), the internal and shell temperatures of the battery cell can be expressed as (37) by substituting ( 33) into (34).…”

Section: Electrothermal Model Of Essmentioning

confidence: 99%

“…φ max is set to 20 • based on the requirements [32]. Thus the whole action space a k = (a k.ESS , a k.ϕ ) includes 9 × 11 actions in total, and the action intervals are set to be 1 MW for the ESS and 2 • for the phase angle, which are fine-grained and reasonable according to the findings in [33], [34].…”

Section: B Action Spacementioning

confidence: 99%

See 2 more Smart Citations

Thermal Constrained Energy Optimization of Railway Cophase Systems With ESS Integration—An FRA-Pruned DQN Approach

Xing

2023

IEEE Trans. Transp. Electrific.

View full text Add to dashboard Cite

Section: Electrothermal Model Of Essmentioning

confidence: 99%

Section: Electrothermal Model Of Essmentioning

confidence: 99%

See 1 more Smart Citation

Thermal Constrained Energy Optimization of Railway Cophase Systems With ESS Integration—An FRA-Pruned DQN Approach

Xing

2023

IEEE Trans. Transp. Electrific.

View full text Add to dashboard Cite

“…In addition, some studies have applied RL methods to wind turbine control [113][114][115][116][117]. RL has also been successfully applied to many aspects related to renewable energy system operations, such as energy storage management [118,119], power system stabilization [120], frequency control [121], wind energy scheduling [122,123] and system maintenance [124,125].…”

Section: Fundamentals Of Rlmentioning

confidence: 99%

Wind farm control technologies: from classical control to reinforcement learning

2022

View full text Add to dashboard Cite

Wind power plays a vital role in the global effort towards net zero. The recent figure shows that 93GW new wind capacity was installed worldwide in 2020, leading to a 53% year-on-year increase. Control system is the core in wind farm operations and has essential influences on the farm’s power capture efficiency, economic profitability, and operation & maintenance cost. However, wind farms’ inherent system complexities and the aerodynamic interactions among wind turbines bring significant barriers to control systems design. The wind industry has recognized that new technologies are needed to handle wind farm control tasks, especially for large-scale offshore wind farms. This paper provides a comprehensive review of the development and most recent advances of wind farm control technologies. This covers the introduction of fundamental aspects in wind farm control in terms of system modelling, main challenges, and control objectives. Existing wind farm control methods for different purposes, including layout optimization, power generation maximization, fatigue loads minimization, and power reference tracking, are investigated. Moreover, a detailed discussion regarding the differences and connections among model-based, model-free and data-driven wind farm approaches is presented. In addition, highlights are made on the state-of-the-art wind farm control technologies based on reinforcement learning - a booming machine learning technique that has drawn worldwide attention. Future challenges and research avenues in wind farm control are also analysed.

show abstract

“…Power production from wind and tidal needs to be traded ahead of time, based on forecasts. RL applications to batteries in this context include management of uncertain generation forecasts [167], [85], management of uncertain generation and market forecasts (Yang et al, [17]) smoothing fluctuations in generation [97], [65] and optimizing the revenue of a wind farm with other generation resources on site [140].…”

Section: E Wind Farm and Tidalmentioning

confidence: 99%

Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

2022

View full text Add to dashboard Cite

The transition to renewable production and smart grids is driving a massive investment to battery storages, and reinforcement learning (RL) has recently emerged as a potentially disruptive technology for their control and optimization of battery storage systems. A surge of papers has appeared in the last two years applying reinforcement learning to the optimization of battery storages in buildings, energy communities, energy harvesting Internet of Things networks, renewable generation, microgrids, electric vehicles and plug-in hybrid electric vehicles. This article reviews these applications through 4 different perspectives. Firstly, the type of optimization problem is analyzed; the literature can be divided to approaches that optimize either financial targets or energy efficiency. Secondly, the approaches for handling user comfort are analyzed for applications that may impact a human user. Thirdly, this paper discusses the approach to model and reduce battery degradation. Fourthly, the articles are categorized by application context and applications likely to attract a high amount of research are identified. The paper concludes with a list of unresolved challenges.

show abstract

Reinforcement learning for fluctuation reduction of wind power with energy storage

Cited by 7 publications

References 37 publications

Thermal Constrained Energy Optimization of Railway Cophase Systems With ESS Integration—An FRA-Pruned DQN Approach

Thermal Constrained Energy Optimization of Railway Cophase Systems With ESS Integration—An FRA-Pruned DQN Approach

Wind farm control technologies: from classical control to reinforcement learning

Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

Contact Info

Product

Resources

About