Safe multi-agent deep reinforcement learning for real-time decentralized control of inverter based renewable energy resources considering communication delay

Guo, Guodong; Zhang, Mengfan; Gong, Yanfeng; Xu, Qianwen

doi:10.1016/j.apenergy.2023.121648

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…The energy management problem of a multienergy hub was transformed into a multi-agent coordination optimization problem based on MADRL, which minimized the system operation cost and carbon dioxide emissions under the premise of meeting the constraints [73]. Guo et al [74] proposed a real-time decentralized control strategy based on MADRL, which made full use of the residual capacity of the photovoltaic inverter to minimize power loss under the premise of ensuring voltage safety. In summary, these studies proposed methods for using MADRL in HECESSs.…”

Section: Deep Reinforcement Learning Applied For Hecessmentioning

confidence: 99%

Hydrogen-electricity coupling energy storage systems: Models, applications, and deep reinforcement learning algorithms

Jiehui,

Su,

Wang

et al. 2024

Clean Energy Sci. Technol.

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With the maturity of hydrogen storage technologies, hydrogen-electricity coupling energy storage in green electricity and green hydrogen modes is an ideal energy system. The construction of hydrogen-electricity coupling energy storage systems (HECESSs) is one of the important technological pathways for energy supply and deep decarbonization. In a HECESS, hydrogen storage can maintain the energy balance between supply and demand and increase the utilization efficiency of energy. However, its scenario models in power system establishment and the corresponding solution methods still need to be studied in depth. For accelerating the construction of HECESSs, firstly, this paper describes the current applications of hydrogen storage technologies from three aspects: hydrogen production, hydrogen power generation, and hydrogen storage. Secondly, based on the complementary synergistic mechanism of hydrogen energy and electric energy, the structure of the HECESS and its operation mode are described. To study the engineering applications of HECESSs more deeply, the recent progress of HECESS application at the source, grid, and load sides is reviewed. For the application of the models of hydrogen storage at the source/grid/load side, the selection of the solution method will affect the optimal solution of the model and solution efficiency. As solving complex multi-energy coupling models using traditional optimization methods is difficult, the paper therefore explored the advantages of deep reinforcement learning (DRL) algorithms and their applications in HECESSs. Finally, the technical application in the construction of new power systems supported by HECESSs is prospected. The study aims to provide a reference for the research on hydrogen storage in power systems.

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Section: Deep Reinforcement Learning Applied For Hecessmentioning

confidence: 99%

Hydrogen-electricity coupling energy storage systems: Models, applications, and deep reinforcement learning algorithms

Jiehui,

Su,

Wang

et al. 2024

Clean Energy Sci. Technol.

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Weighted Average Ensemble-Based PV Forecasting in a Limited Environment with Missing Data of PV Power

Lee,

Son

2024

Sustainability

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Photovoltaic (PV) power is subject to variability, influenced by factors such as meteorological conditions. This variability introduces uncertainties in forecasting, underscoring the necessity for enhanced forecasting models to support the large-scale integration of PV systems. Moreover, the presence of missing data during the model development process significantly impairs model performance. To address this, it is essential to impute missing data from the collected datasets before advancing with model development. Recent advances in imputation methods, including Multivariate Imputation by Chained Equations (MICEs), K-Nearest Neighbors (KNNs), and Generative Adversarial Imputation Networks (GAINs), have exhibited commendable efficacy. Nonetheless, models derived solely from a single imputation method often exhibit diminished performance under varying weather conditions. Consequently, this study introduces a weighted average ensemble model that combines multiple imputation-based models. This innovative approach adjusts the weights according to “sky status” and evaluates the performance of single-imputation models using criteria such as sky status, root mean square error (RMSE), and mean absolute error (MAE), integrating them into a comprehensive weighted ensemble model. This model demonstrates improved RMSE values, ranging from 74.805 to 74.973, which corresponds to performance enhancements of 3.293–3.799% for KNN and 3.190–4.782% for MICE, thereby affirming its effectiveness in scenarios characterized by missing data.

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Safe multi-agent deep reinforcement learning for real-time decentralized control of inverter based renewable energy resources considering communication delay

Cited by 2 publications

References 21 publications

Hydrogen-electricity coupling energy storage systems: Models, applications, and deep reinforcement learning algorithms

Hydrogen-electricity coupling energy storage systems: Models, applications, and deep reinforcement learning algorithms

Weighted Average Ensemble-Based PV Forecasting in a Limited Environment with Missing Data of PV Power

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