A robust game-theoretic optimization model for battery energy storage in multi-microgrids by considering of renewable based DGs uncertainty

Aryanezhad, Majid

doi:10.1016/j.epsr.2021.107591

Cited by 14 publications

(6 citation statements)

References 22 publications

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“…In such methods, values of the uncertain parameters are defined by a continuous set [30]. Some recent papers are highlighted in the followings in order to give to the reader an overview on how RO is used for MG/NMG management [99][100][101][102][103][104][105].…”

Section: Robust Optimizationmentioning

confidence: 99%

“…The results highlight the robustness and efficiency of the controller. In [104], a robust game-theoretic optimization model is developed for NMG economical operation by considering the renewable-based distributed generators uncertainty. The batteries' charging and discharging power is predicted using the column and constraint generation algorithm, particle swarm optimization, and a Markov decision process model of random variables.…”

Section: Robust Optimizationmentioning

confidence: 99%

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A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids

2022

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Microgrids (MGs) and networked (interconnected) microgrids (NMGs) are emerging as an efficient way for integrating distributed energy resources (DERs) into power distribution systems. MGs and NMGs can disconnect from the main grid and operate autonomously, strengthen grid resilience, and help mitigate grid disturbances and maintain power quality. In addition, when supported by sophisticated and efficient management strategies, MGs and NMGs have the ability to enhance power supply reliability. However, their deployment comes with many challenges, in particular regarding the efficient management of DERs. That is why a survey of recent advances in the smart management—the term refers to a variety of planning and control tasks—of MGs and NMGs is presented in this paper. It aims at establishing a picture of strategies and identifying trends in methods. The reader is provided with an in-depth analysis of a variety of papers recently published in peer-reviewed journals: the way the methods are used and the common issues addressed by the scientific community are discussed. Following this analysis, one can especially observe that (1) model-based predictive control (MPC) is emerging as a competitive alternative to conventional methods, in particular in voltage and frequency regulation and DER management (2) due to their ability to handle complex tasks, data-driven strategies are getting more and more attention from the scientific community (3) game theory (GT) is a very good candidate for efficient management of complex systems as NMGs (4) MPC and artificial intelligence are increasingly being used for proper MG islanded operation or to manage electric vehicles (EVs) efficiently.

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Section: Robust Optimizationmentioning

confidence: 99%

Section: Robust Optimizationmentioning

confidence: 99%

A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids

2022

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“…Enough works by foreign researchers are devoted to this topic. In these researches the characteristics of existing power plants for which it is possible to connect hydrogen storage systems are considered in detail [23]- [29]. Researchers have developed various algorithms that allow to create a project of the optimal structure of an energy facility, depending on the target indicators [30]- [32].…”

Section: Introductionmentioning

confidence: 99%

Energy management in hybrid complexes based on wind generation and hydrogen storage

Shklyarskiy,

Andreeva,

Sutikno

et al. 2024

Bulletin EEI

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This work is devoted to the analysis of scientific articles in the field of hybrid energy complexes based on hydrogen technologies and renewable energy sources. A special attention is given to wind generation. The review of the topics of scientific publications indexed in the Scopus database in the field of research is carried out, the most frequently encountered topics and the rarest ones are highlighted. Brief statistics about publications selected for detailed analysis are given. The most interesting direction for studying is development of control systems for hybrid energy complexes. Several traditional approaches, which are commonly used in research on this topic and methods are highlighted. The existing shortcomings and inaccuracies in the overviewed works are identified. Conclusions are drawn about the need to transform existing methods and specific proposals are made to improve management systems to increase the efficiency of decision-making and achieve greater economic benefits. Promising areas of research that also require special attention are formulated.

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“…However, the proposed method in this paper addresses these challenges by incorporating an attention mechanism in the LSTM, resulting in reduced computation time and requiring less data for training. Once the class of PQD is detected, advanced artificial intelligence (AI)‐based techniques can be utilized to employ optimal solutions for mitigating the specific PQ issue [20]. For power systems with significant integration of photovoltaic (PV) sources, an effective approach has been proposed for coordinating various devices, including energy storages (ESs), step voltage regulators (SVRs), load tap changers (LTCs), and others, to enhance the voltage profile and reduce energy loss [21].…”

Section: Introductionmentioning

confidence: 99%

Power quality disturbance signal segmentation and classification based on modified BI‐LSTM with double attention mechanism

Khetarpal,

Nagpal,

Siano

et al. 2023

IET Generation Trans & Dist

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This paper proposes a recurrent neural network based model to segment and classify multiple combined multiple power quality disturbances (PQDs) from the PQD voltage signal. A modified bi‐directional long short‐term memory (BI‐LSTM) model with two different types of attention mechanisms is developed. Firstly, an attention gate is added to the basic LSTM cell to reduce the training time and focus the memory on important PQD signal part. Secondly, an attention layer is added to the BI‐LSTM to obtain the more important part of the voltage signal by assigning weightage to the output of the BI‐LSTM model. This attention gate applied in the LSTM cells improves effective and decisive key information extraction from future and past states of the PQD signal and the addition of attention layer improves the overall decision capability of the model, saving computation time, and increasing PQD classification accuracy. Finally, a SoftMax classifier is applied to classify the combined PQD signal in 96 different combinations. The proposed BI‐LSTM model with attention gate and attention layer mechanism is compared to different baseline models based on recurrent neural network and convolution neural network (CNN). From the simulation study, it is inferred that with the proposed method, the multiple combined PQD signals are easily segmented from the voltage signal which makes the process of PQD classification more accurate with less computation complexity and in less time as compared to popular signal processing based approaches for which classification of multiple PQDs is computationally difficult.

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A robust game-theoretic optimization model for battery energy storage in multi-microgrids by considering of renewable based DGs uncertainty

Cited by 14 publications

References 22 publications

A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids

A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids

Energy management in hybrid complexes based on wind generation and hydrogen storage

Power quality disturbance signal segmentation and classification based on modified BI‐LSTM with double attention mechanism

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