Forecast-Based Consensus Control for DC Microgrids Using Distributed Long Short-Term Memory Deep Learning Models

Alavi, Seyed Amir; Mehran, Kamyar; Vahidinasab, Vahid; Catalào, João P. S.

doi:10.1109/tsg.2021.3070959

Cited by 43 publications

(13 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, an LSTM network is utilized to predict the load demand of a microgrid. The LSTM network is trained on historical load data and can predict the load demand for the next time step based on the current and previous load values [20,21].…”

Section: Introductionmentioning

confidence: 99%

A Deep Reinforcement Learning Method for Economic Power Dispatch of Microgrid in OPAL-RT Environment

et al. 2023

View full text Add to dashboard Cite

This paper focuses on the economic power dispatch (EPD) operation of a microgrid in an OPAL-RT environment. First, a long short-term memory (LSTM) network is proposed to forecast the load information of a microgrid to determine the output of a power generator and the charging/discharging control strategy of a battery energy storage system (BESS). Then, a deep reinforcement learning method, the deep deterministic policy gradient (DDPG), is utilized to develop the power dispatch of a microgrid to minimize the total energy expense while considering power constraints, load uncertainties and electricity price. Moreover, a microgrid built in Cimei Island of Penghu Archipelago, Taiwan, is investigated to examine the compliance with the requirements of equality and inequality constraints and the performance of the deep reinforcement learning method. Furthermore, a comparison of the proposed method with the experience-based energy management system (EMS), Newton particle swarm optimization (Newton-PSO) and the deep Q-learning network (DQN) is provided to evaluate the obtained solutions. In this study, the average deviation of the LSTM forecast accuracy is less than 5%. In addition, the daily operating cost of the proposed method obtains a 3.8% to 7.4% lower electricity cost compared to that of the other methods. Finally, a detailed emulation in the OPAL-RT environment is carried out to validate the effectiveness of the proposed method.

show abstract

Section: Introductionmentioning

confidence: 99%

A Deep Reinforcement Learning Method for Economic Power Dispatch of Microgrid in OPAL-RT Environment

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Despite that, RNN has the drawback of remembering long time sequences [34], which leads the long short-term memory (LSTM). The LSTM is widely adopted in the prediction of the power and energy sector [35]. Nevertheless, it suffers from a large computational burden.…”

Section: Introductionmentioning

confidence: 99%

Resilient operation of DC microgrid against FDI attack: A GRU based framework

Shah

Zhao

2023

International Journal of Electrical Power & Energy Systems

View full text Add to dashboard Cite

“…In particular, deep learning model has been widely applied in many fields with real-time analysis requirements, such as image annotation (Abrahamyan et al, 2021), speech recognition (Hussain et al, 2021), semantic understanding (DE Oliveira et al, 2020;Xie et al, 2021), and achieved good application effects (Zheng et al, 2021). In power system, deep learning model has also been introduced into measurement data completion (Wang et al, 2021), fault identification and line selection (Wu et al, 2021b), load prediction (Alavi et al, 2021;Li L. et al, 2021;Li et al, 2021a;Li et al, 2021b), and state estimation (Huang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Fast reconfiguration method of low-carbon distribution network based on convolutional neural network

Yang²,

Ye³

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

The existing meta-heuristic distribution network reconfiguration (DNR) algorithm has excellent optimization ability through iteration. However, it is difficult to realize the large-scale fast calculation and online real-time response of DNR solution. In order to improve the security and low-carbon economy of distribution network, this paper proposes a fast reconfiguration method of distribution network based on convolutional neural network (CNN). Taking IEEE 33 system and 185 node system as examples, the effectiveness of the proposed method is verified. The reasons why the proposed method can achieve better results are as follows: By mining the historical data of distribution network, the corresponding relationship between load mode (LM) and its optimal topology is established. For a load mode in actual operation, the reconfiguration scheme can be quickly obtained according to the established corresponding relationship. Thus, iterative calculation is avoided and computational efficiency is improved. A multi-branch CNN model is established based on the distribution network structure, and an inception module is introduced into CNN to improve the ability of CNN to extract data features. This model can reduce the dependence on the specific distribution network structure and is easy to expand.

show abstract

Forecast-Based Consensus Control for DC Microgrids Using Distributed Long Short-Term Memory Deep Learning Models

Abstract: Voltage Bus 1 Voltage Bus 2 Voltage Bus 3 Voltage Bus 4 Voltage Bus 5 Fig. 11. Configuration A: Voltage of buses. There is a large voltage offset of 1 V from the nominal 380 V due because of the droop controllers. The voltage becomes 0 after the branch ESS has run out of energy.

Cited by 43 publications

References 48 publications

A Deep Reinforcement Learning Method for Economic Power Dispatch of Microgrid in OPAL-RT Environment

A Deep Reinforcement Learning Method for Economic Power Dispatch of Microgrid in OPAL-RT Environment

Resilient operation of DC microgrid against FDI attack: A GRU based framework

Fast reconfiguration method of low-carbon distribution network based on convolutional neural network

Contact Info

Product

Resources

About