Energy Management Model for a Standalone Hybrid Microgrid through a Particle Swarm Optimization and Artificial Neural Networks Approach

Águila-León, Jesús; Vargas‐Salgado, Carlos; Chiñas-Palacios, Cristian; Díaz-Bello, Dácil

doi:10.1016/j.enconman.2022.115920

Cited by 62 publications

(22 citation statements)

References 43 publications

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“…Additional research should be done to understand better the thin-film PV modules' energy performance and predictability. To enhance the performance of microgrids, Jesus, et al [20] concentrated on creating a method for integrating particle swarm optimization-based artificial networks (PSO-AN) into a selfadaptable system for energy management. Usually, the power system with offline optimization strategies is a more reliable energy source to reduce the power domain issues.…”

Section: Related Workmentioning

confidence: 99%

AI-based efficiency analysis technique for photovoltaic renewable energy system

Alam,

Alshahrani,

Khan

et al. 2023

Phys. Scr.

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Artificial intelligence (AI) in renewable energy technologies plays a crucial part due to its modeling and performance forecasting. Therefore, a novel AI-based evolving generative adversarial Fuzzy network (EGAFN) has been presented in this paper as a forecasting tool for the efficiency analysis of renewable solar energy for four distinct regions. The power ratings from environmental parameters and solar panels were monitored and recorded for a year. The data pre-processing is primarily applied to improve the system's function using a data filter. Furthermore, the data's energy estimation accuracy is enhanced using feature extraction and selection by a multi-objective lionized manta-ray foraging optimizer (MLMRFO). Finally, the hyperparameters of the EGAFN method are optimized by multi-objective optimization. The proposed technique aims to enhance the energy efficiency of PV systems for solar power production forecasting using an optimized multi-objective algorithm. The findings show that the suggested technique's prediction performance is better than earlier methods. Thus, the proposed methodology can assist in increasing energy efficiency and making better use of renewable energy sources.

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Section: Related Workmentioning

confidence: 99%

AI-based efficiency analysis technique for photovoltaic renewable energy system

Alam,

Alshahrani,

Khan

et al. 2023

Phys. Scr.

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“…To see the details of finding the optimal solution of parameters, see ref 50 PSO is an effective method to optimize complex parameter values, has good learning ability, and has been used to solve complex optimization problems in various fields. 51,52 The network construction, training process, and result prediction in this paper were all carried out in MATLAB 2021b. The software package has a deep learning toolkit that can be used to choose the sequence-to-sequence network in the deep network designer to build the LSTM neural network layer.…”

Section: Pso Algorithmmentioning

confidence: 99%

“…Conventionally, most of the ROP prediction methods are single intelligent prediction techniques, where the model parameter values are randomly set, resulting in a long training time and low prediction accuracy. PSO is an effective method to optimize complex parameter values, has good learning ability, and has been used to solve complex optimization problems in various fields. , …”

Section: Model Buildingmentioning

confidence: 99%

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An Advanced Long Short-Term Memory (LSTM) Neural Network Method for Predicting Rate of Penetration (ROP)

Lou

Cheng

et al. 2022

ACS Omega

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Rate of penetration (ROP) is an essential factor in drilling optimization and reducing the drilling cycle. Most of the traditional ROP prediction methods are based on building physical model and single intelligent algorithms, and the efficiency and accuracy of these prediction methods are very low. With the development of artificial intelligence, high-performance algorithms make reliable prediction possible from the data perspective. To improve ROP prediction efficiency and accuracy, this paper presents a method based on particle swarm algorithm for optimization of long short-term memory (LSTM) neural networks. In this paper, we consider the Tuha Shengbei block oilfield as an example. First, the Pearson correlation coefficient is used to measure the correlation between the characteristics and eight parameters are screened out, namely, the depth of the well, gamma, formation density, pore pressure, well diameter, drilling time, displacement, and drilling fluid density. Second, the PSO algorithm is employed to optimize the super-parameters in the construction of the LSTM model to the predict ROP. Third, we assessed model performance using the determination coefficient (R 2 ), root mean square error (RMSE), and mean absolute percentage error (MAPE). The evaluation results show that the optimized LSTM model achieves an R 2 of 0.978 and RMSE and MAPE are 0.287 and 12.862, respectively, hence overperforming the existing methods. The average accuracy of the optimized LSTM model is also improved by 44.2%, indicating that the prediction accuracy of the optimized model is higher. This proposed method can help to drill engineers and decision makers to better plan the drilling operation scheme and reduce the drilling cycle.

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“…The control of MGs can be broadly classified as centralized, decentralized and distributed control [17,18]. Centralized techniques use a central controller which gives a coordinated control at the cost of high communication and computational requirements increasing with the size of system [19,20], system failure due to a single point of failure, data privacy, severe limitations on system modifications [10], general requirement of special solvers [21] and convergence issues [22] and are, therefore, preferred for a system of small size. In decentralized control, each Microsource (MS) is responsible for its own operation based on local measurements without any communication with other MSs [17] leading to severe limitations on system level [10].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Decomposition Based Hierarchical Heuristic Control of Multimicrogrids

et al. 2022

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This paper proposes a novel decomposition based hierarchical heuristic control of multimicrogrids to maximize the utilization of renewable energy sources and load satisfaction. The proposed control is capable of providing comprehensive online dispatch of active and reactive powers in steady state modes of operation according to IEEE 2030.7 while keeping in view the restrictions of the participants (microsources and/or microgrids). The proposed solution is generalized to be adopted with respect to different types of participants and grid structures, modular with respect to the number of its participants and capable of protecting the privacy of its participants to maximum possible extent. The proposed heuristic control decomposes the control problem at four levels of hierarchy namely microsource, unit, microgrid and multimicrogrid level. All the distributed energy sources have their own microsource controllers and all the distributed energy sources of the same type are put under one unit controller. All the unit controllers are

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Energy Management Model for a Standalone Hybrid Microgrid through a Particle Swarm Optimization and Artificial Neural Networks Approach

Cited by 62 publications

References 43 publications

AI-based efficiency analysis technique for photovoltaic renewable energy system

AI-based efficiency analysis technique for photovoltaic renewable energy system

An Advanced Long Short-Term Memory (LSTM) Neural Network Method for Predicting Rate of Penetration (ROP)

A Comprehensive Decomposition Based Hierarchical Heuristic Control of Multimicrogrids

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