Power Capacity Optimization in a Photovoltaics-Based Microgrid Using the Improved Artificial Bee Colony Algorithm

Zhang, Huijuan; Xie, Zili; Lin, Hsiung-Cheng; Li, Shaoyong

doi:10.3390/app10092990

Cited by 15 publications

(10 citation statements)

References 41 publications

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“…e current research on intelligent algorithms for either diagnosis or prediction has been attracting more attention in past years. However, the feature extractor and classifier of neural networks may not suit the load change for maintaining a highly accurate outcome [5][6][7][8]. For this reason, a combination of different neural network methods attempted to achieve a highly possible solution in a variety of disciplines.…”

Section: Introductionmentioning

confidence: 99%

Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis

Lin

2020

Shock and Vibration

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It is crucial to carry out the fault diagnosis of rotating machinery by extracting the features that contain fault information. Many previous works using a deep convolutional neural network (CNN) have achieved excellent performance in finding fault information from feature extraction of detected signals. They, however, may suffer from time-consuming and low versatility. In this paper, a CNN integrated with the adaptive batch normalization (ABN) algorithm (ABN-CNN) is developed to avoid high computing resource requirements of such complex networks. It uses a large-scale convolution kernel at the grassroots level and a multidimensional 3 × 1 small convolution nuclear. Therefore, a fast convergence and high recognition accuracy under noise and load variation environment can be achieved for bearing fault diagnosis. The performance results verify that the proposed model is superior to Support Vector Machine with Fast Fourier Transform (FFT-SVM) and Multilayer Perceptron with Fast Fourier Transform (FFT-MLP) models and Deep Neural Network with Fast Fourier Transform (FFT-DNN).

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Section: Introductionmentioning

confidence: 99%

Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis

Lin

2020

Shock and Vibration

Self Cite

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“…where ω max and ω min denote the maximum and minimum inertia coefficients; K max denotes the maximum Iterations; τ denotes the empirical value, generally within (20,55). Larger ω makes APSO have strong whole region searchability, while smaller ω tends to local search.…”

Section: Apso Algorithmmentioning

confidence: 99%

“…However, the proposed algorithm is too complex. Reference [20] established a mathematical model by using the AHP to analyze the multi-energy capacity ratio and economy. Reference [21] emulated a typical combined cycle power generation unit to analyze the susceptibility of carbon emission and electricity price.…”

Section: Introductionmentioning

confidence: 99%

Research on Capacity Allocation Optimization of Commercial Virtual Power Plant (CVPP)

et al. 2022

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Commercial virtual power plants (CVPP) connect the form of renewable energy resource portfolio to the power market and reduce the risk of the unstable operation of a single renewable energy. Combining different kinds of large-scale renewable energy in CVPP to provide capacity services like base load, peak shaving, and valley-filling, etc., for the system loads is an urgent problem to be solved. Therefore, it is valuable to analyze the capacity allocation ratio of the CVPP to maximize the utilization of all kinds of energy, especially for the large-scale multi-energy base. This paper proposed a multi-energy coordinated operation framework by considering various load demands, including base load and peak shaving for the capacity allocation of CVPP based on the world’s largest renewable energy resource base on the upstream area of the Yellow River. The main procedures of this framework are as follows: (1) A paratactic model satisfying base load and peak shaving is proposed to determine the ability of the CVPP operation model’s capacity services to meet the different demands of the power system load. (2) A hybrid dimension reduction algorithm with a better convergence rate and optimization effect solves the proposed paratactic model based on the ReliefF and the Adaptive Particle Swarm Optimization (APSO). The results show that the large-scale CVPP with different compositions can achieve both of the goals of a stable base load output and stable residual load under different weather conditions. Compared with the operation on sunny days, the base load fluctuation and residual load fluctuation of CVPP on rainy days are reduced by 14.5% and 21.9%, respectively, proving that CVPP can alleviate renewable energy’s dependence on weather and improve energy utilization.

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“…Among them, the ABC algorithm is widely used in various fields recently [30]. Especially, the ABC algorithm has been widely used to solve the MG operation problem and it has been modified to improve performance in recent years [31][32][33]. Generally, the ABC algorithm is based on the food foraging behavior of bees.…”

Section: Improved Multi-level Artificial Bee Colony Algorithmmentioning

confidence: 99%

Two-Stage Optimal Microgrid Operation with a Risk-Based Hybrid Demand Response Program Considering Uncertainty

Ryu

Kim

2020

Energies

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Owing to the increasing utilization of renewable energy resources, distributed energy resources (DERs) become inevitably uncertain, and microgrid operators have difficulty in operating the power systems because of this uncertainty. In this study, we propose a two-stage optimization approach with a hybrid demand response program (DRP) considering a risk index for microgrids (MGs) under uncertainty. The risk-based hybrid DRP is presented to reduce both operational costs and uncertainty effect using demand response elasticity. The problem is formulated as a two-stage optimization that considers not only the expected operation costs but also risk expense of uncertainty. To address the optimization problem, an improved multi-layer artificial bee colony (IML-ABC) is incorporated into the MG operation. The effectiveness of the proposed approach is demonstrated through a numerical analysis based on a typical low-voltage grid-connected MG. As a result, the proposed approach can reduce the operation costs which are taken into account uncertainty in MG. Therefore, the two-stage optimal operation considering uncertainty has been sufficiently helpful for microgrid operators (MGOs) to make risk-based decisions.

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Power Capacity Optimization in a Photovoltaics-Based Microgrid Using the Improved Artificial Bee Colony Algorithm

Cited by 15 publications

References 41 publications

Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis

Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis

Research on Capacity Allocation Optimization of Commercial Virtual Power Plant (CVPP)

Two-Stage Optimal Microgrid Operation with a Risk-Based Hybrid Demand Response Program Considering Uncertainty

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