Research on Economic Optimal Dispatching of Microgrid Based on an Improved Bacteria Foraging Optimization

Lv, Yang; Zhou, Yangkun

doi:10.3390/biomimetics8020150

Cited by 7 publications

(3 citation statements)

References 41 publications

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“…The recent evolution of BFOA has improved its configuration to overcome the shortcomings of standard BFOA in terms of complexity, execution time and convergence curve. The capability of BFOA has yielded promising results when applied to complex optimization challenges within the power system domain, such as load shedding [46,47], electric vehicle charging stations [48], frequency stabilization in hydropower systems [49], control of multi-machine power system [50,51], economic dispatch [52] and power distribution restoration [53]. The hybridization of BFOA with BP neural network [54], multi-layer bidirectional LSTM [55] and ANN [56] in STLF have been found in the literature, which shows excellent performance in improving the forecasting accuracy.…”

Section: Review Of Related Workmentioning

confidence: 99%

A Hybrid Model Based on LSSVM and the Improved BFOA for Sustainability of Daily Electricity Load Forecasting in Malaysia

Zaini,

Sulaima,

Razak

et al. 2024

Preprint

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: Leveraging the sustainability of the power system market, researchers have developed various ML models for forecasting electricity demand. The LSSVM is well suited to handle complex non-linear power load series. However, the less optimal regularization parameter and the Gaussian kernel function in the LSSVM model have contributed to flawed forecasting accuracy and random generalization ability. Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a hybrid model based on the LSSVM optimized by the IBFOA for forecasting the daily electricity load in Peninsular Malaysia. The IBFOA for parameter optimization of LSSVM is introduced. The IBFOA based on the sine cosine equation is proposed to adjust the constant step size in the BFOA, which creates an imbalance between the exploration and exploitation during optimization. Finally, the load forecasting model based on LSSVM-IBFOA is constructed using MAPE as the objective function. Comparative analysis demonstrates the model, achieving the highest R2 (0.9880) and significantly reducing error metrics (MAPE, MAE, RMSE, MSE, NRMSE) compared to the baseline LSSVM (average reduction of 27.72% to 47.72%). Additionally, IBFOA exhibits faster convergence and higher accuracy compared to BFOA, highlighting the accuracy of LSSVM-IBFOA for short-term load forecasting.

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Section: Review Of Related Workmentioning

confidence: 99%

A Hybrid Model Based on LSSVM and the Improved BFOA for Sustainability of Daily Electricity Load Forecasting in Malaysia

Zaini,

Sulaima,

Razak

et al. 2024

Preprint

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“…The operating temperature of PEM-FC ranges between 60 and 100 • C. The entire expense of a car based on the PEM-FC is 500-600 $/kW [57]. Thus, PEM-FCs are employed in several applications for instance transportation [56], airplanes, and distributed generators [58].…”

Section: Overview Of Fcsmentioning

confidence: 99%

MPPT of PEM Fuel Cell Using PI-PD Controller Based on Golden Jackal Optimization Algorithm

Agwa,

Alanazi,

Kraiem

et al. 2023

Biomimetics

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Subversive environmental impacts and limited amounts of conventional forms of energy necessitate the utilization of renewable energies (REs). Unfortunately, REs such as solar and wind energies are intermittent, so they should be stored in other forms to be used during their absence. One of the finest storage techniques for REs is based on hydrogen generation via an electrolyzer during abundance, then electricity generation by fuel cell (FC) during their absence. With reference to the advantages of the proton exchange membrane fuel cell (PEM-FC), this is preferred over other kinds of FCs. The output power of the PEM-FC is not constant, since it depends on hydrogen pressure, cell temperature, and electric load. Therefore, a maximum power point tracking (MPPT) system should be utilized with PEM-FC. The techniques previously utilized have some disadvantages, such as slowness of response and largeness of each oscillation, overshoot and undershoot, so this article addresses an innovative MPPT for PEM-FC using a consecutive controller made up of proportional-integral (PI) and proportional-derivative (PD) controllers whose gains are tuned via the golden jackal optimization algorithm (GJOA). Simulation results when applying the GJOA-PI-PD controller for MPPT of PEM-FC reveal its advantages over other approaches according to quickness of response, smallness of oscillations, and tininess of overshoot and undershoot. The overshoot resulting using the GJOA-PI-PD controller for MPPT of PEM-FC is smaller than that of perturb and observe, GJOA-PID, and GJOA-FOPID controllers by 98.26%, 86.30%, and 89.07%, respectively. Additionally, the fitness function resulting when using the GJOA-PI-PD controller for MPPT of PEM-FC is smaller than that of the aforementioned approaches by 93.95%, 87.17%, and 87.97%, respectively.

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“…Particle position and velocity are the two key aspects of the PSO method optimization [ 37 , 38 ]. Each one of them is referred to as a particle, and each particle’s initial position and velocity in the search space are initialized at random [ 39 ]. The particles’ positions and velocities are updated in accordance with Formulas (11) and (12):

…”

Section: Butterfly Optimization Algorithm Optimized By Psomentioning

confidence: 99%

Towards an Optimal KELM Using the PSO-BOA Optimization Strategy with Applications in Data Classification

et al. 2023

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The features of the kernel extreme learning machine—efficient processing, improved performance, and less human parameter setting—have allowed it to be effectively used to batch multi-label classification tasks. These classic classification algorithms must at present contend with accuracy and space–time issues as a result of the vast and quick, multi-label, and concept drift features of the developing data streams in the practical application sector. The KELM training procedure still has a difficulty in that it has to be repeated numerous times independently in order to maximize the model’s generalization performance or the number of nodes in the hidden layer. In this paper, a kernel extreme learning machine multi-label data classification method based on the butterfly algorithm optimized by particle swarm optimization is proposed. The proposed algorithm, which fully accounts for the optimization of the model generalization ability and the number of hidden layer nodes, can train multiple KELM hidden layer networks at once while maintaining the algorithm’s current time complexity and avoiding a significant number of repeated calculations. The simulation results demonstrate that, in comparison to the PSO-KELM, BBA-KELM, and BOA-KELM algorithms, the PSOBOA-KELM algorithm proposed in this paper can more effectively search the kernel extreme learning machine parameters and more effectively balance the global and local performance, resulting in a KELM prediction model with a higher prediction accuracy.

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Research on Economic Optimal Dispatching of Microgrid Based on an Improved Bacteria Foraging Optimization

Cited by 7 publications

References 41 publications

A Hybrid Model Based on LSSVM and the Improved BFOA for Sustainability of Daily Electricity Load Forecasting in Malaysia

A Hybrid Model Based on LSSVM and the Improved BFOA for Sustainability of Daily Electricity Load Forecasting in Malaysia

MPPT of PEM Fuel Cell Using PI-PD Controller Based on Golden Jackal Optimization Algorithm

Towards an Optimal KELM Using the PSO-BOA Optimization Strategy with Applications in Data Classification

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