Photovoltaic mono and bifacial module/string electrical model parameters identification and validation based on a new differential evolution bee colony optimizer

Background: As more and more renewable energy such as wind energy is connected to the power grid, the static economic dispatch in the past cannot meet its needs, so the dynamic economic dispatch of the power grid is imperative. Methods: Hence, in this paper, we proposed an Improved Differential Evolution algorithm (IDE) based on Differential Evolution algorithm (DE) and Artificial Bee Colony algorithm (ABC). Firstly, establish the dynamic economic dispatch model of wind integrated power system, in which we consider the power balance constraints as well as the generation limits of thermal units and wind farm. The minimum power generation costs are taken as the objectives of the model and the wind speed is considered to obey the Weibull distribution. After sampling from the probability distribution, the wind speed sample is converted into wind power. Secondly, we proposed the IDE algorithm which adds the local search and global search thoughts of ABC algorithm. The algorithm provides more local search opportunities for individuals with better evolution performance according to the thought of artificial bee colony algorithm to reduce the population size and improve the search performance. Results: Finally, simulations are performed by the IEEE-30 bus example containing 6 generations. By comparing the IDE with the other optimization model like ABC, DE, Particle Swarm Optimization (PSO), the experimental results show that obtained optimal objective function value and power loss are smaller than the other algorithms while the time-consuming difference is minor. The validity of the proposed method and model is also demonstrated. Conclusion: The validity of the proposed method and the proposed dispatch model is also demonstrated. The paper also provides a reference for economic dispatch integrated with wind power at the same time.

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A novel online model parameters identification method with anti‐interference characteristics for lithium‐ion batteries

International Journal of Energy Research ◽

10.1002/er.6477 ◽

2021 ◽

Vol 45 (6) ◽

pp. 9502-9517

Author(s):

Heng Miao ◽

Jiajun Chen ◽

Ling Mao ◽

Keqing Qu ◽

Jinbin Zhao ◽

...

Keyword(s):

Lithium Ion Batteries ◽

Lithium Ion ◽

Parameters Identification ◽

Model Parameters ◽

Identification Method

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Solving Resource-Constrained Project Scheduling Problems Using Hybrid Artificial Bee Colony with Differential Evolution

Journal of Computing in Civil Engineering ◽

10.1061/(asce)cp.1943-5487.0000544 ◽

2016 ◽

Vol 30 (4) ◽

pp. 04015065 ◽

Cited By ~ 13

Author(s):

Duc-Hoc Tran ◽

Min-Yuan Cheng ◽

Minh-Tu Cao

Keyword(s):

Differential Evolution ◽

Project Scheduling ◽

Artificial Bee Colony ◽

Scheduling Problems ◽

Resource Constrained ◽

Resource Constrained Project Scheduling ◽

Bee Colony

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USING AN IMPROVED BEE MEMORY DIFFERENTIAL EVOLUTION ALGORITHM FOR PARAMETER ESTIMATION TO SIMULATE BIOCHEMICAL PATHWAYS

Journal of Biological System ◽

10.1142/s0218339014500065 ◽

2014 ◽

Vol 22 (01) ◽

pp. 101-121 ◽

Cited By ~ 1

Author(s):

CHUII KHIM CHONG ◽

MOHD SABERI MOHAMAD ◽

SAFAAI DERIS ◽

MOHD SHAHIR SHAMSIR ◽

LIAN EN CHAI ◽

...

Keyword(s):

Differential Evolution ◽

Differential Evolution Algorithm ◽

Estimation Algorithm ◽

Convergence Time ◽

Unknown Parameters ◽

Estimation Algorithms ◽

Bee Colony ◽

De Algorithm ◽

Cell Cycle Pathway ◽

Parameter Values

When analyzing a metabolic pathway in a mathematical model, it is important that the essential parameters are estimated correctly. However, this process often faces few problems like when the number of unknown parameters increase, trapping of data in the local minima, repeated exposure to bad results during the search process and occurrence of noisy data. Thus, this paper intends to present an improved bee memory differential evolution (IBMDE) algorithm to solve the mentioned problems. This is a hybrid algorithm that combines the differential evolution (DE) algorithm, the Kalman filter, artificial bee colony (ABC) algorithm, and a memory feature. The aspartate and threonine biosynthesis pathway, and cell cycle pathway are the metabolic pathways used in this paper. For three production simulation pathways, the IBMDE managed to robustly produce the estimated optimal kinetic parameter values with significantly reduced errors. Besides, it also demonstrated faster convergence time compared to the Nelder–Mead (NM), simulated annealing (SA), the genetic algorithm (GA) and DE, respectively. Most importantly, the kinetic parameters that were generated by the IBMDE have improved the production rates of desired metabolites better than other estimation algorithms. Meanwhile, the results proved that the IBMDE is a reliable estimation algorithm.

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