Optimal Dispatching of Regional Interconnection Multi‐Microgrids Based on Multi‐Strategy Improved Whale Optimization Algorithm

Wang, Zichen; Dou, Zhenhai; Dong, Jun; Si, Shuqian; Wang, Chen; Liu, Lianxin

doi:10.1002/tee.23566

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…Simulation results showed that the proposed algorithm applied to the model can effectively improve these objectives. Wang et al [17] established an economic dispatch model based on regional interconnected multi-microgrid systems, and proposed an improved whale algorithm using five strategies, namely adaptive inertia weight, dynamic spiral search and generalized binary learning, to solve this problem. The improved algorithm was applied to two arithmetic models of the grid-connected operation and non-grid-connected operation for testing, and it was found that the convergence accuracy and speed of the algorithm have been improved and the results obtained were good.…”

Section: Intelligent Optimization Algorithmmentioning

confidence: 99%

Research on Dynamic Economic Dispatch Optimization Problem Based on Improved Grey Wolf Algorithm

Yang,

Zhang,

Zhu

et al. 2024

Energies

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The dynamic economic dispatch (DED) problem is a typical complex constrained optimization problem with non-smooth, nonlinear, and nonconvex characteristics, especially considering practical situations such as valve point effects and transmission losses, and its objective is to minimize the total fuel costs and total carbon emissions of generating units during the dispatch cycle while satisfying a series of equality and inequality constraints. For the challenging DED problem, a model of a dynamic economic dispatch problem considering fuel costs is first established, and then an improved grey wolf optimization algorithm (IGWO) is proposed, in which the exploitation and exploration capability of the original grey wolf optimization algorithm (GWO) is enhanced by initializing the population with a chaotic algorithm and introducing a nonlinear convergence factor to improve weights. Furthermore, a simple and effective constraint-handling method is proposed for the infeasible solutions. The performance of the IGWO is tested with eight benchmark functions selected and compared with other commonly used algorithms. Finally, the IGWO is utilized for three different scales of DED cases, and compared with existing methods in the literature. The results show that the proposed IGWO has a faster convergence rate and better global optimization capabilities, and effectively reduces the fuel costs of the units, thus proving the effectiveness of IGWO.

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Section: Intelligent Optimization Algorithmmentioning

confidence: 99%

Research on Dynamic Economic Dispatch Optimization Problem Based on Improved Grey Wolf Algorithm

Yang,

Zhang,

Zhu

et al. 2024

Energies

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“…The BFO integrated with various optimization parts has the highest search accuracy and the fastest iteration speed. This paper also compares some other algorithms for BFO optimization, such as the Hormone Regulation based Emotional Bacterial Foraging Algorithm (HR-EBFA) [ 41 ], Bacterial Foraging reinforcement Learning Optimization Algorithm (RL-BFA) [ 42 ], the improved Quantum Bacterial Foraging Algorithm (MQBFA) [ 44 ], and Distribution Estimation based adaptive Bacterial Foraging Algorithm (BFOED) [ 43 ]. The improved bacterial foraging algorithm presented in this paper has better results than the above methods.…”

Section: An Improved Bacterial Foraging Optimization and Its Applicationmentioning

confidence: 99%

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

Zhou

2023

Biomimetics

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This paper proposes an improved Bacterial Foraging Optimization for economically optimal dispatching of the microgrid. Three optimized steps are presented to solve the slow convergence, poor precision, and low efficiency of traditional Bacterial Foraging Optimization. First, the self-adaptive step size equation in the chemotaxis process is present, and the particle swarm velocity equation is used to improve the convergence speed and precision of the algorithm. Second, the crisscross algorithm is used to enrich the replication population and improve the global search performance of the algorithm in the replication process. Finally, the dynamic probability and sine-cosine algorithm are used to solve the problem of easy loss of high-quality individuals in dispersal. Quantitative analysis and experiments demonstrated the superiority of the algorithm in the benchmark function. In addition, this study built a multi-objective microgrid dynamic economic dispatch model and dealt with the uncertainty of wind and solar using the Monte Carlo method in the model. Experiments show that this model can effectively reduce the operating cost of the microgrid, improve economic benefits, and reduce environmental pollution. The economic cost is reduced by 3.79% compared to the widely used PSO, and the economic cost is reduced by 5.23% compared to the traditional BFO.

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“…However, the paper did not intuitively display the power distribution diagram of each unit. Wang et al [ 17 ] used a joint dispatch to solve the optimal dispatch problem of microgrids, but joint dispatch in multiple areas sacrifices the benefits of a single model, and the whale algorithm cannot balance this process well. Zhang Y et al use the butterfly algorithm to optimize microgrids [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Research on Microgrid Optimal Dispatching Based on a Multi-Strategy Optimization of Slime Mould Algorithm

Zhang,

Zhou

2024

Biomimetics

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In order to cope with the problems of energy shortage and environmental pollution, carbon emissions need to be reduced and so the structure of the power grid is constantly being optimized. Traditional centralized power networks are not as capable of controlling and distributing non-renewable energy as distributed power grids. Therefore, the optimal dispatch of microgrids faces increasing challenges. This paper proposes a multi-strategy fusion slime mould algorithm (MFSMA) to tackle the microgrid optimal dispatching problem. Traditional swarm intelligence algorithms suffer from slow convergence, low efficiency, and the risk of falling into local optima. The MFSMA employs reverse learning to enlarge the search space and avoid local optima to overcome these challenges. Furthermore, adaptive parameters ensure a thorough search during the algorithm iterations. The focus is on exploring the solution space in the early stages of the algorithm, while convergence is accelerated during the later stages to ensure efficiency and accuracy. The salp swarm algorithm’s search mode is also incorporated to expedite convergence. MFSMA and other algorithms are compared on the benchmark functions, and the test showed that the effect of MFSMA is better. Simulation results demonstrate the superior performance of the MFSMA for function optimization, particularly in solving the 24 h microgrid optimal scheduling problem. This problem considers multiple energy sources such as wind turbines, photovoltaics, and energy storage. A microgrid model based on the MFSMA is established in this paper. Simulation of the proposed algorithm reveals its ability to enhance energy utilization efficiency, reduce total network costs, and minimize environmental pollution. The contributions of this paper are as follows: (1) A comprehensive microgrid dispatch model is proposed. (2) Environmental costs, operation and maintenance costs are taken into consideration. (3) Two modes of grid-tied operation and island operation are considered. (4) This paper uses a multi-strategy optimized slime mould algorithm to optimize scheduling, and the algorithm has excellent results.

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Optimal Dispatching of Regional Interconnection Multi‐Microgrids Based on Multi‐Strategy Improved Whale Optimization Algorithm

Cited by 7 publications

References 14 publications

Research on Dynamic Economic Dispatch Optimization Problem Based on Improved Grey Wolf Algorithm

Research on Dynamic Economic Dispatch Optimization Problem Based on Improved Grey Wolf Algorithm

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

Research on Microgrid Optimal Dispatching Based on a Multi-Strategy Optimization of Slime Mould Algorithm

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