Solving high-dimensional multi-area economic dispatch problem by decoupled distributed crisscross optimization algorithm with population cross generation strategy

Meng, Anbo; Xu, Xuancong; Zhang, Zhan; Zeng, Cong; Liang, Ruduo; Zhang, Zheng; Wang, Xiaolin; Yan, Baiping; Yin, Hao; Luo, Jianqiang

doi:10.1016/j.energy.2022.124836

Cited by 13 publications

(3 citation statements)

References 45 publications

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“…In order to address population initialization and generation jumping, IRF proposes a counterproductive learning technique. A decoupled distributed crisscross optimization (DDCO) based on population cross generation was proposed for the MASED problem taking into consideration VPE to reduce generation costs [22]. Two evolutionary methods including fast convergence programming (FCP) and multiobjective grey wolf optimizer (MOGWO) were presented for the MASED problem to minimize generation costs and emissions [23,24].…”

Section: Literature Surveymentioning

confidence: 99%

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A Multiobjective Evolutionary Approach for Solving the Multi-Area Dynamic Economic Emission Dispatch Problem Considering Reliability Concerns

Lotfi

2022

Sustainability

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Economic dispatch (ED) problems, especially in multi-area power networks, have been challenging concerns for power system operators for several decades. In this paper, we introduce a novel approach for solving the multiobjective multi-area dynamic ED (MADED) problem in the presence of practical constraints such as valve-point effect (VPE), prohibited operating zone (POZ), multi-fuel operation (MFO), and ramp rate (RR) limitations. Different objective functions including energy not supplied (ENS), generation costs, and emissions are investigated. The reliability objective, which has been less studied in economic dispatch area, distinguishes the proposed study from other studies. A compromise has been made from economic and reliability points of view. The MADED problem in the power system is inherently a complex and nonlinear problem, considering the operational constraint increments and the intricacy of the problem. Hence, the modified grasshopper optimization (MGO) algorithm based on a chaos mechanism is presented to prevent being trapped in local optima. The proposed method is tested on two systems including a 10 unit, 3-zone test system and a 40-unit 3-zone test system, and then, the outcomes are compared with those of other evolutionary techniques such as gray wolf optimization (GWO) and modified honey bee mating optimization (MHBMO). The simulation results demonstrate that the suggested strategy is successful in resolving both single-objective and multiobjective MADED problems.

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Section: Literature Surveymentioning

confidence: 99%

“…where g is the the coefficient of gravity e g and represents a single vector toward earth center. The A Component in (18), can be obtained by (22):…”

Section: Grasshopper Optimization Algorithmmentioning

confidence: 99%

A Multiobjective Evolutionary Approach for Solving the Multi-Area Dynamic Economic Emission Dispatch Problem Considering Reliability Concerns

Lotfi

2022

Sustainability

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“…Ref. [ 1 ] proposed a Latin hypercube sampling method to process the uncertainty of wind and solar data, effectively reducing the impact of wind and light on power grid peak shaving [ 2 ]. Combining the roulette wheel mechanism and Monte Carlo thinking to process wind and light data, using randomly generated scenes to simulate the uncertain process of scenery, this method can effectively reduce the economic cost of hydrothermal unit scheduling.…”

Section: Introductionmentioning

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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A fast data-driven optimization method of multi-area combined economic emission dispatch

2023

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Solving high-dimensional multi-area economic dispatch problem by decoupled distributed crisscross optimization algorithm with population cross generation strategy

Cited by 13 publications

References 45 publications

A Multiobjective Evolutionary Approach for Solving the Multi-Area Dynamic Economic Emission Dispatch Problem Considering Reliability Concerns

A Multiobjective Evolutionary Approach for Solving the Multi-Area Dynamic Economic Emission Dispatch Problem Considering Reliability Concerns

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

A fast data-driven optimization method of multi-area combined economic emission dispatch

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