Fault-Tolerant Multisubset Aggregation Scheme for Smart Grid

Wang, Xiaodi; Liu, Yining; Choo, Kim‐Kwang Raymond

doi:10.1109/tii.2020.3014401

Cited by 74 publications

(71 citation statements)

References 29 publications

(43 reference statements)

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“…Besides, we will try to compare more algorithms, such as jSO [138], LSHADESPACMA [139], EAGDE [140], and EBLSHADE [141]. The problems that can benefit from applying the AGOA are not limited to parameter optimization [142], deployment optimization in node arrangement in the sensor networks [143], or finding the optimal solutions for resource allocation [144]; potential areas in which the proposed GOA-based cores can be used include landslide prediction [145], environmental cases that need cross-optimization [146], [147], target tracking systems that need high accuracy and consistency [148], development of efficient hydrothermal systems [47], development and equipment tracking systems [149], decision making and management of supply chains [150], industrial problems such as the development of the order-picking systems [151], applications in image enhancement [152], smart agriculture [153], and smart grids [154]. Other problems, including social manufacturing optimization [155] and video coding optimization [156], are also exciting topics worthy of investigation.…”

Section: Discussionmentioning

confidence: 99%

Chaotic Arc Adaptive Grasshopper Optimization

et al. 2021

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The grasshopper optimization algorithm (GOA) has become one of the most widely used methods, regardless of its shortcomings in its performance and algorithm design. To further harmonize exploration and exploitation in the GOA, this paper proposed an enhanced variant of the primary method by incorporating chaos theory to address the problems of slow convergence, low solution accuracy, and stagnation. Structural code analysis and previous works reveal that the grasshopper algorithm's internal function is one of these shortcomings' primary sources. The proposed GOA-based method employed an adaptive arc function instead of the grasshopper algorithm's internal function to strengthen and balance the global exploration capabilities and local exploitation capacities without any expensive modifications. Additionally, the chaotic mapping strategy was adopted to update the individual positions of agents iteratively. The individuals near the current optimal solution were disturbed to regenerate the optimal solution and enhance the swarm's diversity. This idea improves the global inspection capacity and discourages falling into local optima. To verify the proposed technique's effectiveness, a comprehensive set of twenty-seven benchmark cases and three engineering design problems were used for validation. We compared the proposed GOA-based method with the WOA, SCA, GOA, DA, CSSA, CGSCA, CLPSO, GL25, and OBWOA. Additionally, we tested the designed GOA against LSHADE, SHADE, EBOwithCMAR, SaDE, MPEDE, and EPSDE. Simulation results demonstrated that the algorithm was substantially superior to the original technique. Its global optimization competence, search accuracy, and convergence performance were notably improved. The results expose the regulation of this internal factor, which significantly affects the quality of the results. Further information about this research and assistance with any request is available at http://aliasgharheidari.com. INDEX TERMSGrasshopper optimization algorithm, swarm intelligence, chaotic maps, engineering design problem.

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Section: Discussionmentioning

confidence: 99%

Chaotic Arc Adaptive Grasshopper Optimization

et al. 2021

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“…In [65], the authors investigated the Korean campus to find the viability of microgrid implementation especially investors. e payback period revealed that the initial cost will cover within a few years and shifted the conventional system into a smart [66][67][68] power system. In [69], the authors analyzed a large number of customers in the US region.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Operation of the Campus Microgrid considering the Resource Uncertainty and Demand Response Schemes

Muqeet

Munir

Ahmad

et al. 2021

Mathematical Problems in Engineering

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Present power systems face problems such as rising energy charges and greenhouse gas (GHG) releases. These problems may be assuaged by participating distributed generators (DGs) and demand response (DR) policies in the distribution system (DS). The main focus of this paper is to propose an energy management system (EMS) approach for campus microgrid (µG). For this purpose, a Pakistani university has been investigated and an optimal solution has been proposed. Conventionally, it contains electricity from the national grid only as a supply to fulfil the energy demand. Under the proposed setup, it contains campus owned nondispatchable DGs such as solar photovoltaic (PV) panels and microturbines (MTs) as dispatchable sources. To overcome the random nature of solar irradiance, station battery has been integrated as energy storage. The subsequent nonlinear mathematical problem has been scheduled by mixed-integer nonlinear programming (MINLP) in MATLAB for saving energy cost and battery aging cost. The framework has been validated under deterministic and stochastic environments. Among random parameters, solar irradiance and load have been taken into consideration. Case studies have been carried out considering the demand response strategies to analyze the proposed model. The obtained results show that optimal management and scheduling of storage in the presence of DGs mutually benefit by minimizing consumption cost (customer) and grid load (utility) which show the efficacy of the proposed model. The results obtained are compared to the existing literature and a significant cost reduction is found.

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“…According to the actual situation, the finite element method is adopted and ANSYS software is used to simulate and solve the ground potential distribution [20,21]. e field circuit coupling method is usually used to calculate DC bias of the main transformer in the urban area.…”

Section: Integrated Model Of "Stray Current-geoelectric Field-magnetic Bias Dc"mentioning

confidence: 99%

Calculation and Analysis of DC Magnetic Bias Current of Urban Main Transformer under the Action of Stray Current

Chen

2021

Mobile Information Systems

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The stray current generated by subway running into the ground makes the main transformer in the urban area in a direct current (DC) bias state. First, the mathematical model of the metro stray current field is established and the Galerkin finite element method is applied to calculate the model. Then, the dynamic model of the stray current-induced geoelectric field under different working conditions is established by using ANSYS software, and the three-dimensional numerical simulation study of the stray current-induced geoelectric field of the subway is carried out. Finally, taking the Urumqi subway in Xinjiang as an example, the stray current-induced geoelectric field is calculated and simulated, and the correctness of the model is verified by comparing with the measured data of DC magnetic bias in the urban substation. The research can provide useful reference for the calculation and treatment of DC bias of the main transformer in the urban area under the action of stray current.

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Fault-Tolerant Multisubset Aggregation Scheme for Smart Grid

Cited by 74 publications

References 29 publications

Chaotic Arc Adaptive Grasshopper Optimization

Chaotic Arc Adaptive Grasshopper Optimization

Optimal Operation of the Campus Microgrid considering the Resource Uncertainty and Demand Response Schemes

Calculation and Analysis of DC Magnetic Bias Current of Urban Main Transformer under the Action of Stray Current

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