A data-driven multi-objective optimization framework for optimal integration planning of solid-state transformer fed energy hub in a distribution network

Gantayet, Amaresh; Dheer, Dharmendra Kumar

doi:10.1016/j.jestch.2022.101278

Cited by 5 publications

(5 citation statements)

References 31 publications

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“…Here's how the convolution operation is calculated: We begin with the initial input data, which is often shown as a 2D matrix with dimensions of 4 by 4. Given that the convolution kernel has dimensions of 2 by 2, the convolution window's size is also equal to the convolution kernel's size 21 . The convolution window's original location is in the 2D matrix's top left corner.…”

Section: Modelling and Solvingmentioning

confidence: 99%

Studying computer communication networks to identify errors using enhanced neural network algorithms

zhang

2024

Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023)

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The stability and reliability of computer communication networks are crucial for the proper functioning of modern society. However, network faults are inevitable, so efficient fault detection systems are needed to quickly identify and solve problems. This study aims to improve the performance of network fault detection by introducing improved neural network algorithms and applying deep learning techniques to this field. First, we studied in detail the various types of faults that may occur in computer communication networks and constructed the corresponding fault datasets. Then, we introduced improved neural network structures, including convolutional neural networks and recurrent neural networks, to better capture the spatio-temporal characteristics of network data. Experimental results show that the improved neural network algorithm exhibits excellent performance in network fault detection. Compared with traditional methods, our model achieves significant improvements in detection rate and accuracy. In addition, the model demonstrates adaptability to different fault types, including traditional faults and emerging network threats. In summary, this study provides an efficient and reliable solution for fault detection in computer communication networks by introducing an improved neural network algorithm. This approach is expected to play an important role in real network operations and improve network stability and security, thus providing more reliable support for information communication in modern society.

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Section: Modelling and Solvingmentioning

confidence: 99%

Studying computer communication networks to identify errors using enhanced neural network algorithms

zhang

2024

Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023)

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“…[140], [141], [142], [143]. Among them, the evolutionary and meta-heuristic algorithms applied to EH energy management and planning problems include: genetic algorithms [144], [145], [146], shuffled frog leaping algorithm [147], grey wolf optimization [148], improved water wave optimization algorithm [149], ϵ-domination based multi-objective evolutionary algorithm [150], differential evolution quantum particle swarm optimization algorithm [151], group search optimizer [152], [153], nondominated sorting genetic algorithm [154], [155], [156], time varying acceleration coefficient gravitational search algorithm [157], [158], time varying acceleration coefficients particle swarm optimization algorithm [159], flower pollination algorithm [160], particle swarm optimization [161], [162], [163], [164], modified teaching-learning based optimization [165], [166], [167], [168], and quantum artificial bee colony algorithm [169]; and, their hybrid versions, such as combination of the multiple-mutations adaptive genetic algorithm with an interior point optimization solver [170], hybrid genetic particle swarm optimization [171], combination of adaptive neuro-fuzzy inference system and genetic algorithms [172], hybrid algorithm of ant-lion optimizer and krill herd optimization [81], hybrid teaching-learning-based optimization and crow search algorithm [173], and hybrid particle swarm -neurodynamic algorithm…”

Section: Appendix a Application Of Evolutionary Algorithms To Operati...mentioning

confidence: 99%

Operation and Planning of Energy Hubs Under Uncertainty—A Review of Mathematical Optimization Approaches

et al. 2023

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Co-designing energy systems across multiple energy carriers is increasingly attracting attention of researchers and policy makers, since it is a prominent means of increasing the overall efficiency of the energy sector. Special attention is attributed to the so-called energy hubs, i.e., clusters of energy communities featuring electricity, gas, heat, hydrogen, and also water generation and consumption facilities. Managing an energy hub entails dealing with multiple sources of uncertainty, such as renewable generation, energy demands, wholesale market prices, etc. Such uncertainties call for sophisticated decision-making techniques, with mathematical optimization being the predominant family of decision-making methods proposed in the literature of recent years. In this paper, we summarize, review, and categorize research studies that have applied mathematical optimization approaches towards making operational and planning decisions for energy hubs. Relevant methods include robust optimization, information gap decision theory, stochastic programming, and chance-constrained optimization. The results of the review indicate the increasing adoption of robust and, more recently, hybrid methods to deal with the multi-dimensional uncertainties of energy hubs.

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“…In [33] mobile energy hubs are considered for enhancing the resiliency of the electricity distribution grids by the shortest path algorithm and minimizing the restoration time of the system by transporting the mobile energy hubs. A datadriven multi-objective modeling is used in [34] for energy loss reduction and voltage profile improvement in energy hub by non-dominated sorted genetic algorithm (NSGA-II). In [35] configuration planning of the energy hub considering load characteristics and spatio-temporal synergistic effects of resources is implemented by Genetic algorithm (GA).…”

Section: Related Studiesmentioning

confidence: 99%

“…The energy balance constraint for electrical energy, natural gas and heat energy are modeled by ( 32)- (34), respectively.…”

Section: P T D P T P T Ed T P T P T Tmentioning

confidence: 99%

Energy scheduling based-two layers interval optimization in a smart energy hub system with multi-performance of the hydrogen storage system and demand side management

Javid

Derakhshan

Hakimi

2023

Preprint

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A smart energy hub (SEH) is one of the significant opportunities in smart grids as a distinguished framework for optimal energy consumption and generation. The energy price uncertainty in energy markets for energy optimization is the main challenge for achieving economic modeling of energy consumption and generation. This paper proposes optimal energy scheduling in a SEH under uncertainty of electricity prices in the day ahead in the energy market. The proposed optimization approach is implemented by a two-layer interval approach considering uncertainties for minimizing energy generation cost. In first layer optimization, electrical demand subject to the optimal level of electrical consumption is modified by the demand shifting strategy by using the demand side management (DSM). In second-layer optimization, the interval approach is used for modeling uncertainty; and energy generation cost is converted to Bi-objective functions including average and deviation costs with conflict nature than each other. Also, the multi-performance of the hydrogen storage system based-electrical and natural gas generation is considered for managing uncertainties in second layer optimization. Since the interval optimization approach is proposed in the second layer, minimizing the average cost and deviation cost are formulated as Bi-objective functions by the augmented epsilon-constraint method. In the following, the LINMAP procedure is utilized to achieve maximum compatibility and the best trade-off consequence in the Pareto frontier of the multi-objective functions. Finally, the proposed optimization approach is modeled as a numerical simulation in the three case studies to verify obtained results by implementing the DSM and hydrogen storage system participation.

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A data-driven multi-objective optimization framework for optimal integration planning of solid-state transformer fed energy hub in a distribution network

Cited by 5 publications

References 31 publications

Studying computer communication networks to identify errors using enhanced neural network algorithms

Studying computer communication networks to identify errors using enhanced neural network algorithms

Operation and Planning of Energy Hubs Under Uncertainty—A Review of Mathematical Optimization Approaches

Energy scheduling based-two layers interval optimization in a smart energy hub system with multi-performance of the hydrogen storage system and demand side management

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