Predicting the stochastic behavior of uncertainty sources in planning a stand-alone renewable energy-based microgrid using Metropolis–coupled Markov chain Monte Carlo simulation

Bakhtiari, Hamed; Alvarez, Manuel

doi:10.1016/j.apenergy.2021.116719

Cited by 39 publications

(11 citation statements)

References 43 publications

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“…41,42 As a result, they may be used as protected miniature power stations in stand-alone or independent machine setups to generate electricity. 43,44 The reliability, consistency, and financial viability all influence the scale of these systems. Solar technology must enhance its process and working materials to offer superior results.…”

Section: Solar Technologymentioning

confidence: 99%

Novel low-carbon energy solutions for powering emerging wearables, smart textiles, and medical devices

Brindha

Sundarrajan

Rao

et al. 2022

Energy Environ. Sci.

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Section: Solar Technologymentioning

confidence: 99%

Novel low-carbon energy solutions for powering emerging wearables, smart textiles, and medical devices

Brindha

Sundarrajan

Rao

et al. 2022

Energy Environ. Sci.

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“…At present, many researchers have studied power load. J. J. Hu et al applied data mining algorithm to power load forecasting [10], and verified that this method can effectively predict power load through simulation tests; H. [11] to simulate the uncertain random load in the operation of independent microgrid of renewable energy. Although the above two methods can realize the function of power load and uncertain random load prediction, they do not specifically classify the power load of users, so their applicability is poor.…”

Section: A Related Workmentioning

confidence: 99%

Classification Method for Power Load Data of New Energy Grid based on Improved OTSU Algorithm

Ma¹,

Li²,

Liu³

et al. 2022

IJACSA

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The classification method for power load data of new energy grid based on improved OTSU algorithm is studied to improve the classification accuracy of power load data. According to the idea of two-dimensional visualization of time series, GAF (Geographical Adaptive Fidelity) method is used to transform the current data of power load in new energy grid into twodimensional image of power load in new energy grid. The intra class dispersion is introduced, and the improved OTSU algorithm is used to segment the foreground and background of the twodimensional image according to the pixel gray value of the twodimensional image and the one-dimensional inter class variance corresponding to the pixel neighborhood gray value. The twodimensional foreground image of power load is taken as the input sample of convolution neural network. The convolution neural network extracts the features of the two-dimensional foreground image of power load through convolution layer. According to the extracted features, the classification results of power load data of new energy grid are output through three steps: nonlinear processing, pooling processing and full connection layer classification. The experimental results show that this method can accurately classify the power load data of new energy grid, and the classification accuracy is higher than 97%.

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“…Furthermore, the difference in the impact of economic policy uncertainty on the investment of traditional and renewable energy enterprises was studied by [25]. Uncertainties in wind velocity, solar radiation, water flow speed, load demand, and electricity price were investigated for planning a stand-alone renewable energy-based micro-grid [26]. Bouchekara et al [27] utilized a multi-objective approach to optimally configure an HRE system consisting of PV, WT, and DG for a micro-grid under load uncertainty.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating the Impact of Economic Uncertainty on Optimal Sizing of Grid-Independent Hybrid Renewable Energy Systems

et al. 2021

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One of the many barriers to decarbonization and decentralization of the energy sector in developing countries is the economic uncertainty. As such, this study scrutinizes economics of three grid-independent hybrid renewable-based systems proposed to co-generate electricity and heat for a small-scale load. Accordingly, the under-study systems are simulated and optimized with the aid of HOMER Pro software. Here, a 20-year average value of discount and inflation rates is deemed a benchmark case. The techno-economic-environmental and reliability results suggest a standalone solar/wind/electrolyzer/hydrogen-based fuel cell integrated with a hydrogen-based boiler system is the best alternative. Moreover, to ascertain the impact of economic uncertainty on optimal unit sizing of the nominated model, the fluctuations of the nominal discount rate and inflation, respectively, constitute within the range of 15–20% and 10–26%. The findings of economic uncertainty analysis imply that total net present cost (TNPC) fluctuates around the benchmark value symmetrically between $478,704 and $814,905. Levelized energy cost varies from an amount 69% less than the benchmark value up to two-fold of that. Furthermore, photovoltaic (PV) optimal size starts from a value 23% less than the benchmark case and rises up to 55% more. The corresponding figures for wind turbine (WT) are, respectively, 21% and 29%. Eventually, several practical policies are introduced to cope with economic uncertainty.

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Predicting the stochastic behavior of uncertainty sources in planning a stand-alone renewable energy-based microgrid using Metropolis–coupled Markov chain Monte Carlo simulation

Cited by 39 publications

References 43 publications

Novel low-carbon energy solutions for powering emerging wearables, smart textiles, and medical devices

Novel low-carbon energy solutions for powering emerging wearables, smart textiles, and medical devices

Classification Method for Power Load Data of New Energy Grid based on Improved OTSU Algorithm

Investigating the Impact of Economic Uncertainty on Optimal Sizing of Grid-Independent Hybrid Renewable Energy Systems

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