Economic Operation and Quality Control in PV-BES-DG-Based Autonomous System

Sharma, Shatakshi; Singh, Bhim; Mishra, Sukumar

doi:10.1109/tii.2019.2935531

Cited by 10 publications

(3 citation statements)

References 24 publications

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“…In this paper, the probability density function (PDF) is used to find the distribution of the design variables due to their continuous nature and the samples used for optimal sizing of the MG are selected using appropriate PDFs [20]. For the analysis of MG, the (i) photovoltaic (PV)/Battery/Diesel generator (DG) based configuration is considered due to its reliable operation in autonomous mode [21] along with (ii) PV/Battery based environmentally friendly configuration. Though the battery provides backup during the intermittent operation and dynamic changes in PV/Battery configuration, the DG ensures higher reliability even when the battery state of charge becomes low in PV/Battery/DG configuration [22].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Techno-Economic Design of Isolated Microgrid

Jeyaprabha

Milanović

2023

IEEE Trans. Power Syst.

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Isolated microgrid (MG) proved its potential to electrify the remote community through the cheaper and greener renewable energy alternatives with higher reliability than the weak grid. The goal of universal electricity access and the climate change mitigation paves the way for many public/private owned microgrids in remote areas. Having in mind various uncertainties associated with the design parameters of the MG (e.g., temperature, irradiation, load), this paper presents a probabilistic approach for optimal sizing and techno-economic assessment of the PV-based MG. The potential environmental impact of the isolated MG is taken into account by considering both, the PV/Battery, and more conventional PV/Battery/Diesel Generator configuration. The effectiveness of multi-year data over the single year data in the probabilistic approach is demonstrated. The probabilistic techno-economic analysis of different configurations facilitates informed decision making regarding the configuration and size of MG components while meeting the reliability of supply and environmental constraints. The feasibility and affordability of the configurations are demonstrated through the distribution of net present cost, levelized cost of energy and the unmet load percentage.

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Section: Introductionmentioning

confidence: 99%

Probabilistic Techno-Economic Design of Isolated Microgrid

Jeyaprabha

Milanović

2023

IEEE Trans. Power Syst.

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“…The Covid-19 pandemic has also highlighted hydropower's resilience and critical role in delivering clean, flexible, reliable, and affordable energy, especially in times of crisis, because the plant operations have been less affected due to the degree of automation with modern Industrial Internet of Things (IIoT) facilities and intelligent system. [6][7][8][9][10][11][12] Generally, the grid controls and dispatches the electricity generation demand to hydropower stations ahead of days. However, a hydropower station's power generation is also affected by many dynamic factors, such as river/reservoir inflows, seasonality, abrupt needs, and gross industrial production, the self-usage of electricity, price, and so forth.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the global weighted average cost of electricity from hydropower projects in 2019 was US $0.047/kWh, making it the lowest‐cost source of electricity in many markets. The Covid‐19 pandemic has also highlighted hydropower's resilience and critical role in delivering clean, flexible, reliable, and affordable energy, especially in times of crisis, because the plant operations have been less affected due to the degree of automation with modern Industrial Internet of Things (IIoT) facilities and intelligent system 6–12 …”

Section: Introductionmentioning

confidence: 99%

HydroFlow: Towards probabilistic electricity demand prediction using variational autoregressive models and normalizing flows

Zhang

Wang

Zhong

et al. 2022

Int J of Intelligent Sys

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We present HydroFlow, a novel deep generative model for predicting the electricity generation demand of large-scale hydropower stations. HydroFlow uses a latent stochastic recurrent neural network to capture the dependencies in the multivariate time series. It not only utilizes the hidden state of the neural network, but also considers the uncertainty of variables related to natural and social factors. We also introduce an endto-end approach based on generative flows to approximate the posterior distribution of time series with exact likelihoods. Our model is powerful as adding stochasticity to different factors (e.g., reservoir capacity and water-flow measurements) and thus overcomes the expressiveness limitations of deterministic prediction methods. It also enables trainable latent transformations that can improve the model interpretability.We evaluate HydroFlow on the data collected from the hydropower stations of a large-scale hydropower development company. Experimental results show that our model significantly outperforms the state-of-the-art baseline methods while providing explainable results.

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A novel robust adaptive control of parallel energy resources in smart island

Gao

Sun

et al. 2021

International Journal of Electrical Power & Energy Systems

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Economic Operation and Quality Control in PV-BES-DG-Based Autonomous System

Cited by 10 publications

References 24 publications

Probabilistic Techno-Economic Design of Isolated Microgrid

Probabilistic Techno-Economic Design of Isolated Microgrid

HydroFlow: Towards probabilistic electricity demand prediction using variational autoregressive models and normalizing flows

A novel robust adaptive control of parallel energy resources in smart island

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