Microgrid Energy Management and Methods for Managing Forecast Uncertainties

Vinothine, Shanmugarajah; Lidula, N. W. A.; Rajapakse, Athula D.; Kaluthanthrige, Roshani

doi:10.3390/en15228525

Cited by 17 publications

(5 citation statements)

References 55 publications

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“…The utilization of "secondary" integral controllers aims to effectively mitigate the system parameter variations (voltage and frequency) that are generated by the droop controllers (11) and (12) in a steady-state approach.…”

Section: Secondary Level Reviewmentioning

confidence: 99%

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Optimized Distributed Cooperative Control for Islanded Microgrid Based on Dragonfly Algorithm

Al-dulaimi,

Kurnaz

2023

Energies

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This study introduces novel stochastic distributed cooperative control (SDCC) in the context of island microgrids (MGs). A proportional resonant (PR) controller and virtual impedance droop control in stationary reference frames are employed in cooperation with distributed averaging secondary control optimized by the dragonfly algorithm (DA). The suggested approach demonstrates the capability to achieve mean-square synchronization for the voltage and frequency restoration of distributed generators (DGs) to ensure efficient active power sharing. Therefore, a sparse communication network has been used to avoid data congestion and reduce the need for extensive communication and information exchange. The proposed system offers an instinctive compromise between voltage regulation and reactive power sharing. A conventional centralized secondary control with PR droop control is simulated for performance evaluation and comparison purposes. In this study, empirical evidence is demonstrated to support the MG’s ability to confront communication failure and its ability to work reliably during plug-and-play operations.

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Section: Secondary Level Reviewmentioning

confidence: 99%

“…Ref. [9] proposes voltage stabilization and harmonic regulation [10,11]. The highest level of tertiary control entails the optimization of global economic dispatch across the network, which is dependent on the current energy markets and pricing.…”

Section: Introductionmentioning

confidence: 99%

Optimized Distributed Cooperative Control for Islanded Microgrid Based on Dragonfly Algorithm

Al-dulaimi,

Kurnaz

2023

Energies

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“…These controllers are in charge of measures and data transmission, the execution of commands, and alarm activation in the case of critical operation. Data collected and acquired are typically transmitted and processed by Energy Management Systems (EMSs) and specialized platforms, facilitating the planning and optimization of power flows [5].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Weather Model Combination for PV Production Forecasting

Buonanno,

Caputo,

Balog

et al. 2024

Energies

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Accurate predictions of photovoltaic generation are essential for effectively managing power system resources, particularly in the face of high variability in solar radiation. This is especially crucial in microgrids and grids, where the proper operation of generation, load, and storage resources is necessary to avoid grid imbalance conditions. Therefore, the availability of reliable prediction models is of utmost importance. Authors address this issue investigating the potential benefits of a machine learning approach in combination with photovoltaic power forecasts generated using weather models. Several machine learning methods have been tested for the combined approach (linear model, Long Short-Term Memory, eXtreme Gradient Boosting, and the Light Gradient Boosting Machine). Among them, the linear models were demonstrated to be the most effective with at least an RMSE improvement of 3.7% in photovoltaic production forecasting, with respect to two numerical weather prediction based baseline methods. The conducted analysis shows how machine learning models can be used to refine the prediction of an already established PV generation forecast model and highlights the efficacy of linear models, even in a low-data regime as in the case of recently established plants.

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“…This intermittency creates a mismatch between energy supply and demand, as renewable energy production does not always align with peak energy consumption periods [3] . As a result, excess renewable energy may be generated during low-demand periods, while insufficient energy is available during high-demand periods [4] . This situation leads to inefficient energy utilization and potential grid instabilities [5] .…”

Section: Introductionmentioning

confidence: 99%

Enhancing Lithium-Air Battery Performance through CoPc@CNT Composites: Electrochemical Analysis and Insights

Yerlikaya

Farsak

2023

Preprint

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Cobalt-phthalocyanine (CoPc) catalysts have shown great promise for enhancing the performance of lithium-oxygen (Li-O2) batteries, offering benefits such as efficient lithium storage, high reversible capacity, improved cycle performance, and enhanced charge and discharge capacity. In this study, we focused on the synthesis of CoPc-carbon nanotube (CNT) composites with porous structure. The composites were prepared by synthesizing CoPc compounds and interacting them with CNTs using a 3D ball mill shaker. Comprehensive spectroscopic techniques including NMR, FTIR, and UV were employed to characterize the newly synthesized phthalonitrile and phthalocyanine compounds. The surface morphologies of the composite materials were investigated using SEM, EDX, mapping and TEM analysis, enabling the determination of particle sizes and chemical compositions. XRD and XPS analyses confirmed that the composite structures were consistent with the existing literature. BET analysis revealed multilayer isotherm for the composites, indicating their favorable properties. The composite catalysts were incorporated into batteries and their performance was evaluated through various electrochemical tests. Notably, the CoPc1-CNT composite exhibited a remarkable discharge capacity of 3400 mAh g-1catalyst in the prepared battery.

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Microgrid Energy Management and Methods for Managing Forecast Uncertainties

Cited by 17 publications

References 55 publications

Optimized Distributed Cooperative Control for Islanded Microgrid Based on Dragonfly Algorithm

Optimized Distributed Cooperative Control for Islanded Microgrid Based on Dragonfly Algorithm

Machine Learning and Weather Model Combination for PV Production Forecasting

Enhancing Lithium-Air Battery Performance through CoPc@CNT Composites: Electrochemical Analysis and Insights

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