Optimized hybrid wind power generation with forecasting algorithms and battery life considerations

Patel, Pranav; Shandilya, Ashish; Deb, Dipankar

doi:10.1109/peci.2017.7935735

Cited by 17 publications

(12 citation statements)

References 12 publications

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“…For wind power applications, commonly-used BESS include lithium-ion, lead-acid and nickel-cadmium batteries. Patel et al have discussed several methodologies to achieve optimized hybrid wind power generation, and we extend these four alternatives to solve the MCDM problem for a wind farm [14].…”

Section: Problem Formulationmentioning

confidence: 99%

Multi-Criteria Decision Making Approach for Hybrid Operation of Wind Farms

et al. 2019

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Hybrid operation of wind farms has been in the limelight in recent years wherein the stochastic nature of wind causes market operators to choose an optimal strategy to maximize profit. The current work deals with a multi-criteria decision making approach to choose the best possible alternatives for a hybrid wind farm operation. A set of three, non-beneficial criteria, namely wind wakes, wind curtailment, and forced outages, were chosen to evaluate the best alternative. Three methods, (i) Simple Additive Weighting (SAW), (ii) the Technique for Order or Preference by Similarity to Ideal Solution (TOPSIS) and (iii) Complex Proportional Assessment (COPRAS), were applied to identify the best alternative, and the results revealed that for all three methods, borrowing deficit power from a neighboring wind farm is the best alternative. Comparative analyses in terms of the data requirement, the effect of dynamic decision matrices, and rank reversal in wind farm application have also been pioneered.

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Section: Problem Formulationmentioning

confidence: 99%

Multi-Criteria Decision Making Approach for Hybrid Operation of Wind Farms

et al. 2019

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“…In [19], authors assessed the effect of electric vehicles on the microgrids operation. It is shown in [20] that the charging demand of electric vehicles needs to be modeled based on accurate inspection and investigation of their random behavior [21] in a complex environment [22]. While modeling the real effects of electric vehicles on the system is a necessity, finding a way to mitigate their effects and change it to opportunities for the microgrid is a research challenge [23].…”

Section: Introductionmentioning

confidence: 99%

An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

et al. 2021

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Renewable microgrids are new solutions for enhanced security, improved reliability and boosted power quality and operation in power systems. By deploying different sources of renewables such as solar panels and wind units, renewable microgrids can enhance reducing the greenhouse gasses and improve the efficiency. This paper proposes a machine learning based approach for energy management in renewable microgrids considering a reconfigurable structure based on remote switching of tie and sectionalizing. The suggested method considers the advanced support vector machine for modeling and estimating the charging demand of hybrid electric vehicles (HEVs). In order to mitigate the charging effects of HEVs on the system, two different scenarios are deployed; one coordinated and the other one intelligent charging. Due to the complex structure of the problem formulation, a new modified optimization method based on dragonfly is suggested. Moreover, a self-adaptive modification is suggested, which helps the solutions pick the modification method that best fits their situation. Simulation results on an IEEE microgrid test system show its appropriate and efficient quality in both scenarios. According to the prediction results for the total charging demand of the HEVs, the mean absolute percentage error is 0.978, which is very low. Moreover, the results show a 2.5% reduction in the total operation cost of the microgrid in the intelligent charging compared to the coordinated scheme.

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“…The study in [15] proposes a stochastic scheme to enable enhanced coordination between the energy storage system and the wind park. Furthermore, the authors in [16] consider the depletion of battery life as a economic factor and provide a comparative study of numerous prediction methods for wind farms such that deficit power can be delivered by the batteries and during stages of excess power, the supplementary power is stored in the batteries in a well-coordinated and efficient manner. Similarly, [17] provides an economic study with wind prediction schemes to determine an appropriate approach that reduces the number of storage devices without damaging their active life.…”

Section: Introductionmentioning

confidence: 99%

Moving Regression Filtering With Battery State of Charge Feedback Control for Solar PV Firming and Ramp Rate Curtailment

Syed

Khalid

2021

IEEE Access

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The surge in energy demand needs to be met with environmentally pleasant resources to reduce the production of greenhouse gases. Solar Photovoltaic (PV) power is a widespread choice as it is accessible in plenty and is comparatively inexpensive. However, the large-scale penetration of intermittent PV power causes multiple variabilities in the grid such as frequency issues and voltage deviations. To counteract these instabilities, Battery Energy Storage System (BESS) is integrated into the grid as it reduces the PV fluctuations and promotes optimal operation. Nevertheless, storage systems are expensive, and thus smoothing filters are coupled with the BESS for cost reduction and power smoothing. Formerly, traditional filters such as Low Pass Filters (LPF), Moving Average (MA), and Moving Median (MM) filters have been proposed for power smoothing. However, these filters have inadequate power tracking capabilities particularly with longer window sizes (W.S) and time constants, which subsequently depreciates the storage system performance. To compensate for the delayed power tracking, larger energy storage systems are required which in turn adds to the overall operational costs. This paper proposes the Moving Regression (MR) filter combined with state of charge (SoC) feedback control for solar PV variability reduction, reduced time delay, decreased battery charging/discharging power, and ramp rate. Simulation results attest that the MR filter achieves better solar power smoothing without increasing the BESS capacity. Also, the performance of the MR filter is less affected with the increase in window sizes. Additionally, the execution of the introduced smoothing filter was found to be superior when assessed against the LPF, MA, MM, Savitsky-Golay (SG), and the Gaussian filter. In comparison to the SG (W.S = 53), the MR (W.S = 45) filter reduces the battery charging/discharging power by approximately 30.48% and as opposed to the LPF (T.C = 48), the peak SoC is reduced by 19.1%.INDEX TERMS Battery energy storage system, low pass filter, machine learning, moving average filter, moving regression filter, renewable energy, solar power smoothing.

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Optimized hybrid wind power generation with forecasting algorithms and battery life considerations

Cited by 17 publications

References 12 publications

Multi-Criteria Decision Making Approach for Hybrid Operation of Wind Farms

Multi-Criteria Decision Making Approach for Hybrid Operation of Wind Farms

An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

Moving Regression Filtering With Battery State of Charge Feedback Control for Solar PV Firming and Ramp Rate Curtailment

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