Influence of Wind Speed Forecasting Error on the Choice of the Number of Balancing System Batteries

In the energy balancing system of distributed generation systems with RES (renewable energy sources), in particular with wind turbines, the effective use of the battery of the balancing system depends on the charge-discharge modes that are implemented. To be effectively used in an energy balancing system, the RES control system should coordinate the processes of energy generation and accumulation in the system through the implementation of operational management with forecasting. Depending on the characteristics of the battery and the accuracy of the measurement or prediction of the energy the battery capacity (or the number of batteries) that will provide the specified control range (controlled operation area) needs to be chosen. Empirical relations (equations) devoted to the dependence of the battery capacity on the discharge current and to the change of voltage at the terminals of the battery during direct current discharge were listed. Among the equations Peukert’s law was chosen. A general view of the dependence of the battery capacity on the discharge current was shown. The formula for Peukert's constant (coefficient) was given. 5 Packert's law limitations were listed including the fact that the effect of temperature on the battery is not taken into account. The influence of depth charge-discharge and the number of discharge cycles on the capacitance was shown. In the process of using the battery and increasing the number of charge-discharge cycles, the capacity decreases. Peukert’s formula was extended to be influenced by temperature: both the Peukert’s capacity and the Peukert’s coefficient depend on the temperature because the Peukert’s coefficient depends on the capacity. For further calculations, a rechargeable battery HZB12-180FA from manufacturer HAZE Battery Campany Ltd was chosen. The temperature was taken into account by empirical dependences from the manufacturer and then they were approximated by 3rd order polynomials. Graphical results of the approximation were shown. The formula of dependency between the power of the wind turbine and the wind speed was shown. The connection between wind speed prediction error, amount of power that could not be obtained because of that and the number of batteries that would provide the specified control range (controlled operation area) was shown. Thus, for calculation of the number of batteries the depth of discharge, temperature and prediction (measurement) error were taken into account. Example dependences of the number of batteries on the wind speed error at temperatures of -20 °C, 0 °C and 20 °C were shown. Curves of dependence of the number of batteries of the balancing system on the ambient temperature and the error of wind speed forecasting was constructed. As an example, when the prediction error increases from 10% to 15%, the number of batteries needs to be increased by 1.17 times, and when the temperature decreases from 20 °C to 0 °C, the number of batteries needs to be increased by 1.48 times. The results of the work can be used at the stage of planning the wind turbine when choosing the number and capacity of the batteries to be installed. Possible areas of further research are using Peukert's formulas, generalized for other or different types of batteries, using other formulas, except for Peukert’s one, for taking into account the dependence of battery capacity on discharge current, using a non-empirical approach to include dependency on temperature.

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Analyzing Time and Cost of Onshore Wind Turbine Construction Methods Using Discrete Event Simulation and Wind Speed Forecasting

Research, Development and Practice in Structural Engineering and Construction ◽

10.3850/978-981-08-7920-4_c-30-0332 ◽

2012 ◽

Author(s):

Hesham Osman ◽

Dina Atef ◽

Moheeb Ibrahim

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Discrete Event Simulation ◽

Discrete Event ◽

Onshore Wind ◽

Wind Speed Forecasting ◽

Event Simulation ◽

Construction Methods ◽

Turbine Construction

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Comparative models for multi-step ahead wind speed forecasting applied for expected wind turbine power output prediction

Wind Engineering ◽

10.1177/0309524x211052015 ◽

2021 ◽

pp. 0309524X2110520

Author(s):

Germaine Djuidje Kenmoé ◽

Hervice Roméo Fogno Fotso ◽

Claude Vidal Aloyem Kazé

Keyword(s):

Neural Networks ◽

Wind Speed ◽

Wind Turbine ◽

Arima Model ◽

Physical Method ◽

Computational Time ◽

Dynamic Neural Networks ◽

Wind Speed Forecasting ◽

Forecasting Models ◽

One Step

This paper investigates six of the most widely used wind speed forecasting models for a combination of statistical and physical methods for the purpose of Wind Turbine Power Generation (WTPG) prediction in Cameroon. Statistical method based on both single static and dynamic neural networks architectures and two hybrid neural networks architectures in comparison to ARIMA model are employed for multi-step ahead wind speed forecasting in two Datasets in Bapouh, Cameroon. The physical method is used to estimate 1 day ahead expected WTPG for each Dataset using the previous predicted wind speed from better forecasting models. The obtained results of multi-step ahead forecasting showed that the ARIMA and nonlinear autoregression with exogenous input neural network (NARXNN) models perform well the wind speed forecasting than other forecasting models in both Datasets. The better performances of ARIMA are achieved with one-step ahead and two-step ahead forecasting, while NARXNN is better with one-step ahead forecasting. But NARXNN models have more computational time than other models such as ARIMA models. Furthermore, the effectiveness of employed hybrid method for WTPG prediction is proven.

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Wind turbine and ultra-capacitor harvested energy increasing in microgrid using wind speed forecasting

Engineering Science and Technology an International Journal ◽

10.1016/j.jestch.2019.08.006 ◽

2019 ◽

Vol 22 (5) ◽

pp. 1161-1167 ◽

Cited By ~ 3

Author(s):

A. Aranizadeh ◽

A. Zaboli ◽

O. Asgari Gashteroodkhani ◽

B. Vahidi

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Wind Speed Forecasting ◽

Ultra Capacitor

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Wind turbine power output smoothing in microgrid using ultra-capacitor with continuous wind speed forecasting and online supervisory control

Journal of Renewable and Sustainable Energy ◽

10.1063/1.4950958 ◽

2016 ◽

Vol 8 (3) ◽

pp. 033301 ◽

Cited By ~ 3

Author(s):

A. Aranizadeh ◽

B. Vahidi ◽

A. Rahiminejad

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Supervisory Control ◽

Power Output ◽

Wind Speed Forecasting ◽

Ultra Capacitor

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Short-term wind speed forecasting system using deep learning for wind turbine applications

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v10i6.pp5779-5784 ◽

2020 ◽

Vol 10 (6) ◽

pp. 5779

Author(s):

Gokhan Erdemir ◽

Aydin Tarik Zengin ◽

Tahir Cetin Akinci

Keyword(s):

Deep Learning ◽

Wind Speed ◽

Wind Turbine ◽

Wind Turbines ◽

Learning Algorithm ◽

Integrated System ◽

Short Term ◽

Wind Speed Forecasting ◽

Short Term Forecasting

It is very important to accurately detect wind direction and speed for wind energy that is one of the essential sustainable energy sources. Studies on the wind speed forecasting are generally carried out for long-term predictions. One of the main reasons for the long-term forecasts is the correct planning of the area where the wind turbine will be built due to the high investment costs and long-term returns. Besides that, short-term forecasting is another important point for the efficient use of wind turbines. In addition to estimating only average values, making instant and dynamic short-term forecasts are necessary to control wind turbines. In this study, short-term forecasting of the changes in wind speed between 1-20 minutes using deep learning was performed. Wind speed data was obtained instantaneously from the feedback of the emulated wind turbine's generator. These dynamically changing data was used as an input of the deep learning algorithm. Each new data from the generator was used as both test and training input in the proposed approach. In this way, the model accuracy and enhancement were provided simultaneously. The proposed approach was turned into a modular independent integrated system to work in various wind turbine applications. It was observed that the system can predict wind speed dynamically with around 3% error in the applications in the test setup applications.

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Short term wind speed forecasting for wind turbine applications using linear prediction method

Renewable Energy ◽

10.1016/j.renene.2007.01.014 ◽

2008 ◽

Vol 33 (1) ◽

pp. 35-41 ◽

Cited By ~ 118

Author(s):

G.H. Riahy ◽

M. Abedi

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Linear Prediction ◽

Prediction Method ◽

Short Term ◽

Wind Speed Forecasting

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THE INFLUENCE OF THE WIND SPEED PREDICTION ERROR ON THE SIZE OF THE STORAGE CONTROLLED OPERATION ZONE IN THE SYSTEM WITH THE WIND GENERATOR

Praci Institutu elektrodinamiki Nacionalanoi akademii nauk Ukraini ◽

10.15407/publishing2020.57.035 ◽

2020 ◽

Vol 2020 (57) ◽

pp. 35-41

Author(s):

K.S. Klen ◽

◽

M.K. Yaremenko ◽

V.Ya. Zhuykov ◽

◽

...

Keyword(s):

Wind Speed ◽

Prediction Error ◽

Average Energy ◽

Interpolation Formula ◽

Wind Speed Prediction ◽

Wind Generator ◽

Prediction Horizon ◽

Speed Prediction ◽

Using Data ◽

Predictor Corrector

The article analyzes the influence of wind speed prediction error on the size of the controlled operation zone of the storage. The equation for calculating the power at the output of the wind generator according to the known values of wind speed is given. It is shown that when the wind speed prediction error reaches a value of 20%, the controlled operation zone of the storage disappears. The necessity of comparing prediction methods with different data discreteness to ensure the minimum possible prediction error and determining the influence of data discreteness on the error is substantiated. The equations of the "predictor-corrector" scheme for the Adams, Heming, and Milne methods are given. Newton's second interpolation formula for interpolation/extrapolation is given at the end of the data table. The average relative error of MARE was used to assess the accuracy of the prediction. It is shown that the prediction error is smaller when using data with less discreteness. It is shown that when using the Adams method with a prediction horizon of up to 30 min, within ± 34% of the average energy value, the drive can be controlled or discharged in a controlled manner. References 13, figures 2, tables 3.

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SHAPING OF AN AUTONOMOUS POWER SYSTEM FOR GUARANTEED POWER SUPPLY WITH THE PREDOMINANCE WIND ENERGY

Alternative Energy and Ecology (ISJAEE) ◽

10.15518/isjaee.2018.22-24.028-050 ◽

2018 ◽

pp. 28-50

Author(s):

S. G. Ignatiev ◽

S. V. Kiseleva

Keyword(s):

Energy Storage ◽

Wind Speed ◽

Wind Energy ◽

Wind Turbine ◽

Wind Turbines ◽

Energy Demand ◽

Diesel Generator ◽

Average Wind Speed ◽

Wind Speeds ◽

Average Wind

Optimization of the autonomous wind-diesel plants composition and of their power for guaranteed energy supply, despite the long history of research, the diversity of approaches and methods, is an urgent problem. In this paper, a detailed analysis of the wind energy characteristics is proposed to shape an autonomous power system for a guaranteed power supply with predominance wind energy. The analysis was carried out on the basis of wind speed measurements in the south of the European part of Russia during 8 months at different heights with a discreteness of 10 minutes. As a result, we have obtained a sequence of average daily wind speeds and the sequences constructed by arbitrary variations in the distribution of average daily wind speeds in this interval. These sequences have been used to calculate energy balances in systems (wind turbines + diesel generator + consumer with constant and limited daily energy demand) and (wind turbines + diesel generator + consumer with constant and limited daily energy demand + energy storage). In order to maximize the use of wind energy, the wind turbine integrally for the period in question is assumed to produce the required amount of energy. For the generality of consideration, we have introduced the relative values of the required energy, relative energy produced by the wind turbine and the diesel generator and relative storage capacity by normalizing them to the swept area of the wind wheel. The paper shows the effect of the average wind speed over the period on the energy characteristics of the system (wind turbine + diesel generator + consumer). It was found that the wind turbine energy produced, wind turbine energy used by the consumer, fuel consumption, and fuel economy depend (close to cubic dependence) upon the specified average wind speed. It was found that, for the same system with a limited amount of required energy and high average wind speed over the period, the wind turbines with lower generator power and smaller wind wheel radius use wind energy more efficiently than the wind turbines with higher generator power and larger wind wheel radius at less average wind speed. For the system (wind turbine + diesel generator + energy storage + consumer) with increasing average speed for a given amount of energy required, which in general is covered by the energy production of wind turbines for the period, the maximum size capacity of the storage device decreases. With decreasing the energy storage capacity, the influence of the random nature of the change in wind speed decreases, and at some values of the relative capacity, it can be neglected.

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