scholarly journals Probabilistic Forecasting of Wind and Solar Farm Output

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 5154
Author(s):  
John Boland ◽  
Sleiman Farah
Keyword(s):  
Electricity Market ◽  
Wind Farms ◽  
Error Variance ◽  
Prediction Intervals ◽  
Electrical Network ◽  
Probabilistic Forecasting ◽  
Short Term ◽  
Solar Systems ◽  
National Electricity Market ◽  
Short Term Forecasting

Accurately forecasting the output of grid connected wind and solar systems is critical to increasing the overall penetration of renewables on the electrical network. This includes not only forecasting the expected level, but also putting error bounds on the forecast. The National Electricity Market (NEM) in Australia operates on a five minute basis. We used statistical forecasting tools to generate forecasts with prediction intervals, trialing them on one wind and one solar farm. In classical time series forecasting, construction of prediction intervals is rudimentary if the error variance is constant—termed homoscedastic. However, if the variance changes—either conditionally as with wind farms, or systematically because of diurnal effects as with solar farms—the task is much more complicated. The tools were trained on segments of historical data and then tested on data not used in the training. Results from the testing set showed good performance using metrics, including Coverage and Interval Score. The methods used can be adapted to various time scales for short term forecasting.

scholarly journals Forecasting of Wind and Solar Farm Output in the Australian National Electricity Market: A Review

Energies ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 370
Author(s):  
John Boland ◽  
Sleiman Farah ◽  
Lei Bai
Keyword(s):  
Electricity Market ◽  
South Australia ◽  
Wind Farms ◽  
Electrical Network ◽  
Renewable Sources ◽  
Solar Systems ◽  
Clear Sky ◽  
National Electricity Market ◽  
The Moment ◽  
Wind Resource

Accurately forecasting the output of grid connected wind and solar systems is critical to increasing the overall penetration of renewables on the electrical network. This is especially the case in Australia, where there has been a massive increase in solar and wind farms in the last 15 years, as well as in roof top solar, both domestic and commercial. For example, in 2020, 27% of the electricity in Australia was from renewable sources, and in South Australia almost 60% was from wind and solar. In the literature, there has been extensive research reported on solar and wind resource, entailing both point and interval forecasts, but there has been much less focus on the forecasting of output from wind and solar systems. In this review, we canvass both what has been reported and also what gaps remain. In the case of the latter topic, there are numerous aspects that are not well dealt with in the literature. We have added discussion on the value of forecasts, rather than just focusing on forecast skill. Further, we present a section on how to deal with conditionally changing variance, a topic that has little focus in the literature. One other topic may be particularly important in Australia at the moment, but may become more widespread. This is how to deal with the concept of a clear sky output from a solar farm when the field is oversized compared to the inverter capacity, resulting in a plateau for the output.

scholarly journals Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market

Data ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 43 ◽  
Author(s):  
Mesbaholdin Salami ◽  
Farzad Movahedi Sobhani ◽  
Mohammad Ghazizadeh
Keyword(s):  
Neural Networks ◽  
Electricity Market ◽  
Simulation Optimization ◽  
Supply And Demand ◽  
Electricity Supply ◽  
Wavelet Neural Networks ◽  
Short Term ◽  
Swarm Optimization ◽  
Forecasting Accuracy ◽  
Short Term Forecasting

The databases of Iran’s electricity market have been storing large sizes of data. Retail buyers and retailers will operate in Iran’s electricity market in the foreseeable future when smart grids are implemented thoroughly across Iran. As a result, there will be very much larger data of the electricity market in the future than ever before. If certain methods are devised to perform quick search in such large sizes of stored data, it will be possible to improve the forecasting accuracy of important variables in Iran’s electricity market. In this paper, available methods were employed to develop a new technique of Wavelet-Neural Networks-Particle Swarm Optimization-Simulation-Optimization (WT-NNPSO-SO) with the purpose of searching in Big Data stored in the electricity market and improving the accuracy of short-term forecasting of electricity supply and demand. The electricity market data exploration approach was based on the simulation-optimization algorithms. It was combined with the Wavelet-Neural Networks-Particle Swarm Optimization (Wavelet-NNPSO) method to improve the forecasting accuracy with the assumption Length of Training Data (LOTD) increased. In comparison with previous techniques, the runtime of the proposed technique was improved in larger sizes of data due to the use of metaheuristic algorithms. The findings were dealt with in the Results section.

scholarly journals Hybrid Forecasting Model for Short-Term Electricity Market Prices with Renewable Integration

2018 ◽  
Vol 11 (1) ◽  
pp. 57 ◽  
Author(s):  
Gerardo Osório ◽  
Mohamed Lotfi ◽  
Miadreza Shafie-khah ◽  
Vasco Campos ◽  
João Catalão
Keyword(s):  
Electricity Market ◽  
Wavelet Transforms ◽  
Fuzzy Inference ◽  
Electricity Sector ◽  
Forecasting Model ◽  
Probabilistic Forecasting ◽  
Short Term ◽  
Market Prices ◽  
Inference System ◽  
Hybrid Particle Swarm Optimization

In recent years, there have been notable commitments and obligations by the electricity sector for more sustainable generation and delivery processes to reduce the environmental footprint. However, there is still a long way to go to achieve necessary sustainability goals while ensuring standards of robustness and the quality of power grids. One of the main challenges hindering this progress are uncertainties and stochasticity associated with the electricity sector and especially renewable generation. In this paradigm shift, forecasting tools are indispensable, and their utilization can significantly improve system operation and minimize costs associated with all related activities. Thus, forecasting tools have an essential key role in all decision-making stages. In this work, a hybrid probabilistic forecasting model (HPFM) was developed for short-term electricity market prices (EMP) combining wavelet transforms (WT), hybrid particle swarm optimization (DEEPSO), adaptive neuro-fuzzy inference system (ANFIS), and Monte Carlo simulation (MCS). The proposed hybrid probabilistic forecasting model (HPFM) was tested and validated with real data from the Spanish and Pennsylvania-New Jersey-Maryland (PJM) markets. The proposed model exhibited favorable results and performance in comparison with previously published work considering electricity market prices (EMP) data, which is notable.

scholarly journals Short-Term Electric Load Forecasting Using Standardized Load Profile (SLP) And Support Vector Regression (SVR)

2019 ◽  
Vol 9 (4) ◽  
pp. 4548-4553
Author(s):  
N. T. Dung ◽  
N. T. Phuong
Keyword(s):  
Machine Learning ◽  
Electricity Market ◽  
Business Strategy ◽  
Learning Algorithm ◽  
Load Forecasting ◽  
Support Vector ◽  
Forecast Errors ◽  
Short Term ◽  
Load Profile ◽  
Short Term Forecasting

Short-term load forecasting (STLF) plays an important role in business strategy building, ensuring reliability and safe operation for any electrical system. There are many different methods used for short-term forecasts including regression models, time series, neural networks, expert systems, fuzzy logic, machine learning, and statistical algorithms. The practical requirement is to minimize forecast errors, avoid wastages, prevent shortages, and limit risks in the electricity market. This paper proposes a method of STLF by constructing a standardized load profile (SLP) based on the past electrical load data, utilizing Support Regression Vector (SVR) machine learning algorithm to improve the accuracy of short-term forecasting algorithms.

scholarly journals Energy Storage for the Australian National Electricity Market Grid

2020 ◽  
Vol 160 ◽  
pp. 01006
Author(s):  
Wael Al-Kouz ◽  
Jamal Nayfeh ◽  
Alberto Boretti
Keyword(s):  
Power Generation ◽  
Energy Storage ◽  
Solar Energy ◽  
Electricity Market ◽  
Environmental Issues ◽  
Battery Storage ◽  
Short Term ◽  
National Electricity Market ◽  
Grid Based

The paper discusses the energy storage needs for the Australian National Electricity Market grid, based on the analysis of the power generation, wind and solar energy facilities and solar rooftop, and the grid demand. It is concluded that significant energy storage, for both powers of storing or delivering energy, and time this power can be stored or released, is needed. While pumped hydro energy storage from retrofitting of existing hydroelectric facilities may certainly provide a rapid short term growth of energy storage, additional expansions are difficult, and battery storage, that is challenging for sustainability, economic and environmental issues, must be considered.

Short Term Forecasting for Utility-Scale Wind Farms — The Power Model Challenge

2009 ◽  
Vol 33 (3) ◽  
pp. 247-257 ◽  
Author(s):  
Jonathan Collins ◽  
Jeremy Parkes ◽  
Andrew Tindal
Keyword(s):  
Wind Farm ◽  
Power Conversion ◽  
Wind Farms ◽  
Power Model ◽  
Short Term ◽  
Production Forecast ◽  
Modelling Techniques ◽  
Short Term Forecasting ◽  
Utility Scale ◽  
Modelling Methods

To date, many studies have focused on the mathematical modelling techniques for forecasting the production from wind farms, looking predominantly at the task of predicting the meteorological conditions at the site. This paper focuses on the final stage of the forecasting process-conversion from a meteorological forecast to a power production forecast. This challenge is particularly significant for utility-scale (>100MW) wind farms, where the simple application of a turbine manufacturer's power curve is insufficient to capture the true behaviour and interaction of the wind turbines over the whole site. This paper shows that the errors introduced by this power conversion process can be significantly reduced through the use of more advanced power modelling methods. A market trading scenario is considered and the increase in annual trading revenue for an example 100MW wind farm when using an advanced power model is estimated to be €180,000.

scholarly journals Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3089 ◽  
Author(s):  
Ke Yan ◽  
Xudong Wang ◽  
Yang Du ◽  
Ning Jin ◽  
Haichao Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Power Consumption ◽  
Real World ◽  
Electricity Market ◽  
Moving Average ◽  
Short Term ◽  
Auto Regressive ◽  
Short Term Forecasting ◽  
Deep Learning Neural Network

Electric power consumption short-term forecasting for individual households is an important and challenging topic in the fields of AI-enhanced energy saving, smart grid planning, sustainable energy usage and electricity market bidding system design. Due to the variability of each household’s personalized activity, difficulties exist for traditional methods, such as auto-regressive moving average models, machine learning methods and non-deep neural networks, to provide accurate prediction for single household electric power consumption. Recent works show that the long short term memory (LSTM) neural network outperforms most of those traditional methods for power consumption forecasting problems. Nevertheless, two research gaps remain as unsolved problems in the literature. First, the prediction accuracy is still not reaching the practical level for real-world industrial applications. Second, most existing works only work on the one-step forecasting problem; the forecasting time is too short for practical usage. In this study, a hybrid deep learning neural network framework that combines convolutional neural network (CNN) with LSTM is proposed to further improve the prediction accuracy. The original short-term forecasting strategy is extended to a multi-step forecasting strategy to introduce more response time for electricity market bidding. Five real-world household power consumption datasets are studied, the proposed hybrid deep learning neural network outperforms most of the existing approaches, including auto-regressive integrated moving average (ARIMA) model, persistent model, support vector regression (SVR) and LSTM alone. In addition, we show a k-step power consumption forecasting strategy to promote the proposed framework for real-world application usage.

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