scholarly journals Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7473
Author(s):  
Hakan Acaroğlu ◽  
Fausto Pedro García Márquez
Keyword(s):  
Wind Energy ◽  
Electricity Markets ◽  
Electricity Market ◽  
Market Price ◽  
Load Forecasting ◽  
Structural Development ◽  
Power Prediction ◽  
Comprehensive Review ◽  
Forecasting Methods ◽  
Area Of Concern

Forecasting the electricity price and load has been a critical area of concern for researchers over the last two decades. There has been a significant economic impact on producers and consumers. Various techniques and methods of forecasting have been developed. The motivation of this paper is to present a comprehensive review on electricity market price and load forecasting, while observing the scientific approaches and techniques based on wind energy. As a methodology, this review follows the historical and structural development of electricity markets, price, and load forecasting methods, and recent trends in wind energy generation, transmission, and consumption. As wind power prediction depends on wind speed, precipitation, temperature, etc., this may have some inauspicious effects on the market operations. The improvements of the forecasting methods in this market are necessary and attract market participants as well as decision makers. To this end, this research shows the main variables of developing electricity markets through wind energy. Findings are discussed and compared with each other via quantitative and qualitative analysis. The results reveal that the complexity of forecasting electricity markets’ price and load depends on the increasing number of employed variables as input for better accuracy, and the trend in methodologies varies between the economic and engineering approach. Findings are specifically gathered and summarized based on researches in the conclusions.

scholarly journals Neural Network Based Model Comparison for Intraday Electricity Price Forecasting

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4557 ◽  
Author(s):  
Ilkay Oksuz ◽  
Umut Ugurlu
Keyword(s):  
Neural Networks ◽  
Electricity Markets ◽  
Electricity Market ◽  
Network Architecture ◽  
Model Comparison ◽  
Market Price ◽  
Absolute Error ◽  
Electricity Price ◽  
Price Forecasting ◽  
Electricity Price Forecasting

The intraday electricity markets are continuous trade platforms for each hour of the day and have specific characteristics. These markets have shown an increasing number of transactions due to the requirement of close to delivery electricity trade. Recently, intraday electricity price market research has seen a rapid increase in a number of works for price prediction. However, most of these works focus on the features and descriptive statistics of the intraday electricity markets and overlook the comparison of different available models. In this paper, we compare a variety of methods including neural networks to predict intraday electricity market prices in Turkish intraday market. The recurrent neural networks methods outperform the classical methods. Furthermore, gated recurrent unit network architecture achieves the best results with a mean absolute error of 0.978 and a root mean square error of 1.302. Moreover, our results indicate that day-ahead market price of the corresponding hour is a key feature for intraday price forecasting and estimating spread values with day-ahead prices proves to be a more efficient method for prediction.

Benefits for Wind Energy in Electricity Markets from Using Short Term Wind Power Prediction Tools; A Simulation Study

2004 ◽  
Vol 28 (1) ◽  
pp. 119-127 ◽  
Author(s):  
Julio Usaola ◽  
Oswaldo Ravelo ◽  
Gerardo González ◽  
Fernando Soto ◽  
Mª Carmen Dávila ◽  
...  
Keyword(s):  
Wind Energy ◽  
Wind Power ◽  
Simulation Study ◽  
Electricity Markets ◽  
Power Prediction ◽  
Short Term ◽  
Prediction Tools ◽  
Wind Power Prediction

scholarly journals Electricity Market Price Assesment Utilizing Hybrid PSO – ANN Algorithm

2019 ◽  
Vol 8 (4) ◽  
pp. 12867-12870
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Market Price ◽  
Competitive Bidding ◽  
Computational Time ◽  
Price Forecasting ◽  
Prediction Errors ◽  
Key Factors ◽  
Bidding Strategies ◽  
Forecasting Errors

Prediction of cost is the most imperative task and the reason for settling on choices in competitive bidding strategies. Reliability, Robustness and optimal benefits for the market players are the fundamental concerns which can be accomplished by a point value anticipating module constitute of diminutive prediction errors, reduced complexity and lesser computational time. Thus in this work, a coordinated methodology dependent on Artificial Neural Networks (ANN) prepared with Particle Swarm Optimization (PSO) is proposed for momentary market clearing costs anticipating in pool based electricity markets. The proposed methodology overcomes the difficulties like trapping towards local minima and moderate convergence as in existing techniques. The work was speculated on territory Spain electricity markets and the outcomes obtained are compared with hybrid models presented in the previous literature. The response shows decline in forecasting errors that are recognized in price forecasting. The total research may help the ISO in finding the key factors that are fit for expectation with low errors.

scholarly journals Load Forecasting for a Campus University Using Ensemble Methods Based on Regression Trees

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2038 ◽  
Author(s):  
María Ruiz-Abellón ◽  
Antonio Gabaldón ◽  
Antonio Guillamón
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Load Forecasting ◽  
Electricity Consumption ◽  
Ensemble Methods ◽  
Regression Trees ◽  
Supervised Machine Learning ◽  
Smart Meters ◽  
Wide Range ◽  
Objective Being

Load forecasting models are of great importance in Electricity Markets and a wide range of techniques have been developed according to the objective being pursued. The increase of smart meters in different sectors (residential, commercial, universities, etc.) allows accessing the electricity consumption nearly in real time and provides those customers with large datasets that contain valuable information. In this context, supervised machine learning methods play an essential role. The purpose of the present study is to evaluate the effectiveness of using ensemble methods based on regression trees in short-term load forecasting. To illustrate this task, four methods (bagging, random forest, conditional forest, and boosting) are applied to historical load data of a campus university in Cartagena (Spain). In addition to temperature, calendar variables as well as different types of special days are considered as predictors to improve the predictions. Finally, a real application to the Spanish Electricity Market is developed: 48-h-ahead predictions are used to evaluate the economical savings that the consumer (the campus university) can obtain through the participation as a direct market consumer instead of purchasing the electricity from a retailer.

scholarly journals Industrial consumers’ electricity market participation options: a case study of an industrial cooling process in Denmark

2021 ◽  
Vol 4 (S2) ◽  
Author(s):  
Nicolas Fatras ◽  
Zheng Ma ◽  
Bo Nørregaard Jørgensen
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Market Price ◽  
Industrial Process ◽  
Operating Conditions ◽  
Comparative Approach ◽  
Market Participation ◽  
Cooling Process ◽  
Process Flexibility

AbstractIn a deregulated market context, industrial consumers often have multiple market participation options available to bid their flexible consumption in electricity markets and thereby reduce their electricity bill. Yet most participation strategies for demand response are developed in a fixed and predefined set of submarkets. Meanwhile, little literature has compared multiple market options for market participants. Therefore, this paper proposes a comparative approach between available market options to evaluate savings from different market participation options. More specifically, this study implements an optimisation program in Python to investigate the impacts of changes in an industrial process’ flexibility on savings with different market participation options. The optimisation program is tested with a case study of an industrial cooling process in three Danish submarkets (day-ahead, intraday, and regulating power markets). The market participation options are formed by different combinations of these three submarkets, and the type and amount of process flexibility are varied by changing time and load constraints in the optimisation program. The results show that bidding in market options with multiple submarkets yields higher savings than single-market bidding, but that increases in available flexibility impact savings in each market option differently. Increased flexibility will only bring additional savings if it allows to take further advantage of price variations in a market option. Additionally, increases in savings with flexibility depend on the considered type of flexibility. These changes in relative savings between market options at each flexibility level imply that the optimal market option is not a static choice for a process with variable operating conditions. The optimal market option for an industrial consumer depends not only on market price signals, but also on the type and amount of available flexibility.

scholarly journals Short-Term Forecasting of Wind Energy: A Comparison of Deep Learning Frameworks

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7943
Author(s):  
Elianne Mora ◽  
Jenny Cifuentes ◽  
Geovanny Marulanda
Keyword(s):  
Wind Energy ◽  
Wind Power ◽  
Electricity Markets ◽  
Electricity Market ◽  
Short Term Memory ◽  
Short Term ◽  
Integrated Networks ◽  
Wind Power Forecasting ◽  
Overall Performance ◽  
Power Forecasting

Wind energy has been recognized as the most promising and economical renewable energy source, attracting increasing attention in recent years. However, considering the variability and uncertainty of wind energy, accurate forecasting is crucial to propel high levels of wind energy penetration within electricity markets. In this paper, a comparative framework is proposed where a suite of long short-term memory (LSTM) recurrent neural networks (RNN) models, inclusive of standard, bidirectional, stacked, convolutional, and autoencoder architectures, are implemented to address the existing gaps and limitations of reported wind power forecasting methodologies. These integrated networks are implemented through an iterative process of varying hyperparameters to better assess their effect, and the overall performance of each architecture, when tackling one-hour to three-hours ahead wind power forecasting. The corresponding validation is carried out through hourly wind power data from the Spanish electricity market, collected between 2014 and 2020. The proposed comparative error analysis shows that, overall, the models tend to showcase low error variability and better performance when the networks are able to learn in weekly sequences. The model with the best performance in forecasting one-hour ahead wind power is the stacked LSTM, implemented with weekly learning input sequences, with an average MAPE improvement of roughly 6, 7, and 49%, when compared to standard, bidirectional, and convolutional LSTM models, respectively. In the case of two to three-hours ahead forecasting, the model with the best overall performance is the bidirectional LSTM implemented with weekly learning input sequences, showcasing an average improved MAPE performance from 2 to 23% when compared to the other LSTM architectures implemented.

scholarly journals A Comparative Study of Pricing Mechanisms to Reduce Side-Payments in the Electricity Market: A Case Study for South Korea

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3395
Author(s):  
Hansol Shin ◽  
Tae Hyun Kim ◽  
Kyuhyeong Kwag ◽  
Wook Kim
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Market Price ◽  
Production Costs ◽  
Market Participants ◽  
Locational Marginal Pricing ◽  
Side Payments ◽  
Sales Company ◽  
Convex Costs ◽  
The Cost

Under marginal-cost pricing, some generators cannot recover their production costs at the market price due to non-convexities in the electricity market. For this reason, most electricity markets pay side-payments to generators whose costs are not sufficiently recovered, but side-payments present the problem of deteriorating transparency in the market. Recently, convex hull pricing and extended locational marginal pricing have been reviewed or gradually introduced to reduce side-payments. Another method is to include non-convex costs in the market price, which is applied in the Korean electricity market. Although it is not generally considered in the electricity market, the Vickrey auction method is also one of the pricing mechanisms that can reduce side-payments. The main purpose of this study is to analyze the financial impact of these alternative pricing mechanisms on market participants through rigorous simulation. We applied the alternative pricing schemes to the Korean electricity market, and the impacts are analyzed by comparing the cost aspect of an electricity sales company and the profit aspect of generation companies. As a result of the simulation study, each pricing mechanism not only differed in the degree to which side-payments are reduced but also has different effects on the type of generators.

scholarly journals Assessing Socioeconomic Impacts of Integrating Distributed Energy Resources in Electricity Markets through Input-Output Models

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4486
Author(s):  
Carmen Ramos Carvajal ◽  
Ana Salomé García-Muñiz ◽  
Blanca Moreno Cuartas
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Power Plants ◽  
Large Scale ◽  
Market Price ◽  
Total Output ◽  
Input Output ◽  
Economic Sectors ◽  
Socioeconomic Impacts ◽  
Virtual Power Plants

In competitive electricity markets, the growth of electricity generated by renewable sources will reduce the market price of electricity assuming marginal cost pricing. However, small renewable distributed generation (RDG) alone cannot modify the formation of electricity prices. By aggregating small RDG units into a Virtual Power Plants (as a single unit market) they are capable of dealing at the wholesale electricity market analogous to large-scale producer following in changes in wholesale prices. This paper investigates the socioeconomic impacts of different type of RDG technologies on Spanish economic sectors and households. To this end, we applied an input-output price model to detail the activities more sensitive to changes in electricity price due to RDG technologies deployment and the associated modifications in income and total output associated with the households’ consumption variation. Detailed Spanish electricity generation disaggregation of the latest available Spanish Input-Output table, which refers to 2015, was considered. It was found that the integration of RDG units in the electricity market project a better situation for the economy and Spanish households. This paper’s scope and information can be used to benefit decision-making with respect to electricity pricing policies.

scholarly journals Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities

2019 ◽  
Vol 11 (4) ◽  
pp. 987 ◽  
Author(s):  
Sana Mujeeb ◽  
Nadeem Javaid ◽  
Manzoor Ilahi ◽  
Zahid Wadud ◽  
Farruh Ishmanov ◽  
...  
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Electricity Market ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Energy Market ◽  
Short Term ◽  
Term Memory ◽  
Forecasting Methods ◽  
Long Short Term Memory

This paper focuses on analytics of an extremely large dataset of smart grid electricity price and load, which is difficult to process with conventional computational models. These data are known as energy big data. The analysis of big data divulges the deeper insights that help experts in the improvement of smart grid’s (SG) operations. Processing and extracting of meaningful information from data is a challenging task. Electricity load and price are the most influential factors in the electricity market. For improving reliability, control and management of electricity market operations, an exact estimate of the day ahead load is a substantial requirement. Energy market trade is based on price. Accurate price forecast enables energy market participants to make effective and most profitable bidding strategies. This paper proposes a deep learning-based model for the forecast of price and demand for big data using Deep Long Short-Term Memory (DLSTM). Due to the adaptive and automatic feature learning mechanism of Deep Neural Network (DNN), the processing of big data is easier with LSTM as compared to the purely data-driven methods. The proposed model was evaluated using well-known real electricity markets’ data. In this study, day and week ahead forecasting experiments were conducted for all months. Forecast performance was assessed using Mean Absolute Error (MAE) and Normalized Root Mean Square Error (NRMSE). The proposed Deep LSTM (DLSTM) method was compared to traditional Artificial Neural Network (ANN) time series forecasting methods, i.e., Nonlinear Autoregressive network with Exogenous variables (NARX) and Extreme Learning Machine (ELM). DLSTM outperformed the compared forecasting methods in terms of accuracy. Experimental results prove the efficiency of the proposed method for electricity price and load forecasting.

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