Search Results (51)

For an electric company, having an accurate forecast of the expected electrical production and maintenance from its wind farms is crucial. This information is essential for operating in various existing markets, such as the Iberian Energy Market Operator—Spanish Hub (OMIE in its Spanish acronym), the Portuguese Hub (OMIP in its Spanish acronym), and the Iberian electricity market between the Kingdom of Spain and the Portuguese Republic (MIBEL in its Spanish acronym), among others. The accuracy of these forecasts is vital for estimating the costs and benefits of handling electricity. This article explains the process of creating the complete dataset, which includes the acquisition of the hourly information of four European wind farms as well as a description of the structure and content of the dataset, which amounts to 2 years of hourly information. The wind farms are in three countries: Auvergne-Rhône-Alpes (France), Aragon (Spain), and the Piemonte region (Italy). The dataset was built and validated following the CRISP-DM methodology, ensuring a structured and replicable approach to data processing and preparation. To confirm its reliability, the dataset was tested using a basic predictive model, demonstrating its suitability for wind energy forecasting and maintenance optimization. The dataset presented is available and accessible for improving the forecasting and management of wind farms, especially for the detection of faults and the elaboration of a preventive maintenance plan. Full article

The growing investment in variable renewable energy sources is changing how electricity markets operate. In Europe, players rely on forecasts to participate in day-ahead markets closing between 12 and 37 h ahead of real-time operation. Usually, transmission system operators use a symmetrical procurement of up and down secondary power reserves based on the expected demand. This work uses machine learning techniques that dynamically compute it using the day-ahead programmed and expected dispatches of variable renewable energy sources, demand, and other technologies. Specifically, the methodology incorporates neural networks, such as Long Short-Term Memory (LSTM) or Convolutional neural network (CNN) models, to improve forecasting accuracy by capturing temporal dependencies and nonlinear patterns in the data. This study uses operational open data from the Spanish operator from 2014 to 2023 for training. Benchmark and test data are from the year 2024. Different machine learning architectures have been tested, but a Fully Connected Neural Network (FCNN) has the best results. The proposed methodology improves the usage of the up and down secondary reserved power by almost 22% and 11%, respectively. Full article

Batteries are essential for the functionality of electric vehicles (EVs), leading to their design with enhanced performance and durability. Consequently, traction batteries are often replaced while they still retain the properties for use in less stressful demanding applications, with lower power and storage requirements. This serves as a notable opportunity for circular economy. The energy management system presented is designed with lithium-ion batteries coming from EVs and repurposed for electricity storage as a smart backup solution for buildings. The system buys and stores energy from the grid during low-cost periods and utilizes the stored electricity to feed the demand, avoiding high electricity prices and smoothing out peak consumptions exceeding a predefined power limit. To illustrate the proposal, a case study is presented based on the Spanish market, analyzing the impact on the electricity savings for end consumers as well as the extended second-life estimation for a pack of batteries. The analysis of the results will help assess if the system is both economically feasible and environmentally sustainable from a circular economy point of view. Full article

The electricity market in Spain, as in many European countries, is organized into daily, intraday, and reserve markets. This project aims to predict the supply curves in the Spanish intraday market that have six sessions with different horizons of application, using information from the market itself. To achieve this, we approximate these curves using a non-uniform grid of points and evaluate the quality of these approximations with a weighted distance, both based on empirical market data. We employ neural network models, including multilayer perceptrons (MLPs), convolutional neural networks (CNNs), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and a Transformer network alongside a naive model for benchmarking. The MLP and CNN models demonstrated significant improvements in predicting these supply curves for the six market sessions. Full article

Battery electric vehicles represent a technological pathway for reducing carbon emissions in personal road transport. However, for the widespread adoption of this type of vehicle, the user experience should be similar to that of combustion engine vehicles. To achieve this objective, a robust and reliable public charging infrastructure is essential. In Spain, the electric recharging infrastructure is growing quickly in metropolitan areas but much more slowly on roads and highways. The upcoming charging stations must be located along high-volume traffic corridors and in proximity to the Trans-European Transport Network. The main contribution of this research is to offer a method for examining the essential electricity infrastructure investments required in scenarios involving substantial electric vehicle adoption. The methodology includes a sensitivity analysis of fleet composition and market share, recharging user behavior, charging station density, and vehicle efficiency improvements. To this end, the authors have developed a simplified probabilistic model, addressing the effect of the involved parameters through a comprehensive scenario analysis. The results show that the actual number of high-capacity charging plugs on Spanish roads is significantly lower than the European regulation requirements for the year 2030 considering an electric vehicle market share according to the Spanish Integrated National Energy and Climate Plan 2021–2030 objectives and it is far from the necessary infrastructure to cover the expected demand according to the traffic flow. Under these circumstances, the charging peak power demand reaches over 7.4% of the current Spanish total power demand for an electric vehicle fleet, which corresponds to only 12% of the total. Full article

Amidst the global energy crisis in 2022, the Spanish and Portuguese governments introduced a subsidy to natural gas (“the Iberian exception”), attempting to lower the wholesale electricity market prices, with the understanding that gas-fired-combined cycle gas turbines (CCGTs) are price-setting technologies most of the time, directly or indirectly. The subsidy succeeded in lowering the market price but induced several other effects, such as (1) the increase in cleared energy in the Spanish market (mostly produced with gas), (2) the bias in the import/export cross-border position between Spain and France (Spain became a net exporter to France immediately), or (3) the consequent increase in congestion rents, which serve to lightly finance the subsidy, among other effects. This paper provides a framework for clustering the different effects based on the market participation phases: the subsidy, the market bidding, the market results, and surplus and rents. Moreover, this paper builds on the theoretical market models, with and without subsidies, and with and without cross-border exchanges. Based on the real market bids, the subsidies, and the generators’ data, we reconstruct the supply and demand curves and simulate the counterfactual market scenarios in order to illustrate and quantify the effects. We highlight the quantification of the theoretical effect of the transfer of rents, from non-fossil to fossil fuel producers, induced by the gas subsidy. Full article

The electricity market in Spain holds significant importance in the nation’s economy and sustainability efforts due to its diverse energy mix that encompasses renewables, fossil fuels, and nuclear power. Accurate energy price prediction is crucial in Spain, influencing the country’s ability to meet its climate goals and ensure energy security and affecting economic stakeholders. We have explored how leveraging advanced GPT tools like OpenAI’s ChatGPT to analyze energy news and expert reports can extract valuable insights and generate additional variables for electricity price trend prediction in the Spanish market. Our research proposes two different training and modelling approaches of generative pre-trained transformers (GPT) with specialized news feeds specific to the Spanish market: in-context example prompts and fine-tuned GPT models. We aim to shed light on the capabilities of GPT solutions and demonstrate how they can augment prediction models by introducing additional variables. Our findings suggest that insights derived from GPT analysis of electricity news and specialized reports align closely with price fluctuations post-publication, indicating their potential to improve predictions and offer deeper insights into market dynamics. This endeavor can support informed decision-making for stakeholders in the Spanish electricity market and companies reliant on electricity costs and price volatility for their margins. Full article

This work proposes a pumped thermal energy storage (PTES) integrated into the power block of a concentrated solar power plant. The power block operates under a Hybrid Rankine–Brayton (HRB) cycle using propane as the working fluid. During PTES charging, some thermal energy is obtained from a dedicated compressor (additional to that of the HRB cycle), which is stored. During discharge, both compressors (HRB and PTES) are off, restoring the consumed energy and resulting in about a 13% increase in nominal power output. The system is also able to store thermal energy that would otherwise be rejected through the condenser if the PTES were turned off, leading to efficiency improvements in some cases. Considering the 2022 Spanish electricity market prices, the proposed PTES integration with 4 h of storage is feasible. The levelized cost of storage is calculated and compared to those of other PTES systems, achieving around a 40% reduction compared with an equivalent PTES Rankine. These results encourage future studies where the proposed PTES could be integrated into other power cycles that include a recompression process. Full article

The existence of chaos is particularly relevant, as the identification of a chaotic behavior in a time series could lead to reliable short-term forecasting. This paper evaluates the existence of nonlinearity and chaos in the underlying process of the spot prices of the Spanish electricity market. To this end, we used daily data spanning from 1 January 2013, to 31 March 2021 and we applied a comprehensive framework that encompassed a wide range of techniques. Nonlinearity was analyzed using the BDS method, while the existence of a chaotic structure was studied through Lyapunov exponents, recurrence plots, and quantitative recurrence analysis. While nonlinearity was detected in the underlying process, conclusive evidence supporting chaos was not found. In addition, the generalized autoregressive conditional heteroscedastic (GARCH) model accounts for part of the nonlinear structure that is unveiled in the electricity market. These findings hold substantial value for electricity market forecasters, traders, producers, and market regulators. Full article

This article presents the methodology and results developed as part of the Integration of Energy Resources through Local Electricity Markets (IREMEL) project, whose aim is to assess the capability of flexibility markets to manage eventual distribution network (DN) congestion produced by a high penetration of distributed energy resources (DERs), including photovoltaic (PV) panels, battery energy storage systems (BESSs), and electric vehicles (EVs). The distribution system simulator OpenDSS has been used to simulate three Spanish DNs under multiple DER penetration scenarios considering an urban and rural low-voltage network and an industrial medium-voltage DN. Likewise, the congestion events detected in the annual simulations have been used to measure the potential of flexibility markets under different DER penetrations and energy pricing. The results suggest that oversized distribution networks could prevent a profitable flexibility market implementation since the simulations developed in this article shows that networks with high congestion levels are prime candidates to solve this issue through a market mechanism. Likewise, the results suggest that a proper price for the energy managed through a local flexibility market (LFM) could have a bigger effect on market viability than DER penetration. Full article

In the Spanish electricity market, after the daily market is held in which prices are set for the next day, the secondary and tertiary markets take place, which allow companies more accurate adjustment of the electricity they are able to offer. The objective of this paper is to predict the final price reached in these markets by predicting the supply curve in advance, which is the aggregate of what companies offer. First, we study a procedure to represent the supply curves, and then we consider different machine learning approaches to obtain the day-ahead supply curves for the secondary market. Finally, the predictions of the supply curves are crossed with the system requirements to obtain the expected price predictions. Histogram-Based Gradient Boosting is the best performing algorithm for predicting supply curves. The most relevant variables for the prediction are the lagged values, the daily market price, the price of gas and values of the wind recorded in the Spanish provinces. Full article

Electrical load forecasting plays a crucial role in planning and operating power plants for utility factories, as well as for policymakers seeking to devise reliable and efficient energy infrastructure. Load forecasting can be categorized into three types: long-term, mid-term, and short-term. Various models, including artificial intelligence and conventional and mixed models, can be used for short-term load forecasting. Electricity load forecasting is particularly important in countries with restructured electricity markets. The accuracy of short-term load forecasting is crucial for the efficient management of electric systems. Precise forecasting offers advantages for future projects and economic activities of power system operators. In this study, a novel integrated model for short-term load forecasting has been developed, which combines the wavelet transform decomposition (WTD) model, a radial basis function network, and the Thermal Exchange Optimization (TEO) algorithm. The performance of this model was evaluated in two diverse deregulated power markets: the Pennsylvania-New Jersey-Maryland electricity market and the Spanish electricity market. The obtained results are compared with various acceptable standard forecasting models. Full article

Intraday markets can be organized as continuous trading or discrete auction sessions. While many studies have attempted to compare the liquidity of these two models, additional external factors specific to each system, such as the balancing market design and number of bidding zones, affect overall market liquidity. In this regard, the Spanish hybrid intraday markets seem like an excellent case study to compare the two market models. Since the two intraday models are implemented in the same system (the Spanish one), the same conditions apply to their implementation. However, a direct comparison of liquidity is still challenging due to two factors: (1) differences exist in market architecture (timing, pricing scheme, bidding formats, etc.) between the two models, which create preferences among market players for one or the other; (2) the opportunistic behavior of market players in the system responding to the market price signals may affect the liquidity dynamics. We demonstrate the relevance of these two factors coming into play in the Spanish intraday markets, first carrying out a qualitative analysis of the market architecture of both models and then empirically analyzing a market manipulation attempt, which we refer to as the 15:10 rush. Our analysis points towards the need for more efficient regulation governing the interaction of the continuous intraday market with intraday auction markets and the potential risks from increased algorithmic trading. Full article

Wind energy forecasting is a critical aspect for wind energy producers, given that the chaotic nature and the intermittence of meteorological wind cause difficulties for both the integration and the commercialization of wind-produced electricity. For most European producers, the quality of the forecast also affects their financial outcomes since it is necessary to include the impact of imbalance penalties due to the regularization in balancing markets. To help wind farm owners in the elaboration of offers for electricity markets, the EOLO predictor model can be used. This tool combines different sources of data, such as meteorological forecasts, electric market information, and historic production of the wind farm, to generate an estimation of the energy to be produced, which maximizes its financial performance by minimizing the imbalance penalties. This research study aimed to evaluate the performance of the EOLO predictor model when it is applied to the different Spanish electricity markets, focusing on the statistical analysis of its results. Results show how the wind energy forecast generated by EOLO anticipates real electricity generation with high accuracy and stability, providing a reduced forecast error when it is used to participate in successive sessions of the Spanish electricity market. The obtained error, in terms of RMAE, ranges from 8%, when it is applied to the Day-ahead market, to 6%, when it is applied to the last intraday market. In financial terms, the prediction achieves a financial performance near 99% once imbalance penalties have been discounted. Full article

The path toward energy transition requires many metals, some of which are scarce in nature or their supply is controlled by a few countries. The European and Spanish situations are particularly vulnerable because of the scarcity of crucial geological mineral resources, especially those known as critical. In this context, the recovery of metals from waste electric and electronic equipment (WEEE) presents an important opportunity to partly alleviate this situation because this region produces most of the WEEE per capita. In this study, 43 different categories of EEE placed in the Spanish market between 2016 and 2021 were assessed, considering the composition of up to 57 elements, with 34 being critical. The results show the great opportunities for urban mining: 1.4 million tons of metals valued at USD 2.43 billion, representing 80% of the mass and 25% of the price of the primary extraction in Spain during that period. In addition, 20,000 tons corresponded to critical metals. However, the short life of EEE and the low traceability and low recovery of metals, especially critical and precious (94% and 87% of their values are lost, respectively), make it necessary to overcome major challenges to develop a new industry capable of moving toward a deeper circular economy. Full article