Energy Forecasting Research Articles

Integrating Machine Learning and MLOps for Wind Energy Forecasting: A Comparative Analysis and Optimization Study on Türkiye’s Wind Data

This study conducted a detailed comparative analysis of various machine learning models to enhance wind energy forecasts, including linear regression, decision tree, random forest, gradient boosting machine, XGBoost, LightGBM, and CatBoost. Furthermore, it developed an end-to-end MLOps pipeline leveraging SCADA data from a wind turbine in Türkiye. This research not only compared models using the RMSE metric for selection and optimization but also explored in detail the impact of integrating machine learning with MLOps on the precision of energy production forecasts. It investigated the suitability and efficiency of ML models in predicting wind energy with MLOps integration. The study explored ways to improve LightGBM algorithm performance through hyperparameter tuning and Docker utilization. It also highlighted challenges in speeding up MLOps development and deployment processes. Model performance was assessed using the RMSE metric, conducting a comparative evaluation across different models. The findings revealed that the RMSE values among the regression models ranged from 460 kW to 192 kW. Focusing on enhancing LightGBM, the research decreased the RMSE value to 190.34 kW. Despite facing technical and operational hurdles, the implementation of MLOps was proven to enhance the speed (latency of 9 ms), reliability (through Docker encapsulation), and scalability (using Docker swarm) of machine learning endeavors.

Revolutionizing power grid loss prediction with advanced hybrid time series deep learning model

Accurate prediction of grid loss in power distribution networks is pivotal for efficient energy management and pricing strategies. Traditional forecasting approaches often struggle to capture the complex temporal dynamics and external influences inherent in grid loss data. In response, this research presents a novel hybrid time-series deep learning model: Gated Recurrent Units with Temporal Convolutional Networks (GRU-TCN), designed to enhance grid loss prediction accuracy. The proposed model integrates the temporal sensitivity of GRU with the local context awareness of TCN, exploiting their complementary strengths. A learnable attention mechanism fuses the outputs of both architectures, enabling the model to discern significant features for accurate prediction. The model is evaluated using well-established metrics across distinct temporal phases: training, testing, and future projection. Results showcase Resulting in encouraging Figures for mean absolute error, root mean squared error, and mean absolute percentage error, the model’s capacity to capture both long-term trends and transitory patterns. The GRU-TCN hybrid model represents a pioneering approach to power grid loss prediction, offering a flexible and precise tool for energy management. This research not only advances predictive accuracy but also lays the foundation for a smarter and more sustainable energy ecosystem, poised to transform the landscape of energy forecasting.

Transfer learning integrating similarity analysis for short-term and long-term building energy consumption prediction

Currently, building energy consumption prediction models are usually based on a large amount of historical operational data in high demands of building operating hours and monitoring systems. However, many buildings may lack operational data due to relatively limited monitoring systems, causing the failure to use data-driven methods to characterize the energy profile. In this context, transfer learning is a promising method to establish the knowledge transfer between many high-quality building operation datasets and a small amount of target building data, and to help predict energy consumption in the target building. This paper studies the possibility of employing transfer learning to achieve both short and long-term building energy consumption prediction. Firstly, a similarity analysis method, based on variable modal decomposition and dynamic time warping, is proposed for identifying the source buildings with the most similar energy features to the target building. Then, transfer learning for long-term prediction air-conditioning energy consumption is developed with weather parameters generated by the Morphing method as inputs. For the short-term single-step prediction, the proposed model CV-RMSE improves 81.3% (AEC) and 77.4% (EEC), respectively, compared to the prediction model that does not implement the transfer learning strategy and directly uses the target BEC data. As for the short-term multi-step prediction, the proposed model CV-RMSE improves 62.0% (AEC) and 65.5% (EEC), respectively. For the long-term prediction, the average CV-RMSE for the whole year is 6.62% and 11.15% for the proposed and directly target domain-based model, respectively. The proposed method explores the practicality of transfer learning in building energy forecasting, contributing to the use of existing building operation data for energy management at different timespan.

Performance analysis of a blockchain enabled domestic power consumption monitoring and forecasting system

Purpose The purpose of this paper is to develop a blockchain-based data capture and transmission system that will collect real-time power consumption data from a household electrical appliance and transfer it securely to a local server for energy analytics such as forecasting. Design/methodology/approach The data capture system is composed of two current transformer (CT) sensors connected to two different electrical appliances. The CT sensors send the power readings to two Arduino microcontrollers which in turn connect to a Raspberry-Pi for aggregating the data. Blockchain is then enabled onto the Raspberry-Pi through a Java API so that the data are transmitted securely to a server. The server provides real-time visualization of the data as well as prediction using the multi-layer perceptron (MLP) and long short term memory (LSTM) algorithms. Findings The results for the blockchain analysis demonstrate that when the data readings are transmitted in smaller blocks, the security is much greater as compared with blocks of larger size. To assess the accuracy of the prediction algorithms data were collected for a 20 min interval to train the model and the algorithms were evaluated using the sliding window approach. The mean average percentage error (MAPE) was used to assess the accuracy of the algorithms and a MAPE of 1.62% and 1.99% was obtained for the LSTM and MLP algorithms, respectively. Originality/value A detailed performance analysis of the blockchain-based transmission model using time complexity, throughput and latency as well as energy forecasting has been performed.

Energy Consumption Forecasting through Data Mining

Forecasting is vital for effective energy management systems to project future energy requirements and establish demand-supply equilibrium. In this study, we propose to evaluate the effectiveness of a new weather-free forecasting model. The model is created using advanced data mining techniques on an extensive database that contains relevant historical power production data. The motivation behind developing a weather-free model stems from the challenges associated with obtaining reliable weather data in specific scenarios and the desire to reduce computational complexity. By eliminating the reliance on weather data, our model offers a promising alternative approach to energy forecasting, potentially enhancing accuracy and efficiency. In this work, our objectives are twofold. Firstly, we aim to evaluate the interplay between anomaly detection techniques and forecasting model accuracy. Anomalies in energy consumption patterns can significantly impact forecasting accuracy, and thus, understanding their detection and management is crucial. Secondly, we seek to determine the optimal performance metric among three defined metrics explicitly tailored for this application. By addressing these objectives, we aim to contribute to advancing energy forecasting methodologies, providing valuable insights for practical implementation in energy management systems. Our findings have the potential to inform decision-making processes and optimize resource allocation in energy distribution networks, ultimately leading to more sustainable and resilient energy infrastructures.

Enhancing photovoltaic energy forecasting: a progressive approach using wavelet packet decomposition

Abstract Accurate photovoltaic (PV) energy forecasting plays a crucial role in the efficient operation of PV power stations. This study presents a novel hybrid machine-learning (ML) model that combines Gaussian process regression with wavelet packet decomposition to forecast PV power half an hour ahead. The proposed technique was applied to the PV energy database of a station located in Algeria and its performance was compared to that of traditional forecasting models. Performance evaluations demonstrate the superiority of the proposed approach over conventional ML methods, including Gaussian process regression, extreme learning machines, artificial neural networks and support vector machines, across all seasons. The proposed model exhibits lower normalized root mean square error (nRMSE) (2.116%) and root mean square error (RMSE) (208.233 kW) values, along with a higher coefficient of determination (R2) of 99.881%. Furthermore, the exceptional performance of the model is maintained even when tested with various prediction horizons. However, as the forecast horizon extends from 1.5 to 5.5 hours, the prediction accuracy decreases, evident by the increase in the RMSE (710.839 kW) and nRMSE (7.276%), and a decrease in R2 (98.462%). Comparative analysis with recent studies reveals that our approach consistently delivers competitive or superior results. This study provides empirical evidence supporting the effectiveness of the proposed hybrid ML model, suggesting its potential as a reliable tool for enhancing PV power forecasting accuracy, thereby contributing to more efficient grid management.

A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities

To achieve the energy transition, energy and energy efficiency are becoming more and more important in society. New methods, such as Artificial Intelligence (AI) and Machine Learning (ML) models, are needed to coordinate supply and demand and address the challenges of the energy transition. AI and ML are already being applied to a growing number of energy infrastructure applications, ranging from energy generation to energy forecasting and human activity recognition services. Given the rapid development of AI and ML, the importance of Trustworthy AI is growing as it takes on increasingly responsible tasks. Particularly in the energy domain, Trustworthy AI plays a decisive role in designing and implementing efficient and reliable solutions. Trustworthy AI can be considered from two perspectives, the Model-Centric AI (MCAI) and the Data-Centric AI (DCAI) approach. We focus on the DCAI approach, which relies on large amounts of data of sufficient quality. These data are becoming more and more synthetically generated. To address this trend, we introduce the concept of Synthetic Data-Centric AI (SDCAI). In this survey, we examine Trustworthy AI within a Synthetic Data-Centric AI context, focusing specifically on the role of simulation and synthetic data in enhancing the level of Trustworthy AI in the energy domain.

Green energy revolution: A unique approach for energy forecasting and optimization towards sustainable energy planning and social development

Green energy revolution: A unique approach for energy forecasting and optimization towards sustainable energy planning and social development

Analysis of variables to determine their influence on renewable energy forecasting using ensemble methods

Forecasting is of great importance in the field of renewable energies because it allows us to know the quantity of energy that can be produced, and thus, to have an efficient management of energy sources. However, determining which prediction system is more adequate is very complex, as each energy infrastructure is different. This work studies the influence of some variables when making predictions using ensemble methods for different locations. In particular, the proposal analyzes the influence of the aspects: the variation of the sampling frequency of solar panel systems, the influence of the type of neural network architecture and the number of ensemble method blocks for each model. Following comprehensive experimentation across multiple locations, our study has identified the most effective solar energy prediction model tailored to the specific conditions of each energy infrastructure. The results offer a decisive framework for selecting the optimal system for accurate and efficient energy forecasting. The key point is the use of short time intervals, which is independent of type of prediction model and of their ensemble method.

A novel seasonal grey prediction model with fractional order accumulation for energy forecasting

To accurately predict sequence data with seasonal characteristics, we combine data restart technology and fractional order accumulation into a novel seasonal grey model (FSGM (1,1, α)). The particle swarm optimization (PSO) algorithm is used to solve the fractional order and background value coefficients of the model, and the effectiveness of FSGM (1,1, α) is verified using three cases. Finally, we use FSGM (1,1, α) to predict quarterly electricity generation in Beijing and Henan Province and quarterly petroleum coke production in China from 2023 to 2027. The research results indicate that, first, FSGM (1,1, α) is reasonable and effective and has the ability to accurately capture the dynamic trend of seasonal data. Second, compared with the grey model (GM (1,1)), seasonal grey model (SGM (1,1)), data grouping grey model (DGGM (1,1)), data grouping seasonal model (DGSM (1,1)), and data grouping seasonal time model (DGSTM (1,1)), which have seasonal characteristics, FSGM (1,1, α) can better fit the original data, achieve higher prediction accuracy, and perform better. Third, from 2023 to 2027, it is predicted that there will be no significant change in Beijing's electricity generation, and the current stable trend will be maintained. Both the power generation in Henan Province and the petroleum coke production in China will steadily increase to a certain extent, with obvious seasonal cyclical fluctuations. Notably, the power generation and petroleum coke production in Henan Province in the fourth quarter of 2027 will increase by 11.50 % and 10.93 %, respectively, compared to those in the fourth quarter of 2023.

Evaluation and intelligent forecasting of energy and exergy efficiencies of a nanofluid-based filled-type U-pipe solar ETC using three machine learning approaches

The thermodynamic modeling of a filled layer U-type was conducted considering the six water-based and Engine Oil (EO)-based nanofluids (NFs). The results demonstrated that Fe2O3/EO NF is efficient from the exergy efficiency point of view. While MWCNT/water NF outperforms from the energy efficiency aspect. Moreover, the outcome indicated that when (Tin−Tamb)/It increased above 0.04 (K m2/W), Ltube has no considerable effect on the exergy efficiency. Still, the length impact is noticeable regarding outlet temperature and energy efficiency. Furthermore, the advantage of the filled-type over the conventional ETSC is more prominent at low m˙ (e.g. 4.0 kg/h). So that ηex in the filled type with Fe2O3/EO is improved by 2.29 % and 3.70 %, respectively, for (Tin−Tamb)/It of 0 and 0.04. Moreover, the increase in m˙ of the water/based and oil-based NFs, respectively, between 6 and 10 kg/h and 8–20 kg/h is suggested from the energy efficiency aspect. The results of machine learning modeling revealed that three models namely group method for data handling (GMDH), response surface method (RSM), and modified response surface model (MRSM) are capable of estimating ηen and ηex. While MRSM outperforms the two other models in terms of (R2 | ηen = 0.9986 and RMSE| ηen = 0.7849; R2 |ηex = 0.9988 and RMSE| ηex= 0.1513).

A wind speed forecasting model using nonlinear auto-regressive model optimized by the hybrid chaos-cloud salp swarm algorithm

Wind energy forecasting is significantly affected by the strong volatility, intermittency and variability of the wind speed sequence itself. Therefore, to improve forecast accuracy and stability, based on nonlinear auto-regressive model with exogenous inputs (NARX) and the hybrid chaos-cloud salp swarm algorithm (CC-SSA), a short-term wind speed prediction method is proposed. Firstly, to reduce the complexity of the original wind speed data and generate subcomponents with different patterns and low complexity, a mixed modal decomposition method is carried out by combining variational modal decomposition (VMD) based on Pearson correlation coefficient and generalized S-transform (GST) based on adaptive sample entropy. Therefore, the complementary advantages of different mode decompositions are obtained by combining the two different subcomponents into a mixed component. Secondly, by using cloud model and chaotic map, the improved CC-SSA algorithm is proposed to improve the convergence performance of salp swarm algorithm (SSA). Finally, by using CC-SSA algorithm to optimize the weights of NARX, the CC-SSA-NARX forecasting model is established to predict wind speed. The experimental results show that for the 1-Step, 2-Step, and 3-Step datasets of the actual wind speed in the studied region, the MAE, MAPE, RMSE, and R2 of the CC-SSA-NARX model are 0.23, 6 %, 0.27, and 0.97 respectively. The proposed model present the highest prediction index among the other 7 comparative models, showing high accuracy and generalization ability in short-term wind speed prediction, it can provide a certain reference for enhancing the stability of wind power generation and promoting the sustainable development of the wind power industry to some extent.

A Review of Solar and Wind Energy Resource Projection Based on the Earth System Model

Many countries around the world are rapidly advancing sustainable development (SD) through the exploitation of clean energy sources such as solar and wind energy, which are becoming the core of the sustainable energy transition. In recent years, the continuous advancement of Earth system models (ESMs) has facilitated numerous studies utilizing them to predict long-term and large-scale meteorological elements, consequently enabling forecasts of wind and solar energy. These forecasts provide critical guidance for formulating national renewable energy policies. Nevertheless, the current literature on ESMs predicting wind and solar energy lacks sufficient integration. Hence, to comprehend the focal points and future research prospects, we conducted this systematic review, employing four academic search tools to comprehensively analyze the relevant literature from the past five years. We summarized the general analytical process and compared the content and conclusions of the literature. The study reveals that future photovoltaic (PV) potential for electricity generation may increase in certain regions but decrease in others, while the global potential for concentrated solar power (CSP) may diminish, influenced by diverse factors and displaying significant regional disparities. In addition, wind resource trends vary in different regions, and forecasts exhibit considerable uncertainty. Therefore, many studies have corrected wind speeds prior to predicting wind energy. Subsequent research endeavors should concentrate on optimizing ESMs, investigating the impacts of technological innovation, and enhancing the prediction and analysis of extreme weather events.

Application and evaluation of the evolutionary algorithms combined with conventional neural network to determine the building energy consumption of the residential sector

Residential uses a significant amount of energy; hence, encouraging sustainability and lessening environmental effects requires minimizing energy consumption in this sector. This study focuses on applying and evaluating evolutionary algorithms combined with conventional neural networks to predict building energy consumption in the residential sector. The primary objectives were to assess the performance of three evolutionary algorithms – Heap-Based Optimizer (HBO), Multiverse Optimizer (MVO), and Whale Optimization Algorithm (WOA) – in comparison to each other and to determine their effectiveness in predicting energy consumption. Each algorithm was integrated into the neural network framework to optimize the prediction model. Training and testing datasets were employed to evaluate the performance of the models. Two key statistical indices, Root Mean Square Error (RMSE) and R-squared (R2), were utilized to assess the accuracy of the predictions. The results of the evaluation demonstrated varying performances among the three evolutionary algorithms. MVO achieved the highest scores for both RMSE (48.55082 in training and 68.44517 in testing) and R2 (0.99184 in training and 0.98236 in testing) on both training and testing datasets, indicating superior predictive accuracy compared to HBO and WOA. These findings underscore the importance of algorithm selection in optimizing predictive models for energy consumption forecasting. Further research may explore hybrid approaches or parameter tuning to enhance the performance of evolutionary algorithms in this domain. Overall, this study contributes to advancing energy forecasting techniques, with potential implications for energy management and conservation efforts in the residential sector.

Investigation of Load, Solar and Wind Generation as Target Variables in LSTM Time Series Forecasting, Using Exogenous Weather Variables

Accurately forecasting energy metrics is essential for efficiently managing renewable energy generation. Given the high variability in load and renewable energy power output, this represents a crucial area of research in order to pave the way for increased adoption of low-carbon energy solutions. Whilst the impact of different neural network architectures and algorithmic approaches has been researched extensively, the impact of utilising additional weather variables in forecasts have received far less attention. This article demonstrates that weather variables can have a significant influence on energy forecasting and presents methodologies for using these variables within a long short-term memory (LSTM) architecture to achieve improvements in forecasting accuracy. Moreover, we introduce the use of the seasonal components of the target time series, as exogenous variables, that are also observed to increase accuracy. Load, solar and wind generation time series were forecast one hour ahead using an LSTM architecture. Time series data were collected in five Spanish cities and aggregated for analysis, alongside five exogenous weather variables, also recorded in Spain. A variety of LSTM architectures and hyperparameters were investigated. By tuning exogenous weather variables, a 33% decrease in mean squared error was observed for solar generation forecasting. A 22% decrease in mean absolute squared error (MASE), compared to 24-h ahead forecasts made by the Transmission Service Operator (TSO) in Spain, was also observed for solar generation. Compared to using the target variable in isolation, utilising exogenous weather variables decreased MASE by approximately 10%, 15% and 12% for load, solar and wind generation, respectively. By using the seasonal component of the target variables as an exogenous variable itself, we demonstrated decreases in MASE of 19%, 12% and 8% for load, solar and wind generation, respectively. These results emphasise the significant benefits of incorporating weather and seasonal components into energy-related time series forecasts.

Physics-inspired and data-driven two-stage deep learning approach for wind field reconstruction with experimental validation

Accurate and reliable wind forecasts for urban blocks play a pivotal role in the construction of zero-energy communities by guiding the selection and placement of wind turbines and the aerodynamic design optimization of ducted openings. While relatively accurate wind fields are available based on numerical methods, their heavy computational cost and discontinuity make it necessary to explore an interactive and end-to-end method. In this study, we develop a physics-inspired and data-driven two-stage deep learning approach that can reconstruct complex wind fields precisely. The proposed method integrates a physical feature extraction model of the flow field with a sparse measurement data-driven error correction approach. In particular, a well-designed and well-trained flow field feature extraction model (original model) can preserve salient features of CFD modelling, while data-driven error correction techniques may harvest the uncertainty features and fill the remaining gaps between the original model predictions and the measured data. The proposed method is verified by a measured dataset from a community in Beijing. Experimental validation illustrates that the proposed algorithm successfully accomplishes wind field reconstruction in complex terrains using sparse datasets. We show that the proposed two-stage strategy exhibits significantly improved prediction results over the purely original method, with an average accuracy improvement of 47.17% and a maximum accuracy improvement of 72.59%. Overall, the proposed method delivers the potential in accurate wind field construction and urban wind energy forecasting.

Forecasting wind power using Optimized Recurrent Neural Network strategy with time‐series data

AbstractFuel prices are rising, bringing attention to the utilization of alternative energy sources (RES). Even though load forecasting is more accurate at making predictions than wind power forecasting is. To address the operational challenges with the supply of electricity, wind energy forecasts remain essential. A certain kind of technology has recently been applied to forecast wind energy. On wind farms, a variety of wind power forecasting methods have been developed and used. The main idea underlying recurrent networks is parameter sharing across the multiple layers and neurons, which results in cycles in the network's graph sequence. Recurrent networks are designed to process sequential input. A novel hybrid optimization‐based RNN model for wind power forecasting is proposed in this research. Using the SpCro algorithm, a proposed optimization method, the RNN's weights are adjusted. The Crow Search Optimization (CSA) algorithm and the Sparrow search algorithm are combined to form the SpCro Algorithm (SSA). The suggested Algorithm was developed using the crow's memory traits and the sparrow's detecting traits. The proposed system is simulated in MATLAB, and the usefulness of the suggested approach is verified by comparison with other widely used approaches, such as CNN and DNN, in terms of error metrics. Accordingly, the MAE of the proposed method is 45%, 10.02%, 10.04%, 33.58%, 94.81%, and 10.01% higher than RNN, SOA+RNN, CSO+RNN, SSA+DELM, CFU‐COA, and GWO+RNN method.

Enhanced Forecasting Accuracy of a Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and a CNN-LSTM Model

Renewable energies have become pivotal in the global energy landscape. Their adoption is crucial for phasing out fossil fuels and promoting environmentally friendly energy solutions. In recent years, the energy management system (EMS) concept has emerged to manage the power grid. EMS optimizes electric grid operations through advanced metering, automation, and communication technologies. A critical component of EMS is power forecasting, which facilitates precise energy grid scheduling. This research paper introduces a deep learning hybrid model employing convolutional neural network–long short-term memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting. The proposed method integrates the variational mode decomposition (VMD) algorithm with the CNN-LSTM model to predict PV power output from a solar farm in Boussada, Algeria, spanning 1 January 2019, to 31 December 2020. The performance of the developed model is benchmarked against other deep learning models across various time horizons (15, 30, and 60 min): variational mode decomposition–convolutional neural network (VMD-CNN), variational mode decomposition–long short-term memory (VMD-LSTM), and convolutional neural network–long short-term memory (CNN-LSTM), which provide a comprehensive evaluation. Our findings demonstrate that the developed model outperforms other methods, offering promising results in solar power forecasting. This research contributes to the primary goal of enhancing EMS by providing accurate solar energy forecasts.

Assessment of Adaptive Coordinated Control of Overcurrent Relay Using Novel Multi-Trial Vector-Based Differential Evolution Algorithm

In this work, we use a unique multi-trial vector-based differentiation algorithm to examine the dynamically coordinated management of over current relays (OCRs) in substations. OCRs are significant for substation protection, and their coordinated control is essential for different sections experiencing varying current levels. Numerous optimization and AI techniques have been explored by researchers to enhance OCR coordinated control performance. This study introduces adaptive control for automatic coordination time adjustment based on forecasting, particularly focusing on integrating wind energy systems with and without reactors in substations. Accurate forecasting is vital for effective adaptive OCR control; hence, a novel multi-trial vector-based evolutionary (MTDE) algorithm is proposed for wind energy forecasting. MATLAB is used for the analysis, comparing the performance of artificial neural network (ANN)-based wind energy forecasting. The proposed method employs ANN for wind energy forecasting in a 110/22KV substation, enhancing OCR coordinated control based on wind energy integration. The ANN is trained using Mean Square Error (MSE), and its weights are tuned using MTDE for improved forecasting accuracy. Simulation results show that the MTDE-tuned ANN significantly enhances wind energy forecasting accuracy compared to conventional ANN, enabling superior adaptive coordinated OCR control, and thereby improving substation protection under fault conditions.

Stochastic economic model predictive control for renewable energy and ancillary services trading with storage

The provision of renewable-based ancillary services (AS) is paramount for the stable operation of power systems featuring high renewable penetration. The combined operation of storage with renewables enables aggregators to increase the reliability of their energy and frequency control AS offers. Existing dispatch strategies for the supply of both energy and AS are usually rule-based or involve tracking a technical reference signal, hence economically suboptimal for aggregators. This study proposes a comprehensive decision framework in which first a stochastic optimization derives bids on energy and AS markets, then stochastic economic model predictive control (SEMPC) optimizes the storage dispatch in order to maximize the profit and minimize the storage degradation, as a function of the predicted renewable production and the expected AS activation. The framework is applied to a real-world case study where storage combined with wind power participates in the energy market, the frequency containment market and the frequency restoration reserve market. The SEMPC-based approach increases market revenue by 15% compared to a standard reference-tracking MPC, and reduces storage degradation by 23%. The stochastic formulation lowers the sensitivity of the economic objectives to renewable energy forecast errors, compared to deterministic approaches.