• Energy Research

AbstractThe effective management of microgrids is important towards transition to sustainable energy paradigm. By optimizing the utilization of different energy sources, such as solar photovoltaic panels and energy storages, it improves the reliability of the grid and develops resiliency in dealing with of challenges of unexpected variations in demand. To this end, the proposed paper presents DeepEMS, a system developed to manage the energy of microgrids through the incorporation of diverse intelligent algorithms. DeepEMS provides dynamic microgrid management through the utilization of Bidirectional Long Short‐Term Memory (BiLSTM) networks, Sliding Linear Programming (SLP), and Random Forest (RF). By implementing these methodologies, DeepEMS can optimize energy consumption throughout the microgrid by dynamically identifying and coordinating the needs of various energy sources. DeepEMS achieves precise multimodal optimization and facilitates integration of storage systems, grid interactions, and renewable energy sources (RES), as demonstrated by simulations and data analytics. DeepEMS presented performance in control, resource allocation, management, and grid utilization. Furthermore, in a comparative analysis with alternative intelligent models including XGBoost, Light GBM, RF, and Decision Trees, DeepEMS consistently demonstrated higher performance as measured by several key performance indicators (KPIs).

AbstractThis paper studies the effect of the number of switching (NOS) per day of capacitor banks on loss reduction in radial distribution systems. To this aim, the daytime (more precisely, 24 h) is divided into different numbers of time segments (equal to the same NOS) for capacitors’ size switching. The resulting non‐linear programming with discontinuous derivatives (called DNLP) model is solved subject to related constraints. The results reveal the impact of hourly switching of capacitor banks on further loss reduction (namely 118.4435, 83.7856, and 101.738 MWh for three IEEE systems) and higher net savings (i.e. k$5.6067, k$4.2772, and k$5.3542 for the same systems) of radial distribution systems compared to daily switching. Then, the hyper‐tuned Random Forest model is trained based on the IEEE 69‐bus network, fine‐tuned by the IEEE 10‐bus network, and fitted by the IEEE 33‐bus network to have an intelligent multi‐classification task with the highest accuracy. Numerical simulation, in both classic and intelligent parts, is presented to demonstrate the performance of DeepOptaCap. For the final step, DeepOptaCast is compared to other intelligent models of Light Gradient Boosting Method (LGBM), Decision Tree, and XGBoost, regarding KPIs of mean absolute percentage error, root mean squared percentage error, mean absolute error, root mean squared error, and coefficient of determination to demonstrate the model's superiority.

In numerous industrial contexts, precise analysis and forecasting of electrical signals within three-phase systems are indispensable. As a result, this work presents DeepPhase, a hybrid framework that combines Long Short-Term Memory (LSTM) neural networks with gradient-boosted regression (GBR) to predict the current, voltage, and power of electrical signals. The performance of the model is evaluated in comparison to benchmark models, namely Bidirectional LSTM (BiLSTM), K-Nearest Neighbors (KNN), and LSTM, which utilize essential Key Performance Indicators (KPIs). As demonstrated by its highest Coefficient of Determination (R2) of 0.999, Mean Absolute Error (MAE) of 6.94 × 10−5, Mean Absolute Percentage Error (MAPE) of 0.07 %, and Root Mean Square Error (RMSE) of 0.000156, DeepPhase consistently exhibits predictive precision. For Three-Phase Current, MAE is 2.13 × 10−3, MAPE is 0.01 %, RMSE is 0.062432, and R2 is 0.960596; and for Three-Phase Voltage, MAE is 9.52E-03, MAPE is 0.03 %, RMSE is 0.014, and R2 is 0.999. The results of this study highlight the effectiveness of DeepPhase in analyzing the dynamics of complex Three-Phase electrical signals. This has significant implications for improving decision-making and control strategies in complex electrical systems.

The transition to sustainable electricity generation depends heavily on renewable energy sources, particularly wind power. Making precise forecasts, which calls for clever predictive controllers, is a crucial aspect of maximizing the efficiency of wind turbines. This study presents DeepVELOX, a new methodology. With this method, sophisticated machine learning methods are smoothly incorporated into wind power systems. The Increased Velocity (IN-VELOX) wind turbine framework combines the Gradient Boosting Regressor (GBR) with the Grey Wolf Optimization (GWO) algorithm. Predictive capabilities are entering a new age thanks to this integration. This research presents DeepVELOX, its structure, and results. In particular, this study presents the considerable performance of DeepVELOX. With a MAPE of 0.0002 and an RMSPE of 0.0974, it gets outstanding Key Performance Indicator (KPI) results. The criteria of Accuracy, F1-Score, R2-Score, Precision, and Recall, with a value of 1, further emphasize its performance. The result of this process is an MSE of 0.0352. The significant reduction in forecast disparities is made possible by this system’s remarkable accuracy. Along with improving accuracy, the integration of machine learning algorithms, including GBR, the GWO algorithm, and wind turbine operations, offer a dynamic framework for maximizing power and energy capture.

Integrating renewable energy sources (RESs), such as offshore wind turbines (OWTs), into the power grid demands advanced control strategies to enhance efficiency and stability. Consequently, a Deep Fractional-order Wind turbine eXpert control system (DeepFWX) model is developed, representing a hybrid proportional/integral (PI) fractional-order (FO) model predictive random forest alternating current (AC) bus voltage controller designed explicitly for OWTs. DeepFWX aims to address the challenges associated with offshore wind energy systems, focusing on achieving the smooth tracking and state estimation of the AC bus voltage. Extensive comparative analyses were performed against other state-of-the-art intelligent models to assess the effectiveness of DeepFWX. Key performance indicators (KPIs) such as MAE, MAPE, RMSE, RMSPE, and R2 were considered. Superior performance across all the evaluated metrics was demonstrated by DeepFWX, as it achieved MAE of [15.03, 0.58], MAPE of [0.09, 0.14], RMSE of [70.39, 5.64], RMSPE of [0.34, 0.85], as well as the R2 of [0.99, 0.99] for the systems states [X1, X2]. The proposed hybrid approach anticipates the capabilities of FO modeling, predictive control, and random forest intelligent algorithms to achieve the precise control of AC bus voltage, thereby enhancing the overall stability and performance of OWTs in the evolving sector of renewable energy integration.

Intelligent predictive models are fundamental in peer-to-peer (P2P) energy trading as they properly estimate supply and demand variations and optimize energy distribution, and the other featured values, for participants in decentralized energy marketplaces. Consequently, DeepResTrade is a research work that presents an advanced model for predicting prices in a given traditional energy market. This model includes numerous fundamental components, including the concept of P2P trading systems, long-term and short-term memory (LSTM) networks, decision trees (DT), and Blockchain. DeepResTrade utilized a dataset with 70,084 data points, which included maximum/minimum capacities, as well as renewable generation, and price utilized of the communities. The developed model obtains a significant predictive performance of 0.000636% Mean Absolute Percentage Error (MAPE) and 0.000975% Root Mean Square Percentage Error (RMSPE). DeepResTrade’s performance is demonstrated by its RMSE of 0.016079 and MAE of 0.009125, indicating its capacity to reduce the difference between anticipated and actual prices. The model performs admirably in describing actual price variations in, as shown by a considerable R2 score of 0.999998. Furthermore, F1/recall scores of [1, 1, 1] with a precision of 1, all imply its accuracy.

The adoption of solar systems has witnessed a remarkable growth rate in recent years, driven by increasing awareness of renewable energy and declining costs of solar technology. Solar systems offer several advantages, including abundant energy source, reduced carbon emissions, and potential cost savings. However, they also face challenges such as intermittency, limited energy storage capacity, and grid integration issues. By incorporating hydrogen in smart grids, these drawbacks can be addressed as hydrogen can serve as a means of energy storage, allowing excess solar energy to be stored as hydrogen and utilized during periods of low solar generation. Hydrogen-incorporated smart grids thus provide a complementary solution to enhance the reliability, stability, and scalability of solar systems, facilitating their integration into the broader energy landscape. Consequently, this chapter aims to provide a comprehensive review of green hydrogen-integrated sector-coupled smart grids and presents prospects for future advancements. The background and significance of hydrogen integration within smart grid systems are established. The fundamentals of hydrogen integration, including its role as an energy carrier and its integration within smart grid systems, are explored. The concept of sector coupling in smart grids is examined, emphasizing the interconnection of different energy sectors and the importance of achieving energy system integration. Existing green hydrogen-incorporated smart grid projects are reviewed, and experiences gathered from successful implementations are analyzed. Technological advancements, such as emerging green hydrogen production and storage technologies, are discussed along with smart grid control and management systems for efficient green hydrogen utilization. Economic and environmental considerations are evaluated, encompassing cost analysis, evaluation of environmental impacts, and identification of economic incentives. Future prospects and research directions are explored, aiming to identify key challenges, address gaps, and highlight areas for further investigation. Overall, through this comprehensive review and exploration of future prospects, a deeper understanding of hydrogen-integrated sector-coupled smart grids and their potential for advancing sustainable energy systems can be achieved.

Artificial intelligence (AI) and machine learning (ML) can assist in the effective development of the power system by improving reliability and resilience. The rapid advancement of AI and ML is fundamentally transforming energy management systems (EMSs) across diverse industries, including areas such as prediction, fault detection, electricity markets, buildings, and electric vehicles (EVs). Consequently, to form a complete resource for cognitive energy management techniques, this review paper integrates findings from more than 200 scientific papers (45 reviews and more than 155 research studies) addressing the utilization of AI and ML in EMSs and its influence on the energy sector. The paper additionally investigates the essential features of smart grids, big data, and their integration with EMS, emphasizing their capacity to improve efficiency and reliability. Despite these advances, there are still additional challenges that remain, such as concerns regarding the privacy of data, challenges with integrating different systems, and issues related to scalability. The paper finishes by analyzing the problems and providing future perspectives on the ongoing development and use of AI in EMS.