This work optimized Power-to-X (PtX) operations in a Danish wind farm, balancing profitability and emissions through single and multi-objective optimization. The analysis was conducted through secondary data and an optimization model developed in Solver in order to evaluate operational scenarios. Among all the different evaluated solutions, Solution 3 emerged as optimal, reducing CO₂ emissions to 3146 kg while maintaining 90% of maximum profit (269,706 DKK). It allocated energy efficiently-745 MWh to the grid, 83 MWh to hydrogen (producing 1479 kg of green hydrogen), and 828 MWh to storage, with zero curtailment. Compared to profit-maximizing (Solution 1: 298,943 DKK, 12,357 kg CO₂) and emission-minimizing (Solution 5: 235,244 DKK, 2336 kg CO₂) alternatives, Solution 3 offers a sustainable compromise, supporting decarbonization goals without significant economic trade-offs. The findings highlight replicable strategies for PtX systems in renewable energy integration for future projects.

Accurate short-term electrical demand forecasting is critical for maintaining operational efficiency and energy security, especially in power-constrained systems like South Africa’s Eskom. Statistical methods like autoregressive integrated moving average (ARIMA) and exponential smoothing often fail to represent nonlinear and regime-dependent trends in power demand. This study presents a dynamic ensemble that combines deep neural networks (DNN) and long short-term memory (LSTM) architectures, which are both augmented by self-organising maps (SOM)-based clustering. The proposed method divides historical hourly load data from the Drakensberg generation plant into discrete temporal regimes using SOM, then trains the DNN and LSTM architectures within each regime, and dynamically combines their predictions. Shapley additive explanations (SHAP) are used to improve the interpretability of the impact of each cluster and time hierarchies, while mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) measures are used to assess prediction performance. The ensemble architecture delivers a higher accuracy, lowering MAPE to 2.20% while consistently outperforming individual benchmark architectures. The deployment on Amazon Web Services (AWS) proves the model’s scalability and appropriateness for real-time applications. Although performance degrades in irregular demand clusters, adaptive re-clustering may alleviate this constraint. Overall, the combined DNN-LSTM-SOM strategy is a reliable, interpretable, and scalable solution for short-term load forecasting, enabling better operational planning and grid dependability in developing energy systems.

Accurate short-term wind speed forecasting is crucial for efficient grid management and the integration of renewable energy sources. This study introduces a hybrid forecasting model that combines enhanced variational mode decomposition (EVMD), discrete wavelet transform (DWT), and an encoder-only Transformer. The dataset is first preprocessed to handle missing values and outliers and then subjected to a two-step decomposition process: EVMD extracts the first nine intrinsic mode functions (IMFs), and DWT is applied to the last IMF from EVMD to further decompose high-frequency components and capture finer-scale variations. The encoder-only Transformer is then used to process and forecast all subseries obtained from both EVMD and DWT, excluding the final IMF from EVMD, and their outputs are aggregated to produce the final prediction. The model was evaluated using data from the Thai Hoa wind farm in Binh Thuan, Vietnam, and compared with six benchmarks (variational mode decomposition (VMD)-Transformer, VMD-long short-term memory (LSTM), Transformer, LSTM, and random forest (RF)) using mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and normalized RMSE (NRMSE) for both single and multi-step forecasting. Results show that the proposed hybrid model consistently outperforms all benchmarks across all evaluation metrics, achieving an RMSE of 0.535 m/s for 1-step and 0.834 m/s for 24-step forecasting, representing up to 1.9% improvement over the best baseline and over 63% improvement compared to the worst-performing model, thereby demonstrating superior accuracy across both forecasting horizons. By leveraging multi-level decomposition and attention mechanisms, the model effectively captures complex wind speed patterns, enhancing forecasting performance and supporting better integration of wind energy into power systems.

The rapid integration of distributed energy resources and power-electronic-interfaced loads requires sophisticated planning techniques for modern radial distribution systems (RDS). In this paper, we propose a comprehensive multi-objective optimization framework for the coordinated placement and size of electric vehicle charging stations (EVCS), distributed generations (DGs), and soft open points (SOPs) in RDS. The developed objective function concurrently minimizes loss, improves the voltage profile, enhances the power factor, reduces harmonic distortion, and maximizes the utilization of substation capacity, subject to operational and technical constraints. Teaching Learning Based Optimization (TLBO) and Harris Hawks Optimization (HHO) were used and compared to evaluate solution resilience and convergence efficiency. The outcomes showed that system coordination consistently improved network efficiency, voltage stability, feeder balance, and power quality. Coordinated multi-device integration delivered better technical performance and ensured steady operation within regulatory voltage and harmonic limits. The robustness and dependability of the suggested optimization framework were validated through statistical analysis, which showed that HHO outperformed TLBO in terms of convergence behavior and solution quality. In addition to improving technical performance, the suggested framework supports the development of sustainable power systems by encouraging the integration of renewable energy sources, grid modernization, and the adoption of electrified transportation. The study supports SDGs 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure), 11 (Sustainable Cities and Communities), and 13 (Climate Action). Overall, the suggested coordinated optimization approach offers a scalable and long-lasting solution for active distribution networks prepared for the future.

Short-term wind power forecasting is essential for wind farm management and reliable grid operations. However, the accuracy of turbine-specific forecasting is often compromised by limitations in sequence modeling and misleading information from the surrounding wind turbines. To address these challenges, we proposed a novel DVTransformer (DTW-VARIMA-Transformer) framework for turbine specific forecast integrating Transformer neural networks and a predictive strategy. This approach integrates spatio-temporal dynamics using wind speed from the surrounding turbines to forecast the wind power of the target turbine in a wind farm. Wind turbines were selected using dynamic time warping (DTW) based metrics, which calculate the dynamic distance between wind speed time series, ensuring the reliability of spatial information. Additionally, we incorporated predicted wind speeds of surrounding turbines using vector autoregressive integrated moving average (VARIMA), alongside historical data, to better capture the influence of future wind conditions on the target turbine power output. The DVTransformer performance was assessed against parallel models under various metrics, including mean absolute error (MAE), mean squared error (MSE), and correlation coefficient (R), demonstrating significant improvements in a multi-step ahead forecasting task. The proposed DVTransformer was first validated on two representative turbines to assess turbine-specific forecasting performance. For 3-step-ahead forecasting, DVTransformer achieved an average MSE of 0.085 for turbine T01, corresponding to reductions of 34.92%, 24.77%, 2.20%, and 27.34% compared to the Fast Fourier Transformer (FFTransformer), Informer, Spatio-temporal Long Short-Term Memory (ST-LSTM), and Spatio-temporal Multi-Layer Perceptron (ST-MLP), respectively. Similarly, for turbine T05, the proposed model attained an average MSE of 0.090, achieving MSE reductions of 38.45%, 23.08%, 2.93%, and 25.96% against the same benchmark models, demonstrating robustness across turbines. To further evaluate computational efficiency and scalability, a training-budget sensitivity analysis was conducted by comparing a DVTransformer trained for a single epoch against a fully trained Transformer. The results showed that the DVTransformer achieved comparable prediction accuracy across all turbines while reducing significant computational time. Evident from incorporating reliable spatial information, employing predictive wind conditions and using Transformer to capture long and short-term dependencies within time sequences increased the overall performance of the proposed method.

This paper addresses the challenge of tracking an arbitrary power profile in a proton exchange membrane fuel cell (PEMFC) in the presence of measurement noise and disturbances. To this end, we used an extended Kalman filter (EKF) to estimate the internal states of the PEMFC in conjunction with an adaptive sliding mode controller (SMC) that has been shown to reduce chatter. The model used by the controller captures the internal dynamics and nonlinearly, and is accurate within 0.1% of the high-fidelity model. We developed the conditions necessary for the stability of the proposed controller based on the Lyapunov stability theorem. We also developed a systematic multi-objective optimization methodology of the controller hyperparameters to simultaneously minimizing tracking error, controller-chatter, and controller input using the non-dominated sorting genetic algorithm II (NSGA-II). The controller performance was demonstrated using multiple simulated experiments. Based on experimental results on desired signal data, we concluded that the proposed controller scheme can track desired power profiles within a 1% error.

Energy storage systems (ESSs) serve as flexible resources that significantly contribute to the integration of uncertain renewable energy sources. They can mitigate power fluctuations through fast charging and discharging in ramping services while providing frequency regulation reserves to maintain system stability. However, traditional methods often neglect the uncertainty of storage capacity and the coupling between ramping and frequency regulation constraints, leading to potential capacity violations and inefficient joint optimization. In this paper, we propose a coordinated ramping-frequency regulation optimization strategy considering ESS capacity uncertainty. A probabilistic confidence-based model was developed to characterize the stochastic nature of capacity, and unified ramping-frequency regulation constraints were formulated for ESSs and thermal units. Nonlinear terms were linearized to derive a tractable mixed-integer quadratic programming (MIQP) model. Case studies verified that the proposed approach effectively accounts for capacity uncertainty, enables shared utilization of ramping and regulation capabilities under unified constraints, and enhances the overall efficiency of system flexibility deployment.