Energy storage plays a significant role in improving the stability of distributed energy, improving power quality and peak regulation in the micro-grid system, which is of great significance to the sustainable development of energy. In grid-connected mode, energy storage is mainly used to reduce the operating costs of micro-grid. Real-time price arbitrage is an important source of energy storage revenue. It is feasible to design arbitrage strategies using Q-learning algorithm. Due to the overestimation of the Q learning algorithm, this paper proposes an arbitrage strategy method based on Double-Q learning. Compared with Q-learning algorithm, Double-Q learning can avoid overestimation and provide more stable and accurate arbitrage strategy for energy storage systems. Since the source of arbitrage in previous studies was limited to electricity prices alone, this paper considers joint arbitrage of electricity and carbon prices. The simulation results show that if adding fluctuate carbon prices to arbitrage sources, the arbitrage profits will increase by more than 110%.

In recent years, the application of machine learning methods has become increasingly common in atmospheric science, particularly in modeling and predicting processes that impact air quality. This study focuses on predicting hydrogen production from solid oxide electrolytic cells (SOECs), a technology with significant potential for reducing greenhouse gas emissions and improving air quality. We developed two models using artificial neural networks (ANNs) and support vector machine (SVM) to predict hydrogen production. The input variables are current, voltage, communication delay time, and real-time measured hydrogen production, while the output variable is hydrogen production at the next sampling time. Both models address the critical issue of production hysteresis. Using 50 h of SOEC system data, we evaluated the effectiveness of the ANN and SVM methods, incorporating hydrogen production time as an input variable. The results show that the ANN model is superior to the SVM model in terms of hydrogen production prediction performance. Specifically, the ANN model shows strong predictive performance at a communication delay time ε = 0.01–0.02 h, with RMSE = 2.59 × 10⁻², MAPE = 33.34 × 10⁻²%, MAE = 1.70 × 10⁻² Nm³/h, and R² = 99.76 × 10⁻². At delay time ε = 0.03 h, the ANN model yields RMSE = 2.74 × 10⁻² Nm³/h, MAPE = 34.43 × 10⁻²%, MAE = 1.73 × 10⁻² Nm³/h, and R² = 99.73 × 10⁻². Using the SVM model, the prediction error values at delay time ε = 0.01–0.02 h are RMSE = 2.70 × 10⁻² Nm³/h, MAPE = 44.01 × 10⁻²%, MAE = 2.24 × 10⁻² Nm³/h, and R² = 99.74 × 10⁻², while at delay time ε = 0.03 h they become RMSE = 2.67 × 10⁻² Nm³/h, MAPE = 43.44 × 10⁻²%, MAE = 2.11 × 10⁻² Nm³/h, and R² = 99.75 × 10⁻². With this precision, the ANN model for SOEC hydrogen production prediction has positive implications for air pollution control strategies and the development of cleaner energy technologies, contributing to overall improvements in air quality and the reduction of atmospheric pollutants.

Based on the basic nonlinear parameter system of the solid oxide electrolysis cell, the data-driven method was used for system identification. The basic model of the solid oxide electrolysis cell was accomplished in Simulink and experiments were performed under a diversified input/output operating environment. The experimental results of the solid oxide electrolysis cell basic parameter system generated 15 datasets. The system identification process involved the utilization of these datasets with the application of nonlinear autoregressive-exogenous models. Initially, data identification came from the Matlab mechanism model. Then, the nonlinear autoregressive-exogenous structures were estimated and selected exploratively through an individual operating condition. In terms of fitness, we conclude that the solid oxide electrolysis cell parameter system cannot be satisfied by a solitary autoregressive-exogenous model for all datasets. Nevertheless, the nonlinear autoregressive-exogenous model utilized S-type nonlinearities to fit a total of 2 validation datasets and 15 estimated datasets. The obtained results were compared with the basic parameter system of a solid oxide electrolysis cell, and the nonlinear autoregressive-exogenous projected output demonstrated an accuracy of over 93% across diverse operational circumstances—regardless of whether there was noise interference. This result has positive significance for the future use of the solid oxide electrolysis cell to achieve the dual carbon goal in China.

This paper studies the control problem of an uncertain nuclear refueling machine (RM) system in the presence of uncertainty, external disturbance, input backlash and asymmetric output constraint. Taking into account the effects of external disturbance and input backlash, the RM system with fuel rod is modeled as a copling partial differential equation-ordinary differential equation (PDE-ODE). Using the backstepping method, an adaptive neural network control scheme is designed to drive the RM and bridge to the desired positions and simultaneously reduce the vibration of fuel rod. A novel asymmetric tangent-type barrier Lyapunov function (Tan-BLF) is constructed to restrict the position tracking error into the given range. The formulation of backlash nonlinearity is transformed into the expected input and nonlinear error. Then the combination of input backlash error and boundary disturbance is defined as a disturbance-like item. Applying the robust control strategy and adaptive technique, two auxiliary input signals are proposed to offset the impact of the disturbance-like item. The adaptive neural network (NN) is employed to compensate for the system uncertainty. The practical stability of the RM system with the proposed control is demonstrated via the theoretical Lyapunov analysis. Simulation verifies the performance of the designed control scheme.