The global horizontal irradiance (GHI) is the most important metric for evaluating solar resources. The accurate prediction of GHI is of great significance for effectively assessing solar energy resources and selecting photovoltaic power stations. Considering the time series nature of the GHI and monitoring sites dispersed over different latitudes, longitudes, and altitudes, this study proposes a model combining deep neural networks and deep convolutional neural networks for the multi-step prediction of GHI. The model utilizes parallel temporal convolutional networks and gate recurrent unit attention for the prediction, and the final prediction result is obtained by multilayer perceptron. The results show that, compared to the second-ranked algorithm, the proposed model improves the evaluation metrics of mean absolute error, mean absolute percentage error, and root mean square error by 24.4%, 33.33%, and 24.3%, respectively.

With the increasing integration of renewable energy sources into the power grid and the continuous expansion of grid infrastructure, real-time preventive control becomes crucial. This article proposes a real-time prediction and correction method based on the extreme gradient boosting (XGBoost) algorithm. The XGBoost algorithm is utilized to evaluate the real-time stability of grid static voltage, with the voltage stability L-index as the prediction target. A correction model is established with the objective of minimizing correction costs while considering the operational constraints of the grid. When the L-index exceeds the warning value, the XGBoost algorithm can obtain the importance of each feature of the system and calculate the sensitivity approximation of highly important characteristics. The model corrects these characteristics to maintain the system’s operation within a reasonably secure range. The methodology is demonstrated using the IEEE-14 and IEEE-118 systems. The results show that the XGBoost algorithm has higher prediction accuracy and computational efficiency in assessing the static voltage stability of the power grid. It is also shown that the proposed approach has the potential to greatly improve the operational dependability of the power grid.

The configuration of energy storage systems in offshore wind farms can effectively suppress fluctuations in wind power and enhance the stability of the power grid. However, the economic balance between the cost of energy storage systems and the fluctuations in wind power remains an urgent challenge to be addressed, especially against the backdrop of widespread spot trading in the electricity market. How to achieve effective wind power stabilization at the lowest cost has become a key issue. This paper proposes three different energy storage configuration strategies and adopts the non-dominated sorting genetic algorithm (NSGA-II) to conduct multi-objective optimization of the system. NSGA-II performed stably in dual-objective scenarios and effectively balanced the relationship between the investment cost of the energy storage system and power fluctuations through the explicit elite strategy. Furthermore, this study analyzed the correlation between the rated power and rated capacity of the energy storage system and the battery life, and corrected the battery life of the Pareto frontier solution obtained by NSGA-II. The research results show that when only considering the investment cost of the energy storage, the optimal configuration was a rated power of 4 MW and a rated capacity of 28 MWh, which could better balance the investment economy and power fluctuation. When further considering the participation of energy storage systems in the electricity spot market, the economic efficiency of the energy storage systems could be significantly improved through the fixed-period electricity price arbitrage method. At this point, the optimal configuration was a rated power of 8 MW and a rated capacity of 37 MWh. The corresponding project investment cost was CNY 242.77 million, and the annual fluctuation rate of the wind power output decreased to 17.84%.

To enhance the prediction accuracy of offshore wind speed, this study employs an interpolation algorithm to improve spatial resolution based on the ERA5 reanalysis dataset. The objective is to identify the optimal interpolation method and apply it to wind energy assessments in the South China Sea. This paper compares the interpolation effects and accuracy of Linear, Cubic, and Bicubic interpolation methods on wind speed data, with the optimal method subsequently applied to evaluate wind resources in the South China Sea for 2023. The findings indicate that, while different interpolation methods minimally affect the correlation of wind speed data, there are notable differences in their impact on overall accuracy. The Cubic interpolation method proved to be the most effective, tripling spatial resolution and reducing wind speed errors in ERA5 data by 26%. Using this method, wind resource assessments were conducted in selected areas of the South China Sea. Results reveal that the annual available operational hours for wind turbines in most parts of the region range from 2000 to 4000 h, with fluctuations in turbine output power increasing alongside available operational hours.

To achieve carbon neutrality goals, offshore wind power combined with a hydrogen energy storage system (OWP-HESS) is critical for integrating intermittent renewables. This study applied a “cradle-to-grave” process-based life cycle assessment (PLCA) to evaluate a 77.4 MW offshore wind farm coupled with a 45.0 MW electrolysis cell system, covering manufacture, transportation, construction, operation and maintenance, and decommissioning phases. It focuses on two hydrogen production routes, alkaline electrolysis (AEL) and proton exchange membrane (PEM), and covers 12 environmental indicators. Moreover, considering optimal economic efficiency, to adapt to the characteristic of “electricity–hydrogen cogeneration”, as well as to facilitate reflecting the efficiency differences between the two electrolysis technologies, the functional unit is defined as “0.4 kWh green electricity + corresponding green hydrogen”. Results show that offshore wind’s environmental impacts mainly come from manufacture (79.00%, driven by concrete/steel), while hydrogen storage impacts focus on operation/maintenance (66.03% for AEL and 96.61% for PEM, driven by electricity). PEM’s green hydrogen global warming potential (GWP) (0.96 kg CO₂-eq/kg) is much lower than AEL’s (1.81 kg CO₂-eq/kg) and China’s fossil-based hydrogen (≈40 kg CO₂-eq/kg). With an initial system lifespan of 25 years, a wind farm capacity factor of 41.30%, and a hydrogen production efficiency of 68.72% (AEL) and 69.89% (PEM), extending system lifespan by 5 years, raising wind farm capacity factor to 43%, and enhancing hydrogen production efficiency to 71% reduce emissions by 16.67%, 4.00%, and 2.16%, respectively. This study clarifies OWP-HESS’s environmental characteristics, confirms PEM’s low-carbon advantage, and provides support for its sustainable development.