In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean, renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind power has been developing in the direction of digitization and intelligence. It is of great significance to carry out research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit for the reduction of the operation and maintenance costs, the improvement of the power generation efficiency, improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms. This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of “offshore wind power engineering and biological and environment”, the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored, especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of power equipment, and digital platforms.

Transformers are playing an increasingly significant part in energy conversion, transmission, and distribution, which link various resources, including conventional, renewable, and sustainable energy, from generation to consumption. Power transformers and their components are vulnerable to various operational factors during their entire life cycle, which may lead to catastrophic failures, irreversible revenue losses, and power outages. Hence, it is crucial to investigate transformer condition assessment to grasp the operating state accurately to reduce the failures and operating costs and enhance the reliability performance. In this context, comprehensive data mining and analysis based on intelligent algorithms are of great significance for promoting the comprehensiveness, efficiency, and accuracy of condition assessment. In this article, in an attempt to provide and reveal the current status and evolution of intelligent algorithms for transformer condition assessment and provide a better understanding of research perspectives, a unified framework of intelligent algorithms for transformer condition assessment and a survey of new findings in this rapidly-advancing field are presented. First, the failure statistics analysis is outlined, and the developing mechanism of the transformer internal latent fault is investigated. Then, in combination with intelligent demands of the tasks in each stage of transformer condition assessment under big data, we analyze the data source in-depth and redefine the concept and architecture of transformer condition assessment. Furthermore, the typical methods widely used in transformer condition assessment are mainly divided into rule, information fusion, and artificial intelligence. The new findings for intelligent algorithms are also elaborated, including differentiated evaluation, uncertainty methods, and big data analysis. Finally, future research directions are discussed.

In order to realize the economic dispatch and safety stability of offshore wind farms, and to address the problems of strong randomness and strong time correlation in offshore wind power forecasting, this paper proposes a combined model of principal component analysis (PCA), sparrow algorithm (SSA), variational modal decomposition (VMD), and bidirectional long- and short-term memory neural network (BiLSTM). Firstly, the multivariate time series data were screened using the principal component analysis algorithm (PCA) to reduce the data dimensionality. Secondly, the variable modal decomposition (VMD) optimized by the SSA algorithm was applied to adaptively decompose the wind power time series data into a collection of different frequency components to eliminate the noise signals in the original data; on this basis, the hyperparameters of the BiLSTM model were optimized by integrating SSA algorithm, and the final power prediction value was obtained. Ultimately, the verification was conducted through simulation experiments; the results show that the model proposed in this paper effectively improves the prediction accuracy and verifies the effectiveness of the prediction model.

In light of the uncertainties associated with renewable energy sources like wind and photovoltaics, this study aims to progressively increase their proportion in the energy mix. This is achieved by integrating carbon capture devices into traditional thermal power plants and enhancing demand-side management measures, thereby advancing low-carbon objectives in the energy and electricity sectors. Initially, the research proposes utilizing the K-means clustering algorithm to consolidate and forecast the fluctuating outputs of renewable energies such as wind and photovoltaics. Further, it entails a comprehensive analysis of low-carbon resources on both the supply and demand sides of the electricity system. This includes installing carbon storage and power-to-gas facilities in carbon capture plants to create a versatile operating model that can be synchronized with wind power systems. Additionally, the limitations of carbon capture plants are addressed by mobilizing demand-side response resources and enhancing the system’s low-carbon performance through the coordinated optimization of supply and demand resources. Ultimately, this study develops an integrated energy system model for low-carbon optimal operation, aimed at minimizing equipment investment, carbon emission costs, and operational and maintenance expenses. This model focuses on optimizing the load and supply distribution plans of the electrical system and addressing issues of load shedding and the curtailment of wind and solar power. Validation through three typical scenarios demonstrates that the proposed scheduling method effectively utilizes adjustable resources in the power system to achieve the goal of low-carbon economic dispatch.

Against the background of the “30 $\times$ 60” target, low-carbon policies and technologies have become the new starting point and destination of energy conservation and emission reduction in energy systems. Power-to-Gas (P2G), as a new energy conversion mode, provides a new way of consuming energy and reducing carbon emissions. An optimal dispatching model of a park-level integrated energy system considering flexible load and power-to-gas (P2G) participation in the carbon trading market is proposed. Firstly, a comprehensive demand response model for electricity and heat is established according to the system’s flexible load characteristics, adding power-to gas equipment and a combined heat and power (CHP) unit to the system. Secondly, against the background of the carbon trading mechanism and considering the incentive effect of P2G on the carbon trading mechanism, a systematic comprehensive carbon trading cost model is established. Finally, the operation objectives of the low-carbon economy, with minimum energy purchase costs, carbon emission costs, and operating costs, are constructed using a CPLEX commercial solver. The results verify the importance of considering the operational cost of P2G and the feasibility of considering both operating economy and wind power accommodation ability together in integrated energy systems.

With the continuous increase in the proportion of wind power construction and grid connection, the deployment scale of state sensors in wind-power-monitoring systems has grown rapidly with an aim on the problems that the communication authentication process between the wind turbine status sensor and the monitoring gateway is complex and the adaptability of the massive sensors is insufficient. A security access control method for a wind-power-monitoring system based on agile authentication mechanism is proposed in this paper. First, a lightweight key generation algorithm based on one-way hash function is designed. The algorithm realizes fixed-length compression and encryption of measurement data of any length. Under the condition of ensuring security, the calculation and communication cost in the later stage of authentication are effectively reduced. Second, to reduce the redundant process of wind turbine status sensor authentication, an agile authentication model of wind turbine status sensor based on a lightweight key is constructed. Constrained by the reverse order extraction of key information in the lightweight keychain, the model can realize lightweight communication between massive wind turbine status sensors and regional gateways. Finally, the proposed method is compared and verified using the wind turbine detection data set provided by the National New Energy Laboratory of the United States. The experimental results show that this method can effectively reduce the certification cost of a wind-power-monitoring system. Additionally, it can improve the efficiency of status sensor identity authentication and realize the agility and efficiency of the authentication process.