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Large-scale application of new energies is a major approach to achieving carbon peaking and carbon neutralization. Conducting research on the safety risk prevention and control strategy of new energy application helps in the establishment of a monitoring, risk prevention, and guarantee system for new energy application and promotes the high-quality development of related industries. Currently, new energy application in China involves various types of emerging infrastructures and faces challenges such as complex causes of safety accidents, incomplete understanding of safety risks, inadequate safety prevention and control technology systems, and lagging construction of a safety regulatory system. This study analyzes the current status and safety situation of new energy application in China and delves into the safety risk prevention and control issues faced by new energy application infrastructures such as charging/swapping stations, energy storage stations, hydrogen fuelling stations, and integrated energy stations. It also summarizes the current situation and experiences abroad regarding safety risk prevention and control. To establish a comprehensive system for preventing and controlling safety accidents before, during, and after they occur, and to enhance the safety risk management and regulatory capabilities of all stakeholders, this study constructs a strategic framework for safety risk prevention and control for new energy application. It clarifies the key tasks in terms of management and technology development and proposes medium- to long-term engineering and technological support projects. Furthermore, we propose development suggestions in terms of top-level design, governance standards, regulatory platforms, technological research and development, talent cultivation, and ecological co-construction, thus to promote the safety research and application of new energies.

In the context of rapid growth in renewable energy installations and increasingly severe consumption issues, this paper designs a 100% green electricity supplied zero-carbon integrated energy station. It aims to analyze its configuration focusing on the following three core features: zero carbon emissions, 100% green electricity supply, and a centralized–distributed system structure. It discusses equipment selection and provides models for configuring upstream green electricity resources, power generation, energy storage, transformer, and energy conversion. The study examines the synergy between lithium-ion battery storage and modular molten salt thermal storage, along with the virtual energy storage characteristics formed by thermal load inertia, supported by mathematical models. Based on the data from a green electricity system in an Eastern Chinese city and typical load profiles, the paper validates a specific configuration for a 100% green electricity supplied zero-carbon integrated energy station, confirming model accuracy and calculating the required scale of upstream green electricity resources. It proves that establishing an electro-thermal storage synergy system is crucial for addressing the significant fluctuations in renewable energy output. It also argues that leveraging thermal load inertia to create virtual storage can reduce the investment in energy storage system construction.

In recent years, countries around the world have been placing increasing emphasis on energy saving and emission reduction. As part of the energy system, integrated energy stations play an important role in the low-carbon transition of the energy sector. However, most of the existing studies on carbon emissions from integrated energy stations focus on the operation phase or a single module within it and lack analysis and prediction of carbon emissions from all phases of the integrated energy station. In this paper, based on the perspective of life cycle assessment, system dynamics is used to establish a life cycle carbon emission model for integrated energy stations from four phases: production & materialization, construction, operation & maintenance, and disposal & recycling. This paper takes wind power integrated energy stations as an example, simulates the characteristics of the life cycle carbon emissions of integrated energy stations, and identifies the key factors affecting the carbon emissions. The simulation results show that the carbon emissions of the integrated energy station are mainly concentrated in the production and operation of the energy station, and the increase in the service life of the energy station will not have a significant impact on the total carbon emissions. This paper provides the theoretical guidance and decision-making basis for the formulation of low-carbon emission reduction measures for integrated energy stations.

With the modernization and intelligent development of agriculture, the energy demand in rural areas continues to increase, which leads to an increased operational burden on the existing rural distribution network. Integrated energy stations (IESs), in rural areas, use renewable energy sources such as biogas, wind power, and photovoltaic as energy inputs, which can fully improve energy efficiency and help reduce the operating load and peak valley difference of rural distribution networks. In this paper, a multistage planning model is proposed for rural distribution networks with IESs based on the robust optimization method. Firstly, a rural distribution network operation framework with IESs is proposed, and a mathematical model of rural IESs is built based on the energy hub (EH). Then, the multistage robust planning model of rural distribution net-works with IESs is developed and typical scenarios of stochastic source and load are generated based on improved k-means. An iterative solution method for a two-stage robust optimization method is proposed based on the nested column constraint generation (NC&CG) algorithm. Finally, the effectiveness of the presented model and solution approach is assessed through case studies on a modified IEEE 33-node distribution network and a real 152-node distribution network.

Regarding the need to decrease carbon emissions, the electric vehicle (EV) industry is growing rapidly in China; the charging needs of EVs require the number of EV charging stations to grow significantly. Therefore, many refueling stations have been modified to integrated energy stations, which contain photovoltaic systems. The key issue in current times is to figure out how to operate these integrated energy stations in an efficient way. Therefore, an effective scheduling model is needed to operate an integrated energy station. Photovoltaic (PV) and energy storage systems are integrated into EV charging stations to transform them into integrated energy stations (PE-IES). Considering the demand for EV charging during different time periods, the PV output, the loss rate of energy storage systems, the load status of regional grids, and the dynamic electricity prices, a multi-objective optimization scheduling model was established for operating integrated energy stations that are connected to a regional grid. The model aims to simultaneously maximize the daily profits of the PE-IES, minimize the daily loss rate of the energy storage system, and minimize the peak-to-valley difference of the load in the regional grid. To validate the effectiveness of the model, simulation experiments under three different scenarios for the PE-IES were conducted in this research. Each object weight was determined using the entropy weight method, and the optimal solution was selected from the Pareto solution set using an order-preference technique according to the similarity to an ideal solution (TOPSIS). The results demonstrate that, compared to traditional charging stations, the daily revenue of the PE-IES stations increases by 26.61%, and the peak-to-valley difference of the power load in the regional grid decreases by 30.54%, respectively. The effectiveness of PE-IES is therefore demonstrated. Furthermore, to solve the complex optimization problem for PE-IES, a novel multi-objective optimization algorithm based on multiple update strategies (MOMUS) was proposed in this paper. To evaluate the performance of the MOMUS, a detailed comparison with seven other algorithms was demonstrated. These results indicate that our algorithm exhibits an outstanding performance in solving this optimization problem, and that it is capable of generating high-quality optimal solutions.