Decarbonization of the electricity sector is crucial to mitigate the impacts of climate change and global warming over the coming decades.The key challenges for achieving this goal are carbon emission trading and electricity sector regulation, which are also the major components of the carbon and electricity markets, respectively.In this paper, a joint electricity and carbon market model is proposed to investigate the relationships between electricity price, carbon price, and electricity generation capacity, thereby identifying pathways toward a renewable energy transition under the transactional energy interconnection framework.The proposed model is a dynamically iterative optimization model consisting of upperlevel and lower-level models.The upper-level model optimizes power generation and obtains the electricity price, which drives the lower-level model to update the carbon price and electricity generation capacity.The proposed model is verified using the Northeast Asia power grid.The results show that increasing carbon price will result in increased electricity price, along with further increases in renewable energy generation capacity in the following period.This increase in renewable energy generation will reduce reliance on carbon-emitting energy sources, and hence the carbon price will decline.Moreover, the interconnection among zones in the Northeast Asia power grid will enable reasonable allocation of zonal power generation.Carbon capture and storage (CCS) will be an effective technology to reduce the carbon emissions and further realize the emission reduction targets in 2030-2050.It eases the stress of realizing the energy transition because of the less urgency to install additional renewable energy capacity.

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The European power grid is one of the largest regional interconnected power grids in the world.It realizes a multinational grid operation, which is rare.The total installed capacity of the European power grid is the largest throughout the world.In addition, the integration and utilization of renewable energy in this grid is a great benchmark for other countries and can help promote energy transformation and achieve a high proportion of renewable energy consumption.Based on the analysis of the existing status of the European interconnected power grid and the development history of this power grid, this paper summarizes four key development stages of the European power grid.In addition, the characteristics of each stage and the development prospect of the European power grid are analyzed.On this basis, this paper gives suggestions for the development and construction of China’s energy internet; this can provide valuable reference for further studies on China’s energy internet.

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Power transmission across the sea is an important part of global energy interconnection (GEI).To support the construction of GEI and to serve the needs of future clean energy trans-sea transportation and offshore wind power development, this study a) analyzes the requirements of the GEI backbone network pertaining to direct current (DC) submarine cable technology, and b) defines the key technical and economic indices of ultrahigh-voltage direct current (UHVDC) submarine cable based on theoretical computations.The research is based on the thermoelectric coupling model and the finite element method.It is shown that the dielectric strength of the insulating materials of the ±800 kV～±1100 kV/ 4000 MW～12000 MW UHVDC submarine cable (extrusion insulation) should be not less than 43～65 kV/mm, while the heat resistance is not less than 110 °C.As the cost of submarine cable is 5～10 times higher than that of the overhead line, the project investment need to be decreased to a level within the economical carrying capacity to guarantee extensive applicability of the HVDC submarine cable technology.

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With increasing global shortage of fresh water resources, many countries are prioritizing desalination as a means of utilizing abundantly available seawater resources.Integrated energy efficiency evaluation is a scientific method for the quantitative analysis of energy efficiency based on multiple indicators and is very useful for investment, construction, and scientific decision-making for desalination projects.In this paper, the energy efficiency evaluation of the micro energy network (MEN) of desalination for multi-source and multi-load is studied, and the basic idea of comprehensive energy efficiency evaluation is analyzed.The process includes the use of a MEN model to establish an integrated energy efficiency evaluation index system, taking into consideration energy, equipment, economic, environmental, and social factors.A combined evaluation method considering subjective and objective comprehensive weights for multi-source multi-load desalination MENs is proposed to evaluate the energy efficiency of desalination and from multiple perspectives.

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Large-scale integration of wind power into a power system introduces uncertainties to its operation and planning, making the power system operation scenario highly diversified and variable.In conventional power system planning, some key operation modes and most critical scenarios are typically analyzed to identify the weak and high-risk points in grid operation.While these scenarios may not follow traditional empirical patterns due to the introduction of large-scale wind power.In this paper, we propose a weighted clustering method to quickly identify a system’s extreme operation scenarios by considering the temporal variations and correlations between wind power and load to evaluate the stability and security for system planning.Specifically, based on an annual time-series data of wind power and load, a combined weighted clustering method is used to pick the typical scenarios of power grid operation, and the edge operation points far from the clustering center are extracted as the extreme scenarios.The contribution of fluctuations and capacities of different wind farms and loads to extreme scenarios are considered in the clustering process, to further improve the efficiency and rationality of the extreme-scenario extraction.A set of case studies was used to verify the performance of the method, providing an intuitive understanding of the extreme scenario variety under wind power integration.

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With the continuous development of power electronic devices, intelligent control systems, and other technologies, the voltage level and transmission capacity of voltage source converter (VSC)-high-voltage direct current (HVDC) technology will continue to increase, while the system losses and costs will gradually decrease.Therefore, it can be foreseen that VSC-HVDC transmission technology will be more widely applied in future large-scale renewable energy development projects.Adopting VSC-HVDC transmission technology can be used to overcome issues encountered by large-scale renewable energy transmission and integration projects, such as a weak local power grid, lack of support for synchronous power supply, and insufficient accommodation capacity.However, this solution also faces many technical challenges because of the differences between renewable energy and traditional synchronous power generation systems.Based on actual engineering practices that are used worldwide, this article analyzes the technical challenges encountered by integrating large-scale renewable energy systems that adopt the use of VSC-HVDC technology, while aiming to provide support for future research and engineering projects related to VSC-HVDC-based large-scale renewable energy integration projects.

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The modelling of the distribution transformer winding is the starting point and serves as important basis for the transformer characteristics analysis and the lightning pulse response prediction.A distributed parameters model can depict the winding characteristics accurately, but it requires complex calculations.Lumped parameter model requires less calculations, but its applicable frequency range is not wide.This paper studies the amplitude-frequency characteristics of the lightning wave, compares the transformer modelling methods and finally proposes a modified lumped parameter model, based on the above comparison.The proposed model minimizes the errors provoked by the lumped parameter approximation, and the hyperbolic functions of the distributed parameter model.By this modification it becomes possible to accurately describe the winding characteristics and rapidly obtain the node voltage response.The proposed model can provide theoretical and experimental support to lightning protection of the distribution transformer.

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Based on the energy storage cloud platform architecture, this study considers the extensive configuration of energy storage devices and the future large-scale application of electric vehicles at the customer side to build a new mode of smart power consumption with a flexible interaction, smooth the peak/valley difference of the load side power, and improve energy efficiency.A plug and play device for customer-side energy storage and an internet-based energy storage cloud platform are developed herein to build a new intelligent power consumption mode with a flexible interaction suitable for ordinary customers.Based on the load perception of the power grid, this study aims to investigate the operating state and service life of distributed energy storage devices.By selecting an integrated optimal control scheme, this study designs a kind of energy optimization and deployment strategy for stratified partition to reduce the operating cost of the energy storage device on the client side.The effectiveness of the system and the control strategy is verified through the Suzhou client-side distributed energy storage demonstration project.

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Conventional analysis methods cannot fully meet the business needs of power grids.At present, several artificial intelligence (AI) projects in a single business field are competing with each other, and the interfaces between the systems lack unified specifications.Therefore, it is imperative to establish a comprehensive service platform.In this paper, an AI platform framework for power fields is proposed; it adopts the deep learning technology to support natural language processing and computer vision services.On one hand, it can provide an algorithm, a model, and service support for powerenterprise applications, and on the other hand, it can provide a large number of heterogeneous data processing, algorithm libraries, intelligent services, model managements, typical application scenarios, and other services for different levels of business personnel.The establishment of the platform framework could break data barrier, improve portability of technology, avoid the investment waste caused by repeated constructions, and lay the foundation for the construction of “platform + application + service” ecological chain.

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With the application of artificial intelligence technology in the power industry, the knowledge graph is expected to play a key role in power grid dispatch processes, intelligent maintenance, and customer service response provision.Knowledge graphs are usually constructed based on entity recognition.Specifically, based on the mining of entity attributes and relationships, domain knowledge graphs can be constructed through knowledge fusion.In this work, the entities and characteristics of power entity recognition are analyzed, the mechanism of entity recognition is clarified, and entity recognition techniques are analyzed in the context of the power domain.Power entity recognition based on the conditional random fields (CRF) and bidirectional long short-term memory (BLSTM) models is investigated, and the two methods are comparatively analyzed.The results indicated that the CRF model, with an accuracy of 83%, can better identify the power entities compared to the BLSTM.The CRF approach can thus be applied to the entity extraction for knowledge graph construction in the power field.

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