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The regional source–grid–load friendly interaction and precision regulation of adjustable resources with high sensitivity provide important support for the development of a new power system, improving its regulation ability and efficiency and promoting the safe, efficient, and reliable operation. Therefore, a precision regulation strategy for a regional source–grid–load friendly interaction based on a double-layer sensitivity model is proposed in this paper. Firstly, from the two perspectives of power grid-application requirements and power node-adjustable resources, the overall implementation process of the double-layer sensitivity analysis method is described, and the layered logic architecture of the precision regulation platform for regional source–grid–load friendly interaction is constructed in order to realize and apply the proposed double-layer sensitivity model and precision-regulation strategy. Secondly, the first-layer power transmission line-sensitivity analysis model is proposed for the power grid side to obtain the power transmission line-sensitivity matrix. A fine-grained second-layer adjustable resources sensitivity-analysis model is further established for the diversified adjustable resource aggregated under the power node to complete the quantitative ranking of adjustable resource-regulation sensitivity. Then, the application process of double-layer sensitivity analysis results is designed in detail, and the regulation sequence and regulation quantity of source and load are defined. Finally, taking a power system with seven lines, five power nodes, two power sources, and seven types of adjustable resources as an example, the effectiveness and rationality of the proposed model and strategy are verified. The results indicate that the proposed precise regulation strategy can accurately select high-sensitivity power nodes and effectively sort high-sensitivity adjustable resources when solving the multiple scenarios' application needs of power grids. [ABSTRACT FROM AUTHOR]

The new power system provides important support for the large-scale integration and efficient consumption of new energy into the power grid. New energy has entered an era of competition with traditional energy, and entire power systems need to be renovated and upgraded to enhance related auxiliary investments. From a systemic perspective, considering the low effective capacity of wind and solar new-energy power generation, it is necessary to increase investment in backup power capacity, flexible power supply, grid connection, and distribution, as well as other operational management system resources, in order to ensure the safe operation and power balance of the power grid. In this study, we focus on the comprehensive benefit evaluation of new investments in the power grid under the new power system, starting from the demand and classification of new investments in the power grid under the new power system. We then focus on typical project investments under the construction of the new power system, determining cash inflows and outflows within the investment cycle, establishing a full life-cycle economic benefit model that adapts to typical investments, and further analyzing the social benefits promoted by typical new investment in the power grid. We construct a social benefit model for various entities involved in power grid investment from the perspective of all members of society, distributed users, and power generators, covering multiple benefits such as economic benefits, reliability benefits, environmental benefits, driving economic benefits, and employment promoting benefits. Finally, we conduct an empirical analysis based on the typical scenario of promoting distributed photovoltaic technology in this new type of power system, targeting full spontaneous self-use. There are three modes of self-use surplus grid-connection and full grid-connection, and the comprehensive benefits of supporting investment in the power grid, such as economic benefits and social benefits, are calculated using the full life-cycle economic benefit evaluation method and the social benefit evaluation evaluation method. This study provides an important reference for solving the problem of missing and difficult to quantify evaluation systems for the new investment benefits of the power grid in the new power system. [ABSTRACT FROM AUTHOR]

With the rapid development of new power systems, diverse new power services have imposed stricter requirements on network resources and performance. However, the traditional method of transmitting request data to the IoT management platform for unified processing suffers from large delays due to long transmission distances, making it difficult to meet the delay requirements of new power services. Therefore, to reduce the transmission delay, data transmission, storage and computation need to be performed locally. However, due to the limited resources of individual nodes in the local power wireless communication network, issues such as tight coupling between devices and resources and a lack of flexible allocation need to be addressed. The collaborative allocation of resources among multiple nodes in the local network is necessary to satisfy the multi-dimensional resource requirements of new power services. In response to the problems of limited node resources, inflexible resource allocation, and the high complexity of multi-dimensional resource allocation in local power wireless communication networks, this paper proposes a multi-objective joint optimization model for the collaborative allocation of communication, storage, and computing resources. This model utilizes the computational characteristics of communication resources to reduce the dimensionality of the objective function. Furthermore, a mouse swarm optimization algorithm based on multi-strategy improvements is proposed. The simulation results demonstrate that this method can effectively reduce the total system delay and improve the utilization of network resources. [ABSTRACT FROM AUTHOR]

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• The ISAO method is proposed. • The point prediction method based on ISAO-BiTCN-BiGRU-SA is proposed. • The interval prediction method based on LINMAP selection is proposed. • A multi-objective optimization method for new power systems operation is proposed. The large amount of source and load uncertainty in new power systems poses challenges to the optimization of power supply and demand balance. The traditional optimization methods have not fully considered the uncertainty characteristics of different sources and loads. In this regard, a supply–demand balance optimization method based on ISAO-BiTCN-BiGRU-SA-IPBLS is proposed. Firstly, the ISAO algorithm is introduced into the hyperparameter optimization of BiTCN-BiGRU-SA, and the source and load interval prediction method based on LINMAP selection is proposed. Afterwards, a multi-objective optimization method for power supply and demand balance based on two-stage robust optimization is proposed. The first stage takes the daily planned output of adjustable power sources as the optimization variable, with daily operating cost, renewable energy delivery rate, and maximum loss in extreme scenarios as the optimization objectives. The second stage takes the daily operation of energy storage as the optimization variable and minimizes the maximum loss in the extreme scenario as the optimization objective. Finally, the method is applied to the county-level new power system in Hunan Province, China. The results show that the MAPE of the load and PV point prediction results in this work decreases by 13.43 % and 16.93 % after introducing the ISAO, respectively. Compared with the traditional Gaussian method, the Euclidean distance of error indicators between the load/PV interval prediction results in this work and the ideal results at an 85 % confidence interval decreases by 53.19 %/100 %. Compared with the traditional optimization method only considering economy, the work's method improves the renewable energy delivery rate by 0.10 and 0.02 respectively, and reduces the maximum loss in extreme scenarios by 76.75 % and 3.62 % respectively on the maximum load day and maximum renewable energy output day. [ABSTRACT FROM AUTHOR]

• The optimal cloud entropy algorithm is utilizd to enhance the gray cloud model, and a dynamic weight allocation evaluation method is integrated based on variable weights for comprehensive state perception. • Dynamic indicators such as voltage deviation, voltage fluctuation, and other dynamic metrics as evaluation criteria are introduced, with a primary focus on the influence of dynamic indicators in scenarios with high new energy penetration rates. • A novel multi-scale information fusion method is implemented. This fusion method effectively addresses the issue of incomplete identification frameworks and reduces the impact of sensor errors. • Cases comparison is conducted in actual transformer operating environments with varying levels of new energy penetration rates to validate the superiority and scalability of the proposed model in transformer state assessment. The power transformer is the most important and critical component in the power grid of the electrical system. Its safe and stable operation is of great significance for the reliable transmission of renewable energy generation and the reliable power supply to end users. With the continuous development of new types of power systems, the load of the power system undergoes drastic changes, resulting in increased volatility and instability, which leads to issues such as overload, harmonics, and short circuits in transformers. Therefore, in order to accurately assess the operating status of transformers and promptly identify any existing conditions, a method is proposed in this paper. This method uses an optimal cloud entropy parameter calculation method and a variable weighting method to optimize the gray-cloud evidence model. Through a novel multi-source information fusion method, the results of various test items are fused to obtain the state awareness results. Meanwhile, different evaluation indicators are selected for different levels of renewable energy penetration and compared with other methods. The results are validated through examples, demonstrating that the method proposed in this paper can accurately reflect the operating status of transformers and has good scalability. [ABSTRACT FROM AUTHOR]

The evaluation of new power system construction is the research foundation for improving the flexible regulation ability and comprehensive operational efficiency of new power systems, and achieve the comprehensive goals of safe power supply, green consumption, and economic efficiency. However, the existing research on the evaluation index system of new power system construction can not fully reflect the main objectives of new power system construction. Therefore, this paper first develops a source-load and green-intelligence multi-level and multi-dimensional evaluation system for new power system construction from source-load side equipment, green power, reliable power supply, and intelligent power consumption. Secondly, a hybrid optimization algorithm is proposed based on fluid search algorithm (FSO) for improving the Long Short-Term Memory (LSTM) neural network parameter updating method. Then, the improved LSTM neural network is applied to the construction evaluation of the new power system. Finally, the simulation results show that the evaluation error of the new power system construction evaluation method is 0.0063, which has a high evaluation. [ABSTRACT FROM AUTHOR]

The construction of a new power system based on new energy sources can support the successful transition of energy consumption toward a low-carbon environment. Large-scale clusters of electric vehicles (EVs) are an important reserve measure supporting the flexibility of the new power system. To summarize the roles of EVs as a reserve measure in practical engineering applications, in this study we analyze three essential elements of EV clusters: control of dispatching, participant behavior, and information communication. First, the characteristics of clusters of EVs that participate in power system operations were analyzed in terms of their physical resources, aggregation and control structure, dispatching strategies, and market mechanisms. Second, the importance of social factors was emphasized, and the limitations of computational and experimental simulation methods used in decision-making behavior research were confirmed. Next, the evolution of hybrid simulation methods integrating human participants, multi-agents, and mathematical models was analyzed. Finally, given the conflicting challenges of maintaining both the security and the economy of the communication network, we discussed the problems and potential solutions relating to full-domain communication coverage, spectrum resource interference, communication security, and customized priority design enabling large-scale clusters of EVs to gain access to the power grid from the perspective of the fusion of the public communication network and the dedicated communication network. The results of our study provide technical and economic reference points and a feasibility guarantee that enable more distributed reserve resources for power systems in various countries and regions. • EVs as a reserve measure take an important role in building a new power system. • Technology and economy factors in dispatching distributed reserve measures analyzed. • Hybrid simulation creates a fusion analysis paradigm of participant behaviors. • Comprehensive communication solution integrating both public and dedicated networks. [ABSTRACT FROM AUTHOR]

• Reveal the complex mechanism in coupling developing CCUS and renewable energy. • Assess the economic effects of CCUS and renewable energy penetration by a CGE model. • Identify the optimal policy intensity to deploy CCUS and renewable energy in power sector. • Explore the impact of CCUS application on carbon peak in China's new power system. • Building a green energy dominated power system is harder than the carbon peak target. Carbon capture, utilization, and storage (CCUS) is identified as a critical decarbonization technology that is expected to be applied globally on a large scale, especially in a thermal power-dominated power system. To achieve a carbon peak in the power sector, assessing the environmental and economic effects, including the costs of CCUS application and the decarbonization efficiency, is essential for making a reasonable CCUS policy. However, there is still a research gap in identifying the proper CCUS application intensity considering the dynamic changing renewable energy percentage and power quantity. Therefore, this study constructs a "dynamic system- computable general equilibrium" (SD-CGE) model to reveal the industrial and macro impacts of CCUS application policies by comprehensively evaluating the social, economic, and environmental effects of CCUS application scenarios where different policy intensities are set. Using China as a typical case, the results show that, first, the policy of applying CCUS combined with renewable energy penetration will contribute to reaching the power sector's carbon peak, however, building a new power system dominated by green power is a more challenging goal. Second, regarding the CCUS application intensity combined with renewable power penetration rate, it is proper to suggest setting the policy intensity as "medium-medium" for the optimal environmental and economic effects. Consequently, the power sector's carbon peak year will be 2025, while green power will dominate the power supply in 2030. Third, through the model's validation check, the models and results in this study are shown to be accurate and robust. This study provides policy support for constructing China's new power system, is conducive to achieving the national carbon peak goal, and provides other countries with theoretical tools to study similar problems. [ABSTRACT FROM AUTHOR]