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Localization of faults in a large power system is one of the most important and difficult tasks of power systems monitoring. A fault, typically a shorted line, can be seen almost instantaneously by all measurement devices throughout the system, but determining its location in a geographically vast and topologically complex system is difficult. The task becomes even more difficult if measurements devices are placed only at some network nodes. We show that regression graph neural networks we construct, combined with a suitable statistical methodology, can solve this task very well. A chief advance of our methods is that we construct networks that produce localization without having being trained on data that contain fault localization information. We show that a synergy of statistics and deep learning can produce results that none of these approaches applied separately can achieve. [ABSTRACT FROM AUTHOR]

Considering the characteristics of the power system, where "the source moves with the load", the load side is primarily responsible for the carbon emissions of the regional power grid. Consequently, users' electricity consumption behavior has a significant impact on system carbon emissions. Therefore, this paper proposes a multi-objective bi-level optimization strategy for source-load coordination, considering dual demand responses for both electricity and carbon. The upper layer establishes a multi-objective low-carbon economic dispatch model for power grid operators, aiming to minimize the system's total operating cost, the total direct carbon emissions of the power grid, and the disparity in regional carbon emissions. In the lower layer, a low-carbon economic dispatch model for load aggregators is established to minimize the total cost for load aggregators. To obtain the dynamic carbon emission factor signal, a complex power flow tracking method that considers the power supply path is proposed, and a carbon flow tracking model is established. NSGA-II is used to obtain the Pareto optimal frontier set for the upper model, and the 'optimal' scheme is determined based on the fuzzy satisfaction decision. The example analysis demonstrates that the interactive carbon reduction effect under the guidance of dual signals is the most effective. This approach fully exploits the carbon reduction potential of the flexible load, enhancing both the economic efficiency and low-carbon operation of the system. [ABSTRACT FROM AUTHOR]

Introduction: In order to dispatch frequency regulation resources in regional power grids efficiently and promote the development of spot markets, China Southern Power Grid (CSG) established the unified frequency regulation control area. However, the existing regional control performance standards (CPS) for evaluating the performance of frequency control in bulk power systems is no longer suitable for the unified frequency control mode. Method: This paper proposed an innovative frequency control performance standard, named tertiary control performance standards (TCPS) and used Gaussian mixture model (GMM) and folded normal distribution (FND) to describe the distributions of frequency deviations and power deviations of tie line. Result and Discussion: Based on these probability models, the parameters of the proposed TCPS can be determined optimized. Finally, case studies were carried out with the practical data from CSG and indicated that the parameters of proposed TCPS index could be calculated and improved the control performance for the real time power balance of the regional power grid with spot markets. [ABSTRACT FROM AUTHOR]

This paper provides an optimization strategy for flexible operation at the system level to guide the real-time flexible ramping of the power grid. Firstly, the concept of regional power grid flexibility is clarified, and the ramping factor is proposed as a flexibility metric. On this basis, taking the output priority of each node as the objective function and considering constraints such as line flow, power balance, and system flexibility, a mathematical model for regional power grid transmitting flexible power externally is proposed. Compared with the traditional models, this model focuses more on flexibility rather than economy. A case analysis based on the improved IEEE 30-Bus System verifies the feasibility and effectiveness of the proposed strategy and its advantages over the traditional models. [ABSTRACT FROM AUTHOR]

The voltage quality decline caused by the high proportion of new energy connected to power grids is a key factor limiting the capacity of new energy consumption. Therefore， an optimal reactive voltage control method based on the maximum capacity of new energy consumption is proposed. The new energy generation forecast data is used to assess the maximum generation capacity， establish the reactive voltage optimization model and solve the voltage control target at each node for forwarding decision control. According to the topological properties and operational characteristics of the regional power grids， dynamic zoning， strategy prediction， alarm blocking， and other concerning functions are designed to improve the safety and stability of the control method. The simulation analysis results of an example show that the method features ideal reactive voltage support and can promote new energy consumption in the regional power grids.

Electricity consumption is a crucial source of carbon emissions. Many studies have focused on the emissions relevant to electricity generation, yet it is also important to evaluate the carbon intensity of final electricity use for the emission assessment of various industries. Considering fossil fuel consumption, regional exchange, and transmission loss, this study proposed a method to assess the emission intensities of electricity generation, supply, and use. Based on statistical data, the carbon intensities of North, Northeast, East, Central, Northwest, and South China power grids were evaluated during the period from 2005 to 2020, and the driving factors including energy structure, energy intensity, clean production, supply structure, and power loss effects were discussed using Logistic Mean Divided Index (LMDI) decomposition. An overall descending trend was observed for the carbon intensity, and the national average carbon intensities of electricity generation, supply, and use were 0.599, 0.599, and 0.622 kgCO2e/kWh, respectively. The carbon intensity of South China gird was the lowest at 0.425 kgCO2e/kWh in 2020. Moreover, the energy intensity effect and clean production effect were identified as the most influential factors of carbon intensity. The proposed method and results can be helpful to assess carbon emissions relevant to final electricity use. [ABSTRACT FROM AUTHOR]

This paper provides a method for simplified description of a regional power grid model aimed to deliver a grid reduction, and improve grid performance observability. The derived power grid model can be used to analyze the regional allocation of the decentralized energy generation and consumption. The expansion of wind and solar generation in the power system affects the residual load. The power balance between electricity consumption and generation was calculated and analyzed based on the temporal and spatial scales. The proposed grid clustering method is a useful approach for performance analysis in systems with a growing share of renewable generation.

Abstract In this study, the “cradle‐to‐gate” greenhouse gas (GHG) intensities of six types of power generation in China are analyzed using a life cycle assessment approach, including wind power, solar photovoltaic power, nuclear power, hydropower, biomass power, and thermal power. According to the mix of regional power grids in China and GHG intensities of various types of power generation, the GHG intensities of hybrid power on regional power grid scale are calculated. The results show that they are closely corresponding to the grid mix of each region. Besides, the value of northeast China is the highest and the largest variation between regions is about twice. Furthermore, the efforts made by the Chinese government promoting energy shift are expected to accelerate the decrease of the electricity system GHG intensity to 376.9 gCO2‐eq/kW h by 2035, about 51.0% lower than that in 2017. And the total GHG emissions are predicted to reach 4.5 × 109 tCO2‐eq in 2035, while in “below 2°C” scenario this value will decrease to 3.6 × 109 tCO2‐eq. This study compiles the life cycle inventory of China's electricity generation on spatial and temporal scale, and can provide suggestions on the development of regional and national electricity systems.

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Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the context of building a new power system, the high proportion of renewable energy access and the high coupling of information and physical systems lead to more complex grid networks and more diverse risk sources, and the importance of regional power grid security evaluation has also increased. From the perspective of power grid risk sources, firstly, a regional power grid security evaluation index system including natural environment, social environment, economic environment, network environment, technical environment and energy environment was established. Secondly, the improved catastrophe theory was used to build the power grid security evaluation model. Finally, a case analysis of the relevant data of a power grid company from 2012 to 2019 was carried out. Compared with the entropy weight method, the evaluation results of regional power grid security obtained from the improved catastrophe theory are more consistent with the actual statistical results of its comprehensive power supply reliability, which proves that the established index system can accurately analyze the source of regional power grid security risk, thus realizing the effective analysis of the development trend of regional power grid security under different external environments. [ABSTRACT FROM AUTHOR]

In this study, the "cradle‐to‐gate" greenhouse gas (GHG) intensities of six types of power generation in China are analyzed using a life cycle assessment approach, including wind power, solar photovoltaic power, nuclear power, hydropower, biomass power, and thermal power. According to the mix of regional power grids in China and GHG intensities of various types of power generation, the GHG intensities of hybrid power on regional power grid scale are calculated. The results show that they are closely corresponding to the grid mix of each region. Besides, the value of northeast China is the highest and the largest variation between regions is about twice. Furthermore, the efforts made by the Chinese government promoting energy shift are expected to accelerate the decrease of the electricity system GHG intensity to 376.9 gCO2‐eq/kW h by 2035, about 51.0% lower than that in 2017. And the total GHG emissions are predicted to reach 4.5 × 109 tCO2‐eq in 2035, while in "below 2°C" scenario this value will decrease to 3.6 × 109 tCO2‐eq. This study compiles the life cycle inventory of China's electricity generation on spatial and temporal scale, and can provide suggestions on the development of regional and national electricity systems. [ABSTRACT FROM AUTHOR]

The Chinese government has deployed ambitious plans for its power system and renewable energy development to achieve carbon neutrality. To assess the decarbonization processes of regional power grids, this study develops an inventory of the power generation structure at the provincial level based on current official power planning, and simulates the power generated and carbon emissions intensity in China's regional power grid from 2020 to 2050 using a dynamic scenario. Results found the following: First, compared with 2020, carbon emissions from the power system in 2050 decreased by approximately 1.8268 BtCO 2 , and the North China regional power grid contributes the most to emission reduction. Second, except for Tibet power grid, the life-cycle carbon emissions coefficients in China's regional power grids maintained a downward trend, with carbon emission coefficients ranging from 0.1439 to 0.2385 kgCO 2 eq/kWh in 2050. Third, cross-regional power exchange can effectively reduce the carbon emission intensity of regional power grids dominated by installed fossil energy and Northwest regional power grid is the largest power output region, reaching 1077.78 TWh in 2050. Overall, this study assesses the prospective decarbonization of China's regional power grids. The findings contribute to the development of power decarbonization planning and provide data references for electrification of energy-consuming sectors. • The projected carbon emissions intensity of each regional power grid is simulated. • Our model considers cross-regional power exchange. • Dynamic scenarios are used to achieve uncertainty in power generation. • The power system is not decarbonized fast enough under current official power planning. • Recommendations are given from CCS technology and power transmission. [ABSTRACT FROM AUTHOR]