In order to quantify the contribution of the mitigation strategies,an extended Kaya identity has been proposed in this paper for decomposing the various factors that influence the CO2 emission.To this end,we provided a detailed decomposition of the carbon intensity and energy intensity,which enables the quantification of clean energy development and electrification.The logarithmic mean divisia index (LMDI) has been applied to the historical data to quantify the contributions of the various factors affecting the CO2 emissions.Further,the global energy interconnection (GEI) scenario has been introduced for providing a systematic solution to meet the 2°C goal of the Paris Agreement.By combining LMDI with the scenario analysis,the mitigation potential of the various factors for CO2 emission has been analyzed.Results from the historical data indicate that economic development and population growth contribute the most to the increase in CO2 emissions,whereas improvement in the power generation efficiency predominantly helps in emission reduction.A numerical analysis,performed for obtaining the projected future carbon emissions,suggests that clean energy development and electrification are the top two factors that can decrease CO2 emissions,thus showing their great potential for mitigation in the future.Moreover,the carbon capture and storage technology serves as an important supplementary mitigation method.

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The growing demand for electricity in large Brazilian cities and the distance to the region with the largest energy production have encouraged the construction of transmission lines thousands of kilometers long,crossing several states of the federation.Therefore,the heterogeneity of requirements in federal,state and district environmental laws tend to lead to uncertainty for the investor regarding procedures and investments necessary for the completion of the project,as well as the time that the requirements will take to be fulfilled.In the present study,we identify and compare the criteria used by the various environmental licensing agencies for classifying transmission system projects in the different procedures (simplified and ordinary) of environmental licensing.It was possible to expose the differences and demonstrate some inconsistencies in the legislation of some Brazilian states.In particular,we note an absence of proportionality between the licensing requirements and the expected environmental impacts for projects on the physical,biological and social environments.Additionally,it was identified subjectivity in the requirement for social participation through public hearings,demanded by the legislation for the projects classified in the ordinary procedure.

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In a multi-energy collaboration system,cooling,heating,electricity,and other energy components are coupled to complement each other.Through multi-energy coordination and cooperation,they can significantly improve their individual operating efficiency and overall economic benefits.Demand response,as a multi-energy supply and demand balance method,can further improve system flexibility and economy.Therefore,a multi-energy cooperative system optimization model has been proposed,which is driven by price-based demand response to determine the impact of power-demand response on the optimal operating mode of a multi-energy cooperative system.The main components of the multi-energy collaborative system have been analyzed.The multi-energy coupling characteristics have been identified based on the energy hub model.Using market elasticity as a basis,a price-based demand response model has been built.The model has been optimized to minimize daily operating cost of the multi-energy collaborative system.Using data from an actual situation,the model has been verified,and we have shown that the adoption of price-based demand response measures can significantly improve the economy of multi-energy collaborative systems.

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Real-time scheduling as an on-line optimization process must output dispatch results in real time.However,the calculation time required and the economy have a trade-off relationship.In response to a real-time scheduling problem,this paper proposes a real-time scheduling strategy considering the operation interval division of distributed generators (DGs) and batteries in the microgrid.Rolling scheduling models,including day-ahead scheduling and hours-ahead scheduling,are established,where the latter considers the future state-of-charge deviations.For the real-time scheduling,the output powers of the DGs are divided into two intervals based on the ability to track the day-ahead and hours-ahead schedules.The day-ahead and hours-ahead scheduling ensure the economy,whereas the real-time scheduling overcomes the timeconsumption problem.Finally,a grid-connected microgrid example is studied,and the simulation results demonstrate the effectiveness of the proposed strategy in terms of economic and real-time requirements.

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With the increase in the power receiving proportion and an insufficient peak regulation capacity of the local units,the receiving-end power grid struggles to achieve peak regulation in valley time.To solve this problem while considering the potential of the large-scale charge load of electric vehicles (EVs),an aggregator-based demand response (DR) mechanism for EVs that are participating in the peak regulation in valley time is proposed in this study.In this aggregator-based DR mechanism,the profits for the power grid’s operation and the participation willingness of the EV owners are considered.Based on the characteristics of the EV charging process and the day-ahead unit generation scheduling,a rolling unit commitment model with the DR is established to maximize the social welfare.In addition,to improve the efficiency of the optimization problem solving process and to achieve communication between the independent system operator (ISO) and the aggregators,the clustering algorithm is utilized to extract typical EV charging patterns.Finally,the feasibility and benefits of the aggregator-based DR mechanism for saving the costs and reducing the peak-valley difference of the receiving-end power grid are verified through case studies.

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Owing to the large-scale grid connection of new energy sources,several installed power electronic devices introduce sub-/supersynchronous inter-harmonics into power signals,resulting in the frequent occurrence of subsynchronous oscillations (SSOs).The SSOs may cause significant harm to generator sets and power systems;thus,online monitoring and accurate alarms for power systems are crucial for their safe and stable operation.Phasor measurement units (PMUs) can realize the dynamic real-time monitoring of power systems.Based on PMU phasor measurements,this study proposes a method for SSO online monitoring and alarm implementation for the main station of a PMU.First,fast Fourier transform frequency spectrum analysis is performed on PMU current phasor amplitude data to obtain subsynchronous frequency components.Second,the support vector machine learning algorithm is trained to obtain the amplitude threshold and subsequently filter out safe components and retain harmful ones.Finally,the adaptive duration threshold is determined according to frequency susceptibility,amplitude attenuation,and energy accumulation to decide whether to transmit an alarm signal.Experiments based on field data verify the effectiveness of the proposed method.

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The inertia response and primary frequency regulation capability of synchronous grids are declining owing to the increasing penetration of inverter-based resources.The fast frequency response (FFR) of inverter-based resources is an important mitigation option for maintaining grid security under the conditions of low inertia and insufficient primary frequency response capability.However,the understanding and technical characteristics of the FFR of inverter-based resources are still unclear.Aiming at solving the aforementioned problems,this paper proposes a definition for FFR based on the impact mechanism of FFR on system frequency.The performance requirements of FFR are clarified.Then,the effects of FFR on system frequency characteristics are further analyzed based on steady-state frequency deviation,the initial rate of change of frequency,and the maximum transient frequency deviation.Finally,the system requirements for FFR and its application effects are verified by simulating an actual bulk power grid,providing technical support for subsequent engineering application.

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In China,regions with abundant wind energy resources are generally located at the end of power grids.The power grid architecture in these regions is typically not sufficiently strong,and the energy structure is relatively simple.Thus,connecting large-capacity wind power units complicates the peak load regulation and stable operation of the power grids in these regions.Most wind turbines use power electronic converter technology,which affects the safety and stability of the power grid differently compared with conventional synchronous generators.Furthermore,fluctuations in wind power cause fluctuations in the output of wind farms,making it difficult to create and implement suitable power generation plans for wind farms.The generation technology and grid connection scheme for wind power and conventional thermal power generation differ considerably.Moreover,the active and reactive power control abilities of wind turbines are weaker than those of thermal power units,necessitating additional equipment to control wind turbines.Hence,to address the aforementioned issues with large-scale wind power generation,this study analyzes the differences between the grid connection and collection strategies for wind power bases and thermal power plants.Based on this analysis,the differences in the power control modes of wind power and thermal power are further investigated.Finally,the stability of different control modes is analyzed through simulation.The findings can be beneficial for the planning and development of large-scale wind power generation farms.

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Partial discharge (PD) detection is an effective means of discovering insulation faults in gas-insulated switchgear (GIS).One of the most extensively used methods in PD detection has historically been the ultrahigh frequency (UHF) method.This study evaluates the chromatic processing methodology and its key factors for feature extraction of UHF signals in GIS.Three types of artificial defects are installed in the GIS tank at 0°,90°,and 180°,respectively.The features of the UHF signals are extracted in the chromatic space,and PD discrimination of the defects is achieved.The influences of processors are studied before the feature selections are suggested.The time-stepping method is proposed to determine the rules of UHF signal frequency characteristics that vary with time.Finally,the process and options of the chromatics-inspired methodology are summarized.

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The analysis of a supervision environment is the first step for a company to enter the new electricity market.Transmission and distribution assets are the main investment targets of a company.The overseas power market belongs to the regulated industry;whether it is a stock M&A project or a green land bidding project,the regulatory environment determines the assets.The level of return and investment risk that guides the operation strategy of existing overseas assets,has a significant impact on the investment and operations of international companies.A comprehensive and rapid assessment of the regulatory environment can help the project teams of international companies understand the macroenvironment of the target electricity market within a short period,quickly identify investment risks,qualitatively analyze the return level of the underlying assets,shorten the decision time,capture investment opportunities,and enhance the team.Efficiency and quality of work are factors of great importance.

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