A hybrid of line commutated converters (LCCs) and modular multi-level converters (MMCs) can provide the advantages of both the technologies.However,the commutation failure still exists if the LCC operates as an inverter in a hybrid LCC/MMC system.In this paper,the system behavior during a commutation failure is investigated.Both halfbridge and full-bridge MMCs are considered.Control strategies are examined through simulations conducted in PSCAD/EMTDC.Additionally,commutation failure protection strategies for multi-terminal hybrid LCC/MMC systems with AC and DC circuit breakers are studied.This paper can contribute to the protection design of future hybrid LCC/MMC systems against commutation failures.

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Due to low investment cost and high reliability,a new scheme called DR-HVDC (Diode Rectifier based HVDC) transmission was recently proposed for grid integration of large offshore wind farms.However,in this scheme,the application of conventional control strategies for stability operation face several challenges due to the uncontrollability of the DR.In this paper,a coordinated control strategy of offshore wind farms using the DR-HVDC transmission technology to connect with the onshore grid,is investigated.A novel coordinated control strategy for DR-HVDC is proposed based on the analysis of the DC current control ability of the full-bridge-based modular multilevel converter (FB-MMC) at the onshore station and the input and output characteristics of the diode rectifier at the offshore.Considering the characteristics of operation stability and decoupling between reactive power and active power,a simplified design based on double-loop droop control for offshore AC voltage is proposed after power flow and voltage–current (I–V) characteristics of the offshore wind farm being analyzed.Furthermore,the impact of onshore AC fault to offshore wind farm is analyzed,and a fast fault detection and protection strategy without relying on communication is proposed.Case studies carried out by PSCAD/EMTDC verify the effectiveness of the proposed control strategy for the start up,power fluctuation,and onshore and offshore fault conditions.

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A unified power flow controller (UPFC) combines the advantages of various flexible alternating current transmission system (FACTS) devices into a powerful format.Using a 500 kV power grid,this study evaluates the selection and use of a UPFC to improve transmission capacity.The “UPFC unit capacity control proportionality coefficient” is introduced to quantify the control effect of the UPFC,and an optimal calculation method for the UPFC capacity is presented.Following the proposal of a UPFC site selection process,the data of an existing power grid is used to conduct simulations.The simulation results show that the UPFC has a strong ability to improve transmission capacity,and its use is greatly advantageous.Additionally,by applying the proposed selection method,the control effect and economic benefits of the UPFC can be comprehensively considered during project site selection.These findings have a guiding significance for UPFC site selection in ultra-high voltage power grids.

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The large-scale utilization and sharing of renewable energy in interconnected systems is crucial for realizing “instrumented,interconnected,and intelligent” power grids.The traditional optimal dispatch method can not coordinate the economic benefits of all the stakeholders from multiple regions of the transmission network,comprehensively.Hence,this study proposes a large-scale wind-power coordinated consumption strategy based on the Nash-Q method and establishes an economic dispatch model for interconnected systems considering the uncertainty of wind power,with optimal windpower consumption as the objective for redistributing the shared benefits between regions.Initially,based on the equivalent cost of the interests of stakeholders from different regions,the state decision models are respectively constructed,and the noncooperative game Nash equilibrium model is established.The Q-learning algorithm is then introduced for high-dimension decision variables in the game model,and the dispatch solution methods for interconnected systems are presented,integrating the noncooperative game Nash equilibrium and Q-learning algorithm.Finally,the proposed method is verified through the modified IEEE 39-bus interconnection system,and it is established that this method achieves reasonable distribution of interests between regions and promotes large-scale consumption of wind power.

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North African countries generally have strategic demands for energy transformation and sustainable development.Renewable energy development is important to achieve this goal.Considering three typical types of renewable energies—wind,photovoltaic (PV),and concentrating solar power (CSP)—an optimal planning model is established to minimize construction costs and power curtailment losses.The levelized cost of electricity is used as an index for assessing economic feasibility.In this study,wind and PV,wind/PV/CSP,and transnational interconnection modes are designed for Morocco,Egypt,and Tunisia.The installed capacities of renewable energy power generation are planned through the time sequence production simulation method for each country.The results show that renewable energy combined with power generation,including the CSP mode,can improve reliability of the power supply and reduce the power curtailment rate.The transnational interconnection mode can help realize mutual benefits of renewable energy power,while the apportionment of electricity prices and trading mechanisms are very important and are related to economic feasibility; thus,this mode is important for the future development of renewable energy in North Africa.

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In the process of large-scale,grid-connected wind power operations,it is important to establish an accurate probability distribution model for wind farm fluctuations.In this study,a wind power fluctuation modeling method is proposed based on the method of moving average and adaptive nonparametric kernel density estimation (NPKDE) method.Firstly,the method of moving average is used to reduce the fluctuation of the sampling wind power component,and the probability characteristics of the modeling are then determined based on the NPKDE.Secondly,the model is improved adaptively,and is then solved by using constraint-order optimization.The simulation results show that this method has a better accuracy and applicability compared with the modeling method based on traditional parameter estimation,and solves the local adaptation problem of traditional NPKDE.

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Distributed energy systems (DES),as an integrated energy system with coupled distributed energy resources,have great potential in reducing carbon dioxide emissions and improving energy efficiencies.Considering the background of urbanization and the energy revolution in China,the study investigates the renewable-based DESs supply modes and their application in China.A new method is proposed to classify DESs supply modes into three categories considering the renewable resource in domination,and their application domains are discussed.A comprehensive model is given for economic and environmental evaluation.Typical case studies show that the renewable-based DES systems can supply the energy in a cost-effective and environment-friendly way.Among them,the biomass waste dominated supply mode can not only achieve “zero” carbon emissions but also “zero” energy consumption,even though not yet economically attractive under the present policy and market conditions.Thus,recommendations are given to promote the further deployment of renewable-based DESs,regarding their supply modes,policy requirements,and issues to be addressed.

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With the rapid development of technologies such as big data and cloud computing,data communication and data computing in the form of exponential growth have led to a large amount of energy consumption in data centers.Globally,data centers will become the world’s largest users of energy consumption,with the ratio rising from 3% in 2017 to 4.5% in 2025.Due to its unique climate and energy-saving advantages,the high-latitude area in the Pan-Arctic region has gradually become a hotspot for data center site selection in recent years.In order to predict and analyze the future energy consumption and carbon emissions of global data centers,this paper presents a new method based on global data center traffic and power usage effectiveness (PUE) for energy consumption prediction.Firstly,global data center traffic growth is predicted based on the Cisco’s research.Secondly,the dynamic global average PUE and the high latitude PUE based on Romonet simulation model are obtained,and then global data center energy consumption with two different scenarios,the decentralized scenario and the centralized scenario,is analyzed quantitatively via the polynomial fitting method.The simulation results show that,in 2030,the global data center energy consumption and carbon emissions are reduced by about 301 billion kWh and 720 million tons CO2 in the centralized scenario compared with that of the decentralized scenario,which confirms that the establishment of data centers in the Pan-Arctic region in the future can effectively relief the climate change and energy problems.This study provides support for global energy consumption prediction,and guidance for the layout of future global data centers from the perspective of energy consumption.Moreover,it provides support of the feasibility of the integration of energy and information networks under the Global Energy Interconnection conception.

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In recent years,renewable energy technologies have been developed vigorously,and related supporting policies have been issued.The developmental trend of different energy sources directly affects the future developmental pattern of the energy and power industry.Energy trend research can be quantified through data statistics and model calculations; however,parameter settings and optimization are difficult,and the analysis results sometimes do not reflect objective reality.This paper proposes an energy and power information analysis method based on emotion mining.This method collects energy commentary news and literature reports from many authoritative media around the world and builds a convolutional neural network model and a text analysis model for topic classification and positive/negative emotion evaluation,which helps obtain text evaluation matrixes for all collected texts.Finally,a long-short-term memory model algorithm is employed to predict the future development prospects and market trends for various types of energy based on the analyzed emotions in different time spans.Experimental results indicate that energy trend analysis based on this method is consistent with the real scenario,has good applicability,and can provide a useful reference for the development of energy and power resources and of other industry areas as well.

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In many regions,international power system interconnections provide economic,energy-security,environmental,and technical benefits.In contrast,such interconnections remain scarce in Northeast Asia.In 2016,after approving a joint memorandum of understanding between major electric power companies from China,Japan,South Korea,and Russia,related initiatives regained momentum in the region.Nevertheless,the corresponding developments in Japan remain limited,mainly owing to the lack of involvement of Japanese electric power companies.This study represents a pioneering attempt to provide an economic assessment based on power exchange prices of a power system interconnection between Japan and South Korea regarding the competitiveness of electric power companies in terms of competitive business segments and strategic consequences.We found that although the position of Japanese generators may slightly deteriorate,that of the supply segment would substantially improve,thus suggesting that more opportunities than threats are derived from the interconnection.This promising outcome may foster the adoption of an interconnection with South Korea considering the positive economic and business perspectives in Japan.Furthermore,realizing the interconnection may improve the energy security and air quality in the region.

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