Owing to increasing environmental concerns and resource scarcity,integrated energy system shave become widely used in communities.Rural energy systems,as one of the important links of the energy network in China,suffer from low energy efficiency and weak infrastructure.Therefore,it is particularly important to increase the proportion of electricity consumption and build an integrated energy system for rural electrification in China (IESREIC) with a rural distribution network as the core,in line with national conditions.In this study,by analyzing the Chinese regional differences and natural resource endowments,the development characteristics of the IESREIC are summarized.Then,according to the existing rural energy problems,key technologies are proposed for the IESREIC,such as those for planning and operation,value sharing,infrastructure,and a management and control platform.Finally,IESREIC demonstration projects and business models are introduced for agricultural production,rural industrial systems,and rural life.The purpose is to propose research concepts for the IESREIC,provide suggestions for the development of rural energy,and provide a reference for the construction of rural energy systems in countries with characteristics similar to those of China.

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Distributed energy resources have been proven to be an effective and promising solution to enhance power system resilience and improve household-level reliability.In this paper,we propose a method to evaluate the reliability value of a photovoltaic (PV) energy system with a battery storage system (BSS) by considering the probability of grid outages causing household blackouts.Considering this reliability value,which is the economic profit and capital cost of PV+BSS,a simple formula is derived to calculate the optimal planning strategy.This strategy can provide household-level customers with a simple and straightforward expression for invested PV+BSS capacity.Case studies on 600 households located in eight zones of the US for the period of 2006 to 2015 demonstrate that adding the reliability value to economic profit allows households to invest in a larger PV+BSS and avoid loss of load caused by blackouts.Owing to the differences in blackout hours,households from the 8 zones express distinct willingness to install PV+BSS.The greater the probability of blackout,the greater revenue that household can get from the PV+BSS.The simulation example shows that the planning strategy obtained by proposed model has good economy in the actual operation and able to reduce the economic risk of power failure of the household users.This model can provide household with an easy and straightforward investment strategy of PV+BSS capacity.

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To promote the utilization of renewable energy,such as photovoltaics,this paper proposes an optimal flexibility dispatch method for demand-side resources (DSR) based on the Stackelberg game theory.First,the concept of the generalized DSR is analyzed and flexibility models for various DSR are constructed.Second,owing to the characteristics of small capacity but large-scale,an outer approximation is proposed to describe the aggregate flexibility of DSR.Then,the optimal flexibility dispatch model of DSR based on the Stackelberg game is established and a decentralized solution algorithm is designed to obtain the Stackelberg equilibrium.Finally,the actual data are utilized for the case study and the results show that,compared to the traditional centralized optimization method,the proposed optimal flexibility dispatch method can not only reduce the net load variability of the DSR aggregator but is beneficial for all DSR owners,which is more suitable for practical applications.

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The integrated energy system (IES) is an important energy supply method for mitigating the energy crisis.A station-and-network-coordinated planning method for the IES,which considers the integrated demand responses (IDRs)of flexible loads,electric vehicles,and energy storage is proposed in this work.First,based on load substitution at the user side,an energy-station model considering the IDR is established.Then,based on the characteristics of the energy network,a collaborative planning model is established for the energy station and energy network of the IES,considering the comprehensive system investment,operation and maintenance,and clean energy shortage penalty costs,to minimize the total cost.This can help optimize the locations of the power lines and natural gas pipelines and the capacities of the equipment in an energy station.Finally,simulations are performed to demonstrate that the proposed planning method can help delay or reduce the construction of new lines and energy-station equipment,thereby reducing the investment required and improving the planning economics of the IES.

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Fast charging stations play an important role in the use of electric vehicles (EV) and significantly affect the distribution network owing to the fluctuation of their power.For exploiting the rapid adjustment feature of the energy-storage system (ESS),a configuration method of the ESS for EV fast charging stations is proposed in this paper,which considers the fluctuation of the wind power as well as the characteristics of the charging load.The configuration of the ESS can not only mitigate the effects of fast charging stations on the connected distribution network but also improve its economic efficiency.First,the scenario method is adopted to model the wind power in the distribution network,and according to the characteristics of the EV and the driving probability,the charging demand of each station is calculated.Then,considering factors such as the investment cost,maintenance cost,discharging benefit,and wind curtailment cost,the ESS configuration model of the distribution network is set up,which takes the optimal total costs of the ESS for EV fast charging stations within its lifecycle as an objective.Finally,General Algebraic Modelling System (GAMS) is used to linearize and solve the proposed model.A simulation on an improved IEEE-69 bus system verifies the feasibility and economic efficiency of the proposed model.

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Considering the complex coupling of multiple energies and the varying load forecasting errors for an integrated energy system (IES),this study proposes a dynamic time-scale scheduling strategy based on long short-term memory (LSTM)and multiple load forecasting errors.This strategy dynamically selects a hybrid timescale which is suitable for a variety of energies for each month.This is obtained by combining the mean absolute percentage error (MAPE) curve of the load forecasting with the error restriction requirements of the dispatcher.Based on the day-ahead scheduling plan,the output of the partial equipment is selectively adjusted at each time-scale to realize multi-energy collaborative optimization and gives full play to the comprehensive advantages of the IES.This is achieved by considering the differences in the response speed for each piece of equipment within the intra-day scheduling.This study uses the IES as an example,and it dynamically determines the time scale of the energy monthly.In addition,this investigation presents a detailed analysis of the output plan of the key equipment to demonstrate the necessity and the advantages of the strategy.

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Electric system planning with high variable renewable energy (VRE) penetration levels has attracted great attention world-wide.Electricity production of VRE highly depends on the weather conditions and thus involves large variability,uncertainty,and low-capacity credit.This gives rise to significant challenges for power system planning.Currently,many solutions are proposed to address the issue of operational flexibility inadequacy,including flexibility retrofit of thermal units,inter-regional transmission,electricity energy storage,and demand response (DR).Evidently,the performance and the cost of various solutions are different.It is relevant to explore the optimal portfolio to satisfy the flexibility requirement for a renewable dominated system and the role of each flexibility source.In this study,the value of diverse DR flexibilities was examined and a stochastic investment planning model considering DR is proposed.Two types of DRs,namely interrupted DR and transferred DR,were modeled.Chronological load and renewable generation curves with 8760 hours within a whole year were reduced to 4 weekly scenarios to accelerate the optimization.Clustered unit commitment constraints for accommodating variability of renewables were incorporated.Case studies based on IEEE RTS-96 system are reported to demonstrate the effectiveness of the proposed method and the DR potential to avoid energy storage investment.

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The close proximity and the necessity of coordination between multiple high-voltage direct currents (HVDCs) raise the issue of grid partitioning in multi-infeed HVDC systems.A multi-objective partition strategy is proposed in this paper.Several types of relationships to be coordinated and complemented are analyzed and formulated using quantitative indices.According to the graph theory,the HVDC partition is transformed into a graph-cut problem and solved via the spectral clustering algorithm.Finally,the proposed method is validated for a practical multi-HVDC grid,confirming its feasibility and effectiveness.

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Development of the medium and low voltage DC distribution system is of great significance to a regional transmission of electric energy,increasing a penetration rate of new energy,and enhancing a safety of the operation of the AC/DC interconnected grid.This paper first summarizes the medium and low voltage DC distribution system schemes and plans put forward by many countries,and then elaborate status of under-construction medium and low voltage DC distribution system project cases in China.Based on these project cases,this paper analyzes key issues involved in the medium and low voltage DC distribution system topologies,equipment,operation control technologies and DC fault protections,in order to provide theoretical and technical reference for future medium and low voltage DC distribution system-related projects.Finally,this paper combines a current China research status to summarize and give a prediction about the future research direction of medium and low voltage DC distribution system,which can provide reference for the research of medium and low voltage DC distribution system.

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With the integration of renewable energy resources,the inertia of power systems significantly reduces,thereby making the system sensitive to operational disturbances.A disturbance-based method is presented herein to estimate inertia,uncovering the influence of renewables on system-resilient operations.The Gaussian process regression method is then used to predict the power system trajectory after disturbance.Extensive tests demonstrate the data-driven method mathematically estimates the inertia of the system as well as predicts the dynamics operations of power grids subject to disturbances.Numerical results also offer insights into the enhancement of system resilience by strategically designing the inertia of power systems.

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