A Two-Layer Fuzzy Control Strategy for the Participation of Energy Storage Battery Systems in Grid Frequency Regulation

To address the frequency fluctuation problem caused by the power dynamic imbalance between the power system and the loadwhen a large number of newenergy sources are connected to the grid,a two-layer fuzzy control strategy is proposed for the participation of the energy storage battery system in FM.Firstly,considering the coordination of FM units responding to automatic power generation control commands,a comprehensive allocation strategy of two signals under automatic power generation control commands is proposed to give full play to the advantages of two FM signals while enabling better coordination of two FM units responding to FM commands;secondly,based on the grid FM demand and battery FM capability,a double-layer fuzzy control strategy is proposed for FM units responding to automatic power generation control commands in a coordinated manner under dual-signal allocation mode to precisely allocate the power output depth of FM units,which can control the fluctuation of frequency deviation within a smaller range at a faster speed while maintaining the battery charge state;finally,the proposed Finally,the proposed control strategy is simulated and verified inMatlab/Simulink.The results show that the proposed control strategy can control the frequency deviation within a smaller range in a shorter time,better stabilize the fluctuation of the battery charge level,and improve the utilization of the FM unit.

Low-Frequency Oscillation Analysis of Grid-Connected VSG System Considering Multi-Parameter Coupling

With the increasing integration of new energy generation into the power system and the massive withdrawal of traditional fossil fuel generation,the power system is faced with a large number of stability problems.The phenomenon of low-frequency oscillation caused by lack of damping and moment of inertia is worth studying.In recent years,virtual synchronous generator(VSG)technique has been developed rapidly because it can provide considerable damping and moment of inertia.While improving the stability of the system,it also inevitably causes the problem of active power oscillation,especially the low mutual damping between the VSG and the power grid will make the oscillation more severe.The traditional time-domain state-space method cannot reflect the interaction among state variables and study the interaction between different nodes and branches of the power grid.In this paper,a frequency-domain method for analyzing low-frequency oscillations considering VSG parameter coupling is proposed.First,based on the rotor motion equation of the synchronous generator(SG),a secondorder VSG model and linearized power-frequency control loop model are established.Then,the differences and connections between the coupling of key VSG parameters and low-frequency oscillation characteristics are studied through frequency domain analysis.The path and influencemechanism of a VSG during low-frequency power grid oscillations are illustrated.Finally,the correctness of the theoretical analysis model is verified by simulation.

Research on Short-Term Load Forecasting of Distribution Stations Based on the Clustering Improvement Fuzzy Time Series Algorithm

An improved fuzzy time series algorithmbased on clustering is designed in this paper.The algorithm is successfully applied to short-term load forecasting in the distribution stations.Firstly,the K-means clustering method is used to cluster the data,and the midpoint of two adjacent clustering centers is taken as the dividing point of domain division.On this basis,the data is fuzzed to form a fuzzy time series.Secondly,a high-order fuzzy relation with multiple antecedents is established according to the main measurement indexes of power load,which is used to predict the short-term trend change of load in the distribution stations.Matlab/Simulink simulation results show that the load forecasting errors of the typical fuzzy time series on the time scale of one day and one week are[−50,20]and[−50,30],while the load forecasting errors of the improved fuzzy time series on the time scale of one day and one week are[−20,15]and[−20,25].It shows that the fuzzy time series algorithm improved by clustering improves the prediction accuracy and can effectively predict the short-term load trend of distribution stations.

Two-Stage Optimal Dispatching of Wind Power-Photovoltaic-Solar Thermal Combined System Considering Economic Optimality and Fairness

Aiming at the problems of large-scale wind and solar grid connection,how to ensure the economy of system operation and how to realize fair scheduling between new energy power stations,a two-stage optimal dispatching model of wind power-photovoltaic-solar thermal combined system considering economic optimality and fairness is proposed.Firstly,the first stage dispatching model takes the overall economy optimization of the system as the goal and the principle of maximizing the consumption of wind and solar output,obtains the optimal output value under the economic conditions of each new energy station,and then obtains the maximum consumption space of the new energy station.Secondly,based on the optimization results of the first stage,the second stage dispatching model uses the dispatching method of fuzzy comprehensive ranking priority to prioritize the new energy stations,and then makes a fair allocation to the dispatching of the wind and solar stations.Finally,the analysis of a specific example shows that themodel can take into account the fairness of active power distribution of new energy stations on the basis of ensuring the economy of system operation,make full use of the consumption space,and realize the medium and long-term fairness distribution of dispatching plan.

Localization method of subsynchronous oscillation source based on high-resolution time-frequency distribution image and CNN

The penetration of new energy sources such as wind power is increasing,which consequently increases the occurrence rate of subsynchronous oscillation events.However,existing subsynchronous oscillation source-identification methods primarily analyze fixed-mode oscillations and rarely consider time-varying features,such as frequency drift,caused by the random volatility of wind farms when oscillations occur.This paper proposes a subsynchronous oscillation sourcelocalization method that involves an enhanced short-time Fourier transform and a convolutional neural network(CNN).First,an enhanced STFT is performed to secure high-resolution time-frequency distribution(TFD)images from the measured data of the generation unit ports.Next,these TFD images are amalgamated to form a subsynchronous oscillation feature map that serves as input to the CNN to train the localization model.Ultimately,the trained CNN model realizes the online localization of subsynchronous oscillation sources.The effectiveness and accuracy of the proposed method are validated via multimachine system models simulating forced and natural oscillation events using the Power Systems Computer Aided Design platform.Test results show that the proposed method can localize subsynchronous oscillation sources online while considering unpredictable fluctuations in wind farms,thus providing a foundation for oscillation suppression in practical engineering scenarios.

Two-Stage Optimal Scheduling of Community Integrated Energy System

From the perspective of a community energy operator,a two-stage optimal scheduling model of a community integrated energy system is proposed by integrating information on controllable loads.The day-ahead scheduling analyzes whether various controllable loads participate in the optimization and investigates the impact of their responses on the operating economy of the community integrated energy system(IES)before and after;the intra-day scheduling proposes a two-stage rolling optimization model based on the day-ahead scheduling scheme,taking into account the fluctuation of wind turbine output and load within a short period of time and according to the different response rates of heat and cooling power,and solves the adjusted output of each controllable device.The simulation results show that the optimal scheduling of controllable loads effectively reduces the comprehensive operating costs of community IES;the two-stage optimal scheduling model can meet the energy demand of customers while effectively and timely suppressing the random fluctuations on both sides of the source and load during the intra-day stage,realizing the economic and smooth operation of IES.

Modeling of Large-Scale Hydrogen Storage System Considering Capacity Attenuation and Analysis of Its Efficiency Characteristics

In the existing power system with a large-scale hydrogen storage system,there are problems such as low efficiency of electric-hydrogen-electricity conversion and single modeling of the hydrogen storage system.In order to improve the hydrogen utilization rate of hydrogen storage system in the process of participating in the power grid operation,and speed up the process of electric-hydrogen-electricity conversion.This article provides a detailed introduction to the mathematical and electrical models of various components of the hydrogen storage unit,and also establishes a charging and discharging efficiency model that considers the temperature and internal gas partial pressure of the hydrogen storage unit.These models are of great significance for studying and optimizing gas storage technology.Through these models,the performance of gas storage units can be better understood and improved.These studies are very helpful for improving energy storage efficiency and sustainable development.The factors affecting the charge-discharge efficiency of hydrogen storage units are analyzed.By integrating the models of each unit and considering the capacity degradation of the hydrogen storage system,we can construct an efficiency model for a large hydrogen storage system and power conversion system.In addition,the simulation models of the hydrogen production system and hydrogen consumption system were established in MATLAB/Simulink.The accuracy and effectiveness of the simulation model were proved by comparing the output voltage variation curve of the simulation with the polarization curve of the typical hydrogen production system and hydrogen consumption system.The results show that the charge-discharge efficiency of the hydrogen storage unit increases with the increase of operating temperature,and H2 and O2 partial voltage have little influence on the charge-discharge efficiency.In the process of power conversion system converter rectification operation,its efficiency decreases with the increase of temperature,while in the process of inverter operation,power conversion system efficiency increases with the increase of temperature.Combined with the efficiency of each hydrogen storage unit and power conversion system converter,the upper limit of the capacity loss of different hydrogen storage units was set.The optimal charge-discharge efficiency of the hydrogen storage system was obtained by using the Cplex solver at 36.46%and 66.34%.

Research on pre-synchronization control strategy of the micro-grid with multi-VSG

Seamless switching is an important guarantee for the inverter to work continuously without stopping when gridconnected power generation system is the transient process from grid-connected to isolated island and from isolated island reclosing. Based on the principle of pre-synchronous operation of traditional synchronous generators, this paper analyzes the principle of pre-synchronous process and its effect on the steady-state of micro grid with multiple virtual synchronous generators. At the same time, the paper analyzes the transient influence of the closed pre-synchronous switch on the power distribution of the micro grid under different phase modes. This paper proposes a pre-synchronous control strategy based on phase recognition. MATLAB/Simulink simulation software is used to verify the correctness and validity of the proposed method.

Review of lithium-ion battery state of charge estimation

The technology deployed for lithium-ion battery state of charge(SOC)estimation is an important part of the design of electric vehicle battery management systems.Accurate SOC estimation can forestall excessive charging and discharging of lithium-ion batteries,thereby improving discharge efficiency and extending cycle life.In this study,the key lithium-ion battery SOC estimation technologies are summarized.First,the research status of lithium-ion battery modeling is introduced.Second,the main technologies and difficulties in model parameter identification for lithium-ion batteries are discussed.Third,the development status and advantages and disadvantages of SOC estimation methods are summarized.Finally,the current research problems and prospects for development trends are summarized.

Investigation on the Dielectric Properties of CO2 and CO2-Based Gases Based on the Boltzmann Equation Analysis

In this paper, the dielectric properties of CO2, CO2/air, CO2/O2, CO2/N2, CO2/CF4, CO2/CH4, CO2/He, C02/H2, CO2/NH3 and CO2/CO were investigated based on the Boltzmann equation analysis, in which the reduced critical electric field strength （E/N）cr of the gases was derived from the calculated electron energy distribution function （EEDF） by solv- ing the Boltzmann transport equation. In this work, it should be noted that the fundamental data were carefully selected by the published experimental results and calculations to ensure the validity of the calculation. The results indicate that if He, H2, N2 and CH4, in which there axe high ionization coefficients or a lack of attachment reactions, are added into CO2, the dielectric properties will decrease. On the other hand, air, O2, NH3 and CFa （ranked in terms of （E/N）cr value in increasing order） have the potential to improve the dielectric property of CO2 at room temperature.

A Data Driven Security Correction Method for Power Systems with UPFC

The access of unified power flow controllers(UPFC)has changed the structure and operation mode of power grids all across the world,and it has brought severe challenges to the traditional real-time calculation of security correction based on traditionalmodels.Considering the limitation of computational efficiency regarding complex,physical models,a data-driven power system security correction method with UPFC is,in this paper,proposed.Based on the complex mapping relationship between the operation state data and the security correction strategy,a two-stage deep neural network(DNN)learning framework is proposed,which divides the offline training task of security correction into two stages:in the first stage,the stacked auto-encoder(SAE)classification model is established,and the node correction state(0/1)output based on the fault information;in the second stage,the DNN learningmodel is established,and the correction amount of each action node is obtained based on the action nodes output in the previous stage.In this paper,the UPFC demonstration project of NanjingWest Ring Network is taken as a case study to validate the proposed method.The results show that the proposed method can fully meet the real-time security correction time requirements of power grids,and avoid the inherent defects of the traditional model method without an iterative solution and can also provide reasonable security correction strategies for N-1 and N-2 faults.

Research on Spatial Statistical Downscaling Method of Meteorological Data Applied to Photovoltaic Prediction

Aiming at the low spatial resolution of meteorological data output from a numerical model in photovoltaic power prediction,a geographically weighted statistical downscaling method considers the influence factors such as normalized vegetation index(NDVI),digital elevation model(DEM),slope direction,longitude and latitude is proposed.This method is based on the correlation between meteorological data and NDVI,DEM,slope direction,latitude and longitude,and introduces DEM and local Moran index to improve the regression model,and obtains 100∗100 m high-resolution meteorological spatial distribution data.Finally,combining the measured data of the study area and the established EOF iterative downscaling method to verify and compare the downscaling results.The results show that the error between the downscaled meteorological data and the measured value is smaller,and the comprehensive downscaling accuracy of the geographically weighted regression method is higher,and the model fitting effect is better.Therefore,this method can effectively improve the influence of errors caused by lower resolution,and provide a more reliable meteorological basis for the prediction of photovoltaic power.

Analysis of Power Quality Problems in Large-Scale Application of Air-Source Heat Pump

With the implementation of electric energy alternatives,the large-scale application of electric energy substitution represented by air-source heat pumps has replaced traditional coal-fired heating,which is beneficial for the environment and alleviates air pollution.However,the large-scale application of airsource heat pumps has brought power quality problems such as voltage sags,harmonic pollution,and three-phase imbalance to the distribution network.This paper studies the fixed-frequency and variablefrequency air-source heat pump,introduces its working principle,analyzes the mechanism of its power quality problem.Moreover,the paper establishes a simulation model for the fixed-frequency heat pump and variable-frequency heat pump to connect to the distribution network.This research mainly studies the impact of large-scale fixed-frequency heat pumps on the depth of voltage sags in the distribution network and the impact of large-scale variable-frequency heat pumps on the harmonic content of the distribution network under different penetration rates and uses measured data to verify the reliability of the simulation results.This paper uses experimental data for the first time to verify the real power quality problems of large-scale heat pumps,which can provide a reference for determining the power quality standards for heat pumps connected to the power grid.At the same time,it can also provide a reference for the power quality management of the distribution network that is actually connected to electric heating.

Optimal Allocation of Comprehensive Resources for Large-Scale Access of Electric Kiln to the Distribution Network

With the significant progress of the“coal to electricity”project,the electric kiln equipment began to be connected to the distribution network on a large scale,which caused power quality problems such as low voltage,high harmonic distortion rate,and high reactive power loss.This paper proposes a two-stage power grid comprehensive resource optimization configuration model.A multi-objective optimization solution based on the joint simulation platform of Matlab and OpenDSS is developed.The solution aims to control harmonics and optimize reactive power.In the first stage,a multi-objective optimization model is established to minimize the active network loss,voltage deviation,and equipment cost under the constraint conditions of voltage margin,power factor,and reactive power compensation capacity.Furthermore,the first stage uses a particle swarm optimization(PSO)algorithm to optimize the location and capacity of both series and parallel compensation devices in the distribution network.In the second stage,the optimal configuration model of the active power filter assumes the cost of the APF as the objective function and takes the harmonic voltage content rate,the total voltage distortion rate,and the allowable harmonic current as the constraint conditions.The proposed solution eliminates the harmonics by uniformly configuring active filters in the distribution network and centrally control harmonics at the system level.Finally,taking the IEEE33 distribution network as the object and considering the change of electric furnace permeability in the range of 20%–50%,the simulation results show that the proposed algorithm effectively reduces the distribution network’s loss,its harmonic content and significantly improve its voltage.

Distributed Robust Optimal Dispatch for the Microgrid Considering Output Correlation between Wind and Photovoltaic

As an effective carrier of integrated clean energy,the microgrid has attracted wide attention.The randomness of renewable energies such as wind and solar power output brings a significant cost and impact on the economics and reliability of microgrids.This paper proposes an optimization scheme based on the distributionally robust optimization(DRO)model for a microgrid considering solar-wind correlation.Firstly,scenarios of wind and solar power output scenarios are generated based on non-parametric kernel density estimation and the Frank-Copula function;then the generated scenario results are reduced by K-means clustering;finally,the probability confidence interval of scenario distribution is constrained by 1-norm and∞-norm.The model is solved by a column-and-constraint generation algorithm.Experimental studies are conducted on a microgrid system in Jiangsu,China and the obtained scheduling solution turned out to be superior under wind and solar power uncertainties,which verifies the effectiveness of the proposed DRO model.

Inter-Provincial Transaction Model in Two-Level ElectricityMarket Considering Carbon Emission and Consumption Responsibility Weights

In the context of the joint operation of China’s intra-provincial markets and inter-provincial trading,how to meet the load demand and energy consumption using inter-provincial renewable energy trading is a key problem.The combined operation of intra-provincial and inter-provincial markets provides a new way for provincial power companies to optimize and clear the intra-provincial power market,complete the intra-provincial consumption responsibility weight index,and consume renewable energy across provinces and regions.This paper combines power generation and consumption within the province,uses inter-provincial renewable energy trading tomeet the load demand within the province and completes the index of intra-provincial consumption responsibility weights.The intra-provincial market trading and inter-provincial market clearing are respectively taken as the upper and lower levels of the model.Under the two-level electricity market operation framework,the upper-level model aims to minimize the expected total operating cost within the province considering the carbon emission cost and the weight of the consumption responsibility,while the lower-level model aims to minimize the inter-provincial renewable energy purchasing cost.Finally,the influence of inter-provincial transaction mechanism,risk aversion coefficient,voucher price,and responsibility weight on operating cost is analyzed.Simulation is used to verify that the proposed model can meet the requirements of the provincial load power consumption and the consumption responsibility weight index,and promote the consumption of renewable energy.

Simulation Study of the Control Strategy of a DC Inverter Heat Pump Using a DC Distribution Network

Photovoltaics,energy storage,direct current and flexibility(PEDF)are important pillars of achievement on the path to manufacturing nearly zero energy buildings(NZEBs).HVAC systems,which are an important part of public buildings,play a key role in adapting to PDEF systems.This research studied the basic principles and operational control strategies of a DC inverter heat pump using a DC distribution network with the aim of contributing to the development and application of small DC distribution systems.Along with the characteristics of a DC distribution network and different operating conditions,a DC inverter heat pump has the ability to adapt to changes in the DC bus voltage and adds flexibility to the system.Theoretical models of the DC inverter heat pump integrated with an ice storage unit were developed.The control strategies of the DC inverter heat pump system considered the influence of both room temperature and varied bus voltage.A simulation study was conducted using MATLAB&Simulink software with simulation results validated by experimental data.The results showed that:(1)The bus fluctuation under the rated working voltage had little effect on the operation of the unit;(2)When the bus voltage was fluctuating from 80%-90%or 105%-107%,the heat pump could still operate normally by reducing the frequency;(3)When the bus voltage was less than 80%or more than 107%,the unit needed to be shut down for the sake of equipment safety,so that the energy storage device could adjust to the sharp decrease or rise of voltage.

A Fault-Tolerant Mobility-Aware Caching Method in Edge Computing

Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to users.In the MEC environment,frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery,ultimately enhancing the quality of the user experience.However,due to the typical placement of edge devices and nodes at the network’s periphery,these components may face various potential fault tolerance challenges,including network instability,device failures,and resource constraints.Considering the dynamic nature ofMEC,making high-quality content caching decisions for real-time mobile applications,especially those sensitive to latency,by effectively utilizing mobility information,continues to be a significant challenge.In response to this challenge,this paper introduces FT-MAACC,a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance mechanisms.This approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached content.Furthermore,it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system’s reliability,availability,and persistence.Empirical results unequivocally demonstrate that FTMAACC outperforms its peer methods in cache hit rates and transmission latency.

Proactive Caching at the Wireless Edge:A Novel Predictive User Popularity-Aware Approach

Mobile Edge Computing(MEC)is a promising technology that provides on-demand computing and efficient storage services as close to end users as possible.In an MEC environment,servers are deployed closer to mobile terminals to exploit storage infrastructure,improve content delivery efficiency,and enhance user experience.However,due to the limited capacity of edge servers,it remains a significant challenge to meet the changing,time-varying,and customized needs for highly diversified content of users.Recently,techniques for caching content at the edge are becoming popular for addressing the above challenges.It is capable of filling the communication gap between the users and content providers while relieving pressure on remote cloud servers.However,existing static caching strategies are still inefficient in handling the dynamics of the time-varying popularity of content and meeting users’demands for highly diversified entity data.To address this challenge,we introduce a novel method for content caching over MEC,i.e.,PRIME.It synthesizes a content popularity prediction model,which takes users’stay time and their request traces as inputs,and a deep reinforcement learning model for yielding dynamic caching schedules.Experimental results demonstrate that PRIME,when tested upon the MovieLens 1M dataset for user request patterns and the Shanghai Telecom dataset for user mobility,outperforms its peers in terms of cache hit rates,transmission latency,and system cost.

Thermodynamic and Transport Properties of Real Air Plasma in Wide Range of Temperature and Pressure

Air plasma has been widely applied in industrial manufacture. In this paper, both dry and humid air plasmas＇ thermodynamic and transport properties are calculated in temperature 300 100000 K and pressure 0.1-100 atm. To build a more precise model of real air plasma, over 70 species are considered for composition. Two different methods, the Gibbs free energy minimization method and the mass action law method, are used to deternfinate the composition of the air plasma in a different temperature range. For the transport coefficients, the simplified Chapman-Enskog method developed by Devoto has been applied using the most recent collision integrals. It is found that the presence of CO2 has almost no effect on the properties of air plasma. The influence of H2O can be ignored except in low pressure air plasma, in which the saturated vapor pressure is relatively high. The results will serve as credible inputs for computational simulation of air plasma.