Power generation expansion planning approach considering carbon emission constraints

Decarbonization of the power sector in China is an essential aspect of the energy transition process to achieve carbon neutrality.The power sector accounts for approximately 40%of China’s total CO_(2) emissions.Accordingly,collaborative optimization in power generation expansion planning(GEP)simultaneously considering economic,environmental,and technological concerns as carbon emissions is necessary.This paper proposes a collaborative mixedinteger linear programming optimization approach for GEP.This minimizes the power system’s operating cost to resolve emission concerns considering energy development strategies,flexible generation,and resource limitations constraints.This research further analyzes the advantages and disadvantages of current GEP techniques.Results show that the main determinants of new investment decisions are carbon emissions,reserve margins,resource availability,fuel consumption,and fuel price.The proposed optimization method is simulated and validated based on China’s power system data.Finally,this study provides policy recommendations on the flexible management of traditional power sources,the market-oriented mechanism of new energy sources,and the integration of new technology to support the attainment of carbon-neutral targets in the current energy transition process.

Difference between grid connections of large-scale wind power and conventional synchronous generation

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.

Adaptive Reclosing Scheme Based on Traveling Wave Injection For Multi-Terminal dc Grids

The hybrid dc circuit breaker(HCB)has the advantages of fast action speed and low operating loss,which is an idealmethod for fault isolation ofmulti-terminal dc grids.Formulti-terminal dc grids that transmit power through overhead lines,HCBs are required to have reclosing capability due to the high fault probability and the fact that most of the faults are temporary faults.To avoid the secondary fault strike and equipment damage that may be caused by the reclosing of the HCB when the permanent fault occurs,an adaptive reclosing scheme based on traveling wave injection is proposed in this paper.The scheme injects traveling wave signal into the fault dc line through the additionally configured auxiliary discharge branch in the HCB,and then uses the reflection characteristic of the traveling wave signal on the dc line to identify temporary and permanent faults,to be able to realize fast reclosing when the temporary fault occurs and reliably avoid reclosing after the permanent fault occurs.The test results in the simulation model of the four-terminal dc grid show that the proposed adaptive reclosing scheme can quickly and reliably identify temporary and permanent faults,greatly shorten the power outage time of temporary faults.In addition,it has the advantages of easiness to implement,high reliability,robustness to high-resistance fault and no dead zone,etc.

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.

Cyber-physical Collaborative Restoration Strategy for Power Transmission System Considering Maintenance Scheduling

In order to improve the ability of power transmission system to cope with compound faults on the communication side and power side,a cyber-physical collaborative restoration strategy is proposed.First,according to the information system’s role in fault diagnosis,remote control of equipment maintenance and automatic output adjustment of generator restoration,a cyber-physical coupling model is proposed.On this basis,a collaborative restoration model of power transmission system is established by studying interactions among maintenance schedule paths,information system operation,and power system operation.Based on power flow linearization and the large M-ε method,the above model is transformed into a mixed integer linear programming model,whose computational burden is reduced further by the clustering algorithm.According to the parameters of IEEE39 New England system,the geographic wiring diagram of the cyber-physical system is established.Simulation results show the proposed restoration strategy can consider the support function of the information system and space-time coordination of equipment maintenance at both sides comprehensively to speed up load recovery progress.

Adaptive electricity theft detection method based on load shape dictionary of customers

With the application of the advanced measurement infrastructure in power grids,data driven electricity theft detection methods become the primary stream for pinpointing electricity thieves.However,owing to anomaly submergence,which shows that the usage patterns of electricity thieves may not always deviate from those of normal users,the performance of the existing usage-pattern-based method could be affected.In addition,the detection results of some unsupervised learning algorithm models are abnormal degrees rather than“0-1”to ascertain whether electricity theft has occurred.The detection with fixed threshold value may lead to deviation and would not be sufficiently flexible to handle the detection for different scenes and users.To address these issues,this study proposes a new electricity theft detection method based on load shape dictionary of users.A corresponding strategy for tunable threshold is proposed to optimize the detection effect of electricity theft,and the efficacy and applicability of the proposed adaptive electricity theft detection method were verified from numerical experiments.

Compensation of body shake errors in terahertz beam scanning single frequency holography for standoff personnel screening

In the terahertz（THz） band, the inherent shake of the human body may strongly impair the image quality of a beam scanning single frequency holography system for personnel screening. To realize accurate shake compensation in imaging processing, it is quite necessary to develop a high-precision measure system. However, in many cases, different parts of a human body may shake to different extents, resulting in greatly increasing the difficulty in conducting a reasonable measurement of body shake errors for image reconstruction. In this paper, a body shake error compensation algorithm based on the raw data is proposed. To analyze the effect of the body shake on the raw data, a model of echoed signal is rebuilt with considering both the beam scanning mode and the body shake. According to the rebuilt signal model, we derive the body shake error estimated method to compensate for the phase error. Simulation on the reconstruction of point targets with shake errors and proof-of-principle experiments on the human body in the 0.2-THz band are both performed to confirm the effectiveness of the body shake compensation algorithm proposed.

Optimization modeling method for coal-to-electricity heating load considering differential decisions

Heating by electricity rather than coal is considered one effective way to reduce environmental problems. Thus, the electric heating load is growing rapidly, which may cause undesired problems in distribution grids because of the randomness and dispersed integration of the load. However, the electric heating load may also function as an energy storage system with optimal operational control. Therefore, the optimal modeling of electric heating load characteristics, considering its randomness, is important for grid planning and construction. In this study, the heating loads of distributed residential users in a certain area are modeled based on the Fanger thermal comfort equation and the predicted mean vote thermal comfort index calculation method. Different temperatures are considered while modeling the users' heating loads. The heat load demand curve is estimated according to the time-varying equation of interior temperature. A multi-objective optimization model for the electric heating load with heat energy storage is then studied considering the demand response(DR), which optimizes economy and the comfort index. A fuzzy decision method is proposed, considering the factors influencing DR behavior. Finally, the validity of the proposed model is verified by simulations. The results show that the proposed model performs better than the traditional method.

Communication Networking Schemes for Wide Area Electric Vehicle Energy Service Network

Electric vehicles (EVs) are an emerging type of mobile intelligent power consumption devices in Smart Grid as new green transport tools. In order to provide a powerful automation and intelligence support for wide area electric vehicles energy service network, we analyze the network infrastructure and communications demands of various terminals, devices and monitoring systems distributed in wide area electric vehicle energy service network. According to interactive user services scenarios and energy operations intelligent monitoring, we propose multimode communication integration architecture for wide area electric vehicle energy service network by means of the fusion of the Internet of Things (IoT) technology. Then, we design different networking schemes in access networks and backbone transmission networks meeting multi-scene and multi-operation interaction requirements. The networking schemes will provide efficient technical support to implement intelligent, cross-regional, interactive energy services for electric vehicle users.

SMS Notifying Design in Intra-Day Scheduling Security Check System

In order to master the future operation and stability of power grid exactly, and gasp the weak point accurately, the requirement of power data quality become strict, and the data timeliness of power gird change into outstandingly more and more, because of this, in this paper propose the SMS notifying method of intra-day scheduling data based on safely data principle. The principle is mainly complied with the data source existed or not, the data is coincident to the power grid model, the data is unbroken or not and it is reasonable with the physical reality, then it can obtain better convergence and reasonable intra-day check power data. In order to accelerate the information and network pace of the power grid, the SMS notifying can monitoring data quality without time delay. It dredge the vast path for the future power market into use with the wide range, then, can more effective to ensure the convergence and accuracy of safe check calculation, it provides an effective guarantee with the safe and stable operation of the power grid, in the same way, it is also an efficient method to provides effective guarantee for power grid safe operation from the data source.

Phases-Controlled Coordinated Charging Method for Electric Vehicles

When private electric vehicles(EVs),which will be the main part of the EVs’cluster in the future,are plugged in power system by single phase power line,can result to three-phase unbalance problem of distribution network.In this work,a phased-controlled coordinated charging method was put forward to solve this problem.Firstly,the impacts of charging load to distribution network was analyzed based on the equivalent circuit;and then an architecture of the control method and its corresponding optimal control model were introduced.The optimal model is a multi-objective optimization model,which includes minimizing load variance of each phase and minimizing the power asymmetrical degree of three-phase load;lastly,three scenarios considering balance and unbalance cases were envisioned to verify the reasonableness of this control method based on IEEE-37 distribution network.Results show that the phased-controlled coordinated charging method can minimize the load variance as well as the negative sequence current.

Univariate Time Series Anomaly Detection Based on Hierarchical Attention Network

In order to support the perception and defense of the operation risk of the medium and low voltage distribution system, it is crucial to conduct data mining on the time series generated by the system to learn anomalous patterns, and carry out accurate and timely anomaly detection for timely discovery of anomalous conditions and early alerting. And edge computing has been widely used in the processing of Internet of Things (IoT) data. The key challenge of univariate time series anomaly detection is how to model complex nonlinear time dependence. However, most of the previous works only model the short-term time dependence, without considering the periodic long-term time dependence. Therefore, we propose a new Hierarchical Attention Network (HAN), which introduces seven day-level attention networks to capture fine-grained short-term time dependence, and uses a week-level attention network to model the periodic long-term time dependence. Then we combine the day-level feature learned by day-level attention network and week-level feature learned by week-level attention network to obtain the high-level time feature, according to which we can calculate the anomaly probability and further detect the anomaly. Extensive experiments on a public anomaly detection dataset, and deployment in a real-world medium and low voltage distribution system show the superiority of our proposed framework over state-of-the-arts.

Model predictive control and improved low-pass filtering strategies based on wind power fluctuation mitigation

The rapid development of renewable energy sources such as wind power has brought great challenges to the power grid. Wind power penetration can be improved by using hybrid energy storage(ES) to mitigate wind power fluctuation. We studied the strategy of smoothing wind power fluctuation and the strategy of hybrid ES power distribution. Firstly, an effective control strategy can be extracted by comparing constant-time low-pass filtering(CLF), variable-time low-pass filtering(VLF), wavelet packet decomposition(WPD), empirical mode decomposition(EMD) and model predictive control algorithms with fluctuation rate constraints of the identical grid-connected wind power. Moreover, the mean frequency of ES as the cutoff frequency can be acquired by the Hilbert Huang transform(HHT), and the time constant of filtering algorithm can be obtained. Then, an improved low-pass filtering algorithm(ILFA) is proposed to achieve the power allocation between lithium battery(LB) and supercapacitor(SC), which can overcome the over-charge and over-discharge of ES in the traditional low-pass filtering algorithm(TLFA). In addition, the optimized LB and SC power are further obtained based on the SC priority control strategy combined with the fuzzy control(FC) method. Finally, simulation results show that wind power fluctuation can be effectively suppressed by LB and SC based on the proposed control strategies, which is beneficial to the development of wind and storage system.

Renewed investigation on Power System Stabilizer design

Power System Stabilizer (PSS) was proposed during 1960s to solve the low frequency oscillation problem raised by the wide application of the high-gain fast-response exciters. The fundamentals of PSS design lie in the angle compensation to increase the damping torque, which, since then, has become an important principle in designing the various power system dampers, such as SVC, TCSC, UPFC. Although many papers have been dedicated to the application of this principle, it is interesting to note that in the real industry applications PSS parameters have to be carefully tuned on site in spite of its mature design theory. So does the classical PSS design theory really meet the PSS design demand? By combining the frequency domain and the time domain analysis, this paper reinvestigates the basic idea behind the classical PSS design theory. The paper clarifies the con-cepts of the synchronous torque as well as the damping torque and proves that the classical PSS design principles based on these concepts are not theoretically sound. Then the paper discusses the Linear Optimal Controller Design method and ana-lyzes its relations with the conventional PID design. By doing so the paper reveals the real mechanism of the PSS and proposes to use more systematic and advanced control tools to enhance the controller performance.

Online Identification of Power Battery Parameters for Electric Vehicles Using a Decoupling Multiple Forgetting Factors Recursive Least Squares Method

Li-ion batteries are widely used in electric vehicles(EVs).However,the accuracy of online SOC estimation is still challenging due to the time-varying parameters in batteries.This paper proposes a decoupling multiple forgetting factors recursive least squares method(DMFFRLS)for EV battery parameter identification.The errors caused by the different parameters are separated and each parameter is tracked independently taking into account the different physical characteristics of the battery parameters.The Thevenin equivalent circuit model(ECM)is employed considering the complexity of battery management system(BMS)on the basis of comparative analysis of several common battery ECMs.In addition,decoupling multiple forgetting factors are used to update the covariance due to different degrees of error of each parameter in the identification process.Numerous experiments are employed to verify the proposed DMFFRLS method.The parameters for commonly used LiFePO4(LFP),Li(NiCoMn)O2(NCM)battery cells and battery packs are identified based on the proposed DMFFRLS method and three conventional methods.The experimental results show that the error of the DMFFRLS method is less than 15 mV,which is significantly lower than the conventional methods.The proposed DMFFRLS shows good performance for parameter identification on different kind of batteries,and provides a basis for state of charge(SOC)estimation and BMS design of EVs.

Review of recent research towards power cable life cycle management

Power cables are integral to modern urban power transmission and distribution systems.For power cable asset managers worldwide,a major challenge is how to manage effectively the expensive and vast network of cables,many of which are approaching,or have past,their design life.This study provides an in-depth review of recent research and development in cable failure analysis,condition monitoring and diagnosis,life assessment methods,fault location,and optimisation of maintenance and replacement strategies.These topics are essential to cable life cycle management(LCM),which aims to maximise the operational value of cable assets and is now being implemented in many power utility companies.The review expands on material presented at the 2015 JiCable conference and incorporates other recent publications.The review concludes that the full potential of cable condition monitoring,condition and life assessment has not fully realised.It is proposed that a combination of physics-based life modelling and statistical approaches,giving consideration to practical condition monitoring results and insulation response to in-service stress factors and short term stresses,such as water ingress,mechanical damage and imperfections left from manufacturing and installation processes,will be key to success in improved LCM of the vast amount of cable assets around the world.

Resilient outage recovery of a distribution system: co-optimizing mobile power sources with network structure

Outage recovery is important for reducing the economic cost and improving the reliability of a distribution system(DS)in extreme weather and with equipment faults.Previous studies have separately considered network reconfigu-ration(NR)and dispatching mobile power sources(MPS)to restore the outage load.However,NR cannot deal with the scenario of an electrical island,while dispatching MPS results in a long power outage.In this paper,a resilient outage recovery method based on co-optimizing MPS and NR is proposed,where the DS and traffic network(TN)are considered simultaneously.In the DS,the switch action cost and power losses are minimized,and the access points of MPSs are changed by carrying out the NR process.In the TN,an MPS dispatching model with the objective of mini-mizing power outage time,routing and power generation cost is developed to optimize the MPSs’schedule.A solu-tion algorithm based on iteration and relaxation methods is proposed to simplify the solving process and obtain the optimal recovery strategy.Finally,numerical case studies on the IEEE 33 and 119-bus systems validate the proposed resilient outage recovery method.It is shown that the access point of MPS can be changed by NR to decrease the power outage time and dispatching cost of MPS.The results also show that the system operation cost can be reduced by considering power losses in the objective function.

Crowdsourced federated learning architecture with personalized privacy preservation

In crowdsourced federated learning,differential privacy is commonly used to prevent the aggregation server from recovering training data from the models uploaded by clients to achieve privacy preservation.However,improper privacy budget settings and perturbation methods will severely impact model performance.In order to achieve a harmonious equilibrium between privacy preservation and model performance,we propose a novel architecture for crowdsourced federated learning with personalized privacy preservation.In our architecture,to avoid the issue of poor model performance due to excessive privacy preservation requirements,we establish a two-stage dynamic game between the task requestor and clients to formulate the optimal privacy preservation strategy,allowing each client to independently control privacy preservation level.Additionally,we design a differential privacy perturbation mechanism based on weight priorities.It divides the weights based on their relevance with local data,applying different levels of perturbation to different types of weights.Finally,we conduct experiments on the proposed perturbation mechanism,and the experimental results indicate that our approach can achieve better global model performance with the same privacy budget.

Operating system network security enhancement scheme based on trusted storage

Data storage security has become the core of many network security issues.In order to achieve trusted storage and trusted measurement of network community data,this paper proposes a secure storage model based on trust extension for existing trusted storage technologies.In the process of document encryption,the key information is encrypted as well as decentralized stored by optimizing the ciphertext inverted index structure and update policy to ensure the security of index information.In the process of user access control mechanism,SAML and XACML are used in combination with role-based access control in order to achieve flexible and efficient authorization and access control.In the process of result query,ontology technology is introduced to better express the user’s query intention and improve the query accuracy.A large number of experiments demonstrate the effectiveness and feasibility of the scheme.