Abstract:The conventional power systems are evolving as smart grids. In recent times cyberattacks on smart grids have been increasing. Among diferent attacks, False Data Injection (FDI) is considered as an emerging threat that has signifcant impact. By exploiting the vulnerabilities of IEC 61850 Generic Object-Oriented Substation Events (GOOSE) and Sampled Values (SV) attackers can launch diferent FDI attacks. In this paper, a real-time set up capable of simulating FDI on GOOSE and SV protocols is developed to evaluate the impact of such attacks on power grid. IEC 62351 stipulates cybersecurity guidelines for GOOSE and SV, but only at communication or Information Technology (IT) level. Hence there is a need to develop a holistic security both at IT and Operation Technology (OT) level. In this regard, a novel sequence content resolver-based hybrid security scheme suitable to tackle FDI attacks on GOOSE and SV is proposed. Furthermore,the computational performance of the proposed hybrid security scheme is presented to demonstrate its applicability to the time critical GOOSE and SV protocols.

Abstract:High voltage direct current (HVDC) systems are efcient solutions for the integration of large-scale renewable energy sources with the main power grids. The rapid development of the HVDC grid has resulted in a growing interest in DC circuit breakers (DCCBs). A fast and reliable circuit breaker is a necessary requirement in the development of large scale HVDC grids. This paper provides a comprehensive review and survey of the HVDC CBs and discusses potential research directions. Operational principles and the main features of various DCCBs are described and their merits and shortcomings are also highlighted.

Abstract:Open communication system in modern power systems brings concern about information staleness which may cause power system frequency instability. The information staleness is often characterized by communication delay. However, communication delay is a packet-centered metric and cannot refect the requirement of information freshness for load frequency control (LFC). This paper introduces the age of information (AoI), which is more comprehensive and informative than the conventional communication delay modeling method. An LFC controller and communication are integrated into the design for LFC performance improvement. An AoI-aware LFC model is formulated frst, and considering each allowable update period of the smart sensor, diferent AoI-aware PI controllers are then designed according to the exponential decay rate. The right AoI-aware controller and update period are selected according to the degree of frequency fuctuation of the power system. Case studies are carried out on one-area and two-area power systems. The results show the superior performance of the AoI-aware controllers in comparison to the delay-dependent controllers.

Abstract:This paper applies the innovative idea of DLCI to PV array reconfguration under various PSCs to capture the maximum output power of a PV generation system. DLCI is a hybrid algorithm that integrates multiple meta-heuristic algorithms. Through the competition and cooperation of the search mechanisms of diferent metaheuristic algorithms, the local exploration and global development of the algorithm can be efectively improved to avoid power mismatch of the PV system caused by the algorithm falling into a local optimum. A series of discrete operations are performed on DLCI to solve the discrete optimization problem of PV array reconfguration. Two structures (DLCI-I and DLCI-II) are designed to verify the efect of increasing the number of sub-optimizers on the optimized performance of DLCI by simulation based on 10 cases of PSCs. The simulation shows that the increase of the number of sub-optimizers only gives a relatively small improvement on the DLCI optimization performance. DLCI has a signifcant efect on the reduction in the number of power peaks caused by PSC. The PV array-based reconstruction system of DLCI-II is reduced by 4.05%, 1.88%, 1.68%, 0.99% and 3.39%, when compared to the secondary optimization algorithms.

Abstract:Lithium-ion batteries (LIBs) are crucial for the large-scale utilization of clean energy. However, because of the complexity and real-time nature of internal reactions, the mechanism of capacity decline in LIBs is still unclear. This has become a bottleneck restricting their promotion and application. Electrochemical impedance spectroscopy (EIS) contains rich electrochemical connotations and signifcant application prospects, and has attracted widespread attention and research on efcient energy storage systems. Compared to traditional voltage and current data, the state-of-health (SOH) estimation model based on EIS has higher accuracy. This paper categorizes EIS measurement methods based on diferent principles, introduces the relationship between LIBs aging mechanism and SOH, and compares the advantages of diferent SOH estimation methods. After a detailed analysis of the latest technologies, a review is given. The insights of this review can deepen the understanding of the relationship between EIS and the aging efect mechanism of LIBs, and promote the development of new energy storage devices and evaluation methods.

Abstract:The power router (PR) is a promising piece of equipment for realizing multi-voltage level interconnection and fexible power control in the future distribution power grid. In this paper, a hybrid PR (HPR) topology based on power-frequency transformer electromagnetic coupling with converters is proposed for the medium distribution power grid. The power-frequency transformer is used to undertake power transmission, voltage conversion, and other main tasks, while the power electronic converters are combined to achieve active control. Equivalent magnetic and electrical circuit models are established to help discuss the operating principle of the proposed HPR. Additionally, the power fow and control principle of the HPR in diferent operating conditions are analyzed, with the control system design scheme presented. The theoretical analysis results are verifed by MATLAB/Simulink+Plecs simulation and a controller hardware-in-the-loop study, as well as a down-scale experimental test, indicating that the proposed HPR is fexible in active voltage support and current control.

Abstract:Meteorological changes urge engineering communities to look for sustainable and clean energy technologies to keep the environment safe by reducing CO2 emissions. The structure of these technologies relies on the deep integration of advanced data-driven techniques which can ensure efcient energy generation, transmission, and distribution. After conducting thorough research for more than a decade, the concept of the smart grid (SG) has emerged, and its practice around the world paves the ways for efcient use of reliable energy technology. However, many developing features evoke keen interest and their improvements can be regarded as the next-generation smart grid (NGSG). Also, to deal with the non-linearity and uncertainty, the emergence of data-driven NGSG technology can become a great initiative to reduce the diverse impact of non-linearity. This paper exhibits the conceptual framework of NGSG by enabling some intelligent technical features to ensure its reliable operation, including intelligent control, agent-based energy conversion, edge computing for energy management, internet of things (IoT) enabled inverter, agent-oriented demand side management, etc. Also, a study on the development of data-driven NGSG is discussed to facilitate the use of emerging data-driven techniques (DDTs) for the sustainable operation of the SG. The prospects of DDTs in the NGSG and their adaptation challenges in real-time are also explored in this paper from various points of view including engineering, technology, et al. Finally, the trends of DDTs towards securing sustainable and clean energy evolution from the NGSG technology in order to keep the environment safe is also studied, while some major future issues are highlighted. This paper can ofer extended support for engineers and researchers in the context of data-driven technology and the SG.

Abstract:Some double-circuit transmission lines are untransposed, which results in complex coupling relations between the parameters of the transmission lines. If the traditional modal transformation matrix is directly used to decouple the parameters, it can lead to large errors in the decoupled modal parameter, errors which will be amplifed in the fault location equation. Consequently, it makes the fault location results of the untransposed double-circuit transmission lines less accurate. Therefore, a new modal transformation method is needed to decouple the parameter matrix of untransposed double-circuit transmission lines and realize the fault location according to the decoupled modal parameter. By improving the basis of the Karrenbauer matrix, a modal transformation matrix suitable for decoupling parameters of untransposed double-circuit transmission lines is obtained. To address the diffculties in solving the fault location equation of untransposed double-circuit transmission lines, a new fault location method based on an improved Karrenbauer matrix and the quantum-behaved particle swarm optimization (QPSO) algorithm is proposed. Firstly, the line parameter matrix is decomposed into identical and inverse sequence components using the identical-inverse sequence component transformation. The Karrenbauer matrix is then transformed to obtain the improved Karrenbauer matrix for untransposed double-circuit transmission lines and applied to identical and inverse sequence components to solve the decoupled modal parameter. Secondly, based on the principle that voltage magnitudes at both ends are equal, the fault location equation is expressed using sequence components at each end, and the QPSO algorithm is introduced to solve the equation. Finally, the feasibility and accuracy of the proposed method are verifed by PSCAD simulation. The simulation results fully demonstrate that the innovative improvement on the basis of the traditional modal transformation matrix in this paper can realize the modal transformation of the complex coupling parameters of the untransposed double-circuit transmission lines. It causes almost no errors in the decoupling process. The QPSO algorithm can also solve the fault location equation more accurately. The new fault location method can realize the accurate fault location of untransposed double-circuit transmission lines.

Abstract:This paper addresses a two-stage stochastic-robust model for the day-ahead self-scheduling problem of an aggregator considering uncertainties. The aggregator, which integrates power and capacity of small-scale prosumers and fexible community-owned devices, trades electric energy in the day-ahead (DAM) and real-time energy markets (RTM), and trades reserve capacity and deployment in the reserve capacity (RCM) and reserve deployment markets (RDM). The ability of the aggregator providing reserve service is constrained by the regulations of reserve market rules, including minimum ofer/bid size and minimum delivery duration. A combination approach of stochastic programming (SP) and robust optimization (RO) is used to model diferent kinds of uncertainties, including those of market price, power/demand and reserve deployment. The risk management of the aggregator is considered through conditional value at risk (CVaR) and fuctuation intervals of the uncertain parameters. Case studies numerically show the economic revenue and the energy-reserve schedule of the aggregator with participation in diferent markets, reserve regulations, and risk preferences.

Abstract:Subsequent commutation failure (SCF) can be easily generated during the frst commutation failure (CF) recovery process in a line-commutated converter-based high voltage direct-current system. SCF poses a signifcant threat to the safe and stable operation of power systems, and accurate prediction of CF is thus important. However, SCF is afected by the operating characteristics of the main circuit and the coupling efects of sequential control response in the inverter station. These are difcult to predict accurately. In this paper, a new SCF prediction method considering the control response is proposed based on the physical principle of SCF. The time sequence and switching conditions of the controllers at diferent stages of the frst CF recovery process are described, and the corresponding equations of commutation voltage afected by diferent controllers are derived. The calculation method of the SCF threshold voltage is proposed, and the prediction method is established. Simulations show that the proposed method can predict SCF accurately and provide useful tools to suppress SCF

Abstract:With the increasing scale of distribution networks and the mass access of distributed generation, traditional centralized fault location methods can no longer meet the performance requirements of speed and high accuracy. Therefore, this paper proposes a fault segment location method based on spiking neural P systems and Bayesian estimation for distribution networks with distributed generation. First, the distribution network system topology is decoupled into single-branch networks. A spiking neural P system with excitatory and inhibitory synapses is then proposed to model the suspected faulty segment, and its matrix reasoning algorithm is executed to obtain a preliminary set of location results. Finally, the Bayesian estimation and contradiction principle are applied to verify and correct the initial results to obtain the fnal location results. Simulation results based on the IEEE 33-node system validate the feasibility and efectiveness of the proposed method.

Abstract:The uncertainties associated with multi-area power systems comprising both thermal and distributed renewable generation (DRG) sources such as solar and wind necessitate the use of an efcient load frequency control (LFC) technique. Therefore, a hybrid version of two metaheuristic algorithms (arithmetic optimization and African vulture’s optimization algorithm) is developed. It is called the ‘arithmetic optimized African vulture’s optimization algorithm (AOAVOA)’. This algorithm is used to tune a novel type-2 fuzzy-based proportional–derivative branched with dual degree-of-freedom proportional–integral–derivative controller for the LFC of a three-area hybrid deregulated power system. Thermal, electric vehicle (EV), and DRG sources (including a solar panel and a wind turbine system) are connected in area-1. Area-2 involves thermal and gas-generating units (GUs), while thermal and geothermal units are linked in area-3. Practical restrictions such as thermo-boiler dynamics, thermal-governor dead-band, and generation rate constraints are also considered. The proposed LFC method is compared to other controllers and optimizers to demonstrate its superiority in rejecting step and random load disturbances. By functioning as energy storage elements, EVs and DRG units can enhance dynamic responses during peak demand. As a result, the efect of the aforementioned units on dynamic reactions is also investigated. To validate its efectiveness, the closed-loop system is subjected to robust stability analysis and is compared to various existing control schemes from the literature. It is determined that the suggested AOAVOA improves ftness by 40.20% over the arithmetic optimizer (AO), while frequency regulation is improved by 4.55% over an AO-tuned type-2 fuzzy-based branched controller.

Abstract:This work presents a control approach based on sliding-mode-control (SMC) to design robust H∞ state feedback controllers for load frequency regulation of delayed interconnected power system (IPS) with parametric uncertainties. Considering both state feedback control strategy and delayed feedback control strategy, two SMC laws are proposed. The proposed control laws are designed to improve the stability and disturbance rejection performance of delayed IPS, while stabilization criteria in the form of linear matrix inequality are derived by choosing a Lyapunov–Krasovskii functional. An artifcial time-delay is incorporated in the control law design of the delayed feedback control structure to enhance the controller performance. A numerical example is considered to study the control performance of the proposed controllers and simulation results are provided to observe the dynamic response of the IPS.

Abstract:With the rapid development of wind power, the pressure on peak regulation of the power grid is increased. Electrochemical energy storage is used on a large scale because of its high efciency and good peak shaving and valley flling ability. The economic beneft evaluation of participating in power system auxiliary services has become the focus of attention since the development of grid-connected hundred megawatt-scale electrochemical energy storage systems (ESS). Based on the relationship between power and capacity in the process of peak shaving and valley flling, a dynamic economic beneft evaluation model of peak shaving assisted by hundred megawatt-scale electrochemical ESS considering the equivalent life of the battery is proposed. The model considers the investment cost of energy storage, power efciency, and operation and maintenance costs, and analyzes the dynamic economic benefts of different energy storage technologies participating in the whole life cycle of the power grid. Then, according to the current ESS market environment, the auxiliary service compensation price, peak-valley price diference and energy storage cost unit price required to make the energy storage technology achieve the balance of payments are calculated, and the economic balance points of diferent energy storage types are clarifed. Finally, based on the measured data of diferent provincial power grids, the economies of six energy storage types applied to three provincial power grids are compared and analyzed, and the rationality and efectiveness of the relevant models proposed are verifed. The work has theoretical guiding signifcance for the economic beneft evaluation of hundred megawatt-scale electrochemical energy storage.