The switching frequency of power electronic converters can be as high as hundreds of kilohertz in some cases. The time-step of the real-time simulation is generally suggested to be about one percent of the switching period to accurately locate the switch event. That brings great computation burden in unit time. To break this limitation, this article proposes a real-time simulation method based on the idea of switching period synchronization (SPS). The simulation time is synchronized with the reality time at an interval of the switching period, instead of the time-step. The switching period is adopted as the synchronization interval and also as the main time-step, which is further divided into several variable sub-time-steps determined by switch events within the switching period. To implement the proposed real-time simulation on the field-programmable gate array-based platform, an electromagnetic transient model in discrete-state-space form is proposed to simulate the circuit with the variable subtime-steps. The hardware-in-the-loop simulation of an on-board charger shows that the proposed method can realize the real-time simulation with a switching frequency of 200 kHz. The accuracy, efficiency, and applicability of the proposed method have been further validated.

Power electronic converters are gradually developing towards modularization and cascaded structures to adapt to high voltage, large capacity, and various energy conversion scenarios. However, the real-time simulation models of cascaded power electronic converters are mainly aimed at specific topologies. For example, commercial real-time simulators have developed various package models for modular multilevel converter (MMC). But package models are difficult to meet the simulation requirements of more new topologies such as solid-state transformer (SST). This paper proposes a general real-time simulation method for cascaded power electronic converters based on N-port submodules of any topology and any cascaded structure. Firstly, a general equivalent model of N-port submodules is built. Based on the equivalent model, the topology of each N-port submodule can be customized, and the simulation efficiency can be improved by eliminating the internal nodes of each submodule. Secondly, the incidence matrix is redefined to describe the cascade structures of submodules, which is more flexible than the traditional method and can avoid the decoupling between submodules. Cascaded power electronic converters based on single-port, dual-port and four-port submodule are simulated on the NI-PXI based real-time simulation platform, which verifies the universality and efficiency of the proposed method.

The traditional harmonic state estimation methods are unable to cope with the unappreciable harmonic states caused by the insufficient grid measurement configuration in practical applications, and thus have greater limitations in the application of the unknown harmonic source localisation problem. To address this situation, the paper proposes an unknown harmonic source localisation technique based on orthogonal matching pursuit (OMP) for unobservable systems. Firstly, the harmonic injection currents of sparse signal buses with low attenuation speed are selected as state variables to reconstruct the traditional harmonic state estimation equations; secondly, the OMP algorithm is proposed to achieve the accurate localisation of unknown harmonic sources and the precise calculation of harmonic currents. Finally, the simulation proves that the proposed method has high positioning accuracy, computational precision and operation speed in the multiharmonic source positioning work.

The high penetrability of renewable energy and the increasing demand for hydrogen energy pose challenges to system scheduling and the accommodation of photovoltaic and wind power. Therefore, a data-driven multienergy economic scheduling method with experiential knowledge bases is proposed to enhance system operational efficiency and economic performance. Firstly, the Wasserstein generative adversarial network is used to enhance wind power and photovoltaic historical samples. The K-medoids clustering algorithm and & phi;-divergence are applied to achieve scenario reduction and acquire fuzzy sets, eliminating the need for assumptions about their probability distributions. Secondly, a kernel density estimation is used to improve the accuracy of characterizing the probability distribution of energy loads. Based on fuzzy sets, the distributionally robust optimization is used to achieve optimal scheduling in an integrated electricity-hydrogen-heat system. This technique enhances the accommodation capacity of photovoltaic and wind power while reducing operational costs. Finally, a decision-making technique based on an experiential knowledge base is studied, aiming to swiftly match corresponding decision variables by evaluating the similarity of labeled state variables. Simulation results show that the proposed method improves the decision-making speed by about 0.8 times and reduces operating costs and photovoltaic or wind power curtailment by at least 1%. The method can provide fast and efficient day-ahead economic scheduling for digitized integrated energy systems.

With the development of power system, power electronic devices are widely used. Electromagnetic transient simulation technology is an important means of power system analysis. Due to the limitation of high-frequency switching on the simulation step size, the efficiency of electromagnetic transient simulation is extremely low. The complex and changeable topology of power electronics leads to low versatility of the existing equivalent models. Equivalent models of common modules such as half-bridge and full-bridge have been developed by common simulation platforms, but for complex and changeable power electronic topologies, the uses can only use separated components to build models, which lacks of simulation efficiency and flexibility. So, a general equivalent modeling method suitable for cascading power electronic topologies is proposed, which not only improves the simulation efficiency by using node shrinkage, but also can construct an equivalent model for custom arbitrary topology, which improves the versatility of the equivalent modeling method. Based on the proposed general equivalent model, a simulation algorithm for cascading power electronic topologies is designed. By comparing the simulation accuracy and simulation efficiency of the general equivalent model and the detailed model, it can be verified that the proposed equivalent model can improve simulation efficiency with high simulation accuracy.

Charging behaviours of electric vehicles (EVs) exhibit substantial randomness, making accurate prediction or modelling challenging. Furthermore, as the number of EVs continues to increase, charging stations are diversifying their offerings to accommodate distinct charging characteristics, addressing a wide spectrum of EV charging needs. Previous research mostly focused on the randomness of EVs while neglecting the heterogeneity in charging infrastructure. Therefore, this paper introduces a decentralized collaborative optimal method for EV charging stations, taking into account the varying facility types and the power limitations. First, a decentralized collaborative framework is proposed. The energy boundary model and the average laxity of EVs contribute to transforming the optimization problem into a Markov Decision Process (MDP) with uncertain transitions. Then, multi‐agent deep deterministic policy gradient multi‐individuals (MADDPG‐MI) algorithm is developed to train several heterogeneous agents presenting different types of charging facilities. Each agent makes decisions for multiple homogenous charging piles. Numerous simulation studies validate that the proposed method can effectively reduce charging costs and manages in scenarios involving either homogeneous or multiple heterogeneous charging facilities. Moreover, the MADDPG‐MI algorithm demonstrates performance consistency among multiple decision‐making units while consuming lower training resources offering enhanced scalability.

Despite the enormous global potential of ocean wave energy, it has yet to achieve a level of maturity and economic competitiveness that would result in a substantial impact. Challenges include direct integration into weak or isolated microgrids, a high proportion of uncertain marine environments, nonlinear dynamics, oscillating water column (OWC) device limitations, slower response times, unplanned power outages, power fluctuations, high capital and operational costs in ocean wave energy conversion (OWEC) systems. To this end, a new independent multi-stage design approach is proposed for the performance enhancement of an OWC-based OWEC system. Firstly, an airflow and rotational speed optimal control stage enhances power capture in the Wells turbine-based OWC plant. Secondly, compared to conventional control, the proposed permanent magnet synchronous generator control incorporates an adaptive nonlinear back-stepping control algorithm based on Lyapunov stability theory. Thirdly, introducing reconfigurable control into the conventional six-leg power converter ensures the uninterrupted operation of an OWEC system. Lastly, a model-predictive control-based energy management system is integrated with a bidirectional DC-DC converter that delivers steady power from the gridconnected OWC OWEC system. Hence, MATLAB simulations ensure the overall performance enhancement and feasibility of the OWEC system application and verify that the proposed multi-stage solution is efficient, robust, and reliable.

Fully harnessing the ocean wave's renewable energy resources could benefit coastal countries. However, ocean wave energy harvesting systems encounter several challenges, i.e., marine uncertainties, long-distance mainte-nance, power fluctuations, irregular wave currents, non-linear generator dynamics, turbine limitations, cost optimization, and power smoothing issues. To overcome these challenges, this paper proposes a new multi-stage con-trol design approach for performance evaluation of the os-cillating water column (OWC)-based ocean wave energy conversion (OWEC) system. The first stage optimizes the Wells turbine by implementing an efficient airflow control strategy. It achieves maximum power-harvesting ability by eliminating stalling phenomena. In the second stage, we investigate the robustness of the permanent magnet syn-chronous generator-based OWEC system by designing adaptive back-stepping controllers, taking into account the Lyapunov stability theory. It accomplishes precise speed regulation for optimal power extraction while delivering reduced delay response and percentage errors. To ensure the OWEC system's availability, the third stage incorporates fault-ride-through capabilities. It executes a fault reconfig-urable control for a parallel converter configuration, elimi-nating only the faulty leg instead of the entire power con-verter. In the fourth stage, a supercapacitors-based energy management system achieves power smoothing, even when the OWC plant output power fluctuates. We accomplish this by implementing a model predictive control strategy. Finally, the Matlab/Simulink results verify that the presented mul-ti-stage control for the OWC OWEC system is an effective design approach, offering an optimal, robust, reliable, and power-smoothing solution.

This paper proposes a Fast Harmonic Analysis (FHA) method for efficient harmonic analysis in distribution power networks with converter-interfaced distributed energy resources (CIDERs). The integration of CIDERs, such as photovoltaic and wind power, introduces harmonic distortion due to power electronic converters. Harmonics can impact power transmission efficiency, damage equipment, and cause measurement errors. The FHA method utilizes time-domain Kron reduction and considers harmonic interactions between different frequencies to accelerate simulation speed and improve accuracy. A comparison with the harmonic power flow (HPF) method is conducted, and the harmonic characteristics of distribution power networks with CIDER integration are analyzed using FHA. The proposed FHA method offers a valuable contribution to effective harmonic analysis and management in distribution power networks with CIDERs.

As a hub connecting power production and consumption, the urban power grid (UPG) is the basis for realizing the carbon-neutral goal. To ensure carbon-neutral progress, it is necessary to conduct an effective analysis of carbon-neutral potential from the UPG perspective. Given this, this paper proposes a multi-stage decision model that integrates Decision Making Trial and Evaluation Laboratory (DEMATEL) and Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) methods under the rough-fuzzy (RF) environment. Firstly, a multi-dimensional attribute system is innovatively established, and then the rough-fuzzy number is presented to assemble assessment information of groups and to manipulate internal and external uncertainties during decision process. Subsequently, the RF-DEMATEL is utilized to determine the importance-weights of the attributes, in which the interrelationships between attributes are fully considered. Finally, the RF-MARCOS is developed and applied to obtain carbon-neutral potential analysis results based on utility functions in relation to the ideal and anti-ideal solutions. A case study is conducted based on the proposed methodology, and the stability and robustness of the results are verified through sensitive and comparative analysis. This research contributes theoretically and practically to related literature by providing a systematic model capable of handling decision problems with complexity and uncertainty.

The development of smart grids allows residential customers to participate in demand response (DR) programs to aid power grid management through HEMS (Home Energy Management Systems), but similar electricity consumption behavior among customers based on time-of-use electricity prices may lead to the problem of peak load shift, also known as peak rebounds. This article proposes a multi-level interactive optimization model considering individual sensitivity for DR. The model consists of community energy aggregators (CEAs), which perform as an intermediate processing layer between customers and power grid. Customer terminals perform energy management for home appliances, electric vehicles, energy storage systems, and renewable energy generation. The scheduling problem is decomposed into smaller parallel decision problems that are easier to solve. Renewable generation especially photovoltaic power generation is predicted and used to mitigate the influence of energy generation uncertainty. By introducing numerical responsiveness of customers, the model deals with uncertainty on the subjective level of customers. As indicated in numerical analyses, the model is a good compromise between stochastic optimization depending on idealized probability models and robust optimization sacrificing cost to meet worst case scenarios. The proposed method was compared with existing optimizationbased methods for peak shaving. Compared with coordinated load management, our method reduced the peak load and average cost by 13.36% and 18.96%, respectively. Compared with robust optimization, our method achieved similar effect while handling the uncertainty in customers and PV.

The rapid growth of electric vehicles (EVs) is an unstoppable worldwide development trend. An optimal charging strategy for large-scale EVs is able to deal with the randomness of EVs charging and satisfy charging demands of users while ensuring safe and economic operation of the power system. The current centralized and model-based methods failed to overcome the randomness charging problem of the large-scale EVs. Thus, the paper proposes a decentralized approach based on model-free deep reinforcement learning (DRL) to determine the optimal strategy for reducing EVs charging cost considering power limit of the charging station (CS), users' charging demands and fair charging fees. First, a decentralized framework and a dynamic energy boundary (DEB) model of single EV which discretizes the charging demand are proposed. Second, the problem as a Markov Decision Process (MDP) with unknown transition probability is formulated. Moreover, the recurrent deep deterministic policy gradient (RDDPG) based approach is proposed to determine the charging strategy for all charging piles in the CS. Finally, digital simulation studies are conducted to demonstrate the effectiveness of the proposed approach in charging cost reduction and fair charging fees. In addition, the RDDPG-based approach has great scalability which can apply a small-scale model to solve a large-scale problem without being retrained.

Although fault locating in power distribution systems has been widely studied, very few research works consider fault severity assessment, i.e., fault influence on other lines. The main reason behind this is the lack of a unified standard to quantify and assess the severity of different type of faults. In this paper, a novel power distribution system fault locating and severity assessment method is proposed based on elasticity network mapping. The principle of the proposed method is based on elastic coefficient, which reflects the state of the elastic spring by its numerical value. The partial derivative of distribution line power flow to its phase difference, denoted as dis-tribution rate (DR), is analyzed to map the line fault occurrence to the numerical change in DR. Besides, the method normalizes the DR values of different distribution lines into standardized values between 0 and 1, so that normalized comparison can be achieved. Because DR value is inversely proportional to the severity the fault, by comparing the DR of each line, the fault location and severity can be accurately evaluated. Simulation tests are performed in 10 different test feeders with around 35% of nodes installed with measurement units, reporting a 99% accuracy.

With the development of renewable energy sources （RESs）， more distributed generators with random nature are connected to the distribution network in the form of microgrid clusters. Meanwhile， the traditional centralized economic dispatch also faces the challenge of complex information interaction. In addition， the flexible topology of microgrid clusters urgently requires a information interaction mechanism considering data privacy for microgrid clusters. To address the decentralized and autonomous characteristics of microgrid clusters， an optimal dispatch algorithm of complementary consumption and collaboration for RESs considering information interaction in microgrid clusters is proposed. The proposed algorithm takes reducing the cost of power regulation and guarantying the overall real-time power balance of the microgrids as the goal， and transforms the economic dispatch problem into the consensus based problem of incremental cost in the power distribution process. Case simulation results show that the proposed distributed algorithm has good convergence and obtains good optimal effect under the conditions of power constraints， topology changes and link failures.

Since a great share of renewables is integrated into the grids in the form of distributed energy resources (DERs) by Voltage Source Converters (VSCs), there is an increasing need to improve order-reduction techniques for analyzing grids due to the high order of VSCs. Coherency identification and equivalence is a method traditionally used for building reduced-order models of synchronous machines (SMs). With the electrification of power grids, there are researches focusing on the aggregation of VSCs. However, most of the existing researches assumed that all the VSCs are connected to one bus and only took control and filtering parameters into consideration. In this paper, certain network structures are also considered as control and filtering parameters in coherency identification. An order-reduced model for a distribution network with PQ-controlled VSCs integration considering network topologies is proposed. Kron reduction is conducted after coherency identification to simplify the model and intuitively show the relationship between the coherency of VSCs and the connection impedance. Finally, validations of the equivalent model are verified in a modified IEEE 33-bus distribution network model.

In order to tackle the problem of intelligent charging and discharging management of electric vehicles (EVs) in the situation of lack of charging facilities, a method based on deep reinforcement learning is proposed. First, according charging demands of EVs both plugging in a certain charging pile and in the subsequent queuing, a dynamic energy boundary (DEB) is proposed to adjust the charging and discharging power boundary for the charging EV. Then, the charging scheduling problem is converted to a Markov Decision Process (MDP), and the reward function is designed to minimize the charging cost, charging time and maximize the satisfaction for users’ charging demands. Finally, deep deterministic policy gradient (DDPG) algorithm is used to solve the MDP of continuous charging states and actions. The numerical simulation results show that the proposed method can effectively deal with the charging scheduling problem for EVs considering the lack of charging facilities. According to the real-time situation of EVs queuing, the proposed method can help charging piles adjust the charging process locally, so as to satisfy the charging demand of users as soon as possible and effectively reduce the charging cost.

随着直流源储荷的接入与柔性直流电力电子设备的发展,含多种分布式电源(distributed generation,DG)的交直流混联电网被认为是未来电网的主流架构,这给系统操作带来了更多灵活性,但也加剧了量测数据不足的问题,使得系统状态估计求解更加困难.目前成熟的交流状态估计未计及直流系统状态量和量测量的特征及各类DG出力的不确定性,且通常默认换流器控制方式已知,控制方法未知时的伪量测建模技术有待研究.采用计及电压源换流器(voltage source converter,VSC)损耗的模型,对可观型VSC和不可观型VSC分别采用基于控制信息和基于高斯混合模型(Gaussian mixture model,GMM)的伪量测建模方法,在加权最小二乘法(weighted least squares,WLS)的基础上,提出一种改进的交直流混联电网状态估计算法.在经过修改的IEEE-14节点系统中进行大量仿真计算分析,结果表明所提算法不依赖于量测配置和调度中心与VSC间的通信过程,伪量测数据动态描述能力优于6次采样扩展卡尔曼滤波算法(extend Kalman filter,EKF),具有良好的实用性和抗差性,未来可扩展到电力电子化电力系统状态估计中使用.

Detecting and classifying the occurrence of multiple class events is important in enhancing the wide-area situational awareness ability of power distribution systems. Multiple class event classification based on voltage magnitude measurement has not been largely deployed due to the inadequate elucidation between the occurrence of events and the corresponding eigenvalue perturbations in the measurement data. Herein, a detection and classification method for multiple class events in power distribution systems is proposed based on an eigenvalue fluctuation model. We first elucidated the interrelation between event occurrence and the eigenvalue fluctuations in the system voltage magnitude measurement data. Based on the discovery that different classes of events lead to their respective data eigenvalue behavior, we proposed detection criteria C SRL , C MLP1 , and C MLP2 to quantitatively assess these behaviors. Since the different value changes in C SRL , C MLP1 and C MLP2 can indicate the occurrence of different classes of events, we trained and utilized a supervised classifier to achieve multiple event detection and classification. Simulation tests are performed in 7 different test feeders. Eight classes of events can be accurately detected and classified with only voltage magnitude measurement data. An 80% correct detection rate can be obtained with only a 20% measurement device penetration rate.

Disturbances such as natural disasters or man-made attacks exert serious influences on power system, resulting in an increasing awareness of resilience enhancement strategies. As the integration of microgrids, distributed generators (DGs) and power electronic devices adds to the vulnerability of power distribution network to disruptions, it is highly required to enhance the resilience against failure. Soft open points (SOPs) are flexible power electronic devices, which can balance power distribution under normal operation, and supply restoration under abnormal conditions. Thus, the application of SOPs can boost resilience both in pre-failure prevention and post-failure recovery. In this paper, to maximize the effect of SOPs on the boost of resilience of distribution network, a mixed integer non-linear optimization problem is proposed to schedule the siting and sizing of SOPs based on multi-stage elastic mechanical model. A bi-level algorithm is used to tackle the SOP planning problem. The selection of location and capacity is solved by genetic algorithm, after which the control strategies of SOP are optimized by particle swarm algorithm with given planning scheme to obtain the maximum resilience. Finally, case studies on the IEEE 33-bus system and IEEE 123-node test feeder are used to verify the effectiveness and efficiency of the proposed method.