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The digital controller of the grid-connected converter achieves closed-loop control by sampling key voltage and current signals. High-frequency switching of power electronic switches introduces high-frequency ripples into the signals, which may cause sampling aliasing. Severe distortion may happen in the output current of the converter because of the sampling aliasing. This paper explains how the aliasing problem arises by analyzing the spectrum of sampling-controlled signals. The explanation shows the severity of the aliasing problem and the necessity of adopting anti-aliasing methods. Next, this paper analyzes the impact of commonly used anti-aliasing low-pass filters on current control stability. From the conclusions, adopting a low-pass anti-aliasing filter reduces the stability margin of the current loop, which needs improvement. So this paper proposes a novel hybrid anti-aliasing filter based on the combination of the notch and low pass analog filters. An experiment on a 6kVA prototype verifies the proposed anti-aliasing method. This paper shows distortion and step-response oscillation of the output current when proposed and traditional anti-aliasing methods adopted. The comparison shows the advantages of the proposed anti-aliasing method.

Maintaining energy balance and economical operation is significant for energy hub (EH) which serves as the central component. Implementing real‐time regulation for heating and cooling equipment within the EH is challenging due to their slow response time in response to the stochastic fluctuation in renewable energy sources and demands while the opposite is true for electric energy storage equipment (EST), a conventional single timescale energy management strategy is no longer sufficient to take into account the operating characteristics of all equipment. With this motivation, this study proposes a deep reinforcement learning based two‐timescale energy management strategy for EH, which controls heating & cooling equipment on a long timescale of 1 h, and EST on a short timescale of 15 min. The actions of the EST are modelled as discrete to reduce the action spaces, and the discrete‐continuous hybrid action sequential TD3 model is proposed to address the problem of handling both discrete and continuous actions in long timescale policy. A joint training approach based on the centralized training framework is proposed to learn multiple levels of policies in parallel. The case studies demonstrate that the proposed strategy reduces the economic cost and carbon emissions by 1%, and 0.5% compared to the single time‐scale strategy respectively. [ABSTRACT FROM AUTHOR]

In order to further improve the accuracy of distributed photovoltaic (DPV) power prediction, this paper proposes a support vector machine (SVM) model based on hybrid competitive particle swarm optimization (HCPSO) with consideration of spatial correlation (SC), for realizing short‐term PV power prediction tasks. Firstly, the spatial correlation analysis is conducted on the distributed PV stations. The k‐means clustering method based on morphological similarity distance improvement and mutual information function is used to select the best reference station and the best delay, which generates strongly correlated PV sequences. Then, a hybrid algorithm of particle swarm optimization (PSO) and sine cosine algorithm (SCA) in a competitive framework (HCPSO) is proposed, aiming to fuse the fast convergence capability of PSO algorithm with the global search capability of SCA algorithm, while enabling the algorithm to effectively handle high‐dimensional optimization problems based on a competitive mechanism. Finally, the HCPSO algorithm is combined with SVM algorithm, which expands the applicable scenarios of the SVM model and effectively improves the accuracy of PV short‐term prediction. [ABSTRACT FROM AUTHOR]

The access of large‐scale distributed generation (DG) easily leads to energy imbalance in distribution network. To deal with this issue, this paper proposes an energy optimal schedule method for distribution network considering the participation of source‐load‐storage aggregation groups (SAGs). Firstly, the system model consisting of distribution network layer and SAGs layer is established, and the schedule objectives and constraints of each layer are also given. Secondly, considering the fluctuation on the load side, a forecasting method based on Adaboost integrated convolutional neural networks and bidirectional long‐short term memory is proposed. Then, the improved sparrow search algorithm (ISSA) is proposed by using the tent map and Levy flight on the original sparrow search algorithm. At the same time, by introducing Pareto dominance relation and adaptive grid algorithm, the multi‐objective sparrow search algorithm (MOSSA) is derived. After that, a two‐layer optimization framework (ISSA–MOSSA) is proposed to solve the studied system. The simulation results verify the accuracy of the proposed load forecasting model, the superiority of ISSA as well as MOSSA, and the effectiveness of ISSA–MOSSA in solving the energy optimal schedule problem of the distribution system with the access of DG. [ABSTRACT FROM AUTHOR]

Accurate and dynamic identification of key nodes that can efficiently improve the safety and economy of active distribution network (ADN) is of great significance for optimising the operation level of ADN with time‐varying characteristics. In this study, a dynamic identification method for the key nodes of ADN operation optimisation requirements is proposed. Firstly, considering the influence of nodes on ADN operation performance, the importance of nodes in the network structure, and the controllable potential of nodes, a multi‐angle key node identification index system is constructed. Secondly, considering the influence of the ADN operation state on node importance, the classification boundary of ADN operation state was set to determine the operation state of the ADN at different times. Thirdly, the order relation analysis method and variation coefficient method were used to comprehensively evaluate the key nodes of the ADN under different operating states. Finally, the technique for order preference by similarity to an ideal solution is used to sort the comprehensive evaluation results of key nodes to achieve the effective identification of key nodes. The effectiveness and feasibility of the proposed method are verified on 33‐bus and 118‐bus test systems considering the time sequence variation in distributed generation and load. [ABSTRACT FROM AUTHOR]

In this paper, a neural network model called Long Short-Term Temporal Convolutional Network (LST-TCN) model is proposed for short-term load forecasting. This model refers to the 1-D fully convolution network, causal convolution, and void convolution structure. In the convolution layer, a residual connection layer is added. Additionally, the model makes use of two networks to extract features from long-term data and periodic short-term data, respectively, and fuses the two features to calculate the final predicted value. Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) are used as comparison algorithms to train and forecast 3 h, 6 h, 12 h, 24 h, and 48 h ahead of daily electricity load together with LST-TCN. Three different performance metrics, including pinball loss, root mean squared error (RMSE), and mean absolute error (RASE), were used to evaluate the performance of the proposed algorithms. The results of the test set proved that LST-TCN has better generalization effects and smaller prediction errors. The algorithm has a pinball loss of 1.2453 for 3 h ahead forecast and a pinball loss of 1.4885 for 48 h ahead forecast. Generally speaking, LST-TCN has better performance than LSTM, TCN, and other algorithms. [ABSTRACT FROM AUTHOR]

With the development of distribution network, distributed generation such as wind and photovoltaic (PV) power will become increasingly prominent in the near future. PV is widely constructed because of advantages it has. However, the volatility and randomness of PV makes it more complex than traditional energy in the security assessment of distribution network. Based on risk theory, considering the randomness of PV, node low voltage risk index and line overload risk index are established in this paper. Also, K (N - 1 + 1) principle for distribution network which is developed from traditional (N-1) deterministic principle is applied to reflect the flexible structure of distribution network. IEEE three-feeder example system is utilized to investigate the influence of PV power on the security assessment of distribution network.

In allusion to the randomness of output power of distributed generation (DG), a reliability evaluation model based on sequential Monte Carlo simulation (SMCS) for distribution system with DG is proposed. Operating states of the distribution system can be sampled by SMCS in chronological order thus the corresponding output power of DG can be generated. The proposed method has been tested on feeder F4 of IEEE-RBTS Bus 6. The results show that reliability evaluation of distribution system considering the uncertainty of output power of DG can be effectively implemented by SMCS.

The traditional power decoupling control of modular multilevel converter (MMC) is based on the phase synchronization achieved by the phase-locked loop (PLL), and MMC intricate inner dynamics are ignored, which jeopardizes the system stability and power decoupling. To improve the performance, a nonlinear phase-unsynchronized power decoupling control for MMC is proposed without PLL. At first, the MMC model as a nonlinear multi-input multi-output system is developed. The developed model illustrates that the MMC power coupling is caused by the ac current as well as the MMC capacitor voltages. Then, through the input–output linearization method, the proposed method decouples the MMC output powers without the phase synchronization. Involving integral control, the dynamics of MMC output powers are designed as second-order systems so that the control parameters can be easily determined based on the required transient performance. To verify the accuracy and correctness of the developed model, a comparison of MMC dynamics with the proposed control is conducted between the numerical solution in MATLAB and the EMT simulation in PSCAD/EMTDC. Moreover, seven cases in the EMT simulations and four cases in the experimental results demonstrate the desired phase asynchronization and power decoupling performance of the proposed method compared with the conventional $dq$ control. At last, the system zero dynamic stability is investigated and the influences of MMC control parameters, ac-side voltage, dc-side voltage and equilibrium points are analyzed to evaluate the stability performance of the proposed method. [ABSTRACT FROM AUTHOR]

Aiming at the demand for medium- and high-voltage port access capability in energy router, this study proposes a quad-port DC/DC converter topology scheme based on modular multilevel converter (QP-M2DC). Compared with the traditional multiterminal energy routing converter, it has the advantages of high modularity, strong flexibility, and high power density. In addition, for the modular structure on the medium- and high-voltage sides, this study proposes a narrow phase-shift cyclic modulation strategy, which reduces the system need for voltage balance control and simplifies the overall system control. This study comprehensively introduces and analyses the QP-M2DC topology, working principle, high-frequency link equivalent, and power characteristics, then establishes an equivalent model of system control, and proposes a control scheme for a multiterminal energy routing converter. Finally, a simulation model of the system is established through PLECS, and the simulation results show that in such a narrow phase-shift modulation strategy, the proposed topology can have stable operation in a variety of patterns, reduce the capacitance, and achieve better voltage balance at the same time. The experimental results show the converter efficiency of up to 97.8%. It further shows the superiority of the proposed topology structure and the correctness and effectiveness of the proposed control schemes. [ABSTRACT FROM AUTHOR]

One of the main challenges in microgrid system energy management is dealing with uncertainties such as the power output from renewable energy sources. The classic two-stage robust optimization (C-TSRO) method was proposed to cope with these uncertainties. However, this method is oriented to the worst-case scenario and is therefore somewhat conservative. In this study, focusing on the energy management of a typical islanded microgrid and considering uncertainties such as the power output of renewable energy sources and the power demand of loads, an expected-scenario-oriented two-stage robust optimization (E-TSRO) method is proposed to alleviate the conservative tendency of the C-TSRO method because the E-TSRO method chooses to optimize the system cost according to the expected scenario instead, while ensuring the feasibility of the first-stage variables for all possible scenarios, including the worst case. According to the structural characteristics of the proposed model based on the E-TSRO method, a column-and-constraint generation (C & CG) algorithm is utilized to solve the proposed model. Finally, the effectiveness of the E-TSRO model and the solution algorithm are analysed and validated through a series of experiments, thus obtaining some important conclusions, i.e., the economic efficiency of system operation can be improved at about 6.7% in comparison with the C-TSRO results. [ABSTRACT FROM AUTHOR]

As a result of distributed energy development, the demand for energy storage grows more rapidly. The optimization of energy storage allocation is urgently needed. The economic benefits and characteristics of storage allocation have been thoroughly explored, and a mathematical model of economy was established. The centralized configuration of energy storage can make the best use of surplus electricity and reduce charging loss. The peak cutting and valley filling effect is quite obvious. Furthermore, some allocation strategies of storage were proposed. Based on the data of a user-side transformer and photovoltaic power generation, the cost under different strategies and the configuration scheme with the lowest cost is calculated. The example was used to analyze and compare the benefits under various strategies, and the results show it works in cost-saving. The strategy of similarity storage allocation has a positive effect on energy and cost-saving. [ABSTRACT FROM AUTHOR]

With the increase in renewable energy, improving the utilization rate of renewable energy is of great practical significance. The microgrid has been proved effective in addressing this issue. As a flexible load, electric vehicles are connected to the grid on a large scale, which will have an impact on the grid. In order to solve this problem, this paper proposes a microgrid energy management model for electric vehicle charging stations, which takes into account the economics of microgrid operation and the stability of grid operation. Subsequently, this paper proposes an evolutionary multiobjective optimization algorithm to deal with constraints. Finally, this paper verifies the effectiveness of the proposed model and algorithm through experiments. [ABSTRACT FROM AUTHOR]

In this paper, the research status of topology and control strategy of energy storage grid-connected system is analyzed, and aiming at the working characteristics of the repurposed battery, a cascade power electronic transformer (CPET) with independent DC output is proposed. The working principle of current fed isolated bidirectional DC-DC converter (CF-IBDC) and cascaded H-bridge (CHB) is analyzed, and the decoupling control strategy is designed. In this paper, a hierarchical control strategy is designed for the repurposed battery energy storage (RBES) grid-connected system based on CPET, which consists of three layers: energy layer, power layer, and state of charge (SOC) layer. The energy layer responds to active and reactive power scheduling instructions, the power layer controls the grid-connected current and tracks the grid voltage, and the SOC layer equates the charged state of repurposed batteries. A 3 MVA/12 kV three-phase grid-connected simulation system was established, and a 1 kW single-phase system experiment platform was designed. The simulation and experimental results can verify the correctness of the theoretical analysis and the feasibility of the control strategy. [ABSTRACT FROM AUTHOR]

This article investigates and compares various modulation methods and capacitor voltage-balancing algorithms of a modular multilevel converter for solid-state transformer applications. Characteristics of capacitor charging and discharging are analyzed for the existing single-step alternating voltage balancing and the conventional sorting algorithms with phase-shift (PS) modulation and nearest level control (NLC) methods. Based on the analysis, the single-step alternating algorithm leads to a low-frequency voltage ripple, while the conventional sorting algorithm introduces additional switching actions. To address these issues, two new voltage-balancing algorithms are proposed: a multistep alternating voltage-balancing algorithm, and a currentless sorting algorithm. The performance of the proposed algorithms with the PS and NLC modulation methods is comprehensively investigated and compared based on theoretical analysis and experimental results. The study results demonstrate the capability of the proposed methods to balance capacitor voltages while reducing the low-frequency voltage ripple and avoiding additional switching actions. [ABSTRACT FROM AUTHOR]

The energy crisis and increasing concerns about the environment in modern society have fostered the development of the integrated energy system (IES) in smart grid. The energy hub (EH) is a minimum construction of the IES. The chief purpose of this paper is to propose a Lyapunov optimization-based energy management strategy for the energy hub with an energy router. First, this paper describes a configuration of the EH with an energy router and sets the basic operating principles of the energy router. Second, a method is proposed to analyze the energy balance equations of the EH. Furthermore, the problem of energy management is formulated considering the constraints of energy balance equations, energy storage, flexible loads, and other constraints of operation. Finally, energy storage and flexible electric loads are modeled as stochastic processes, and three virtual queues are constructed to relax these constraints. The Lyapunov drift-plus-penalty function is applied to formulate a relaxed form of the energy management problem. Case studies of a single EH and two-connected EHs are performed to test the effectiveness of the proposed strategy, and the results are compared with a greedy algorithm. [ABSTRACT FROM AUTHOR]

Accurately identifying the key links in active distribution network (ADN) that affect the system's safety and stability is of great significance to improve the efficiency of operation and maintenance and risk early warning for distribution network (DN). In this study, the node resilience (NR) is first proposed as an indicator for structural stability identification of DN. Then, a critical node identification method considering the security, economy and structure stability in ADN is proposed, which uses the NR, the utility risks of branch load change, node voltage deviation and line loss variation as the identification indicators. Also, combining with subjective experience and objective data, the comprehensive evaluation method of critical nodes is improved, and the multilevel and multidimensional evaluation model of critical nodes is constructed. Furthermore, the time sequence of load change and photovoltaic output is considered. Finally, the rationality and effectiveness of the proposed method are proved by taking IEEE 33-bus system and an actual ADN as examples. [ABSTRACT FROM AUTHOR]

A deeper coupling of information and power would make active distribution networks (ADNs) evolve into typical cyber-physical systems (CPSs). The unreliability of cyber systems can affect the security of ADNs. In this article, a methodology based on cyber-power joint analysis is presented to quantitatively evaluate the operational risk of ADNs caused by cyber contingencies. First, a typical CPS structure of ADNs is demonstrated and the influence ways of cyber contingencies are discussed. Then, a CPS modeling framework is proposed to analyze the information flow and cyber contingencies of cyber systems. To determine the best data transmission paths in cyber networks, a routing algorithm based on the link-state protocol is presented. Furthermore, in view of the response of ADNs to physical failures, an optimization model is developed to minimize the load shedding that results from the over-limited voltage. Finally, the expected load curtailment and the expected fault coverage used to quantify potential system losses are calculated using Monte Carlo simulation. The simulation results explain the effectiveness and practicability of the proposed models and methods. [ABSTRACT FROM AUTHOR]

This paper proposes an extended-state-observer-based distributed robust secondary voltage and frequency control for an autonomous microgrid (MG) with inverter-based distributed generators (DGs) considering the uncertainties from models and measurement noise. The MG is considered as a multi-agent system where each DG is defined as an agent and its controller only requires its own information and the information of its neighbors, but each DG obtains noisy measurements of the states of itself and its neighbors easily due to stochastic noise. Therefore, in this paper, an extended state observer is employed to estimate the accurate state information of each DG, which is significantly influenced by measurement noise. Furthermore, the distributed controllers based on a fast terminal sliding mode surface and an adaptive super-twisting algorithm are designed to track the voltage reference and to fasten the convergence rate against disturbances and uncertainties caused by parameter perturbation. Moreover, the distributed frequency controllers are also designed to restore the frequency and to guarantee the accurate active power sharing without power information of DGs. Finally, the effectiveness of the propose control strategy is illustrated by the simulation of an autonomous MG in MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

The operating principle of the clamp circuits like clamping diodes and flying capacitor in three-level LLC converter is different from other three-level converters, because the mean value of the resonant tank current is always zero and pulse-frequency modulation control is generally used instead of pulsewidth modulation control. And the converter with only the clamping diodes or flying capacitor is difficult to realize voltage balance. In this paper, the characteristics of the three-level LLC converter with single clamp circuit are first analyzed. Then, the operation principle of the hybrid-clamped circuit under the unbalanced voltages on input capacitors is described with the mode analysis, based on which the mechanism for voltage autobalance is elaborated and the effects of two clamp circuits are derived. Then, the design of the clamping diodes and the flying capacitor is also involved. Finally, a three-level LLC converter prototype is built and the experimental results verify the validity of the theoretical analysis and design. [ABSTRACT FROM AUTHOR]

Transfer capacities of urban distribution networks need to be increased to fulfill the increasing load demands and to accommodate distributed generation (DG). However, there are limited spaces to build new substations and lines, and curtailment of DGs may happen due to voltage violation during DG outputs fluctuation. This paper proposes and analyzes various methods to convert some existing ac MV lines to dc lines in order to form a hybrid ac/dc distribution network, based on which transfer capacities of lines can be increased, and flexible power shift can be achieved through a voltage source converter between two lines. The increases of transfer capacities are quantified. Optimal operation to fully utilize the increased capacities is achieved, in which losses are minimized in day-ahead scheduling, and node voltages are regulated real-time within security ranges based on limited measurement. Not only reactive power but also real power optimization are designed to maximize load supply and DG accommodation. A sensitivity method is proposed considering relatively large r/x ratio of an MV distribution network, which is effective for the real-time voltage regulation. Simulations are performed to verify the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal design algorithm for distributed secondary voltage control in islanded microgrids (MGs), including communication topology and controller gains. First, upon the consensus-based secondary voltage control, the sufficient condition for network connectivity of communication topology is revealed by the reachability matrix. A multi-objective optimization criterion is first proposed for the network design, taking the convergence performance, network-relevant time delays, and communication costs into consideration. After obtaining the Pareto frontier of this multi-objective model, an optimal network is selected to meet the practical requirements. Based on static output feedback, a small-signal dynamic model of an MG installed with a secondary voltage controller is established, where the distributed secondary voltage controller can be converted into an equivalent decentralized controller. Thereby, a linear quadratic regulator is formulated for the near-optimal design of controller parameters. Our approach customizes the optimal design framework of the topology and controller, which have been largely ignored in the existing literatures. Therefore, it promises to improve the performance of distributed secondary control. The effectiveness of the proposed methodology is verified by a simulation study. [ABSTRACT FROM AUTHOR]