Climate change is progressively reshaping the spatiotemporal dynamics of renewable energy sources such as wind and solar, intensifying the complexity and uncertainty of long-term power system planning. Existing planning frameworks are largely focused on climate mitigation strategies but often overlook the critical dimension of climate adaptation, limiting their efficacy in managing evolving climatic risks. In response, this article proposes a multistage stochastic low-carbon planning framework that incorporates climate-related uncertainties into system planning decision-making. By embedding climate evolution trajectories into the planning horizon, the proposed approach determines optimal stage-wise planning pathways that jointly accommodate mitigation goals and adaptation imperatives under long-term climate uncertainties. First, a systematic climate uncertainty modeling approach is developed to capture both scenario uncertainty and climate response uncertainty through the construction of a representative scenario tree. Second, to reconcile the temporal mismatch between coarse-resolution climate projections and the finegrained requirements of power system planning, a climate-consistent temporal downscaling method is proposed to transform long-term climate projections into high-resolution, hourly level data. Third, to address the computational complexity inherent in the multistage planning problem, a tailored decomposition-based stochastic dual dynamic programming algorithm is developed, which operates on a stage-wise clustered scenario tree to leverage the tree’s structural compactness for accelerated convergence and scalable optimization under climate-related uncertainties. Numerical studies demonstrate that the proposed climate-adaptive planning framework enhances the power system’s ability to manage climate-induced risks while maintaining cost-effectiveness across a wide range of plausible climate futures.

The integration of electric vehicles (EVs) into power systems via vehicle-to-grid (V2G) technology offers new opportunities for bidirectional energy exchange and resource allocation. However, time delays and adversarial attacks in communication networks can undermine the coordination of EV aggregators and power systems. To address this challenge, this paper presents a hybrid counterfactual learning approach for control of EV aggregators in the multi-area power systems V2G and load frequency control (LFC) framework. The proposed hybrid approach integrates counterfactual multi-agent learning, adversarial training, and monotonic neural network (CMA-HMNN). The multi-agent counterfactual learning marginalizes the impact of individual actions on the overall reward, thereby better coordinating controllable resources and reducing variances in adversarial multi-agent training. By enforcing deviation-command monotonicity constraints within the neural network architecture, the proposed approach can satisfy Lyapunov stability conditions and improve the stability of power systems with integrated EVs. Adversarial training based on the fast gradient sign method (FGSM) is applied to enhance the robustness of the networks against perturbations. Even under concurrent time-varying communication delays and malicious threats from communication networks, the method effectively coordinates multiple generation resources and EV aggregators. Compared with four DRL-based control methods, the superiority of the proposed method is verified on the three-area power system and IEEE 39-bus power system with wide EVs integrations.

The large-scale integration of distributed energy resources into active distribution networks may significantly intensify voltage fluctuations and increase network losses. Traditional model-based reactive power optimization approaches depend on existence of accurate system models. On the other hand, conventional reinforcement learning methods largely ignore the spatial characteristics of the active distribution networks during training, allowing agents to have an inadequate perception of the system state. To address these challenges, this paper proposes a multi-agent deep reinforcement learning approach that integrates graph learning with reinforcement learning for the learning of reactive power optimization strategies in active distribution networks. Specifically, the active distribution network is divided into multiple regions, with each region being controlled by an agent. The agents collaborate to achieve the global reactive power optimization goal. The perception capability of the agents is enhanced by adopting graph attention networks during the feature extraction phase. In the training phase, a primal-dual method is employed to manage constraints effectively. During the execution phase, each agent controls the photovoltaic inverters, electric springs, and capacitor banks based on the strategies developed in the training phase. The performance of the proposed approach is validated by a series of experiments on the IEEE-33 system, along with comparisons versus some existing data-driven deep reinforcement learning methods.

Lithium-ion battery charging involves many factors such as electricity, heat, aging, etc. Shortening the charging time of lithium-ion batteries while limiting aging and temperature rise is an urgent issue that needs to be addressed. In order to solve this problem, the pseudo-two-dimensions (P2D) model is used to describe the electrical behavior of batteries. Then, a parameter identification method is proposed using a deep deterministic policy gradient for P2D model. Besides, a multi-objective charging model considering time, health loss, and temperature rise is constructed by combining P2D, equivalent heat, and aging mechanism models. A multi- objective charging optimization strategy is proposed using Deep Q-Network algorithm. Under the condition of strong coupling of multiple parameters in the P2D model, the identification accuracy is improved by more than 20% compared to traditional optimization algorithms. Under fast charging conditions, the charging time decreases by 19%, and the health loss only increased by 2.5%. Finally, the impact of multi-stage current variance on battery health loss is discussed.

For voltage sag position tracing (VSPT) through deep learning methods, model performance deteriorates rapidly under changed scenes. Moreover, time and effort are wasted in retraining numerous models for all different scenes. Therefore, a self-adaptive VSPT method which can response to changed scene is urgently needed. In this article, a deep transfer learning for self-adaptive VSPT under changed scenes is proposed. For accurate VSPT under original scene, a deep learning method via temporal iTransformer is presented, which can enhance local feature extraction capability while retaining the iTransformer’s global perspective. For self-adaptive VSPT under changed scenes, a deep transfer learning based on feature-decoupling is further presented. Here, domain invariant features are calculated via feature-decoupling module, and the difference between source domain features and target domain features is adaptively minimized via feature transference. We test the proposed method via simulation and experimental platform, verifying that the proposed deep transfer learning has satisfactory domain adaptability for self-adaptive VSPT under changed scenes.

A reliable adaptive-memory-derivative (AMD) event-triggered quantized sliding mode load frequency control (QSMLFC) method is proposed for the multiarea interconnected wind power system under frequency-based deception attacks. An AMD event-trigger scheme is proposed to promote the wind power system operation while saving the network resources, and the reliable AMD event-triggered QSMLFC method aims to reduce the frequency deviations of the interconnected wind power systems. A frequency-based deception attack model is developed for analyzing the security issues in network communications for wind power systems. The hysteresis quantizer is used to lower the communication rate. To validate the correctness of the control method, a sufficient reliability criterion is derived to prove the applicability of the AMD event-triggered QSMLFC. Three numerical examples and an IEEE 39-bus system simulation are presented to demonstrate that the reliable AMD event-triggered QSMLFC method can provide satisfactory stability performance for the wind power system under frequency-based deception attacks.

With the advancement of artificial intelligence, deep reinforcement learning is emerging as an effective solution for distribution system service restoration. However, traditional deep reinforcement learning approaches are typically tailored for training agents in specific scenarios, limiting their ability to adapt rapidly to new environments. Furthermore, the spatial characteristics of the distribution network are largely ignored during the training, constraining the state perception capabilities of agents. To address these issues, this paper proposes a meta-based graph reinforcement learning approach that combines graph learning, meta-learning, and reinforcement learning for the learning of service restoration strategies in distribution network. The agent trained by such an approach possesses the feature perception capability of graph learning, allowing it to acquire deeper service restoration strategies from latent graph features. Moreover, the agent also has the fast adaptation ability of meta-learning, enabling it to quickly adapt to new restoration scenarios. Experimental results demonstrate that the proposed approach outperforms existing results of both specialized and generalized strategies.

This paper studies the dynamic state estimation problem for cyber-physical distribution systems (CPDSs) with false data injection attacks (FDIAs) and delayed measurements. In view of the characteristics of multiple measurement types, the equivalent current measurement transformation technique is adopted to make the measurement equation be expressed in the form of linear measurement model. Based on the mixed measurements of phasor measurement units (PMUs) and distribution remote terminal units (DRTUs), a novel model is constructed using Bernoulli distributed random variables to describe the delay phenomena. Further, in order to improve the transmission efficiency of the measurement data, an mechanism is introduced in the network transmission process to minimise the amount of data transmission in the network while ensuring the performance of system state estimation. A measurement model based on the event-triggered mechanism is developed, and an event-triggered square root cubature Kalman filter (ET-SRCKF) algorithm incorporating delayed measurements is designed to implement the state estimation of CPDSs, which can obtain the optimal estimation of the states under delayed measurements. Finally, simulated examples are conducted on the IEEE 33-bus test system, and the effectiveness of the proposed method is illustrated by numerical simulations.

This paper presents a novel non-fragile output sliding mode load frequency control (OSMLFC) strategy designed for multi-area interconnected power systems that incorporate electric vehicles (EVs), particularly in the presence of frequency-triggered denial-of-service (DoS) attacks. We delve into the realm of network communication security concerning load frequency control (LFC) power systems combined with EVs, investigating a real-time frequency-triggered DoS attack by combining real-time frequency dynamics with event-triggering mechanisms. A non-fragile output sliding mode control (SMC) method is proposed, strategically devised to balance the load and frequency aspects of the power systems. Then, a sufficient stability criterion is derived to ensure the non-fragile$H_\infty$stability of the power system integrated with EVs, even when subjected to the perturbations caused by real-time frequency-triggered DoS attacks. The efficacy of our proposed approach and the characteristics of the real-time frequency-triggered DoS attacks are validated through extensive simulations.

This article investigates the resiliently distributed fixed-time control of frequency recovery and power allocation in a multi-terminal high voltage direct current (MTDC) system against denial-of-service (DoS) attacks. An MTDC system typically consists of several AC areas, on which the DoS attacks may cause communication faults by blocking communication channels, preventing certain AC areas from sending message and damaging related facilities. A novel distributed security control scheme is proposed in this paper, which introduces attack detection method and communication repair mechanism to restore the paralyzed topology caused by DoS attacks. By extension, a resiliently distributed fixed-time control is presented under this frame. The proposed control scheme can not only realize frequency restoration but also accomplish active power sharing under DoS attacks. Furthermore, different from existing control strategies, the advanced scheme can guarantee the convergence time without considering the initial value, which helps improve the robustness and stability of the MTDC system. The resilient stability of the proposed scheme is proved by Lyapunov-Krasovskii stability theory. Finally, case studies on an MTDC system are conducted to demonstrate the effectiveness and validity of the proposed controller. Note to Practitioners—MTDC system is a large-scale power system connecting various AC grids. It has the characteristics of distributed and high intelligence, which is prone to be attacked by an adversary. As an index to measure the safe and stable operation of MTDC system, frequency is the focus of this paper. We propose a novel topology recovery mechanism for MTDC systems under DoS attack and design a resilient fixed-time secondary frequency controller based on the idea of multi-agent. The experimental results show that under DOS attack, the proposed topology recovery mechanism and controller can recover the frequency to the rated value in a fixed time and realize the proportional distribution of active power. In practical application, engineers can learn from the controller to resist DoS attack and realize the stable operation of large-scale distributed power system.

Although the current sharing control of dc microgrids has been widely studied, the high communication bandwidth and global communication network structure information demands hander the renewable energy consumption. Thus, this article proposes a fully distributed dynamic edge-event-triggered current sharing control strategy for multibus dc microgrids with power coupling. First, the system model with power coupling is built, which is further switched to the linear heterogeneous multiagent systems with unknown disturbance. It is an indispensable preprocessing for controller design. Furthermore, the fully distributed current sharing control strategy is proposed through adaptive coupling weights. Note that the global communication network structure information demand is eliminated. Moreover, the fully distributed dynamic edge-event-triggered mechanism is proposed to reduce communication bandwidth. Compared with the previous dynamic event-triggered mechanisms applied into dc microgrids, the continuous communication between neighboring agents is avoided, and controller updating frequency is reduced. Finally, the simulation and experimental results verify the proposed control performance.

The large-scale grouping of the battery system leads to the inconsistency of the battery pack. Aiming at tacking this issue, an inconsistency evaluation method is deployed for the battery pack based on an improved Gaussian mixture model (GMM) and feature fusion approach. Specifically, the proposed adaptive forgetting factor recursive least squares (AFFRLS) algorithm allows the open-circuit voltage and other parameters to be jointly identified without the open circuit voltage-state of charge (OCV-SOC) test. An online capacity estimation approach with the extended Kalman particle filter (EPF) is put forward for capacity estimation. Further, an improved GMM is proposed to visualize battery pack inconsistency, using the K-means++ algorithm to initialize category centers. The standard deviation coefficient approach quantifies the inconsistency. Finally, the real-life vehicle data are performed to validate the effectiveness of the proposed method. The experimental results show that the proposed method can evaluate the battery parameters accurately. With the increase in service time, the inconsistency of the battery pack is gradually deteriorating.

Though voltage-regulation and current-sharing of distributed generations (DGs) in dc-microgrids have been widely studied, additional communication links or independent modulation circuit should be added to achieve information transmission. To accomplish precise current-sharing/voltage-regulation without additional communication devices, this article proposes a communication-free intelligent edge control regarding voltage and current for dc-microgrids. First, the power-information dual modulation (PIDM) is designed to achieve information exchange among DGs and eliminate additional communication devices. Second, the cooperative control problem with two coupled targets, i.e., accurate voltage-regulation and current-sharing, is converted into a matter of optimal control. Therefore, the voltage-regulation and current-sharing could be solved concurrently. In addition, the control objective function of each DG is switched to provide the optimal controller and minimize the voltage/current control deviation, which is further switched to solve the Hamilton–Jacobi–Bellman (HJB) function. In order to solve this HJB function, which is difficult to obtain analytical solution, an intelligent edge control strategy with PIDM is proposed to solve the HJB function. Therefore, the precise voltage-regulation and current-sharing can be accomplished. Finally, the proposed control approach is verified through simulation results.

Although the cyber-physical system stability is widely studied, scholars focus more on system stability with communication time delay. Therein, grid-connected inverters with the digital control system are regarded as one simplest and typical cyber-physical system. Meanwhile, the switching/sampling frequency of the inverter is always selected as low as possible from an efficiency viewpoint, resulting in unavoidable delay time. This delay time is apt to cause the system instability, which is more prone to severity under weak grid. To this end, this paper provides a minimum switching/sampling frequency for grid-connected inverters. Firstly, the system impedance model with equivalent delay time is constructed, which is based on padé approximate approach. This equivalent delay time consists of three parts, i.e., sampling delay time in cyber/physical level, calculation delay time in cyber level and pulsewidth modulation delay time in physical level, which reflects the cyber-physical interaction impact. Furthermore, the stability forbidden criterion is applied to make the switching/sampling frequency solving process become Hurwitz matrix identification problem through space mappings. Based on these space mappings, an adaptive step search approach is adopted to obtain the minimum switching/sampling frequency. Finally, the proposed approach can well evaluate the system stability under different frequencies through simulation and experiment.

Distributed renewable energy source is an advisable solution for dc microgrids to reduce fuel consumption and CO2emission. In such microgrids, the installation of two or more battery energy storage (BES) units is utilized to compensate the power imbalance between the sources and loads. Nevertheless, energy management with numerous BES units does not simultaneously consider the impacts of distributed generators (DGs) and constant power loads (CPLs). Since the inaccurate current sharing will shorten the lifetime of the batteries and cause instability problem, this article proposes a distributed secondary$H_{\infty }$consensus approach based on the dynamic event-triggered communication method to realize accurate current sharing and efficient operation in the presence of numerous DGs and CPLs. First, the whole state-space function model of the dc microgrid consisting of DGs, batteries, resistive loads, and CPLs, is first built in detail. This model is further transformed into standard linear heterogeneous multiagent systems, which provides an indispensable preprocessing for advanced control strategy application. Then, the distributed secondary$H_{\infty }$consensus approach based on the foresaid systems is designed to achieve accurate current sharing. For reducing the communication among batteries and the controller updating frequency, the dynamic event-triggered communication method is proposed. Compared with existing event-triggered methods, the communication and controller updating frequency of the proposed dynamic event-triggered method have been reduced a lot. Additionally, the proposed method can not only avoid the Zeno behavior, but also obtain the lowest bound of the sampled time interval. Finally, the numerical simulation results and experimental results verify the effectiveness of the proposed control strategy.

The high penetration of renewable energy sources always leads to the fluctuation of the droop coefficients which are designed in inverse proportion to their rated capacity, and power electronics devices are prone to static instability. Although the impedance-based approaches have been widely studied to deal with this problem, the stability-oriented droop coefficients region identification due to the fluctuation of renewable energy sources is not provided. Thus, this article proposes an impedance-based approach to assess the droop coefficients stability region in the power system consisting of numerous distributed generators (DGs). First, the modified phase margin and opposing argument (MPMOA) forbidden criterion is constituted to acquire the complementary space of the droop coefficients stability region. The MPMOA forbidden criterion is first transformed into the condition that the generalized return-ratio matrix is Hurwitz through mirror, rotation, and translation mapping. In comparison with the previous simplified stability criteria, the conservatism of the proposed criterion is reduced a lot. Thereafter, the droop coefficients stability region is directly calculated by the generalized return-ratio matrix and guardian map theory. Eventually, the simulation and experimental results are provided to validate the conservatism and effectiveness of the impedance-based stability region identification approach. Therein, the simulation results illustrate that the proposed droop coefficients stability operation criterion has lower conservatism than these of the previous simplified stability criteria. Furthermore, the simulation and experimental results illustrate that the proposed stability-oriented droop coefficients region identification approach can provide an effective parameter stability region.

High-voltage direct current (HVDC) is increasingly utilized for long-distance electric power transmission, mainly due to its low resistive losses. In this paper, a decentralized control strategy is proposed to address the secondary frequency restoration and real power sharing problem for multi-terminal direct current (MTDC) transmission systems. We establish a sufficient stability condition to guarantee that the designed decentralized leaky integral controller can restore the frequency to its nominal value. Furthermore, the proposed controller can adjust the real power sharing ratio according to different working conditions. An MTDC system consisting of 4 AC systems is built in MATLAB Simulink environment. Numerical simulations are conducted to validate the effectiveness of proposed decentralized control approach.

The high penetration of power electronic converter loads in dc microgrid causes system stability issue, or also known as constant power load issue, due to their negative impedance characteristics. The stability concern will be more complicated for a self-disciplined microgrid that allows plug and play of various distributed generations (DGs). This article proposes a robust droop-based controller for decentralized power sharing in a dc microgrid considering large-signal stability. For each DG interface converter subsystem, the interactions with other DG interface converters and loads are estimated by a nonlinear disturbance observer (NDO) utilizing the subsystem's own information to achieve decentralized power sharing and fast voltage regulation. With the uncertainties of circuit parameters modeled as a lumped disturbance term and compensated by an NDO, the proposed controller can significantly enhance the robustness against the uncertainties of circuit parameters. The large-signal stability of the whole interconnected system is proved by the backstepping algorithm and Lyapunov theorem. The efficacy and large-signal stability of the proposed approach are verified by both simulations and experiments.

Cascading failure is an intrinsic threat of power grid to cause enormous cost of society, and it is very challenging to be analyzed. The risk of cascading failure depends both on its probability and the severity of consequence. It is impossible to analyze all of the intrinsic attacks, only the critical and high probability initial events should be found to estimate the risk of cascading failure efficiently. To recognize the critical and high probability events, a cascading failure analysis model for power transmission grid is established based on complex network theory (CNT) in this paper. The risk coefficient of transmission line considering the betweenness, load rate and changeable outage probability is proposed to determine the initial events of power grid. The development tendency of cascading failure is determined by the network topology, the power flow and boundary conditions. The indicators of expected percentage of load loss and line cut are used to estimate the risk of cascading failure caused by the given initial malfunction of power grid. Simulation results from the IEEE RTS-79 test system show that the risk of cascading failure has close relations with the risk coefficient of transmission lines. The value of risk coefficient could be useful to make vulnerability assessment and to design specific action to reduce the topological weakness and the risk of cascading failure of power grid.

This paper presents a coordination secondary control strategy for autonomous hybrid AC/DC microgrids with global power sharing operation. In the previous work, primary control with droop control method was applied to distributed generators to ensure local power sharing without communication links in either ac or dc microgrid. An extension of local power sharing for individual microgrid is the global power sharing for hybrid microgrids. With global power sharing control, all distributed generators throughout the hybrid system are capable of sharing the entire ac and dc loads without any information exchange. The coexistence of local power sharing and global power sharing in hybrid system is thereafter defined as generalized primary control, which is usually employed for fully decentralized power management throughout the whole hybrid system. To eliminate the inherent voltage/frequency deviations caused by the generalized primary control, the secondary control is usually applied to each distribution generator for voltage/frequency restoration. This however will degrade the performance of global power sharing operation because of its characteristics of voltage/frequency deviation dependence. To achieve voltage/frequency restoration while maintaining the global power sharing operation, a coordination secondary control was proposed and the effectiveness of the proposed method has been verified by the simulation results.

This paper presents a novel hybrid modulation method based isolated high frequency (HF) bidirectional inverter for AC microgrid application. Bidirectional current-fed converter has been employed to interface the energy storage and intermediate fluctuating dc bus. A new hybrid modulation techniques consisting of fluctuating-dc-modulation (FDM) at low frequency (LF) scale and secondary modulation (SM) at HF scale has been proposed for the current-fed converter. The zero current soft switching (ZCS) of primary side devices and zero voltage soft switching (ZVS) of secondary side devices of front-end current-fed full-bridge converter is realized. The back-end full bridge inverter has been utilized to interface the fluctuating dc bus and AC microgrid. A rotating partial hybrid pulsewidth modulation (RP-HPWM) has been introduced. One leg of inverter is commutated at low switching frequency while the other leg is operated at high switching frequency for partial period of line frequency cycle. Besides the high switching frequency operated switches are only commutated at low value of current flowing through them. Thus, switching losses of the back-end inverter can be considerably reduced. Rotating mechanism is used to equalize the switching losses of four switches of the inverter. The low frequency (LF) fluctuating dc bus allows the reduction of dc-link electrolytic capacitor. This paper presents operation and analysis of the complete inverter topology implementing the proposed hybrid modulation scheme. Simulation results using PSIM9.0.4 and experimental results from a lab prototype clearly validate effectiveness of the proposed modulation scheme.

This paper presents a six-pulse low frequency (LF) fluctuating high voltage dc bus based power system architecture for the fuel cell vehicles (FCVs) application. A novel hybrid modulation techniques consisting of six-pulse modulation (SPM) at LF scale and secondary modulation and 33% sine pulse width modulation (33% SPWM) at high frequency (HF) scale. Three-phase ac waveforms are produced for the propulsion system in FCVs. It significant reduces the switching losses of the bidirectional dual-stage inverter: 1) the zero current soft switching (ZCS) of primary side devices and zero voltage soft switching (ZVS) of secondary side devices of front-end current-fed full-bridge converter is realized. 2) at any moment, only one leg of back-end 3-phase inverter is switched at HF while the other two legs are kept in on or off state. This tremendously reduces the bidirectional inverter's switching losses and improves the system efficiency. Besides, switching loss can be further reduced since all the inverter switches are switched at the low current range. The LF fluctuating high voltage dc bus allows the elimination of large electrolytic dc-link capacitor. This paper presents the operation, analysis, and design of a bidirectional inverter implementing the proposed hybrid modulation technique. Simulation results obtained from power electronics simulation software PSIM and experimental results from the lab prototype clearly validate the effectiveness of the proposed modulation technique.

Economic operation is a major concern for microgrids. Conventionally, economic dispatch of distributed generations (DGs) are solved by centralized control with optimization algorithms or distributed control with consensus algorithm. To improve the reliability, scalability and economy of microgrids, a fully decentralized economic power sharing strategy is proposed in this paper. The proposed method is based on frequency/incremental cost droop (f/IC) characteristics and incremental cost (IC) functions of DGs. ICs of DGs reach equality with the convergence of system frequency. Power dispatch of each DG is automatically achieved based on its relevant incremental cost function. Therefore, by using this method, the incremental cost of each DG will reach equality autonomously and the total operating cost can be optimized without any communication or central controllers. Simulation platform of an autonomous AC MG with three DGs is built in Matlab/Simulink to verify the effectiveness of the proposed method.

Three-phase three-wire shunt active power filters (APFs) usually employ very large electrolytic capacitors in the dc-link to mitigate utility side harmonics. These capacitors are however known to be bulky and of short operating lifetime, particularly for systems where high ripple currents exist. This paper presents the concept of dc-link compensator (DLC) that aims to decouple the harmonic power from the dc-link of APF. With proper system sizing and design, most of the harmonic power can be eliminated by this DLC circuit and very small electrolytic capacitors or even film type capacitors can be used instead. Moreover, DLC itself is constructed with small passive components and features very simple circuit configuration. Experimental results are provided to show its effectiveness.

Increase of DC-compatible loads, popularization of renewable distributed generations (DGs) and development of power electronic converters have boosted the applications of DC microgrid in household level. Proper control algorithms for autonomous operation of DC microgrid which coordinates the operation of energy storages, sources and loads become the key challenge. Comparing with centralized energy management system, distributed control eliminates communication link, thus system response speed and reliability could be enhanced. DC-bus signaling (DBS) in which the bus voltage is regarded as global indicator for system power balance is an effective solution for power sharing. Bus voltage band is divided into few sub-regions around the nominal value. Various elements are prioritized based on system control objectives and scheduled to operate with different mode in respective sub-region. Bus voltage variation induced by power supply/demand difference activates operation mode change of system elements autonomously. A lab-scale DC microgrid with integration of modular solar Photovoltaic (PV) system, battery energy storage (BES) and DC loads was developed to verify the feasibility and effectiveness of proposed control algorithm.

This paper presents an efficiency improved single-phase power factor correction (PFC) converter with its target application to plug-in hybrid electric vehicle (PHEV) charging systems. The proposed PFC converter features sinusoidal input current, three-level output characteristic, and wide range of output DC voltage. Moreover, the involved DC/DC buck conversion stage may only need to convert partial input power rather than full scale of input power, and therefore the system overall efficiency can be much improved. Through proper control of the buck converter, it is also possible to mitigate the double-line frequency ripple power that is inherent in a single-phase AC/DC system. Both simulation and experimental results are presented to show the effectiveness of this converter.