Abstract:With the rapid integration of renewable energy, wide-band oscillations caused by interactions between power electronic equipment and grids have emerged as one of the most critical stability issues. Existing methods are usually studied for local power systems with around one hundred nodes. However, for a large-scale power system with tens of thousands of nodes, the dimension of transfer function matrix or the order of characteristic equation is much higher. In this case, the existing methods such as eigenvalue analysis method and impedance-based method have difficulty in computation and are thus hard to utilize in practice. To fill this gap, this paper proposes a novel method named the smallest eigenvalues based logarithmic derivative (SELD) method. It obtains the dominant oscillation modes by the logarithmic derivative of the k-smallest eigenvalue curves of the sparse extended nodal admittance matrix (NAM). An oscillatory stability analysis tool is further developed based on this method. The effectiveness of the method and the tool is validated through a local power system as well as a large-scale power system.

Abstract:Realistic uncertainties of renewable energies and loads may possess complicated probability distributions and correlations, which are difficult to be characterized by standard probability density functions and hence challenge existing uncertainty propagation analysis (UPA) methods. Also, nonintrusive spectral representation (SR)-based UPA methods can only estimate system responses at each time point separately, which is time-consuming for analyzing power system dynamics. Thus, this paper proposes a generic multi-output SR (GMSR) method to effectively tackle the above limitations by developing the generic correlation transformation and multi-output structure. The effectiveness and superiority of GMSR in efficiency and accuracy are demonstrated by comparing it with existing SR methods.

Abstract:Under-frequency load shedding (UFLS) serves as the very last resort for preventing total blackouts and cascading events. Fluctuating operating conditions and weak resilience of the future grid require UFLS adapt to various operating conditions and non-envisioned faults. This paper develops a novel data-enabled Koopman-based load shedding (KLS) to achieve the optimal one-shot load shedding for power system frequency safety. The KLS yields a network that facilitates a coordinate transformation from the delay-embedded space to a new space, wherein the dynamics can be expressed in a linear manner. The network is specifically tailored to effectively track parameter variations in the dynamic model of the power system. Linear dynamics support the development of a real-time decided load shedding strategy, while parameter tracking enables the adaptability of the KLS to non-envisioned operating conditions and faults. To address approximation inaccuracies and the discrete nature of load shedding, a safety margin tuning scheme is integrated into the KLS framework, ensuring that the system frequency trajectory remains within the safety range. Simulation results show the adaptability, prediction capability, and control effect of the proposed KLS.

Abstract:The increasing penetration of renewable energy resources degrades the frequency stability of power systems. The present work addresses this issue by proposing a look-ahead dispatch model of power systems based on a linear alternating current optimal power flow framework with nonlinear frequency constraints. Meanwhile, the poor efficiency for solving this formulation is addressed by introducing a physics-informed neural network (PINN) to predict key frequency-control parameter values accurately. The PINN ensures that the learned results are applicable to the original physical frequency dynamics model, and applying the predicted parameter values enables the resulting dispatch model to be solved quickly and efficiently using readily available commercial solvers. The feasibility and advantages of the proposed model are demonstrated by the results of numerical computations applied to a modified IEEE 118-bus test system.

Abstract:In DC power systems dominated by power electronic devices, constant power loads (CPLs) and saturation components significantly impact large-signal stability. During the large-signal stability analysis process, the presence of multiple state variables and high-order system poses substantial challenges. To address this, considering the complete control dynamics, this paper proposes an equivalent single-machine (ESM) model of the droop-based DC power systems to reduce the complexity of the large-signal analysis. Building on the proposed ESM model, considering the dynamics of CPL and saturation constraints, a region of attraction (ROA) estimation algorithm based on sum of squares (SOS) programming is proposed, which significantly reduces the conservativeness compared with other existing methods. Furthermore, a control parameter optimization algorithm based on SOS programming is proposed with the aim of expanding the ROA. Furthermgre, with the aim of expanding the ROA, controller sythesis is conducted with proposed control parameter optimization algorithm based on SOS programming. Ultimately, simulation experiments validate the accuracy of the proposed ESM model and the proposed ROA estimation algorithm, as well as the effectiveness of the control parameter optimization algorithm.

Abstract:The oscillatory stability analysis of multi-converter-fed systems (MCFSs) with modest computational resources needs a precise parametric reduced-order impedance model (PROIM). However, the traditional Krylov subspace based parametric model order reduction (KS-PMOR) method has difficulty in building precise PROIM for MCFSs. This is because the factors related to the errors of PROIM are complicated and coupled. To fill this gap, the factors associated with the accuracy of the KS-PMOR method are estimated by defining three indicators: the convergence error, cumulative error, and rank of projection matrix. Using the three indicators, a frequency-domain adaptive parametric model order reduction (FDA-PMOR) method is developed to form the precise PROIM of MCFSs for the accurate and fast oscillatory stability analysis. The accuracy of the obtained PROIM using the proposed FDA-PMOR method and its efficiency in actual oscillatory stability analysis are validated by three MCFSs with different scales, i.e., a small-scale MCFS with four paralleled converter-based renewable energy generators (CREGs), a real-time simulation-based MCFS with eighteen paralleled CREGs, and a larger MCFS with ninety paralleled CREGs.

Abstract:In a DC grid with dedicated metallic return (DMR), the coupling effects among the positive pole, negative pole, and DMR conductors must be considered, which makes fault identification particularly difficult. In addition, the identification of high-impedance faults remains a major challenge for DC grid protection. To address these issues, this study proposes an adaptive single-end protection method for DC grid based on the transient mean value of the current limiting reactor (CLR) modal voltage. First, a fault analysis model of the DC grid with DMR is established using the Clarke transformation. The characteristics of CLR modal voltage are then clarified. A fault pole-selection method based on a novel modulus phase plane is next proposed. A threshold scaling factor based on the differential of DC bus voltage is then constructed to enhance the sensitivity and rapidity of the protection, which can adaptively modify the threshold according to the fault severity. Finally, a simulation model of a four-terminal DC grid with DMR is developed in PSCAD/EMTDC. The speed and reliability of the proposed protection method are verified by simulations and experiments.

Abstract:The single-ended fault location based on travelling waves (TWs) is commonly used for long-distance high-voltage AC transmission lines. However, it relies on high sampling frequency and accurate capturing of the TW head arrival time. Accordingly, this study establishes a transient analytical method for fault location based on the similarity between the transient recorded waveform and output waveforms of analytical calculation model. In the proposed method, fuzzy constraints of fault features are constructed through time-distance and waveform-scaling correlations while considering the deviation factors of the frequency-dependent wave velocity and TW head arrival time. Accordingly, the high-dimensional space of the fitting problem is transformed into a one-dimensional implicit function fitting problem containing only the fault distance, thereby enabling the waveform comparison problem to be quickly solved based on fault TW features. Under the fuzzy constraints proposed in this study, the proposed method requires only a relatively vague identification of the TW head, and the requirements for sampling frequency are also more lenient. In addition, a sliding window scheme is adopted for enhancing the TW morphology characteristics. Finally, the proposed method is tested using PSCAD, and the simulations validate the fault location accuracy of the proposed method.

Abstract:Traditional protection methods are not suitable for hybrid (cable and overhead) transmission lines in voltage source converter based high-voltage direct current (VSC-HVDC) systems. Accordingly, this paper presents the robust fault detection, classification, and location based on the empirical wavelet transform-Teager energy operator (EWT-TEO) and artificial neural network (ANN) for hybrid transmission lines in VSC-HVDC systems. The operational scheme of the proposed protection method consists of two loops ① an EWT-TEO based feature extraction loop, ② and an ANN-based fault detection, classification, and location loop. Under the proposed protection method, the voltage and current signals are decomposed into several sub-passbands with low and high frequencies using the empirical wavelet transform (EWT) method. The energy content extracted by the EWT is fed into the ANN for fault detection, classification, and location. Various fault cases, including the high-impedance fault (HIF) as well as noises, are performed to train the ANN with two hidden layers. The test system and signal decomposition are conducted by PSCAD/EMTDC and MATLAB, respectively. The performance of the proposed protection method is compared with that of the traditional non-pilot traveling wave (TW) based protection method. The results confirm the high accuracy of the proposed protection method for hybrid transmission lines in VSC-HVDC systems, where a mean percentage error of approximately 0.1% is achieved.

Abstract:Most permanent magnet synchronous generator (PMSG) based wind generation systems currently employ grid-following control, relying on a phase-locked loop (PLL) for grid connection. However, it leads to a lack of inertia support in the system. To address this, the virtual inertia control (VIC) is crucial for improvement, yet it introduces potential instability due to torsional oscillation interaction with PLL and low-frequency oscillations, which is an underexplored area. This paper presents a comprehensive analysis of the grid-connected PMSG-based wind generation system. It confirms the necessity of employing a full-order model for studying stability on the quasi-electromechanical timescale (QET) by a comparison with the reduced-order model. Then, a comprehensive modal analysis is conducted to analyze the effect of VIC parameters, shaft inertia time constant, PLL parameters, and torsional oscillation damping (TOD) controller gain on the interaction of QET oscillations under two typical control strategies. The occurrence of interaction and mode conversion is observed when the oscillation frequency and root loci of the torsional, PLL, and low-frequency oscillations are close. Finally, a theoretical analysis is validated via simulation verification in Simulink. These findings offer a valuable guidance for industrial PMSG applications considering VIC.

Abstract:The optimal torque (OT) method, which is preferred for its simplicity, is widely employed in maximum power point tracking (MPPT) control strategies for wind energy capture in wind turbine generators (WTGs). Based on the OT method, the decreased torque gain (DTG) method is developed to improve turbine acceleration through a reduction of the torque gain coefficient. However, the DTG method does not fully align with the acceleration performance required by wind turbines, which subsequently limits improvements in wind energy capture efficiency. To address these concerns, a novel MPPT control strategy is proposed, which introduces redefined torque curve and torque command conceptualized based on a higher-order function relative to rotor speed. Additionally, an adaptive algorithm for the periodic update of the torque command is suggested to better accommodate the variability of turbulent wind speeds, thus aiming to improve the wind energy capture efficiency. The effectiveness of the proposed MPPT control strategy is substantiated through the wind turbine simulator (WTS)-based experiments.

Abstract:Building integrated energy systems (BIESs) are pivotal for enhancing energy efficiency by accounting for a significant proportion of global energy consumption. Two key barriers that reduce the BIES operational efficiency mainly lie in the renewable generation uncertainty and operational non-convexity of combined heat and power (CHP) units. To this end, this paper proposes a soft actor-critic (SAC) algorithm to solve the scheduling problem of BIES, which overcomes the model non-convexity and shows advantages in robustness and generalization. This paper also adopts a temporal fusion transformer (TFT) to enhance the optimal solution for the SAC algorithm by forecasting the renewable generation and energy demand. The TFT can effectively capture the complex temporal patterns and dependencies that span multiple steps. Furthermore, its forecasting results are interpretable due to the employment of a self-attention layer so as to assist in more trustworthy decision-making in the SAC algorithm. The proposed hybrid data-driven approach integrating TFT and SAC algorithm, i.e., TFT-SAC approach, is trained and tested on a real-world dataset to validate its superior performance in reducing the energy cost and computational time compared with the benchmark approaches. The generalization performance for the scheduling policy, as well as the sensitivity analysis, are examined in the case studies.

Abstract:Mobilized energy storage (MES) can provide a variety of services for power systems, including peak shaving, frequency regulation, and congestion alleviation. In this paper, we develop an MES sharing approach based on temporal-spatial network (TSN) toward systemwide temporal-spatial flexibility enhancement, specifically in which the heavy-duty vehicles can exchange batteries at the energy storage stations connected with power grids. To achieve the temporal-spatial coordination of transportation and power systems, we propose a coordinated scheduling model. A decentralized algorithm based on the improved optimality condition decomposition (OCD) algorithm is proposed to address the information asymmetry between transportation and power systems while enhancing computational efficiency. Case studies based on IEEE 30-/118-bus and transportation systems demonstrate that MESs using the proposed approach can significantly improve the utilization of batteries while reducing operating costs by over 40% compared with stationary energy storages (SESs).

Abstract:Hybrid energy storage is considered as an effective means to improve the economic and environmental performance of integrated energy systems (IESs). Although the optimal scheduling of IES has been widely studied, few studies have taken into account the property that the uncertainty of the forecasting error decreases with the shortening of the forecasting time scale. Combined with hybrid energy storage, the comprehensive use of various uncertainty optimization methods under different time scales will be promising. This paper proposes a multi-time-scale optimal scheduling method for an IES with hybrid energy storage under wind and solar uncertainties. Firstly, the proposed system framework of an IES including electric-thermal-hydrogen hybrid energy storage is established. Then, an hour-level robust optimization based on budget uncertainty set is performed for the day-ahead stage. On this basis, a scenario-based stochastic optimization is carried out for intra-day and real-time stages with time intervals of 15 min and 5 min, respectively. The results show that ① the proposed method improves the economic benefits, and the intra-day and real-time scheduling costs are reduced, respectively; ② by adjusting the uncertainty budget in the model, a flexible balance between economic efficiency and robustness in day-ahead scheduling can be achieved; ③ reasonable design of the capacity of electric-thermal-hydrogen hybrid energy storage can significantly reduce the electricity curtailment rate and carbon emissions, thus reducing the cost of system scheduling.

Abstract:To address the strong thermoelectric coupling of the combined heat and power (CHP) units, the low utilization rate of energy storage, and the underexploitation of load-side resource flexibility in integrated energy systems (IESs), this paper proposes an optimal scheduling model of an IES in low-carbon communities considering flexibility of resources and the segmental control of solid oxide fuel cells (SOFCs). Firstly, by replacing the gas turbine (GT) in the CHP unit with an SOFC array to reduce carbon emissions and simultaneously weakening the thermoelectric coupling of the CHP unit, the segmental control method is used to control the SOFC array to improve the overall efficiency of the CHP unit. Secondly, coupled interactions among different types of energy storage equipments are mobilized through the integrated energy storage system to make full use of the remaining space in the heat and natural gas storage tanks. Finally, load-side flexible resources are utilized by considering transferable, substitutable, and heat loads, taking into account the thermal inertia of the building and categorizing rooms based on floors, orientations, and room area. Additionally, different user characteristics are characterized, and the flexible resources of building heating periods in northern cities in China are tapped in depth according to the actual factors. Compared with the traditional model, the optimal scheduling model proposed in this paper can reduce the wind abandonment rate and the carbon emission of community-integrated energy system (CIES) by 4.54% and 70.63%, respectively, and increase the utilization rate of heat and natural gas storage tanks by 12.34% and 30.52%, respectively, and lower the total cost by ¥2183.6 under the premise of ensuring user comfort during energy consumption, which promotes the economic and low-carbon operation of the CIES.

Abstract:This paper develops a physics-guided graph network to enhance the robustness of distribution system state estimation (DSSE) against anomalous real-time measurements, as well as a deep auto-encoder (DAE)-based detector and a Gaussian process-aided residual learning (GARL) to deal with challenges arising from topology changes. A global-scanning jumping knowledge network (GSJKN) is first designed to establish the regression rule between the measurement data and state variables. The structural information of distribution system (DS) and a global-scanning module are incorporated to guide the propagation of scarce measurements in the graph topology, contributing to valid estimation precision in sparsely measured DSs. To monitor the topology changes of the network, a DAE network is employed to learn an efficient representation of the measurements of the system under a certain topology, which can achieve online monitoring of the network structure by observing the variation tendency of the reconstruction error. When the topology change occurs, a Gaussian process with a composite kernel is applied to the modeling of the pre-trained GSJKN residual to adapt to the new topology. The embedding of the physical structural knowledge enables the proposed GSJKN method to restore the missing/noisy values utilizing the adjacent measurements, which enhances the robustness to typical data acquisition errors. The adopted DAE network and special GARL-based transfer method further allow the DSSE method to rapidly detect and adapt to the topology change, as well as achieve effective quantification of the estimation uncertainties. Comparative tests on balanced and unbalanced systems demonstrate the accuracy, robustness, and adaptability of the proposed DSSE method.

Abstract:A novel planning tool for optimizing the placement of electric springs (ESs) in unbalanced distribution networks is introduced in this study. The total voltage deviation is used as the optimization criterion and is calculated when the ESs operate at their maximum reactive power either in the inductive or capacitive modes. The power rating of the ES is adjusted on the basis of the available active power at the bus. And in the optimization problem, it is expressed as the power ratio of the noncritical load (NCL) and critical load (CL). The implemented ES model is flexible, which can be used on any bus and any phase. The model determines the output voltage from the parameters and operating conditions at the point of common coupling (PCC). These conditions are integrated using the backward/forward sweep method (BFSM) and are updated during power flow calculations. The problem is described as a mixed-integer nonlinear problem and solved efficiently using an improved BFSM-based genetic algorithm, which computes power flow and ES placement simultaneously. The effectiveness of this method is evaluated through testing in IEEE 13-bus and 34-bus systems.

Abstract:Rural electrification is a crucial component of the power system that requires urgent innovation and transformation to enhance electrification levels. However, various challenges hinder the progress in rural electrification, primarily due to remote locations and unique consumption patterns. To effectively coordinate the local energy distribution, an energy management framework utilizing peer-to-peer (P2P) based interactive operations is proposed, which minimizes the reliance on long-distance transmission while enhancing the rural electrification level. The proposed P2P-based energy management framework incorporates various distributed generation resources across rural areas, facilitating direct energy transactions between neighboring community-based villages. Additionally, the P2P energy trading is modeled as a Nash bargaining (NB) problem, which accounts for the allocation of network loss costs and operational state of the rural distribution network. To protect the privacy of individual villages, an improved adaptive alternating direction method of multipliers (AADMM) is proposed to solve the NB problem. The AADMM utilizes a local curvature approximation scheme during parameter updates, allowing for automatic adjustments of the fixed penalty parameter within the standard alternating direction method of multipliers (ADMM). This enhancement improves the convergence rates without requiring central oversight. Simulation results demonstrate significant reductions in operational costs for both the overall network and individual village participants. The proposed P2P-based energy management framework also enhances the bus voltage stability and reduces the line transmission power, thereby further enhancing rural electrification levels. The adaptability and extensibility of this framework are further validated using the IEEE 33-bus and 118-bus distribution systems. Additionally, the AADMM shows higher convergence rates compared with the standard ADMM.

Abstract:The volatility of increasing distributed generators (DGs) poses a severe challenge to the supply restoration of active distribution networks (ADNs). The integration of power electronic devices represented by soft open points (SOPs) and mobile energy storages (MESs) provides a promising opportunity for rapid supply restoration with high DG penetration. Oriented for the post-event rapid restoration of ADNs, a bi-level supply restoration method is proposed considering the multi-resource coordination of switches, SOPs, and MESs. At the upper level (long-timescale), a multi-stage supply restoration model is developed for multiple resources under uncertainties of DGs and loads. At the lower level (short-timescale), a rolling correction restoration strategy is proposed to adapt to the DG and load fluctuations on short timescales. Finally, the effectiveness of the proposed method is verified based on a modified practical distribution network and IEEE 123-node distribution network. Results show that the proposed method can fully utilize the coordination potential of multiple resources to improve load restoration ratio for ADNs with DG uncertainties.

Abstract:In this paper, a set of distributed secondary controllers is introduced that provide active regulation for both steady-state and transient-state performances of an islanded DC microgrid (MG). The secondary control for distributed converter interfaced generation (DCIG) not only guarantees that the system converges to the desired operating states in the steady state but also regulates the state variations to a prescribed transient-state performance. Compared with state-of-the-art techniques of distributed secondary control, this paper achieves accurate steady-state secondary regulations with prescribed transient-state performance in an islanded DC MG. Moreover, the applicability of the proposed control does not rely on any explicit knowledge of the system topology or physical parameters. Detailed controller designs are provided, and the system under control is proved to be Lyapunov stable using large-signal stability analysis. The steady-state and transient-state performances of the system are analyzed. The paper proves that as the perturbed system converges, the proposed control achieves accurate proportional power sharing and average voltage regulation among the DCIGs, and the transient variations of the operating voltages and power outputs at each DCIG are regulated to the prescribed transient-state performance. The effectiveness of the proposed control is validated via a four-DCIG MG system.

Abstract:This paper proposes a novel parallel hybrid deep reinforcement learning (DRL) approach to address the real-time energy management problem for microgrid (MG). As the proposed approach can directly approximate a discrete-continuous hybrid policy, it does not require the discretization of continuous actions like regular DRL approaches, which avoids accuracy degradation and the curse of dimensionality. In addition, a novel experience-sharing-based parallel technique is further developed for the proposed approach to accelerate the training speed and enhance the training robustness. Finally, a safety projection technique is introduced and incorporated into the proposed approach to improve the decision feasibility. Comparative numerical simulations with several existing MG real-time energy management approaches (i.e., myopic policy, model predictive control, and regular DRL approaches) demonstrate the effectiveness and superiority of the proposed approach.

Abstract:As the penetration of intermittent renewable energy resources in microgrids (MGs) continues to grow globally, optimal operation management becomes increasingly crucial due to the variability of these sources. One potential solution to this challenge is the use of demand response (DR) programs, which are practical and relatively low-cost options. However, ensuring the security of MG operation also requires evaluating its flexibility by determining the acceptable boundaries of uncertain variables. Additionally, in real-world operational decision-making problems, there is a simultaneous optimization of multiple objectives, including the maximization of system flexibility and the minimization of system cost. This paper presents a methodology for developing a cost-aware flexibility evaluation method for MGs connected to the upstream grid, which are subject to volatile market prices. The model is based on the feasibility analysis of the uncertain space of wind power generation and load, and it also investigates the level of inflexibility present in the system. The impact of the DR program on the flexibility of MGs is quantified through a case study. The case study confirms the success of the proposed method and underscores the significance of cost modeling in flexibility evaluation problems.

Abstract:Thermostatically controlled loads (TCLs) on the demand side have been a vital energy resource in smart grids. To efficiently utilize the large-scale TCLs and enhance the flexibility of micro-community systems, this paper proposes a distributed coordinated control strategy based on the distributed model predictive control (MPC). To achieve the adaptive coordinated control among TCLs and consider user comfort constraints, a distributed dual-layer internal control strategy based on MPC is established on a scalable communication network. This strategy achieves the efficient utilization of TCLs in a distributed manner and notably improves the convergence speed through sparse network communication between neighbors. For external resource utilization of TCLs, a multi-timescale scheduling framework is proposed to realize the pre-allocation of electricity. Furthermore, the feasibility of the proposed distributed coordinated control strategy is confirmed through comparative case analysis.

Abstract:This paper presents a holistic pricing and distributed scheduling framework for multi-microgrid system (MMGS) that considers the supply‒demand relationships of the coupled electricity‒carbon market to promote collaborative market trading within the MMGS for economic and environmental benefit improvement. Initially, an operation model of each microgrid is developed by synthetically considering electricity-carbon operational constraints related to generation units and energy storage units. Then, a collaborative optimization strategy of the MMGS is established according to the Nash bargaining game (NBG) model with the objective of maximizing overall operational revenue. To determine the trading schedule, an accelerated prediction-correction-based alternating direction method of multipliers (PCB-ADMM) algorithm is employed to derive the optimal scheduling strategy of MMGS in a distributed manner, ensuring the privacy preservation of individual microgrids. For electricity-carbon pricing, a supply-demand ratio (SDR) based pricing strategy is proposed to dynamically update electricity and carbon allowance prices, which fundamentally guides and incentivizes each microgrid to trade within the MMGS preferentially rather than with an upstream distribution network. Finally, a study case verifies the effectiveness of the proposed framework in enhancing the operation economy and environmental friendliness of the entire MMGS.

Abstract:Under weak grid conditions, grid impedance is coupled with a control system for voltage source converter based high-voltage direct current (VSC-HVDC) systems, resulting in decreased synchronization stability. Unfortunately, most studies are based on the assumption that impedance ratio (R/X) is sufficiently small to ignore the effects of grid impedance. In this study, we establish a dynamic coupling model that includes grid impedance and control loops, revealing the influence mechanism of R/X on synchronization stability from a physical perspective. We also quantify the stability range of R/X in the static analysis model and introduce a sensitivity factor to measure its effect on voltage stability. Additionally, we utilize a dynamic analysis model to evaluate power angle convergence, proposing a corresponding stability criterion. We then present a method of synchronous voltage reconstruction aimed at enhancing the grid strength. Theoretical analysis shows that this method can effectively mitigate the effects of coupling between grid impedance and the controller under weak grid conditions, ensuring stable operation even under extremely weak grid conditions. Experiments validate the accuracy and effectiveness of the analysis and method.

Abstract:The power loss minimization and DC voltage stability of the multi-terminal direct current (MTDC) system with large-scale wind farm (WF) cluster affect the stability and power quality of the interconnected power grid. This paper proposes a distributed optimal voltage control (DOVC) strategy, which aims to optimize voltage distribution in MTDC and WF systems, reduce system power losses, and track power dispatch commands. The proposed DOVC strategy employs a bi-level distributed control architecture. At the upper level, the MTDC controller coordinates power flow, DC-side voltage of grid-side voltage source converters (GSVSCs), and WF-side voltage source converters (WFVSCs) for power loss minimization and DC voltage stabilization of the MTDC system. At the lower level, the WF controller coordinates the controlled bus voltage of WFVSC and the active and reactive power of wind turbines (WTs) to maintain WT terminal voltages within feasible range. Then, the WF controller minimizes the power loss of the WF system, while tracking the optimal command from the upper-level control strategy. Considering the computational tasks of multi-objective optimization with large-scale WF cluster, the proposed DOVC strategy is executed in a distributed manner based on the alternating direction method of multipliers (ADMM). An MTDC system with large-scale WF cluster is established in MATLAB to validate the effectiveness of the proposed DOVC strategy.

Abstract:The virtual synchronous generator (VSG), utilized as a control strategy for grid-forming inverters, is an effective method of providing inertia and voltage support to the grid. However, the VSG exhibits limited capabilities in low-voltage ride-through (LVRT) mode. Specifically，the slow response of the power loop poses challenges for VSG in grid voltage support and increases the risk of overcurrent, potentially violating present grid codes. This paper reveals the mechanism behind the delayed response speed of VSG control during the grid faults. On this basis, a compound compensation control strategy is proposed for improving the LVRT capability of the VSG, which incorporates adaptive frequency feedforward compensation (AFFC), direct power angle compensation (DPAC), internal potential compensation (IPC), and transient virtual impedance (TVI), effectively expediting the response speed and reducing transient current. Furthermore, the proposed control strategy ensures that the VSG operates smoothly back to its normal control state following the restoration from the grid faults. Subsequently, a large-signal model is developed to facilitate parameter design and stability analysis, which incorporates grid codes and TVI. Finally, the small-signal stability analysis and simulation and experimental results prove the correctness of the theoretical analysis and the effectiveness of the proposed control strategy.

Abstract:The maximum power transfer capability (MPTC) of phase-locked loop (PLL)-based grid-following inverters is often limited under weak-grid conditions due to passivity violations caused by operating-point-dependent control loops. This paper reveals and compares the mechanisms of these violations across different control strategies. Using admittance decomposition and full-order state-space models for eigenvalue analysis, MPTC limitations from control loops and their interactions are identified. The small-signal stabilities of different control loops are compared under varying grid strength, and both static and dynamic MPTCs for each control mode are examined. This paper also explores how control loop interactions impact the MPTC, offering insights for tuning control loops to enhance stability in weak grids. For example, fast power control improves the MPTC when paired with a slow PLL, while power control has minimal effect when the PLL is sufficiently fast. The findings are validated through frequency scanning, eigenvalue analysis, simulations, and experiments.

Abstract:Fractional-order control (FOC) has gained significant attention in power system applications due to their ability to enhance performance and increase stability margins. In grid-connected converter (GCC) systems, the synchronous reference frame phase-locked loop (SRF-PLL) plays a critical role in grid synchronization for renewable power generation. However, there is a notable research gap regarding the application of FOC to the SRF-PLL. This paper proposes a fractional-order SRF-PLL (FO-SRF-PLL) that incorporates FOC to accurately track the phase angle of the terminal voltage, thereby improving the efficiency of grid-connected control. The dynamic performance of the proposed FO-SRF-PLL is evaluated under varying grid conditions. A comprehensive analysis of the small-signal stability of the GCC system employing the FO-SRF-PLL is also presented, including derived small-signal stability conditions. The results demonstrate that the FO-SRF-PLL significantly enhances robustness against disturbances compared with the conventional SRF-PLL. Furthermore, the GCC system with the FO-SRF-PLL maintains stability even under weak grid conditions, showing superior stability performance over the SRF-PLL. Finally, both simulation and experimental results are provided to validate the analysis and conclusions presented in this paper.

Abstract:The introduction of fully controlled devices to build hybrid line commutated converter (H-LCC) has become a new idea to solve the commutation failure. However, existing H-LCC has not considered the implementation of a targeted firing angle control strategy during AC faults, with the objective of enhancing their power transmission and fault response performance. For this reason, this paper proposes an optimized control method for firing angle of H-LCC, designated as flexible virtual firing (FVF). This method first analyzes the influence of alterations in firing angle on reactive power, commutation process and associated action paths. By combining prediction and dynamic search, it optimizes the natural commutation process through the utilization of dynamic boundary and minimum commutation area difference. This can mitigate the impact of AC faults on H-LCC and DC system, thereby improving power transmission and defense to commutation failure, which is beneficial for improving the stability of AC/DC power grids. Finally, the simulation results in PSCAD/EMTDC verify the effectiveness of the proposed method.