Abstract:The rapid development of large-scale offshore wind power (OWP) calls for more cost-effective and reliable collection and transmission technologies. This paper explores three emerging collection technologies: medium-frequency alternating current (AC) collection, direct current (DC) collection, and AC collection without substation; and three transmission technologies: voltage source converter-based high-voltage direct current based on compact modular multilevel converters (MMCs), diode rectifier unit (DRU) based high-voltage direct current (DRU-HVDC), and high-voltage direct current (HVDC) based on hybrid converters. It systematically reviews recent research advancements in these technologies, analyzes critical technical challenges, and identifies key future development trends, providing practical insights to guide the design and optimization of OWP projects. At the collection level, a higher frequency reduces the size of critical equipment in offshore platforms but also leads to increased cable costs. DC offshore wind farms offer advantages such as lower cable costs. However, the design of high-power DC transformers presents challenges. At the transmission level, the size and weight of MMCs can be minimized through topology improvement and control optimization. The practical deployment of DRUs and HVDC systems depends on the technology maturity of grid-forming wind turbines. Moreover, critical aspects of hybrid converters such as capacity design, coordinated control, and stability analysis require further in-depth investigation.

Abstract:With the increasing integration of large-scale renewable energy (RE) sources into power systems, electricity-hydrogen coupling system has emerged as a transformative solution through flexible energy conversion and complementary utilization of electricity and hydrogen. It effectively addresses structural challenges in conventional energy systems regarding spatiotemporal regulation, environmental constraints, and supply security while creating significant opportunities in technological innovation and industrial transformation, accelerating the transition from traditional fossil fuels to clean energy. This paper reviews the strengths and limitations of the electricity-hydrogen coupling system in production, storage, and utilization in scenarios of high RE penetration. It examines the architectural frameworks and current development status of key technologies within the electricity-hydrogen coupling system, and builds on their operational characteristics across multiple timescales to analyze both short-term energy balance control and medium- and long-term optimal dispatch. This paper further investigates representative application scenarios, systematically evaluates demonstration projects deployed, and critically analyzes prevailing challenges alongside prospective research pathways.

Abstract:Due to structural differences and parameter mismatches, power oscillation may arise when the diesel generator (DG) and virtual synchronous generator (VSG) operate in parallel, especially when the periodic pulsed load (PPL) is integrated. This paper analyses the power oscillation mechanism in the paralleled system of DG and VSG and provides an in-depth discussion of the novel phenomenon of power oscillation induced by PPL. The results show that power oscillation is amplified as the PPL pulse frequency approaches the inherent oscillation frequency of the paralleled system. Furthermore, the inherent control delay of the DG speed governor exacerbates the power oscillation. To address this issue, a dynamic phase compensator (DPC) is proposed and integrated into the VSG control loop. By detecting the difference between the instantaneous output power of VSG and its steady-state theoretical value, the proposed DPC provides additional phase compensation to the VSG output phase, effectively suppressing power oscillation for the paralleled system of DG and VSG integrated with PPL. Finally, experimental results validate the theoretical analysis and demonstrate the effectiveness of the proposed DPC.

Abstract:This paper analyzes the effect of a frequency estimator in a grid-forming (GFM) synchronization control on the stability and control performance. GFM control for power converters has been proposed as a promising solution to enhance the stability and resilience of electrical systems dominated by power electronics. However, no consensus has been reached on the control structure for this operation mode. Moreover, the interactions between different GFM schemes and frequency estimators are not completely defined in the literature. In this paper, the effect of adding a frequency estimator to the two main industry-class synchronization controls, i.e., droop and virtual synchronous machine (VSM), is studied. Additionally, different AC voltage measurement points, tuning, and structures of frequency estimator are considered. Two distinct analyses are performed to discuss the characteristics of different configurations① an analytical study on the control performance of different configurations, and ② a small-signal analysis to ensure system stability. Finally, the results are validated using dynamic simulations, followed by a discussion. This paper concludes that the droop should be avoided when applying a frequency estimator, and other structures such as the VSM are more desirable.

Abstract:The increasing use of distributed generation has revealed limitations in conventional power electronic converters, which are unable to provide sufficient inertia and damping support to the grid. As a result, virtual synchronous generator (VSG) has gained widespread adoption for regulating the output voltage and frequency. However, VSG may encounter challenges such as generating large inrush currents and power fluctuations during on-grid switching, significantly reducing the efficacy of virtual synchronous control strategies. Therefore, this study optimizes the dynamic performance of VSG based on grid-connected switching control strategy using radial basis function neural network (RBFNN) integrated nonlinear active disturbance rejection control (NLADRC) approach. In comparison with the conventional pre-synchronization control strategy, the proposed strategy effectively suppresses system variable oscillations through the NLADRC approach. This facilitates the rapid restoration of system output frequency, voltage, and power to the steady state, thereby enhancing transient stability. Moreover, the RBFNN-NLADRC approach leverages the robust fitting capability of the network for obtaining dynamic parameter information, which allows for gain parameter tuning, further enhancing the effectiveness of the proposed strategy. Finally, verifications conducted in MATLAB/Simulink and a Starsim hardware-in-the-loop environment illustrate the superiority and feasibility of the proposed strategy.

Abstract:The application of a virtual synchronous generator (VSG) to provide virtual inertia in isolated microgrids has emerged as a promising control strategy for converter-interfaced renewable energy sources. However, tuning the VSG parameters requires an accurate characterization of frequency dynamics, which remains a challenge, particularly in hybrid microgrids combining conventional and renewable units. In this context, this paper proposes a reduced-order model to support the parameter tuning of VSGs in an isolated hybrid microgrid composed of an oil and gas facility powered by gas generators connected to an offshore wind turbine. A VSG-based control strategy is applied on the grid-side converter of the wind turbine, allowing it to contribute to frequency regulation and inertia emulation. An analytical formulation is developed to determine the frequency nadir, its time of occurrence, and the rate of change of frequency based on the fixed parameters of the gas generators and the tunable parameters of the VSG. A procedure for parameter tuning to attain the desired frequency dynamics is derived from the analytical formulation. Simulations based on MATLAB/Simulink Simscape Electrical model demonstrate the effectiveness of the proposed procedure by illustrating its consistency in guiding parameter tuning and achieving the desired frequency dynamics.

Abstract:The traditional power system dominated by synchronous generators is gradually evolving into a modern power system featured by high-penetrated renewable energy. As a key technology for high-penetrated renewable energy, the grid-forming voltage source converter (GFM-VSC) has received increasing attention. However, the large-disturbance stability analysis of power systems with multiple GFM-VSCs is still a challenging problem due to various limitations of existing methods, including huge computational burden and difficulty in considering network losses. This paper is intended to address these issues from the perspective of reduced-order modeling and domain of attraction (DA) estimation. The innovations involve three aspects. First, the reduced-order modeling method for power systems with multiple GFM-VSCs is proposed using the standard dual-time-scale model in singular perturbation theory. Second, an expanding annular domain (EAD) algorithm is developed to estimate the DA with an entire boundary to analyze the large-disturbance stability of power systems. Third, the conditions of using the reduced-order modeling method based on singular perturbation theory have been clarified. The validity of the reduced-order modeling method is illustrated on a modified 39-bus system with 10 GFM-VSCs.

Abstract:To reduce the cost of offshore wind power generation systems, the configuration of the offshore wind farm employing doubly-fed induction generator (DFIG) connected to the diode rectifier unit-based high-voltage direct current (DRU-HVDC) system has emerged as an attractive solution. The control strategy of the DFIG plays a crucial role in ensuring reliable operation of the offshore wind power generation system due to the uncontrollable nature of the diode rectifier unit (DRU). This paper proposes a self-synchronized grid-forming control strategy for the DFIG in offshore wind farm connected to DRU-HVDC system. Considering the unique power characteristics of the DRU, the proposed strategy constructs a novel power synchronization control loop, which achieves self-synchronization of the DFIGs in offshore wind farm without any communication network. Additionally, the harmonic distortion induced by the natural commutation characteristic of the DRU introduces significant electromagnetic ripples to the DFIG through the stator windings. To mitigate this, an electromagnetic oscillation reduction method based on harmonic current injection is incorporated into the structure of the proposed strategy. Simulation results based on MATLAB/Simulink validate the effectiveness of the proposed strategy and the electromagnetic oscillation reduction method.

Abstract:The high penetration of renewable energy sources interfaced throush power electronic converters often leads to small-signal stability issues. Therefore, it is critical to quantify the impact of control parameters in multiple grid-connected converters on the small-signal stability of power system. To this end, this paper derives the small-signal stability criterion and provides the quantitative analysis of parameter sensitivity for multiple grid-connected converter systems (MGCCSs) based on extended Gershgorin theorem, thereby clarifying the influence of control parameters on the small-signal stability and providing the foundation for adaptive control. Crucially, leveraging this sensitivity analysis, we propose an adaptive control strategy involving targeted parameter adjustment for the identified weak links to ensure that the system operates with a specified stability margin. Both theoretical analysis and simulation prove the effectiveness of the proposed adaptive control strategy in the improving the small-signal stability of MGCCSs. Importantly, the proposed adaptive control strategy also demonstrates the significant potential for online application to adaptively compensate the small-signal stability margin in real time.

Abstract:The coordinated dispatch of interconnected grids characterized by maldistributed sustainable energy encounters challenges with regional privacy. Thus, this study proposes a non-iterative robust economic dispatch method for interconnected grids based on tie-line power transfer regulation. The economic dispatch model with uncertainties is transformed into a two-layer robust model and further treated as a single-layer linear model by strong duality theorem. Then, the intra-regional submodules are established by temporal and spatial decomposition to enable parallel execution. The inter-regional power transfer feasible region (PTFR) and intra-regional operation cost feasible region (OCFR) are evaluated using multi-parameter programming theory to protect the private and sensitive information of each region and to ensure cost efficiency of dispatch results. Additionally, the boundaries of feasible regions are adjusted by the conservatism budget to address multiple fluctuation intervals of stochastic factors. Finally, the information of feasible regions is shared between each intra-regional operator along with central coordination layer, generating feasible regions of joint economic dispatch along with inter-regional power transfer constraints. The intra-regional dispatch strategy could be rapidly obtained following the decision-making of inter-regional dispatch by mapping relations. Case studies by three modified IEEE test systems demonstrate the preciseness and effectiveness of the proposed method.

Abstract:Power grids face significant threats from severe disturbances, often triggered by extreme weather, leading to widespread cascading power outages. Although intentional controlled islanding (ICI) is an effective last-resort operational mitigation strategy employed by system operators worldwide to prevent complete cascading blackouts, the impact of large-scale disturbances, particularly weather-induced cascading outages, on when and where to implement the ICI, is neither adequately considered nor reflected in current operational decision-making standards and procedures. This paper proposes a holistic cascading-driven ICI framework that seamlessly integrates advanced weather-related event modelling and cascading risk quantification of high-impact low-probability (HILP) (or tail-risk) events by using a novel ICI based on decision-making mechanism for enhancing the power grid operational resilience. The proposed framework provides a portfolio of mitigation actions proportional to cascading impacts, differentiating between tail-risk events and expected (average) events typically addressed in reliability-oriented studies and current industry practices, while being tailored to both near-real-time operations and short-term operational planning. The proposed framework involves system splitting around black-start units while forming stable and self-sufficient islands, thereby enhancing reliability and resilience. Studies on the IEEE 39-bus and IEEE 118-bus systems demonstrate the effectiveness with a significant improvement in served demand across all simulated initiating events, including up to N - 6 contingencies.

Abstract:The increase in the penetration rate of renewable energy exacerbates the rise in system short-circuit level. Thus, short-circuit constraints (SCCs) are crucial in the co-optimization of transmission and generation expansion planning. The deregulated environment further complicates this process by assigning responsibilities for transmission and generation to separate market entities. This paper proposes a multi-period co-optimization method of transmission and wind turbine generation expansion planning to address this challenge. The transmission expansion planning (TEP) problem limits the short-circuit level, which could be elevated by lines, synchronous generators, and wind turbine generators. The method is formulated as a tri-level mixed-integer linear programming (MILP) problem, where an equilibrium problem with equilibrium constraints is formed at the second and third levels. This problem is restructured into a MILP problem with Nash equilibrium conditions via complementarity problem reformulation. We propose an iterative algorithm targeting the SCCs to solve it. The effectiveness of the proposed method is validated on the IEEE 24-bus reliability test system through comparisons with three existing TEP methods, analyzing the impact of SCCs and generation expansion planning on TEP and the system operating cost under a deregulated environment.

Abstract:High penetration of wind power into power grids deteriorates system frequency stability. Wind turbines (WTs) are required by grid codes to participate in primary frequency regulation (PFR) by adjusting their rotor speed to utilize the stored kinetic energy. However, frequency support causes a change in rotor speed, and hence, the PFR capability of a wind farm is limited by a time-varying boundary. As the mechanical transient process of the WT is determined by wind speed, it is necessary to forecast the PFR capability of wind farms based on wind speed distribution, to arrange the system scheduling plan while considering dynamic safety. In this paper, a physics-informed probability distribution assessment method is proposed for the PFR capability of wind farms considering wind speed uncertainty. Constructing the analytical correlation relationship between state variables based on Koopman-operator-theory-based state space transformation, the probability density function of the maximum feasible droop coefficient of a wind farm is derived based on the wind speed probability distribution. The simulation results demonstrate that the proposed method achieves a five-order-of-magnitude reduction in computational time compared with the Monte Carlo and time-domain simulation methods, and possesses the advantages of independence from physical parameters and random sampling errors, as well as a simple analytical expression of the probability distribution of PFR capability.

Abstract:Conventional joint operation of integrated electricity and heating systems faces severe challenges, including non-convex models and computation complexity. Additionally, there are adverse impacts from the uncertainties of renewable distributed generators, as well as electrical and thermal loads. This paper proposes an optimal joint operation method of integrated electricity and heating systems based on multi-agent deep reinforcement learning (DRL) method. Firstly, a new hydraulic-thermal flow algorithm that is compatible with DRL training environment is developed. Then, a stochastic distributed optimization model is formulated with multiple agents to minimize network power losses while avoiding operation constraint violations under the spatial and temporal uncertainties. Last, a multi-agent deep deterministic policy gradient is adopted combined with offline neural network training via exploration in a virtual environment and online optimization of joint operation. A numerical case study indicates the effectiveness of the proposed method and solution robustness against spatial and temporal uncertainties.

Abstract:The deployment of wayside energy storage systems (WESSs) in substations is a key strategy for enhancing the energy efficiency of urban rail transit (URT). Existing research on energy management in traction power system (TPS) with WESS primarily focuses on improving the utilization of surplus regenerative braking energy in the system. However, due to the ambiguous output characteristics of TPS with WESS, it is challenging to address the optimization of overall system energy consumption from the perspective of power flow control, marking a significant distinction from flexible traction power systems (FTPSs) based on bidirectional substations. To address this issue, this paper proposes an integrated energy storage substation (IESS) control method and develops a steady-state equivalent model along with a DC power flow model for TPS with IESSs. Furthermore, under the framework of optimal power flow, this paper achieves both optimized control and flexible power supply for the TPS with IESS. Simulation results based on real-world operation scenarios demonstrate that the proposed method effectively optimizes the TPS power flow and reduces the total system energy consumption, offering new insights into the construction of FTPS based on the IESS.

Abstract:Constructing self-sustained highway transportation energy systems (HTESs) hinges on effective sustainable energy planning along highways. Addressing the complex spatial-temporal distribution characteristics of sources and loads presents a formidable challenge in accurately determining the siting and sizing of sustainable energy installations. In this study, we utilize a map rasterization approach and decentralized connection models for quantifying the spatial-temporal distribution characteristics of sources and loads. Leveraging these insights, the source-load-network cooperative operation models in uncertain scenarios, which seamlessly integrate highway and electricity networks, are built and embedded in the multi-objective robust planning model, enabling dynamic resource and demand management. The proposed planning model simultaneously optimizes the capacity, location, and connectivity of wind and photovoltaic power plants in HTES, while improving the robustness. Moreover, a multi-objective-oriented evaluation framework that adjusts the planning priorities based on three key dimensions – investment economy, self-sustained operation, and energy utilization efficiency – is formulated. The dynamic weight allocation mechanism enables tailored planning schemes that address diverse operational objectives effectively. Simulations of an actual HTES validate the effectiveness of the proposed planning model, demonstrating its capability to harmonize the inherent variabilities in the spatial-temporal distribution of sources and loads. The results highlight the significant variability in outcomes based on different objective orientations, underscoring the adaptability potential of the proposed planning model in designing futuristic HTES.

Abstract:The smart grid infrastructure has recorded extensive real-time electricity consumption data, particularly at the levels of distribution transformers and below for short-term load forecasting (STLF). However, training individual short-term load forecasting model (SLFM) for each STLF scenario at these levels substantially increases the computational costs. To address this challenge, this paper proposes a transfer learning-based model training method for STLF. The proposed method is rooted in transfer learning principles and tailored to the unique characteristics of the aforementioned levels, incorporating several key steps. First, an approach for extracting key peak and valley points based on peak width and peak prominence is proposed for simplifying the evaluation of load sequence similarity. Subsequently, these key points are clustered using a density-based spatial clustering of applications with noise approach to ensure proper alignment along the time axis. Secondly, temporal and distribution similarity metrics are introduced to establish a performance guarantee for the transferred SLFM. Subsequently, a hierarchical clustering method groups load sequences, utilizing temporal similarity to quantify distances among sequences and distribution similarity to optimize cluster number selection. To minimize generalization error and further reduce computational costs, a modified bagging method is proposed and applied during the transferred SLFM fine-tuning. Empirical evidence from a study conducted in Guiyang, China demonstrates that the proposed method maintains the SLFM performance without degradation and significantly reduces computational costs by a minimum of 92.23% across multiple scenarios.

Abstract:This paper proposes an online hierarchical volt/var control (VVC) for unbalanced distribution networks using diagonal-scaling alternating direction method of multipliers (DS-ADMM). Under the hierarchical VVC strategy, local photovoltaic (PV) agents only exchange limited information with the center agent and adjust reactive power outputs in real time, with the goal of minimizing the voltage deviations and reactive power regulation costs in the time-varying environment. A diagonalized auxiliary matrix is constructed and developed from the Hessian matrix using preconditioning methods, which is then combined with alternating direction method of multipliers (ADMM) to design the DS-ADMM with improved convergence speed. The DS-ADMM is applied to the hierarchical VVC strategy, further improving the tracking capability and performance for time-varying environmental changes. Simulation studies on a modified IEEE 123-bus unbalanced distribution network are conducted to verify the effectiveness of the hierarchical VVC strategy using DS-ADMM and its robustness under non-ideal communication conditions, and its scalability is further validated on the modified IEEE 8500-node test feeder.

Abstract:The form of hybrid AC/DC is a trend in power distribution systems. The resilience against extreme weather depends on the coordination of cyber and physical systems. Therefore, it is necessary to study the post-disaster recovery of AC/DC hybrid cyber-physical distribution systems (CPDSs). Voltage source converters (VSCs) are critical cyber-physical devices in hybrid AC/DC distribution systems (HDSs) that offer flexibility in post-disaster recovery. However, existing literature on the role of VSC commonly ignores the unreliable communication. In this paper, we quantify the impact of communication failures on VSCs and propose an adaptive switching model of VSC control modes that enhances both the emergency islanding and service restoration phases of post-disaster recovery. This paper also introduces a scheduling model of multi-type repair resources including power failure repair crews, communication failure repair crews, and emergency communication vehicles for joint the restoration of CPDSs. The system recovery model is also presented. Finally, a novel optimization framework combining adaptive switching of VSC control modes, scheduling of multi-type repair resources, and system recovery is proposed to improve the post-disaster recovery efficiency. The effectiveness and superiority of the proposed framework are demonstrated through numerical experiments in a modified IEEE 123-bus system.

Abstract:The integration of numerous distributed energy resources into distribution networks (DNs) can induce large voltage fluctuations and network loss. We introduce a collaborative active and reactive power optimization (CARPO) method for DNs and microgrids (MGs) to efficiently improve the voltage quality and mitigate network loss. First, the CARPO method and models for the DNs and MGs (DMs) are intended to reduce voltage deviations, minimize network loss, and improve the operation efficiency of the entire system. Second, to protect MGs, we aggregate privacy-preserving feasible operation regions of the active and reactive power outputs from distributed energy resources in MGs. A scaled-down MG equivalent model, which ensures high accuracy, is derived for optimal DN operation. Third, based on the equivalent projection theory, the optimal operation flow of DMs with non-iterative projection method is achieved to reduce the computational complexity. The DM model is decomposed into sub-models for the DM levels. The optimal solutions of the coordination variables are obtained for MG power scheduling. Finally, the proposed CARPO method is evaluated through simulation in a modified IEEE 33-bus DN. The results demonstrate that the proposed CARPO method can optimize the system operation and improve the economy of DMs.

Abstract:The global transition toward net zero emissions has accelerated the integration of distributed generators (DGs), particularly renewable energy sources (RESs), energy storage systems, plug-in electric vehicles (PEVs), and fuel-cell electric vehicles (FEVs). Therefore, we propose a decentralized energy management model tailored to the operational dynamics of a community of independent microgrids (MGs) at the transmission level, integrated with DGs, PEVs, FEVs, and hydrogen-based technologies, forming power- and hydrogen-based microgrids (P&HMGs). Managed by a third-party aggregator, P&HMGs strategically participate in the wholesale electricity market (WEM) by consolidating bids and offers. The WEM operates between generators and suppliers. The participating generators in WEM are connected to the transmission level, including power plants and large-scale RESs. The strategic behavior of P&HMGs is modeled using bi-level programming that unveils the potential of P&HMGs to synergize and participate in WEM as a price-maker. Moreover, to cope with the data privacy of P&HMGs and improve the scalability and security of MGs, a fast alternating direction method of multipliers (ADMM) running on a mobile edge computing (MEC) system is proposed as a decentralized energy management approach. Further, a bidirectional long short-term memory (BiLSTM) network considering robust optimization is presented to control the intermittency of electrical load and RESs. The results obtained from case studies confirm a considerable reduction in operation costs in light of the proposed model.

Abstract:As renewable energy and environmental protection gain prominence, community microgrid has become crucial for promoting resource sharing and improving energy efficiency. This paper presents a multi-stage optimization strategy of community microgrid considering fair allocation and risk management, utilizing the Vickrey-Clarke-Groves (VCG) mechanism and the glue value-at-risk (GlueVaR) method. The proposed strategy integrates carbon with the collective self-consumption (CSC) framework, using GlueVaR to manage uncertainties in photovoltaic (PV) power generation by balancing economic performance with extreme risk management. Compared with traditional risk management, the GlueVaR method offers a more comprehensive characterization of both tail risks and central tendency, enabling more robust decision-making under uncertainties. The VCG mechanism ensures accurate supply and demand reporting, thereby optimizing resource allocation. The proposed strategy aims to promote fair allocation, enhance community welfare, reduce carbon emissions, and optimize energy utilization. A distributed alternating direction method of multipliers (ADMM) algorithm is employed to improve the computational efficiency and preserve the privacy of community members, making the proposed strategy scalable to various community microgrid sizes. Case studies confirm that the proposed strategy significantly enhances community welfare, reduces carbon emissions, and strengthens system stability and security. Furthermore, by fostering fair and transparent transactions among members, the cohesion of the community is reinforced for long-term sustainability.

Abstract:The proliferation of distribution-level green electricity and hydrogen resources entails an efficient local energy market (LEM). However, the existing LEM designed for electricity-hydrogen trading falls short of modeling multi-level mechanisms and accounting for the carbon intensity of hydrogen production. To bridge this gap, we propose a carbon-aware multi-level LEM for electricity-hydrogen trading based on a distributionally robust game framework, where hydrogen-based microgrids (HMGs) supply hydrogen to heterogeneous hydrogen users (HUs) including hydrogen refueling stations and industrial users. In this game framework, the coordination between HMGs and HUs is cast as a multi-leader multi-follower Stackelberg game. Specifically, HMGs determine an integrated hydrogen-carbon price, and carry out electricity trading through a non-cooperative game. Meanwhile, HUs act as followers, adjusting hydrogen purchasing strategies. Furthermore, the self-dispatching of HMGs and HUs is modeled as distributionally robust optimization problems considering source-load and hydrogen demand uncertainties, respectively. To hedge against these uncertainties, a novel Bayesian nonparametric hybrid ambiguity set is constructed based on local Wasserstein balls and moment information. Finally, the equilibrium of the proposed game framework is theoretically proved, and a distributed algorithm is developed to obtain this equilibrium. Comparative studies validate that the proposed game framework outperforms the existing ones, demonstrating a total income increasement of 12.3% and a carbon emission reduction of 11.6%.

Abstract:Flexible ramping product (FRP) trading has emerged as a highly effective solution to cope with the volatility and uncertainty introduced by the increasing integration of renewable energy sources. This paper proposes a bidding method for electric vehicle aggregators (EVAs) in the FRP trading market. To effectively articulate the spatiotemporal operational characteristics intrinsic to EVAs, a charging and swapping flexibility aggregation model is formulated. The model is developed by accurately simulating the charging and swapping demands of plug-in electric vehicles and battery-swapping electric vehicles in different charging modes. A novel bilevel optimization model is developed to address the conflicting objectives in the FRP trading market between the EVAs and electric vehicles (EVs), aiming to optimize the incentive prices and charging strategies. The upper level optimizes the bidding profits of EVAs, whereas the lower level models the EV charging behavior using the charging and swapping flexibility aggregation model. To solve the high computational complexity of the high-dimensional nonconvex optimization problem owing to the vast number of EVs, a data-driven evolutionary algorithm incorporated with a zebra optimization algorithm is adopted. Owing to the limited data available for training high-quality agent models in real scenarios, a semi-supervised learning-based tri-training algorithm is adopted to enhance the efficiency of data utilization. Case studies validate the effectiveness of the proposed method.

Abstract:The growing deployment of electric mobility calls for static and dynamic grid studies to investigate to which extent it affects the grid operation and how to validate the countermeasures. Detailed electric vehicle (EV) models, which allow analyzing electrical variables at the EV charger and battery levels, are inadequate for this purpose, as they can have an excessive complexity and are computationally burdensome for large-scale grid studies. To address this issue, we exploit a detailed EV model using an analytical approach, and develop an equivalent model of EVs with fast chargers that is easy to implement and computationally efficient, while retaining adequate accuracy. Simulation results of distribution and transmission systems, modified by adding fleets of EVs, are used to demonstrate the compatibility of the proposed model for static and dynamic grid studies, even when different cathode chemistries and charging strategies are adopted.

Abstract:Voltage interaction between the rectifier and inverter buses is recognized as a critical factor in embedded direct current (EDC) transmission systems, where at least two ends are within a single synchronous AC network, as it significantly affects power flow distribution, voltage stability, and power system planning. Conventional methods for evaluating voltage interaction are insufficient to accurately represent the complicated interplay between responses of the AC-DC network and the internal controllers within EDC transmission systems. To address this issue, this paper proposes an analytical calculation method of a novel voltage interaction evaluation index for various types of EDC transmission systems, which enables precise evaluation of the voltage interaction between the rectifier bus and inverter bus in an EDC transmission system. The proposed method comprehensively accounts for the influence of voltage interaction under small disturbances through the AC network, as well as the influence of voltage interaction under the same disturbance between converter buses through internal controller responses. Numerical simulations are used to analyze the parametric dependence of the index, and its accuracy is demonstrated through dynamic simulation.

Abstract:This paper proposes a centralized secondary control for real-time steady-state optimization of multi-terminal high-voltage direct current (HVDC) grids, considering both voltage and current limits. This control begins with detailed dynamic models of key grid components, including modular multilevel converter (MMC) stations and their control layers, followed by the derivation of a quasi-static input-output model suitable for steady-state control. Using this model, a general optimization problem is formulated, and the associated Karush-Kuhn-Tucker (KKT) conditions are characterized. A secondary controller based on primal-dual dynamics is then proposed to adjust the voltage setpoints of dispatchable MMCs, ensuring convergence to a steady state that satisfies the optimal conditions. The inclusion of current constraints necessitates partial knowledge of the network model, which naturally supports a centralized framework. To reduce the communication burden, a communication triggering mechanism is introduced that limits message exchanges between the control center and MMC stations without degrading performance. The proposed controller is validated through case studies using an offshore multi-terminal HVDC grid with heterogeneous MMC stations, simulated in MATLAB/Simulink. Results confirm that the proposed controller drives the system to optimal operation, while significantly reducing the communication burden without compromising performance.

Abstract:The sending-end system of line-commutated converter based high-voltage direct current (LCC-HVDC) systems is vulnerable to transient voltage disturbances (TVDs), posing a significant threat to voltage stability. This paper proposes a novel strategy to maximize the dynamic voltage support (DVS) capability of LCC-HVDC systems under various TVDs. The physical mechanisms underlying DVS in LCC-HVDC systems are systematically analyzed, forming the basis for an optimization model that maximizes the DVS capability while incorporating security constraints at both the rectifier and inverter ends. To address the challenge of directly solving the model, an optimality analysis with intuitive geometric interpretations is performed. Based on these insights, a two-stage optimal DVS control strategy for LCC-HVDC systems is developed to iteratively approach the optimal solution through coordinated control of the rectifier and inverter stations. The effectiveness and superiority of the proposed strategy in supporting the sending-end system are validated through dynamic simulations, and its applicability under practical operating conditions is discussed.

Abstract:Stealthy false data injection attacks (SFDIAs) targeting state estimation can bypass the bad data detection module, mislead operators with false system states, and potentially result in erroneous decisions and physical damages. While most existing studies focus on single-step SFDIAs, multi-step SFDIAs pose a greater threat due to their forward-looking nature, where each step is strategically planned to amplify the cumulative impact. Therefore, this paper focuses on multi-step SFDIAs and presents a vulnerability assessment framework that leverages a Markov decision process (MDP) and bi-level optimization to quantify the system vulnerability to this type of attack. The MDP models the sequential and strategic nature of these attacks, with states reflecting evolving system conditions influenced by prior actions. At each state, actions derived through bi-level optimization identify attack vectors that maximize line overloads, potentially triggering the tripping of transmission lines. The MDP is solved using Q-learning, enabling the calculation of a vulnerability index that assists operators in assessing the impact of multi-step SFDIAs and identifying the attacker ’ s most critical action at each step of multi-step SFDIAs. The effectiveness of the proposed vulnerability assessment framework is validated through simulations on the IEEE 39-bus test system.

Abstract:Despite recent progress in solving the state estimation problem, its real-time performance remains challenged by the presence of bad data, increasing computational demands for detection and identification. A state estimator uses neighboring measurements to estimate the system states, similar to how a graph neural network (GNN) refines node embeddings (bus states) based on messages from neighboring nodes. This paper proposes a GNN-based framework that detects and identifies bad data before providing measurements to the state estimator. The framework incorporates grid topology, employs node and edge features, and exploits correlations of measurement data to enhance identification accuracy. Specifically, an edge-conditioned GNN is developed to transform graph-based features into categories that detect bad measurements and identify their sources. The generated dataset uses historical load profiles and includes conventional and synchrophasor measurements to emulate real-life applications. The proposed framework is tested on MATPOWER 6-bus and IEEE 14-, 30-, 118-, and 300-bus systems. The results demonstrate high accuracy and illustrate graph-learning patterns. Thus, operators can take preventive actions before the bad measurements propagate through the state estimator.