Extreme events may cause upstream-grid voltage loss, forcing an active distribution network from grid-connected operation into partitioned islanded operation. Therefore, supply-security zoning plans that can be deployed promptly are of significant practical value. Existing studies mainly rely on topological connectivity and steady-state feasibility, and thus cannot ensure that the resulting zoning plans satisfy dynamic frequency security requirements at the initial stage of islanding. To address preventive supply-security needs under a known topology, this paper proposes a preventive supply-security zoning method for active distribution networks considering dynamic frequency security constraints. First, a unified modeling framework is established to characterize supply-zone formation, load restoration, and steady-state feasible operation. Then, a supply-zone-level aggregated frequency-response model is developed based on the initial source-load imbalance, through which the maximum frequency deviation, the rate of change of frequency (RoCoF), and the frequency-security margin are incorporated into the zoning process as endogenous constraints and evaluation indices. Finally, a hierarchical optimization model is formulated to jointly consider priority restoration of critical loads, overall supply-security capability, and implementation cost. Case studies on a modified IEEE 33-bus system show that, compared with a preventive zoning method considering only static constraints, the proposed method reduces the risks of frequency-limit violations and excessive RoCoF at the initial stage of islanding while maintaining the same level of critical-load restoration. The proposed method supports the offline generation and online deployment of supply-security plans for active distribution networks.

Distribution system restoration (DSR) has become increasingly challenging due to frequent topology changes and complex nodal interactions, especially in systems with a high penetration of distributed energy resources (DERs). Existing deep reinforcement learning (DRL) methods remain limited in representing inter-nodal relationships and incorporating domain prior knowledge. Therefore, this paper proposes a graph attention-based coupling-aware reinforcement learning method. From a non-Euclidean spatial perspective, the proposed method uses the distribution power transfer factor (DPTF) to quantify the strength of electrical coupling between nodes. The resulting coupling strengths are embedded as entries of the graph adjacency matrix, allowing the model to capture complex nodal interactions driven by power transfer. An aware graph attention network (AGAT) is further developed, where adjacency matrix with prior knowledge is introduced as a bias term in the attention coefficient calculation. This design guides GAT to generate differentiated node representations enriched with physical information. Based on the extracted graph features, proximal policy optimization (PPO) is employed to determine restoration decisions. Case studies on the IEEE 34-bus system demonstrate that the proposed method outperforms benchmark algorithms in training convergence, restored power, and online decision efficiency, enabling fast and effective distribution system restoration.

Among disasters that may lead to large-scale black-outs of power systems, wind storms introduce spatio-temporal variations in restoration security risks, making large-scale power system restoration more difficult. Power system restoration during wind storms requires coordinated efforts among the regional independent system operators, transmission system operators, and distribution system operators. However, existing research mainly focuses on the coordination between transmission system operators and distribution system operators, which limits its applicability to large-scale blackouts caused by wind storms. Therefore, a spatio-temporal coordinated restoration method based on restoration security risk assessment for multi-voltage-level power systems (MVLPSs) is proposed in this paper. Typhoons, known for their high wind speeds and destructive power, are considered as the disaster scenario. First, a spatio-temporal restoration security risk assessment approach is proposed to reduce additional control costs caused by restoration security risks. Then, a spatio-temporal coordinated restoration framework for MVLPSs is established, and a triple-level optimization model for the spatio-temporal coordinated restoration of MVLPSs is proposed to maximize the net restoration benefits of MVLPSs during the full-stage restoration process. Finally, case studies on an actual 379-bus MVLPS in China are conducted to verify that the proposed method can achieve higher net restoration benefits compared with existing restoration methods.

In recent years, frequent power outages in distribution networks caused by extreme natural disasters have severely impacted socioeconomic activities. With the rapid development of electric vehicles (EVs) and smart cities, postdisaster restoration of urban distribution networks faces new opportunities and challenges. The interdependence of the power, transportation, and communication networks makes post-disaster recovery of the power system more complex. To address these issues, this paper proposes an urban distribution network resilience enhancement strategy based on vehicle-to-grid (V2G) integration and the interdependence among urban power, transportation, and communication networks, aiming to improve the load restoration efficiency and resilience of distribution systems under natural disasters. First, a distribution line failure rate model is established using a genetic algorithm-optimized backpropagation neural network, and stochastic fault scenarios of the distribution network under extreme weather conditions are generated according to the predicted failure probabilities. Subsequently, a coordinated post-disaster restoration model is formulated. This model comprehensively captures the interdependencies among the power, communication, and transportation networks, optimizing EV scheduling for V2G support. Simulation results on the IEEE 14-bus test system demonstrate that the proposed method can effectively improve restoration efficiency and enhance the resilience of the distribution network compared with traditional approaches considering only the power network.

The rapid restoration of power systems is a critical task in responding to large-scale blackout events. Wide-Area Frequency Oscillations (WAFOs), a common issue during the restoration process, can pose a significant threat to system stability and recovery efficiency. To mitigate the impact of WAFOs on power systems, this paper focuses on the sequence impedance model of doubly-fed induction generators (DFIGs), considering the effects of frequency coupling. The Nyquist stability criterion, based on impedance analysis, is then employed to analyse the wide-area frequency oscillations in the wind turbine grid-connected systems. It is emphasized that two factors—namely the dynamic characteristics of the DC bus and the coupling effect of grid impedance—must be considered when building model. Finally, the theoretical analysis is validated through simulation results.

With the significant growth in installed wind power capacity, the penetration of power electronics has increased substantially. A notable effect of this heightened integration of power electronics into the power system is a decrease in the system’s overall inertia, leading to a deterioration in grid stability. Consequently, these alterations pose fresh obstacles to maintaining frequency stability within power systems. However, the ability of wind turbines based on grid-following converters to support weak grids is limited. They fail to meet the operational stability requirements for high shares of renewable energy in the grid. Virtual synchronous generator (VSG)-type converter can enhance system inertia damping while independently regulating AC voltage and frequency, thus providing active support for the system in weak grids with a strong frequency dynamic response capability. In this paper, impedance models of permanent magnet synchronous generators (PMSGs) are developed using an impedance modeling approach. Simulation results validate that the control strategy in this paper offers remarkable stability support for the system.

Inverter-based resources (IBRs) are expected to support black start in case of blackout events, as they can significantly speed up restoration process. This paper proposes a coordinated control scheme to improve frequency and voltage stability for hybrid black start resource (HBSR) participated restoration. HBSR includes a line-commutated converter (LCC)-based high-voltage direct-current (HVDC) system and a self-starting generator. In the proposed scheme, outputs of different resources are controlled in a coordinated manner to suppress disturbances caused by inrush loads, where an active power error controller (PEC) and a reactive power error controller (QEC) are developed based on an analytical model. The analytical model calculates desired DC current reference considering the coupling effects between active power and reactive power under weak grid conditions. PEC is designed as a multi-variable system, which regulates active power of the LCC HVDC line so that frequency variation can be damped and eliminated properly. QEC observes reactive power related security margin and regulates the inverter extinction angle to reduce the risk of commutation failure. Simulations are executed on an HBSR integrated test network based on the CIGRE Benchmark model. Results show that in HBSR dominated black start, frequency deviations and voltage fluctuations can be suppressed more speedily and stably by the proposed control method, and the extinction angle is adapted for more secure commutation in case of insufficient reactive power.

The conventional multi-objective optimization method of microgrid power supply grid architecture mainly uses MLIML (multi-load intermediate main line) to obtain multi-segment correction parameters, which are easily affected by the dynamic changes of power extreme values, resulting in poor optimization indicators. Therefore, a multi-objective optimization method for a rural microgrid’s power supply network architecture is proposed based on an internal search algorithm. That is, a multi-objective optimization model for the microgrid power supply grid architecture is constructed, and an internal search algorithm is used to generate a multi-objective optimization process for the microgrid power supply grid architecture, thereby achieving multi-objective optimization of the microgrid power supply grid architecture. The experimental results show that the multi-objective optimization method of the designed wind and solar power supply grid architecture for rural microgrids has good optimization results. All indicators meet the economic requirements and have certain application values. It has contributed to improving the power supply and distribution quality of the rural microgrid and reducing the comprehensive operation risk.

Abstract This paper introduces a novel algorithm, LSTM-DDQN, designed to address the stochastic economic dispatch problem in microgrids. The inherent variability and randomness of photovoltaic (PV) energy pose challenges for accurate output prediction. Traditional approaches, including physical and statistical methods, often fall short in terms of accuracy. For microgrids with a high proportion of PV generation, we propose a new algorithm that combines Long Short-Term Memory (LSTM) neural networks with the Double Deep Q-Network (DDQN) algorithm. The algorithm leverages the LSTM model to capture the uncertainty of the learning environment and optimizes the Q-value iteration rules in the DDQN algorithm, thereby enhancing the training speed of the neural network. Experimental results demonstrate the algorithm’s remarkable dispatch capabilities in managing the interplay between PV generation, battery capacity, and overall load demand. This algorithm provides robust support for the stable operation of microgrids.

With the increasing complexity and scale of power systems, the risk of large-scale power grid blackouts triggered by adverse weather or human-induced damages has significantly heightened. This emphasizes the critical necessity to enhance power generation speed and load recovery while minimizing outage impact. This study introduces a series of indices that can be utilized to measure the capacity of power system recovery, taking into account the failure probability during extreme events. Subsequently, a systematic and standardized evaluation system is proposed to uniformly assess and compare the performance of power system recovery capability under extreme disasters, considering different restoration schemes according to established standards. The evaluation system estimates power grid losses resulting from extreme disasters, which directly affect generator and transmission line outage states. By comparing resilience metrics before and after such failures occur, this approach provides a comprehensive evaluation of power system resilience in response to extreme disasters.

To achieve carbon neutrality, the flexible resources on the transmission and distribution side should be fully utilized to deal with high proportion of renewable energy. A tri-layer interactive framework based on game model is constructed, taking into account transmission system operators (TSO), distribution system operators (DSO), and load aggregator (LA). Then, the corresponding tri-layer model is built to realize hierarchical economic dispatch which employs price-based demand response to reduce wind power curtailment. Moreover, a KKT(Karush-Kuhn-Tucker) condition integrated analytical target cascading (ATC) method is proposed to solve the tri-layer model in a distributed way. Finally, the coordinated hierarchical economic dispatch result can be obtained by iteratively solving TSO and DSO based mix-integer quadratic programming (MIQP) models. The proposed method respects independent operations of TSO and DSOs, coordinating flexible resources of the source side and load side in the TS-DS economic dispatch optimization. The wind power curtailment is reduced, and the computational efficiency is promoted in a distributed way. The effectiveness of the proposed method is validated using T6D2 and T24D3 systems, showing good performance.

The ever-increasing couplings between electricity and gas systems highlight the coordinated restoration decisions for interdependent electricity and gas system (IEGS) to enhance resilience. However, the distinct timescales of power and gas flow rates as well as information privacy concern cause additional complexities in making restoration decisions, and large calculation scale and nonconvexity also results in computational obstacles. To address these issues, this paper proposes a hybrid spatio-temporal scale decentralized restoration strategy for IEGS to enhance restoration efficiency and security. First, in the spatial scale, the network sectionalizing and the restoration processes in sectionalized electricity and gas systems are coordinated in a decentralized manner with limited boundary information interactions to respect information privacy. Next, the impacts of distinct timescales of power and gas flow rates are explored to accelerate restoration, and a linear gas flow dynamics model is also proposed to capture the gas transmission dynamics with high accuracy, enhancing restoration security. Furthermore, a decomposition-based alternating direction method of multipliers algorithm is proposed to efficiently solve the proposed IEGS restoration strategy, showing good calculation time and convergence performance. Finally, the effectiveness of the proposed restoration strategy is validated in a T118N20 test system and a real-world system, demonstrating the enhanced restoration efficiency, security alongside commendable computational performance.

This study focuses on the scheduling of a microgrid integrated with electric vehicles, employing a reinforcement learning algorithm to devise an optimal economic operation strategy. The approach addresses the challenges of renewable energy generation’s randomness and the economic and safety concerns arising from the extensive integration of electric vehicles into the microgrid. The paper explores the reinforcement learning-based energy management of storage devices within the microgrid. A Deep Q-Network (DQN) method is utilized to formulate the microgrid storage scheduling problem as a Markov Decision Process (MDP). The study involves a detailed simulation of a residential user microgrid, considering various parameters such as battery storage capacity, charging and discharging rates, and electric vehicle load models. The reinforcement learning algorithm demonstrates the capability of real-time response to power changes in the microgrid, ensuring operational cost minimization even under multiple input uncertainties.

Abstract In the past few years, the connection of extensive power grids with a substantial influx of renewable energy has resulted in a growing hazard of significant power failures. Conventional black-start systems are inadequate in promptly and adaptively recuperating from such outages. Consequently, the emergence of new energy generation integrated with energy storage technology to deliver black-start services has become a prevailing trajectory. In this research article, hybrid energy storage is implemented for the wind and solar power generation system to address the issue of inadequate inertia support capacity during the black-start process. The system incorporates a virtual synchronizer to manage and replicate its inertia and damping features, thereby enhancing the black-start capability of renewable energy generation. The effectiveness of the proposed strategy is verified through simulation in Matlab/Simulink.

High penetration of renewable energy has instigated stochastic power injection at interconnection between transmission system (TS) and distribution system (DS). This paper delves into the intricate collaborative risk based stochastic dispatching challenges due to contingencies and the integration of renewable sources in the TS and DS. A framework of risk constrained probabilistic problem (RCPC) is presented, which is divided into Probabilistic coordination (PC) and risk-based stochastic security-constrained unit commitment (RSSCUC). The PC is a stochastic linear problem and RSSCUC is a stochastic mixed integer non-linear problem. Therefore, the probabilistic analytical target cascading along with polynomial chaos expansion (PCE) has been utilized for solving the PC problem for coupled TS and DS. Here, PCE has provided the proposed algorithm with the needed capability to present stochastic shared variables as coefficients for PC. Further, the benders decomposition al-gorithm has been utilized for solving RSSCUC, by dividing it into master problem and sub problem to generate feasibility cuts. The proposed technique reduces the computational burden on the system, as it requires single stochastic coordination problem for all scenarios as an alternative of each coordination problem for each scenario and polynomial coefficients instead of multiple scenarios for each shared variable between TS and DS. Different case studies have been performed utilizing the 6-bus system and IEEE 118-bus system as TSs, 7-bus, 9-bus, 85-bus and 69-bus systems as DSs. Results depict the efficacy of the proposed method.

Abstract This study introduces a unique method employing the Multi-Agent Deep Deterministic Policy Gradient (MADDPG), a sophisticated deep reinforcement learning algorithm, for efficient voltage control in photovoltaic (PV) power distribution networks. The algorithm’s design emphasizes minimizing the need for communication and effectively managing delays, which are pivotal in ensuring consistent and reliable control in environments with fluctuating renewable energy sources. In simulations using the IEEE-33 power distribution system, the research demonstrates the algorithm’s ability to ensure stable and efficient network functioning, even under varying environmental and load conditions. This highlights its potential as a robust solution for modern and renewable energy-integrated power systems.

The feasibility of utilizing wind power as an alternative black start power source is challenged due to the requirement for stable power, voltage, and frequency during major grid blackouts. However, the inherent fluctuation in wind power generation and the way it is controlled pose a significant challenge. This research proposes an innovative solution that involves integrating energy storage devices with wind turbines and employing a coordinated control method between them while adjusting the control systems of the wind turbines. The proposed system integrates wind energy storage with black start power supply, emphasizing the coordinated management of multiple types of energy clusters and an advanced control method based on improved virtual synchronous machines (VSMs). This approach is particularly groundbreaking as it fulfills both rapid black start requirements and sustained stable operation of renewable energy generator units, which are crucial for their role as black start power sources. The proposed black start scheme in this paper is validated through simulation scenarios of an actual power grid, demonstrating its capability to facilitate the startup and stability control of the load required for conventional power plant black starts.

Stability analysis of grid-connected inverters is advanced through impedance analysis in this study, which leads to the development of a comprehensive mathematical model and simulation. Control parameters, notably the voltage inner-loop control parameter $K_{v}$, the proportional gain $K_{p}$ of the phase-locked loop (PLL), and the integral gain $K_{i}$, were meticulously adjusted to assess their effects on subsynchronous oscillations. It was determined that while the proportional gain $K_{p}$ and integral gain $K_{i}$ have a marginal impact on sub-synchronous oscillations, the voltage inner-loop control parameter $K_{i}$ significantly influences these oscillations. The insights gained from this analysis contribute to a deeper understanding of, and potential solutions for, synchronous oscillation issues. The ability to mitigate such oscillations facilitates the integration of a higher proportion of renewable energy sources, thereby enhancing the stability and reliability of the electrical power system. This enhancement is of critical importance in the pursuit of sustainable energy transitions and the advancement of clean energy sources.

A synergistic restoration strategy offers a promising solution for expediting restoration of integrated electricity and gas system (IEGS) following blackouts, particularly under the increasing energy interdependence between these two subsystems. However, achieving synergistic restoration is challenging in 1) distinct operational characteristics between electric and gas flow rates, leading to difficulties in accurately modeling the restoration processes with significantly varying system states; 2) potential risks posed by uncertainties, such as fluctuating renewable energy and random contingencies, undermining the effectiveness of restoration strategy. To address these challenges, this paper proposes a novel distributionally robust risk-resistant synergistic restoration strategy for IEGS. Firstly, to accurately capture the dynamics of slow gas flow relative to instantaneous power flow, we propose a high-fidelity linear dynamic gas flow model with multiparametric disaggregation technique, demonstrating satisfactory accuracy in restoration scenarios. Then, the risks by uncertainties of renewable energy and contingencies are handled within distributionally robust framework. Moreover, the IEGS restoration problem is reformulated as a two-stage robust optimization problem by convex conservative approximation and strong duality theory, which is solved by a nested column-and-constraint generation algorithm. Finally, simulation results validate the effectiveness of the proposed restoration strategy, showing the improved restoration efficiency, security, and risk-resistant performance.

The critical challenge of enhancing the restoration efficiency of regional power systems amidst the increasing penetration of renewable energy generation in transmission and distribution grids is addressed in this paper. To tackle these challenges, a novel method for decision-making on the resilience restoration of power transmission and distribution networks is proposed. Considering the uncertainty of load and wind power during restoration, an coordinated restoration of transmission and distribution optimization model is constructed. By establishing a boundary-coupled model, this method facilitates independent and coordinated solutions for restoration plans in both networks while ensuring information privacy protection among different dispatching level of electric power companies. The simulation results demonstrate the effective enhancement of the proposed method on both transmission and distribution network restoration processes, particularly in the context of integrating large-scale renewable energy plants.