To address the issue that transient voltage cannot be stabilized solely by the system's own reactive power regulation capability after faults occur at some weak nodes in high-penetration wind-photovoltaic hybrid grid-connected system, a dynamic reactive power planning method considering transient voltage stability for high-penetration wind-photovoltaic hybrid grid-connected system is proposed. Based on the system's own reactive power regulation capability, this method constructs a reactive power planning benchmark scenario using typical output scenarios of wind power and photovoltaic power, and determines the installation locations and compensation capacities of the dynamic reactive power compensation devices through multiple power flow calculations. Key fault nodes are identified using transient voltage stability recovery index to preliminarily determine the system's weak nodes. On this basis, sensitivity index are used to determine the installation nodes for dynamic reactive power compensation devices. An optimization model for dynamic reactive power planning is established with the objective of improving system transient voltage stability at the lowest investment cost for dynamic reactive power compensation devices. The compensation capacities at reactive power compensation nodes are optimized using a Particle Swarm Optimization algorithm based on Differential Evolution.

The flexibility provided by Distributed Energy Resources (DER) and Demand Response (DR) can enhance the ability of distribution networks to accommodate renewable generation and new high-peak/coincident loads. However, flexibility is not infinite, and its use in providing local services to distribution system operators (DSOs) can limit its availability for system ancillary services, required by transmission system operators (TSOs) to ensure security and adequacy. As the energy transition progresses, competition for flexibility between TSOs and DSOs will increase. Similar interaction appears between Medium Voltage (MV) and Low Voltage (LV) grids, for instance, when smaller DSOs are supplied by MV distribution networks operated by a different DSO. The Secondary Substation (SS), the interface between the MV and the LV, can provide ancillary services by changing its working point to keep the MV network operation within technical boundaries. Forecasting the SS behaviour is challenging due to the limited observability of LV networks and the uncertainty of generation/consumption patterns. This paper proposes a methodology that identifies and forecasts the amount of flexibility LV can offer without compromising its operation. To address data scarcity, a suitable methodology for developing and using synthetic networks is also proposed. Case studies demonstrate the validity of the proposed approach.

The modeling of interdependent networks for Cyber-Physical Power Systems (CPPS) remains an important and challenging research topic in the study of modern power systems. In such interdependent network models, the operational status of power system nodes is jointly determined by both the control and monitoring systems within the cyber layer. This paper first selects dominant nodes based on the theory of reactive power and voltage partitioning to achieve localized control within their respective partitions, and further incorporates the regulation capability of generator nodes to construct a comprehensive control system model. Subsequently, considering the widespread deployment of monitoring nodes, a one-to-one correspondence between power nodes and monitoring nodes is established, leading to the formulation of a monitoring system model. Based on these two subsystems, a ”multiple-to-multiple” interdependence model for CPPS is proposed.In this study, node survival rate and controllability are employed as key vulnerability metrics. Simulation results validate the effectiveness of the proposed model. Compared with traditional models, the proposed approach results in lower network losses under failure scenarios, demonstrating its superior performance and robustness. This work lays a foundational basis for integrated modeling of Cyber-Physical Power Systems.

The increasing integration of Renewable Energy Sources (RES) in distribution networks causes a change in the operation of the grid which may affect the reliability of distribution transformers through mechanisms that extend beyond thermal stress induced by conventional loading alone. This paper proposes two statistical methodologies to investigate the potential impact of RES on distribution transformer failure rates, and applies them as example to the service area of a Dutch distribution system operator. First, the temporal analysis method reveals significant hourly correlations between failure rates and renewable generation profiles, although no seasonal trends are observed. However, the lack of a rising trend in these correlation values indicates that the increased capacity of RES has not yet impacted the failure rate. Second, a spatial analysis employs a Poisson Geographically Weighted Regression model to assess localized correlations between failures and different types of RES. While large-scale photovoltaic (PV) plants show no statistically significant spatial correlation with failures, the analysis reveals local positive correlations for residential PV and wind generation. Even though the analyses do not reveal increased failure rates due to increasing RES penetration, the findings suggest localized stress on assets and show the need for further research into long-term aging effects.

Climate change-induced extreme disasters cause significant economic losses to distribution system operators (DSOs). This paper proposes a resilience enhancement strategy considering insurance investment budgets. The approach simulates distribution system (DS) reconfiguration from insurers’ perspective to determine affordable insurance budgets for DSOs, optimizing resilience within these constraints. First, k-means clustering with Hausdorff distance processes historical typhoon tracks, generating typical fault scenarios via Monte Carlo simulation. The strategy is modeled as a scenario-based stochastic mixed-integer linear program (SMILP). During planning, insurers optimize DS reconfiguration under fault scenarios to determine investment strategies. During operation, insurers develop planning strategies based on these investments, including DG and ESS allocation plus insurance reserves. Commercial solvers and genetic algorithms solve the planning and operation stage models, respectively. Testing on the IEEE 33-bus system verifies the strategy’s validity. Results demonstrate that the combined planning-operation approach considering insurance budgets effectively enhances resilience by reducing both economic losses and load shedding during extreme events. This insurance-investment framework provides DSOs with a practical mechanism to improve disaster preparedness while managing financial constraints.

Existing research on optimal power flow (OPF) for the transient stability of power systems primarily focus on aperiodic instability when suffering large disturbances. This type of instability arises from insufficient synchronous torque and typically manifests as first or second swing instability. However, transient instability can also occur as periodic oscillation instability due to inadequate damping torque. Traditional transient stability constrained OPF (TSC-OPF) methods are inherently limited in their ability to optimize the damping level of power systems. While small-signal stability-constrained OPF (SSSC-OPF) methods can improve damping ratios under small disturbances, their reliance on linearization theory makes them unsuitable for strongly nonlinear transient stability problems. To fill this gap and distinguish itself from conventional TSC-OPF approaches, this paper proposes a novel large-disturbance dynamic stability constrained OPF model (LDDSC-OPF) that simultaneously optimizes both damping torque and synchronous torque under large disturbances. The proposed approach utilizes Prony analysis to establish a large-disturbance dynamic stability criterion through the critical trajectory-eigenvalue (TE) that quantifies system damping levels. This criterion, formulated as transient stability constraints and combined with power system dynamic equations, is incorporated into a conventional OPF framework to formulate the LDDSC-OPF problem. An efficient iterative solving method based on TE sensitivity is developed to determine optimal generation rescheduling while maintaining specified security margins. Comprehensive simulations on the WSCC 3-machine 9-bus system and New England 10-machine 39-bus system validate the effectiveness of both the proposed model and solution method.

Reliable operation of standalone PV/battery microgrids is challenged by uncertainties in both load demand and solar irradiance. Although model predictive control (MPC) has shown promise for power management, its performance deteriorates when prediction errors exceed certain thresholds, leading to suboptimal maximum power point tracking (MPPT) and voltage regulation. This work presents an enhanced offset-free MPC strategy that incorporates real-time irradiance prediction updates through a physics-informed correction mechanism. The proposed method dynamically adjusts the forecasts to compensate for the inaccuracies of the prediction, maintaining the stability of the system under significant uncertainty. Comparative simulations demonstrate that conventional MPC fails to ensure proper MPPT operation during periods of high prediction error, resulting in PV voltage deviations and reduced efficiency. In contrast, the proposed approach maintains consistent tracking performance while preserving DC bus voltage stability and battery state-of-charge (SoC) limits. Key innovations include (1) an adaptive prediction correction framework that improves irradiance estimation accuracy, (2) robust performance across varying levels of uncertainty, and (3) effective power balance under realistic operating conditions. The method proves particularly valuable for remote microgrid applications where prediction reliability is critical but often compromised by environmental variability.

The emergence of high-voltage direct current integrated (HVDC) power systems introduces significant out-of-distribution (OOD) challenges to machine learning-based analysis of cascading failures (CFs). Existing algorithms often suffer from poor interpretability and generalization in handling diverse CF propagation modes. To address these issues, this paper proposes a novel Causal Graph Attention (CGAT) model that captures critical information from CF events to accurately predict propagation paths and impacts. Specifically, by employing a graph attention mechanism, the model extracts both causal and trivial features as distinctive attention dual subgraphs, which aids in differentiating shortcuts in causal variables and improving prediction accuracy. These subgraphs also provide visual representations that highlight the key elements influencing CF propagation. By incorporating interventions, the CGAT model enhance prediction capabilities across a variety of scenarios that include HVDC control strategies and renewable energy sources (RES). Extensive experiments on augmented IEEE 30-bus and 2737-sop systems demonstrate the effectiveness and superiority of the proposed CGAT model.

As power systems become more dynamic, due to less power system inertia, increasingly sophisticated methods are required to provide frequency containment services. This paper focuses on a technique for rapidly detecting and measuring synchronous machine speed changes. The primary applications of this work are the triggering of inertia services, such as battery energy storage systems, and the validation of the power system’s response to faults. The method employs electrical measurements readily available to system operators. Analysis of transients captured by event recorders at bulk generation and an accompanying simulation with Matlab are used to develop and validate the technique. The work includes the impact of synchronous machine internal power losses and changes in stored magnetic energy. Machine angular acceleration is employed as a proxy for rate-of-change-of-frequency (ROCOF) and calculated on significantly shorter timescales than conventional estimation methods. The technique is used to analyze system transients caused by faults and loss of generation. Approaches for combining the technique with conventional frequency estimation for implementation in real-time or wide-area deployments are discussed.

The way we use our power distribution grids is changing profoundly. Electrification of transport, heating and decentral generation introduce new usage patterns, storage and IoT change existing ones. Grid usage is becoming more volatile: there is an increase of weather-dependent applications, dynamic tariffs and price incentives enable users to adapt their usage. This affects the power flows in the distribution grids. Day-to-day operations and the grid planning needs to change towards a paradigm where this volatility is taken better into account. In this article, we propose a decision framework for distribution grid planning and -operation that incorporates effects of more volatile usage patterns. The framework contains an approach for usage pattern classification and metrics for measuring the level and type of dynamic usage in a grid. Based on level and type of volatility, the framework supports the DSO in choosing a mode of operation of operation that matches the level and type of volatility as well as the risk appetite for specific parts of the distribution grid. The approach is applied to the grids of the Dutch DSO Stedin, making use of the Integrated Infrastructure outlook 2030-2050 (II3050) Stedin’s Energy Transition Impact Assessment Model (SETIAM) datasets

Large-scale photovoltaic (PV) integration threatens new distribution system safety, with frequent extreme scenarios exacerbating carrying capacity issues. This paper proposes an enhancement method: First, establish a comprehensive capacity assessment method focusing on power quality and safety indicators. Second, integrate assessment results into a planning-operation two-layer model: the planning layer maximizes PV capacity by determining optimal siting and sizing, while the operation layer optimizes economic dispatch for cost efficiency. Third, solve the model effectively using the Football Team Training Algorithm (FTTA) and second-order cone relaxation. Finally, validate the method using a 68-node system from a major Northwest China PV town, generating typical scenarios based on extreme-condition PV output models. The approach demonstrably enhances system carrying capacity under extreme conditions.

In power systems with high penetration of distributed generations (DGs), multi-modal broadband harmonic oscillations pose a serious threat to secure and stable operation. Existing active and passive damping methods are limited in their capability to effectively suppress resonance peaks. Considering background harmonics and system impedance characteristics, this paper proposes a novel direct-mounted damping device to suppress broadband harmonic oscillations in the distribution network. In accordance with IEC specified electromagnetic compatibility standards for low-voltage grids, a unified quantitative indicator for broadband oscillation analysis is established. Additionally, a general procedure for damping device parameter design is developed based on modal analysis and the particle swarm optimization (PSO) algorithm. Simulation results show that the proposed topology achieves superior broadband oscillation suppression. Experiments on a multi-inverter grid-connected system further demonstrate the generality and effectiveness of the proposed method in suppressing multi-modal broadband harmonic oscillations within high-DG-penetration distribution networks.

In current engineering practice, the phase of the short-circuit current provided by a grid-connected energy storage plant is affected by the pre-fault status of charging and discharging. When energy storage encounters a fault in the transmission line during charging, the phase difference is likely to be large between the short-circuit current it provides and the short-circuit current provided by the grid on the other side of the line, leading to a risk of no-trip failure in traditional current differential protection (CDP). To address the above problems, this paper proposes a modified current differential protection criterion based on adaptive phase compensation (MCDP). By judging the state of energy storage charging and discharging state as well as the presence of negative sequence currents, current phases on both sides of the line will be compensated at different angles, so as to reduce the phase difference and improve the sensitivity of the protection without reducing the reliability. The performance test of the MCDP is carried out through PSCAD simulation. The results show that the MCDP has good performance under different fault conditions and ability to resist fault resistance.

High penetration of residential photovoltaic (PV) units has caused severe voltage issues in the distribution feeders. During seasons with low demand and high PV generation, distribution feeders face increasing power quality issues. Continuous operation in system conditions with these voltage issues reduces the lifetime of distribution equipment and can cause severe monetary loss. Legacy VAr support devices are too sparse and slow to mitigate these issues. With the introduction of the IEEE 1547-2018 standard, Distributed Energy Resources (DERs) are allowed to participate in providing VAr support to mitigate these issues. This paper proposes a novel Unified Mode Selection (UMS) framework within a real-time VAr optimization tool for distribution networks. This tool is developed based on an effective unbalanced distribution AC Optimal Power Flow (ACOPF) formulation. The proposed model has the ability to select between the Volt-VAr and Watt-VAr operational modes for each VAr enabled smart inverters, and to simultaneously optimize the controller set-points to optimally dispatch each inverter, minimizing operational costs and mitigating the voltage issues. The tool is tested on a snapshot of a distribution network in Arizona and compared against using only Volt-VAr or Watt-VAr operational mode with default settings. The results confirm the effectiveness of the model.

The growing penetration of renewable energy needs a large amount of flexible resource to regulate in power systems. Current research mainly focuses on individual flexibility of resources, the synergistic flexible potential of thermal units with multi-energy storage systems remains underexplored. Therefore, this paper proposes a distributionally robust optimal dispatch method for multiple flexible resources including thermal units and multiple energy storage. Firstly, a refined operation model of units and energy storage is constructed. Specifically, oxygen-enriched deep peaking of units and coordinated operation of battery and hydrogen energy storage are considered. Secondly, a data-driven distributionally robust optimization (DRO) model is established to cope with the uncertainties of renewable energy. An inexact columns and constraint generation (i-C&CG) method is then developed to solve the model efficiently. Finally, a case study of a provincial power system is conducted, and the results verify the validity of the proposed method.

The classical power generation expansion planning (PGEP) that takes environment as an exogenous variable is mainly used to optimize the specific expansion scheme for power companies. It is not suitable for the national energy strategy which is closely related to macro-economy growth and environmental protection. To address this complex problem, a novel multi-disciplinary framework for power generation mix planning is proposed from the perspective of economic growth. In this model, an endogenous economic growth model is introduced to describe the equilibrium relationships of various sectors in macro-economic, thus making economy and environment the endogenous variables. By establishing a new soft-linking between these two models, it permits policymakers to evaluate the alternatives and trade-offs between economic growth and environmental protection within long-term planning horizon while guaranteeing the feasibility of planning scheme. To demonstrate the effectiveness of the proposed methodology, a case study of China from 1990 to 2040 is implemented.

Weather events can cause power outages and damage the grid infrastructure. Resilience is the ability of the power grid to quickly recover from disruptions such as weather events. Enhancing the resilience of the distribution systems is required for continued grid operation. A solution to enhance the resilience of a distribution system is to proactively deploy resources (e.g., backup generators, repair crews, additional conductors) in the most vulnerable regions to provide faster service restoration. In this paper, first, we propose a method to model the impact of extreme weather events on power grids. Then, we use Q-learning to identify the worst impact zones while considering possible propagating paths of extreme weather. Finally, we develop a game-theoretic approach to allocate resources to the worst impact zones at the minimum cost. Simulations are conducted on a modified IEEE 123-bus test system. The results prove the effectiveness of the proposed work on enhancing grid resilience.

This paper firstly analyses the fault equivalent circuit of DG (Distributed Generation) as a high-frequency harmonic source, and then deduces the fault characteristics of the electrical quantities at both ends on the basis of high-frequency harmonic injection, pointing out the weaknesses: easily affected by the load capacity of the T-connected load. Afterwards, this paper draws the equivalent circuit diagram of the positive sequence component after the fault and derives the difference expression of the virtual electrical quantity at the opposite end under internal or external faults at different locations, and then analyses the fault characteristics of positive sequence under the industrial frequency. On this basis, this paper proposes a double-terminal protection scheme based on the combination of high-frequency harmonic injection and virtual positive sequence differential principle. Finally, the protection scheme is verified on the MATLAB/SIMULINK platform. And the proposed protection method can identify faults within 30ms, with a capability of enduring transient resistance up to 200 Ω, noise immunity reaching 200 dB, and being unaffected by the size of T-connected load capacity or the fault location.