The DC collection and transmission scheme based on diode rectifier units (DRUs) has strong potential for large-scale and low-cost renewable energy delivery. However, its uncontrollable nature may introduce frequency stability risks to 100% renewable power plants. This paper focuses on a 100% renewable power plant equipped with a virtual synchronous generator (VSG)-based grid-forming energy storage (ES) and connected to a DRU converter station. The objective is to mitigate frequency instability by improving the control strategy of the ES system. To address the rapid frequency drop under severe disturbances, a novel fast frequency support strategy is proposed. The main contributions are as follows: (1) a dual-layer model predictive control (MPC) framework that rapidly adjusts the total output power of the ES system for frequency support and (2) a state-of-charge (SoC) balancing mechanism that coordinates multiple internal ES units. Simulation results show that the proposed strategy improves the frequency response speed, reduces the frequency deviation and extends the effective support time of the ES system, thereby enhancing the frequency stability of the 100% renewable power plant.

With the rapid development of new energy, power consumption becomes prominent in renewable-rich regions. Diode Rectifier Unit (DRU) based High Voltage Direct Current (HVDC) is adopted to solve the issue, however, DRU lacks grid-forming capability, making it urgent to deploy grid-forming energy storage to ensure the safe operation of the power grid. At the same time, due to climate impacts, wind farm power generation exhibits strong randomness and volatility. When actual generation does not match the planned output, an “imbalance penalty cost” must be incurred. Therefore, the energy storage system of the wind farm must smooth power fluctuations on a short time scale and balance power output on a longer time scale to reduce the economic costs associated with imbalances. This paper proposes a grid-forming hybrid energy storage system capable of scheduled power generation for wind farms. An objective function is established which includes battery operating losses, wind farm imbalance penalty costs, and overall system benefits. Based on ultra-short-term wind power forecasting, model predictive control (MPC) rolling optimization is applied to control the grid-forming hybrid energy storage system. To minimize the annual economic cost, the capacity ratio of the grid-forming hybrid energy storage system is optimized.

Aggregated energy storage system (AESS) is an im portant means of frequency regulation under power disturbance, but its capacity limitations make it hard to cope with high power shortages under inclement weather. A straightforward idea is to regulate frequency by reasonably combining the load response with the AESS. The coordination of the load and Energy storage (ESs) within AESS needs to be seriously handled to prevent frequency second drop caused by output mismatch, which is a challenging continuous-discrete control problem due to their inconsistent control step size. Meanwhile, the decision needs to be performed quickly to prevent large frequency devation, which may be constrained by the non-global observability of the system under inclement weather, thus making the coordination more difficult. In this paper, a sliding mode observer is designed to rapidly evaluate the power disturbance of non-globally observ able systems. Subsequently, a dual-layer model predictive control considering various specified constraints is designed to allocate load response and ESs output in the frequency regulation process. By adopting different control step sizes for the upper and lower layers, the continuous-discrete control problem is well handled. The effectiveness of the entire supplemental frequency control is validated under various power disturbances.

Due to the influence of fault control strategies in inverter-based power source, the performance of conventional current differential protection may further deteriorate in fully renewable power system. This paper first introduces the fault control strategies of fully renewable power system and analyzes the underlying mechanisms responsible for the degradation of current differential protection performance. To address this issue, a novel longitudinal protection scheme based on Current Distribution Entropy (CDE) is proposed. The proposed method utilizes the high sensitivity of the entropy-based index to phase-angle variations to distinguish between internal and external faults. Simulation results obtained using PSCAD/EMTDC demonstrate that the proposed protection scheme can accurately identify various fault types within 5 ms after fault occurrence, while exhibiting strong robustness against transition resistance and external disturbances.

Current research on the collaborative planning of flexibility resources in modern power systems under extreme weather conditions remains insufficient. This paper systematically reviews relevant research advances in collaborative flexible resource planning for addressing extreme weather events. It analyzes the impacts of extreme weather conditions on modern power systems， clarifying their characteristics of low probability but high risk， dual imbalance， and regional variability. The paper summarizes primary challenges faced by generation， grid， load， and storage components under extreme weather conditions. Subsequently， it systematically categorizes flexibility resources across these components and analyzes the strengths and limitations of diverse resources in responding to extreme weather. Furthermore， it reviews collaborative planning models for flexibility resources that accommodate strong uncertainties under extreme weather conditions and identifies shortcomings in existing research on flexible resource planning. Finally， the paper outlines key issues and offers forward-looking perspectives for future research on flexibility resources in new power systems under extreme weather， providing a reference for the planning of such systems that considers the uncertainties associated with extreme weather.

In order to fully exploit the advantages of water transportation in terms of cost and convenience, a new waterway hydrogen chain integrating the vessel-mounted transferable hydrogen production equipment (THPE), hydrogen vessel (HV), and hydrogen refueling station (HRS) is designed in this paper. The medium-term operation characteristics of the THPE which consider the long-time scale berthing position adjustment and short-time scale power regulation ability are modelled. The dynamic relationship between the hydrogen charging rate and real-time hydrogen storage of HVs is also evaluated. Additionally, the hydrogen market interaction mechanism between HRS and other hydrogen sources is formulated based on Bertrand model. On this basis, a medium-term scheduling strategy of the power system integrating waterway hydrogen chains is developed. This model is linearized into a mixed-integer linear programming (MILP) problem using an accuracy-aware adaptive piecewise linearization approximation method to improve solution efficiency. Finally, case studies on a modified IEEE-30-node power system and river network indicate that the proposed strategy can reduce the cost of the integrated electric-hydrogen system by 16,094 thousand yuan (18.2%).

Conventional cyber-physical defense mechanisms for power grids typically depend on real-time threat detection and isolation of compromised devices. However, the growing integration of smart grids with large-scale IoT networks introduces new vulnerabilities, enabling cyber-physical attacks to be initiated from IoT-enabled load devices. Since these devices are mostly owned and operated by third parties, large-scale detection and isolation are difficult to deploy and often incur prohibitive costs. Unlike conventional attacks, this emerging threat may not immediately destabilize the power grid during its cumulative propagation process, thereby creating a window for risk-driven decision-making. Leveraging this characteristic, we propose an adaptive risk defense framework (ARDF) that proactively assesses risks, tolerates manageable disturbances, and intervenes only when the assessed risk exceeds a prescribed threshold. Specifically, we develop a delayed differential equation model for infection dynamics over a centralized-distributed topology. Then, we construct a stability analysis model to quantify the effect of distributed disturbances and identify the most severe attack vector. Subsequently, by evaluating grid resilience, we introduce a risk assessment method for such attacks, which can identify attack scenarios with tangible risks. Numerical simulations on the standard IEEE 39-bus system demonstrate the effectiveness of the proposed framework for risk assessment.

There are emerging challenges posed by the integration of a large number of distributed generations (DGs) into power distribution networks. Thus this paper proposes a novel protection that combines negative-sequence quantity sorting with 5G communication. For information exchange, a 5G communication method with timestamps is proposed, further reducing the synchronous communication requirements of the proposed protection scheme by leveraging the communication characteristics of distribution networks. Simulation results demonstrate the successful application of the proposed criteria to lines with unpredictable branches without relying on data synchronization communication. The new protection scheme significantly enhances fault clearance speed, ensuring that the fault’s maximum response time remains within 50 ms. In comparison to amplitude differential protection, the proposed protection exhibits stronger adaptability and operational stability against unpredictable branches.

Against the background of carbon peak and carbon neutrality, the islanded renewable energy collection and transmission system via a multi-terminal Diode Rectifier Unit (DRU) offers significant economic advantages. However, its DC fault characteristics are complex, and the adaptability of existing protection schemes needs to be verified. Focusing on the novel multi-terminal DRU-CLCC transmission system, this paper analyzes its topology and the steady-state operating characteristics of the DRU, clarifies the coordinated operation logic and setting method of the Siemens traveling wave protection based on voltage variation rate, voltage variation, and current variation, and builds three-terminal and two-terminal system models in PSCAD/EMTDC for simulation tests. The results show that the protection can effectively discriminate internal and external faults and achieve pole selection, but lacks branch selectivity. The multi-terminal topology reduces the tolerance to high-resistance faults. The three-terminal system shows slightly better anti-noise performance than the twoterminal system, yet severe maloperation still occurs under high noise levels. Overall, the Siemens traveling wave protection cannot be directly applied to multi-terminal DRU-CLCC systems. The findings can provide a basis for protection optimization of such systems.

Due to the inherent characteristics of wave resources, wave energy generation (WEG) exhibits impulsive characteristics that needs to be smoothed out before being utilized. The degradation loss of a single battery system to consume the impulsive power is significant. Considering the high energy density and storage portability of hydrogen, a hybrid system combining battery and flexible water electrolysis system is a promising choice for wave energy consumption. However, more diverse and complex consumption devices will introduce more operational conflicts and coordination challenges. Therefore, the paper firstly explores the dynamic characteristics of WEG, taking into account the comprehensive external environment and internal control factors. Then, the contradictory boundaries and collaborated domains between the characteristics of impulsive WEG and the consumption devices are clarified through theoretical analysis. Subsequently, a movable electric-hydrogen supplier (MEHS) powered by wave energy was proposed. Considering the requirements of internal multi-energy flow coupling and external multi-spatiotemporal interaction, a scheduling model is established to schedule energy capture, storage, and supply behavior of MEHS. The simulation results show that the proposed flexible scheduling framework can effectively match the dynamic characteristics of different devices, and maximize the utilization of wave energy resources for island microgrids.

With the high proportion of wind and photovoltaic power and other new energy sources integrated into the power grid, the short-circuit characteristics of their power electronic interfaces differ fundamentally from those of traditional synchronous generators. This often leads to convergence difficulties in iterative calculations based on the conventional Newton–Raphson method. Existing improvements mainly fall into two types: one enhances convergence by refining new energy models and improving iterative strategies, while the other focuses on improving the solver for nonlinear equation systems itself. However, these methods still exhibit insufficient convergence and robustness in sparse matrix computations for large-scale power grids with high penetration of renewable energy. To address this, this paper first develops a piecewise function model for the fault transient output characteristics of doubly-fed induction generators (DFIGs) and photovoltaic (PV) power sources, aiming to accurately represent their strong nonlinearity. Subsequently, for the resulting large-scale sparse nonlinear equation system, an iterative computation method based on Newton–HSS (Newton–Hermitian and Skew-Hermitian Splitting) is proposed. This method employs Newton iteration in the outer layer to handle nonlinearity, while the inner layer efficiently solves the linear system using the HSS splitting technique. Through parameter adjustment, it achieves adaptive allocation of computational effort, significantly improving convergence speed and robustness while ensuring accuracy. Simulation results verify that compared with traditional methods, the proposed approach exhibits superior convergence performance in short-circuit calculations for large-scale power grids with high penetration of renewable energy.

Existing power system flexibility resource planning primarily focuses on normal weather scenarios， proving inadequate to address challenges posed by frequent extreme weather events and high-penetration renewable energy integration. To address this challenge， this paper proposes a novel flexibility resource planning method for extreme weather conditions. First， extreme weather is categorized into environmental-type and disaster-type events， with tailored solutions such as repurposing retired thermal units as standby generators and deploying mobile energy storage vehicles for emergency power supply. Second， a coordinated optimization model for generation， load， and storage flexibility resources under different weather conditions is developed. System flexibility is evaluated using five metrics including annual imbalance risk coefficient. Third， an optimization model is developed to minimize investment and operational costs， with their constraints incorporated， and the solution process is simplified by linearization. Case study based on Hubei Province's power and meteorological data shows that the proposed method， by introducing flexibility resources under extreme weather conditions， reduces system imbalance risk coefficient and decreases total system costs， thereby enhancing flexibility and economic efficiency of modern power systems.

Electric vehicle service equipment (EVSE) usually adopts an Internet of Things (IoT) architecture, making it vulnerable to cyber-attacks. Attackers could compromise massive EVSEs from the Internet and threaten grid security by injecting disturbing power through cross-domain attacks. The fundamental defense measure is to locate and eliminate the disturbance sources, which can be approached from either the power grid domain or the Internet domain. Nevertheless, with existing location methods on power grid domain, we can only locate disturbance at transmission level; with methods deployed on Internet domain, we can indeed identify compromised EVSEs, but it is easily confused with other malicious activities. In this article, we propose a disturbance source localization scheme for cross-domain attacks by fusing the information from the Internet and power grid domains. First, the attack behavior on the Internet domain is characterized based on the widely adopted communication protocol. Then, the observable attack features on the power grid domain are analyzed to numerically evaluate the infection rate of EVSEs. Finally, a multiview learning method is adopted to combine the heterogeneous information obtained from multiple domains. The simulation results verify the advantages of the proposed strategy.

The combination of multiterminal diode rectifier units (DRUs) for DC power collection and Line Commutated Converter (LCC)-based transmission is a highly competitive solution for provincial-level renewable energy accommodation. However, due to the uncontrollable nature of diode devices, such systems depend heavily on gridforming energy storage (ES) for stable operation and fault ride-through. This dependence exacerbates the vulnerability of the receiving-end grid to power deficits. As system inertia declines, traditional Under Frequency Load Shedding (UFLS) struggles to mitigate rapid frequency drops due to its delayed activation and limited precision. While integrating Demand Response (DR) can enhance UFLS, its unpredictable response delays present a significant challenge. To overcome this obstacle, this paper proposes an optimal coordination strategy for UFLS and DR. First, a cumulative distribution function (CDF) model is developed to characterize the uncertain delays of DR. Then, a comprehensive system frequency response model is formulated to capture the joint effects of UFLS and DR. Ultimately, this approach enables the proposed strategy to dynamically determine the optimal DR shedding amount in real time under varying communication conditions. Numerical simulations demonstrate that the proposed strategy achieves secure and precise load shedding at lower cost.

With the evolution of cyber-physical power systems, the widespread integration of vulnerable distributed loads has increased the feasibility of dynamic load-altering attacks (DLAA). DLAA can trigger forced oscillations and threaten power-grid stability, but their effect can be significantly weakened once defenders identify and remove the dominant disturbance nodes. Motivated by this defense process, this article proposes a coordinated cyber attack designed to deceive the transient-energy-based localization system. Specifically, we first analyze the operating mechanism of disturbance localization on the grid side and clarify how disturbance removal decisions are made. Then, we develop a coordinated attack model in which compromised Internet of Things loads excite forced oscillations, while false data are injected into a limited number of phasor measurement units to manipulate the transient-energy distribution perceived by defenders. Under limited attack resources and stealth constraints, an analytical model is further established to construct the optimal attack vector, thereby deceiving the transient-energy-based localization system and misleading the subsequent removal decision. Numerical studies on the IEEE 39-bus system validate the effectiveness of the proposed approach.

Currently, insulation faults in the DC system of photovoltaic (PV) power stations are handled by a full shutdown strategy of inverters, and fault branch localization relies on manual inspection, resulting in low efficiency and poor accuracy, leading to prolonged unplanned outages and substantial power generation losses. This paper proposes an integrated solution combining high-precision insulation monitoring and intelligent fault line selection, which ensures the reliability of line selection criteria through improved measurement accuracy and achieves automatic fault isolation via optimized line selection strategies. The paper analyzes the mathematical essence of the ill-conditioned measurement equations of the traditional bridge method under severe single-pole grounding faults, establishes a dual-channel heteroscedastic noise model, and utilizes the inherent physical constraint that the sum of the positive and negative pole-to-ground voltages always equals the bus voltage to transform the ill-posed inverse problem into an equality-constrained optimal estimation problem, deriving an analytical solution in the sense of constrained least squares. A collaborative monitoring strategy of “balanced bridge monitoring first, unbalanced bridge precision measurement afterward” is proposed. An automatic fault line selection and isolation algorithm based on sequential branch switching is designed, which leverages the operational characteristic that PV systems allow short-term branch interruption, enabling automatic identification and isolation of faulty branches and automatic restoration of non-faulty branches without installing any leakage current sensors. Experimental results show that under severe fault conditions with a single-pole insulation resistance as low as 22 kΩ, the proposed method limits the error to within 5%; the proposed line selection strategy can complete identification and isolation of all faulty branches within at most two rounds of switching.

In order to address the synergistic optimization of energy efficiency improvement in the waste incineration power plant (WIPP) and renewable energy accommodation, an electricity-hydrogen-waste multi-energy system integrated with phase change material (PCM) thermal storage is proposed. First, a thermal energy management framework is constructed, combining PCM thermal storage with the alkaline electrolyzer (AE) waste heat recovery and the heat pump (HP), while establishing a PCM-driven waste drying system to enhance the efficiency of waste incineration power generation. Next, a flue gas treatment method based on purification-separation-storage coordination is adopted, achieving spatiotemporal decoupling between waste incineration and flue gas treatment. Subsequently, a two-stage optimal dispatching strategy for the multi-energy system is developed: the first stage establishes a day-ahead economic dispatch model with the objective of minimizing net system costs, while the second stage introduces model predictive control (MPC) to realize intraday rolling optimization. Finally, The optimal dispatching strategies under different scenarios are obtained using the Gurobi solver, followed by a comparative analysis of the optimized operational outcomes. Simulation results demonstrate that the proposed system optimizes the output and operational states of each unit, simultaneously reducing carbon trading costs while increasing electricity sales revenue. The proposed scheduling strategy demonstrates effective grid peak-shaving functionality, thereby simultaneously improving the system’s economic performance and operational flexibility while providing an innovative technical pathway for municipal solid waste (MSW) resource utilization and low-carbon transformation of energy systems.

Under the "dual-carbon" strategy, remote distribution networks are increasingly integrating distributed energy resources such as wind, photovoltaic, and energy storage systems, forming hybrid AC/DC configurations. Existing studies largely focus on static islanding schemes based on fixed renewable outputs, overlooking priority restoration of critical loads and the severe fluctuations in renewable generation under extreme weather. To address this, this paper proposes a virtual-node equivalent modeling approach to develop an islanded reconfiguration model that considers both static power-flow security and transient frequency/voltage stability. The effectiveness of the method is verified using a modified IEEE-33 bus system.

To address the problem of selecting an “appropriate” charging station for emergency charging during the journey of electric vehicles, this paper proposes a basic architecture of an intelligent charging navigation system composed of the power system, traffic system, charging stations, and on-board navigation terminals. The concept of a charging time window is introduced into a “reservation-based charging + consumption” service model for electric vehicle charging prediction. On this basis, a dynamic dispatching model based on a rolling time axis is designed, enabling the charging process of users to be freed from the constraints of queuing time and time-dependent charging service fees. Case simulations show that intelligent charging navigation for electric vehicles based on reservation charging service can effectively improve the users’ charging experience while taking into account both the operating state of the power grid and the benefits of charging station operators.

In view of the special dispatching demands of isolated islands in low-density periods of renewable energy power generation, the defects of the traditional dispatching mode when applied to isolated power generation systems are analyzed, and the idea of reasonably extending the daily scheduling cycle is proposed to adapt to the application of flexible energy resources in the form of energy packages under various uncertain scenarios. Under the multi-party cooperative power supply strategy for isolated islands, we analyze the shortcomings of key element modeling. A global optimal model of energy scheduling for isolated islands considering low-density energy output periods is constructed based on a refined element model, and a corresponding solution is proposed for the nonlinear constraints. The reasonability and effectiveness of the refined model, the global optimal model, and the assumption of an extended scheduling cycle are verified by theoretical analysis and case simulation.