[Objective] Under the "Carbon peaking and carbon neutrality" goals, as a critical technology for adapting power systems to high proportions of renewable energy, the application advantages of grid-forming energy storage (GFMES) have not been fully exploited. Therefore, there is an urgent need to clarify its active support mechanisms and planning methods across multiple scenarios in the new power system. [Methods] This paper summarizes the voltage support, frequency support, and inertia support mechanisms of GFMES from the perspective of grid-forming control. Planning methods are then reviewed for normal operating conditions, small-disturbance conditions, and large-disturbance conditions. Combined with engineering demonstrations, the planning roles, key requirements, and multi-condition coordination issues of GFMES are further analyzed in renewable energy export systems, HVDC receiving-end nearby grids, and microgrids. [Results] Existing studies have established condition-oriented planning frameworks: normal-operation planning mainly emphasizes techno-economic performance; small-disturbance planning focuses on system strength and frequency stability; large-disturbance planning highlights whole-process coordination of prevention, fault ride-through, and post-fault recovery. Meanwhile, the functional role, dominant constraints, and planning requirements of GFMES differ significantly across application scenarios. [Conclusions] GFMES planning exhibits strong scenario dependence and multi-condition coupling. System strength, voltage/frequency security margins, and fault recovery requirements are the key constraints governing siting and sizing. Future work should develop coordinated planning methods that jointly consider technical constraints and economic performance.
[Objective] With the increasing penetration of power electronic devices in the new power systems， the system is gradually evolving from a “physical synchronization” paradigm dominated by synchronous generators to a “control-based synchronization” paradigm dominated by grid-connected inverters. Due to the strong nonlinearity， limited overcurrent capability， and multi-inverter coupling characteristics of grid-connected inverters， synchronization stability issues exhibit multi-level features， extending from local dynamics to system-wide interactions.[Methods] To address this， this paper presents a comprehensive review from two perspectives， single-machine infinite-bus systems and hybrid systems. For the single-machine infinite-bus systems， virtual power angle dynamic models describing the synchronization behavior of grid-following （GFL） inverters， virtual synchronous generator-based， and virtual oscillator-based grid-forming （GFM） inverters with the grid are introduced. Based on the virtual power angle curves， the key factors affecting the synchronization stability of different inverter types are analyzed， and representative stabilization control strategies are summarized. For hybrid systems， considering the interactive coupling among control loops of GFL and GFM inverters， equivalent power angle dynamic models under both islanded and grid-connected modes are established， and the mechanisms of synchronization instability caused by multi-machine interactions， together with corresponding stabilization control strategies， are systematically reviewed.[Conclusions] Based on an analysis of existing research progress， future research directions on the synchronization stability of grid-connected inverters are discussed from the perspectives of multi-time-scale modeling， multi-scenario stability assessment， and coordinated stabilization control.
[Objective] When uncoordinated control strategies are employed during the parallel-connected operation of multiple energy storage converters in new-type power systems, they cause unbalanced state of charge (SOC) distribution among energy storage systems, as well as frequency overshoot and low-frequency oscillations. To address these problems, this paper proposes an improved virtual synchronous generator (VSG) control strategy that combines SOC-based power balancing and adaptive fundamental damping design. [Methods] First, a grid-forming parallel-connected energy storage system model is established using voltage-source-type VSG control as the grid-forming outer loop. Second, a set of active power balancing coefficients is derived based on the real-time SOC of energy storage systems. Third, a control strategy combining fundamental damping with adaptive damping is designed on the basis of these balancing coefficients. Finally, the performance and effectiveness of the proposed control strategy are verified and analyzed using the MATLAB/Simulink digital simulation environment. [Results] Using an aluminum battery pack as the energy storage system, after introducing the proposed improved VSG control strategy, a power distribution difference of 5% to 7% is observed between the high-SOC and low-SOC systems. Under typical disturbance conditions, the frequency drop amplitude is reduced by approximately 20% and the overshoot amplitude is reduced by approximately 30%; under complex operating conditions, the frequency deviation is reduced by 10% to 15%. [Conclusions] The proposed control strategy maintains SOC balance among parallel-connected units by setting balancing coefficients, thereby reasonably optimizing power distribution. Furthermore, by incorporating an adaptive damping strategy based on the obtained balancing coefficients to correct frequency deviations, the proposed approach effectively enhances the dynamic response performance, frequency stability and multi-machine coordinated operation capability of energy storage systems. Therefore, the proposed control strategy holds certain theoretical significance and engineering application value for the construction of new-type power systems with high penetration of renewable energy.
[Objective] Heatwaves， featured by extensive influence and high intensity， are among the meteorological disaster events that exert the most significant impacts on China’s power system. Industrial consumers， possessing substantial power demand and considerable regulation potential， represent high-quality resources for ensuring a guaranteed power supply. However， existing studies have not fully accounted for the coupled effect of temperature and humidity on temperature-sensitive loads under humid heatwaves， which hinders the accurate prediction of load gaps. Furthermore， traditional power consumption control overlooks the industrial chain coupling characteristics inherent in industrial clusters. In engineering practice， loads are usually curtailed at a uniform proportion， which tends to trigger passive shutdowns of upstream and downstream enterprises and cascading supply disruptions， resulting in massive economic losses. To address these issues， this paper proposes a power consumption control method for industrial clusters considering industrial chain coupling under humid heatwaves.[Methods] First， by integrating multiple meteorological factors including temperature and humidity， the source-load sequence of the power system under high-temperature scenarios is generated to improve the accuracy of load gap forecasting. Second， based on the industrial chain coupling characteristics of industrial clusters， an evaluation model of internal load regulation capability is established， taking into account the production features of industrial consumers. Furthermore， an optimal operation model for regional power grids with a high proportion of industrial loads is constructed， with the objective of minimizing both the planned regulation loss of industrial loads and the coupled loss of the industrial chain.[Results] Simulation verification was conducted on a regional power grid with a high proportion of industrial loads in eastern China. The results demonstrate that the proposed method significantly reduces load regulation losses and cascading supply disruption losses within the industrial chain compared with traditional control methods.[Conclusions] The proposed method can effectively address the unique challenges of power consumption control for industrial clusters under humid heatwaves， remedy the deficiencies of traditional methods that ignore industrial chain coupling， and mitigate the comprehensive loss under supply-demand imbalance. This approach provides scientifically sound and feasible technical support for power systems managing peak summer loads.
[Objective] Aiming at the problem that the frequent occurrence of extreme meteorological events poses a severe challenge to the safe and stable operation of distribution networks， a robust strategy for enhancing the resilience of distribution networks considering the generation of extreme meteorological load scenarios is proposed.[Methods] Firstly， aiming at the problem of scarce historical samples of extreme weather， a conditional denoising diffusion probability model based on the correlation sample expansion strategy is proposed. Combined with meteorological conditions and dual transfer learning， extreme weather load scenarios are generated. Secondly， a pre-fault two-stage robust optimization model for the is established based on the generated scenarios. Among them， the first stage involves decision-making on the location of the material transfer warehouse and the pre-allocation plan for materials； In the second stage， considering the worst-case scenario of the fuzzy distribution of failure scenarios and the decision-maker's risk preference， the load loss cost caused by material shortage is minimized. Finally， in the post-fault stage， a spatio-temporal optimization model considering the collaborative scheduling of maintenance teams and mobile power sources was established， aiming to minimize power outage losses and achieve efficient collaboration between fault repair and load recovery.[Results] The simulation results show that the dual transfer learning method for generating extreme load scenarios based on the conditional denoising diffusion probability model to reduce errors is significantly superior to methods such as generative adversarial networks. The distribution network resilience enhancement scheme based on robust optimization significantly improves the efficiency of resource allocation and achieves a better balance between economy and robustness.[Conclusions] The proposed load scenarios and robust strategies effectively address the dual challenges of scarce historical data and uncertainty in emergency decision-making under extreme weather conditions.
[Objective] To reduce the total energy costs for commercial entities, satisfy renewable energy consumption mandates, improve the utilization rate of new energy, and alleviate reverse overload of distribution transformers, this study develops a configuration strategy for user-sited power sources including wind, solar and energy storage. The strategy accounts for time-of-use pricing curve, load guideline and load baseline demand response (DR). Furthermore, the constraints allowing reverse power transmission are incorporated, and the effectiveness of participating in three types of DR to minimize total energy costs is evaluated and compared. [Methods] A two-layer optimization model was established with the objective of minimizing total energy costs. This model involves alternating iterations between the configuration of user-sited power sources during the planning phase and the determination of the load baseline during the operational phase. The upper-level model considers the maximum values of multiple predicted new energy curves at any given time to establish a user-sited power source configuration model, and solves the capacity of new energy, energy storage, and nominal discharge time. The lower-level model calculates the mathematical expectation of total energy costs based on the outputs of the upper-level model and the probability of multiple predicted new energy curves. The mathematical expectation of transmission curves is used as the load baseline and fed back to the upper-level model. This iterative process continues until the results of two consecutive user-sited power source configuration and the load baseline are identical. By setting a reverse transmission constraint (not exceeding 20% of photovoltaic generation) and adjusting time-of-use pricing, benchmark incentive prices, and load guidelines, the influence on economic indicators was analyzed. A large commercial complex in a coastal province of China served as a case study for calculation and evaluation to verify the strategy's effectiveness from the perspective of reducing total energy costs. [Results] From the perspective of mathematical expectation, the proposed model reduced total energy costs by 2% to 10% compared to control schemes without user-sited power sources. The similarity when participating in baseline DR ranged from 98% to 100%, significantly higher than the 82% to 86% observed with guideline DR. Compared to guideline DR, participating in baseline DR reduced total energy costs by an additional 6% to 10%. Furthermore, the proportions of photovoltaic reverse transmission remained within 0% to 10%. [Conclusions] The strategy proposed in this paper satisfies the renewable energy consumption weights mandated provincial power grids while achieving high similarity between the load baseline and the transmission curve. The inclusion of reverse transmission constraints allows for an increased scale of user-sited power sources, significantly reducing the total energy costs for commercial users.
[Objective] With the increasing penetration of distributed wind and photovoltaic generation in distribution networks， their inherent variability and intermittency have imposed higher requirements on network flexibility. Inadequate flexibility may lead to issues such as wind and solar power curtailment and power flow violations. Enhancing flexibility by exploiting the adjustable capabilities of nodal hosting and grid transfer capacity is regarded as an effective approach. This paper proposes a coordinated planning method for nodal-grid flexibility resources in active distribution networks.[Methods] First， flexibility resource models at both the nodal and grid levels were developed to accurately represent the nodal hosting capacity and grid transmission capability. Second， a flexibility demand model and a corresponding evaluation index system were constructed to quantify the system's flexibility supply capacity. Then， four types of flexibility resources—soft open points （SOP）， hybrid energy storage systems （HESS）， demand response （DR）， and network reconfiguration—were considered， and an optimization strategy for their coordinated allocation was proposed. To account for operational uncertainties， time-series-correlated scenarios were generated. The resulting optimization problem was formulated as a mixed-integer second-order cone programming model using the big-M method and second-order cone relaxation techniques.[Results] Case studies based on an improved IEEE 33-bus distribution system demonstrated that the proposed time-series-correlated scenario generation approach more accurately reflects the operational characteristics of distribution networks compared to traditional uncertainty-handling methods. Although the coordinated allocation strategy increased annual investment to some extent， it reduced the total annual cost by 16.9%， ensured a balance between flexibility supply and demand， and mitigated power flow fluctuations in the grid.[Conclusions] The proposed method fully leverages the complementary strengths of various flexibility resources， significantly enhancing distribution network flexibility and alleviating grid congestion. This work provides valuable insights and technical support for planning distribution networks with high penetration of distributed wind and solar generation.
[Objective] Due to their energy storage capabilities， electric vehicles （EVs） can serve as flexible resources for grid interaction. The large-scale integration of EVs into microgrids creates new opportunities for optimal scheduling.This paper proposes a two-layer stochastic model predictive control （SMPC）-based optimal scheduling strategy for microgrids.[Methods] In the upper-layer， scenario analysis is employed to handle uncertainties in photovoltaic output， load demand， and the number of EVs. A set of representative scenarios is generated， and an optimization model is established to achieve economic dispatching through rolling optimization. In the lower layer， a dynamic power allocation strategy for charging piles is developed based on a broadcast control method. This strategy enables efficient allocation of upper-layer dispatch instructions while considering the charging and discharging regions of EVs.[Results] Case studies indicate that the proposed optimal scheduling method enhances robustness while maintaining economic efficiency， outperforming both deterministic models and robust optimization （RO） approaches. Moreover， the proposed power allocation strategy for charging piles reduces communication burdens compared with traditional centralized power allocation strategies and demonstrates satisfactory tracking performance.[Conclusions] The proposed optimal scheduling strategy improves the economic efficiency and flexibility of microgrid operation while satisfying EV charging demands. The broadcast control algorithm balances communication efficiency and system scalability， making it suitable for large-scale， plug-and-play scenarios.
[Objective] Due to insufficient measurement devices, the observability of distribution networks is relatively poor, which undermines the accuracy of state estimation. To address this issue, this paper proposes a prior-enhanced state estimation method for weakly observable distribution networks based on dynamic partitioning, which enables full-node state estimation utilizing measurements from observable areas. [Methods] First, node observability analysis and real-time dynamic partitioning are performed in accordance with the available measurement data. On this basis, a regional state mapping model is constructed to realize real-time state estimation in unobservable areas. Second, leveraging the prior state information from historical system states, a prediction error covariance matrix is formulated to establish a prior-enhanced extended Kalman filter (PEEKF) model. This method avoids updating the error covariance during posterior estimation, thereby improving estimation efficiency while maintaining accurate estimation states in the observable areas. Finally, the states of observable areas are mapped to the states of unobservable areas through the state mapping model, yielding the state estimates for the unobservable areas of the distribution networks. [Results] Simulation tests are conducted on the IEEE 33-node and 95-node systems. The average absolute percentage error of the algorithm remains consistently below 0.4% in both systems. Compared with the Extended Kalman Filter, Unscented Kalman Filter, and Adaptive Interpolation Kalman Filter, the proposed method achieves significantly higher estimation accuracy. [Conclusions] The proposed method can effectively perform dynamic partitioning and state mapping, and achieve high real-time and high-precision state tracking of weakly observable distribution networks.
[Objective] Constrained by practical operating conditions， load forecasting often faces the dual challenges of low data quality and variable load distribution. To address this， an ultra-short-term load forecasting method considering historical data missing and load temporal heterogeneity is proposed in this paper.[Methods] First， to reconstruct missing time-series data such as load， a Convolutional Neural Network-Bidirectional Long Short-Term Memory （CNN-BiLSTM） neural network is embedded within the Generative Adversarial Imputation Network （GAIN） to capture the spatiotemporal dependencies. During training， to prevent the model from focusing solely on the imputation error of observable values， random noise is introduced to replace partial observable values， enabling explicit measurement of the generation bias corresponding to the aforementioned noise. Second， to reveal load heterogeneity at finer temporal scales， clustering is applied to load input samples for the forecasting model in the training set to identify different load distribution patterns. Finally， a unified forecasting model is fine-tuned according to different pattern-specific samples， constructing personalized sub-models. During online forecasting， the most suitable sub-model is dynamically selected based on the similarity between the current load input sample and the centers of each pattern， eliminating reliance on external information such as calendar and weather data.[Results] Case studies demonstrate that the proposed missing data reconstruction method achieves lower reconstruction errors compared with traditional methods. Based on this， the constructed forecasting model yields higher prediction accuracy. Incorporating the construction and selection strategy for personalized forecasting sub-models further decreases forecasting errors.[Conclusions] Experimental results confirm the practical engineering value of the proposed method in real-world operational scenarios.
[Objective] The high-voltage DC cascade energy storage system （HVDCC-ESS） offers advantages in power-decoupled control and scalable capacity， positioning it as a key direction for large-scale energy-storage technologies. A dedicated reliability-evaluation method for HVDCC-ESS is therefore proposed to quantify system reliability and support planning and operational decision-making.[Methods] First， the ESS is partitioned into three subsystems， AC field， DC field，and battery field， according to topology and operating mode. A multi-layer framework （component-subsystem-system） is established to cope with the difficulties of numerous components， intricate state transitions and cross-coupled functions. Second， considering electrical equipment and internal topology of each subsystem， reliability models that incorporate redundancy and spare-parts policies are built by combining fault-tree analysis， r-out-of-n structure and Markov models. Finally， a common-cause outage separation model is adopted to handle simultaneous subsystem failures， yielding a reliability-evaluation algorithm and multi-dimensional indices （probability， frequency and duration）.[Results] The actual project-based results demonstrate that the proposed model quantifies reliability at component， subsystem and system levels， enabling redundancy and spare-part schemes that satisfy required maintenance intervals and durations. The model adapts to varying operating conditions， maintenance levels and ageing scenarios.[Conclusions] The developed approach quantifies multi-layer， multi-dimensional reliability indices of HVDCC-ESS， providing a reliability basis for product development， optimal system configuration ， and operation and maintenance strategies.
[Objective] To address practical issues such as limited transmission channels for large-scale wind-solar bases in desert and arid regions of China and insufficient local consumption, this paper proposes a collaborative planning method for an integrated electricity-hydrogen energy system (IEHS) that considers solid-state hydrogen transport interactions. The method aims to alleviate the coupling bottleneck between renewable energy curtailment and the disconnection across hydrogen production, storage, transport and utilization. [Methods] First, an IEHS model is established, comprising electrolyzers, hydrogen storage tanks, gas-solid conversion units, and hydrogen vehicle loads. Solid-state hydrogen transport vehicles (SHTVs) are introduced to enable efficient interregional hydrogen transport and energy interaction. A bilevel planning model for hydrogen production, storage, transport, and utilization is then formulated. The upper-level objective minimizes equipment capacity configuration costs and the lower-level objective minimizes system operation optimal dispatch costs. The bilevel model is transformed into a single-level linear model using the Karush-Kuhn-Tucker (KKT) conditions and the Big-M method. [Results] The results demonstrate that adopting SHTVs for cross-regional hydrogen interaction reduces annual electricity purchase costs and renewable energy (wind and solar power) curtailment costs. Compared with independent operation in each region, the annual total system cost decreases by RMB 74.79 million, representing a reduction of 17.7%. In addition, relative to hydrogen tube trailer (HT)-based interaction, the annual transport cost decreases by RMB 15.52 million, or 67.9%. [Conclusions] The proposed method facilitates the accommodation of wind and solar power while reducing CO₂ emissions by 9,450 t, thereby effectively improving the overall economic and environmental performance of the system.
[Objective] The deployment of energy storage in industrial parks not only reduces electricity costs and demand power charges but also generates additional revenue through participation in grid peak shaving and frequency regulation services. However， the stochastic volatility of photovoltaic （PV） generation presents significant challenges for the collaborative application of energy storage systems in demand power management and peak shaving. While electrochemical energy storage demonstrates millisecond response capabilities in mitigating the random fluctuations of PV generation， its high investment costs and limited cycle lifespan restrict its large-scale application on the demand side. In contrast， gravitational energy storage is highly competitive in terms of daily investment costs due to its low cost and long operational lifespan. To address these issues， this study proposes an innovative two-layer iterative robust planning method for a hybrid energy storage system aimed at managing demand power charges and facilitating peak shaving and frequency regulation in industrial parks. [Methods] The upper-layer model， based on information gap decision theory （IGDT）， seeks to minimize the annual operating costs of the system by optimizing the capacity allocation of gravitational and electrochemical energy storage， with the results transmitted to the lower-layer model. The lower-layer model employs model predictive control to achieve adaptive dynamic control of demand charges through rolling optimization. The success of demand power defense serves as the criterion for determining whether feedback is needed for capacity reallocation in the upper layer. [Results] Simulation results indicate that the proposed approach enhances the accuracy of demand management by 49.4%， improves the comprehensive performance index for peak shaving and frequency regulation by 42%， and reduces the annual operating costs of the system by 21%. [Conclusions] The proposed method provides significant theoretical support for the planning of energy storage systems in industrial parks.
[Objective] The widespread integration of renewable energy into multi-park integrated energy systems （MPIES） exacerbates supply-side fluctuations due to the intrinsic intermittency of renewable resources. These variations directly impact market operations and system stability， imposing dual challenges on market participants： managing uncertainties and ensuring equitable allocation of cooperative benefits.This paper proposes a two-stage robust Stackelberg game trading strategy for MPIES that accounts for uncertainties associated with wind and photovoltaic power.[Methods] First， a stochastic scenario generation method is adopted to construct an adjustable robust uncertainty set for wind and photovoltaic outputs based on multi-scenario weighting. Second， by leveraging strong duality theory and Karush-Kuhn-Tucker （KKT） conditions， the Stackelberg game between the operator and MPIES is reformulated as a mixed-integer linear programming （MILP） problem. Finally， a two-stage robust Stackelberg game model is established from a risk-response perspective， and the column-and-constraint generation （C&CG） algorithm is used to iteratively solve the problem to generate the final trading strategy.[Results] Case studies verify that the proposed strategy enhances the risk-response capabilities of all market entities and reduces deviations in expected returns during the game process.[Conclusions] The proposed strategy achieves the synergistic integration of the Stackelberg game and robust optimization within a two-stage framework， effectively balancing risks and benefits under uncertainties， thereby improving overall decision-making quality and economic robustness of the system.
[Objective] To address the issue of high operational costs in existing distribution networks and multi-microgrids caused by insufficient exploitation of synergistic flexibility， this paper proposes a coordinated optimal scheduling strategy for distribution-microgrids considering multi-resource trading of energy， reserve， and carbon emissions.[Methods] First， by fully considering the heterogeneity in market access qualifications among individual microgrids， a novel asymmetric microgrid alliance （AMGA） framework is established to enable differentiated entities participating in spot and reserve market bidding. On this basis， a Stackelberg game strategy between the distribution network operator （DNO） and AMGA is designed， incorporating a multi-level market clearing mechanism. In the upper-level， the DNO fully accounts for the power flow constraints of the distribution network and implements dynamic pricing of energy， reserve， and carbon targeting different market entities. In the lower-level， AMGA explores the energy transfer potential of Electric Vehicles （EV） to achieve coordinated scheduling of multi-microgrids. Subsequently， an optimization algorithm integrating a bisection method is employed to iteratively solve the scheduling model.[Results] The proposed strategy effectively reduces the operational costs of distribution- microgrid system， improves system economic performance by 12.83%， and ensures efficient grid operation.[Conclusions] This method achieves collaborative optimization and fair benefit distribution among multiple entities while balancing economic efficiency and environmental sustainability.
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