Abstract:The increasing integration of renewable energy poses the dual challenges of renewable curtailment and supply shortage due to its temporal variability and high uncertainty. Optimal planning of renewable and storage is critical for ensuring power supply reliability and enhancing renewable energy accommodation. This paper develops a co-planning model of renewable and storage considering capacity credit (CC) constraints. A refined multi-time-scale CC assessment method based on improved scenario selection is proposed to quantify power supply capability of renewable and storage, thereby accounting for their capacity value and establishing system adequacy constraints. To improve computational efficiency, an accelerated operational simulation algorithm and an iterative solving approach are introduced. Through case studies on the RTS-GMLC system and a practical power system in Northeast China, the proposed method improves computational efficiency by 14 times compared to year-round operation without affecting accuracy, while effectively ensuring supply reliability under extreme weather conditions and maintaining economic efficiency.

Abstract:In this paper, the application of flux-coupling-type superconducting fault current limiters (FC-SFCLs) in a renewable power system (RPS) with paralleled synchronous and virtual synchronous generators is studied. First, the configuration of the RPS and the working principles of the FC-SFCLs are elaborated, and the potential influences of the FC-SFCLs on the RPS are theoretically investigated from the perspectives of system stability enhancement and fault current limitation. Then, the conceptual design of the FC-SFCLs based on YBCO tapes is carried out and the superconducting coil's quench resistance is determined. Digital simulations confirm the adequacy of the electrical properties and dissipated energy of the FC-SFCLs, while the relatively short current-limiting duration ensures the generated heat in the FC-SFCLs is within acceptable limits. Moreover, using the FC-SFCLs can suppress the fault currents, mitigate the rotor speed deviation, and alleviate the imbalanced power for the RPS. Hence, the stability improvement of the RPS is fulfilled, and the effectiveness of the FC-SFCLs in the RPS is validated.

Abstract:Smart grids rely on precise timing synchronization for efficient operation and real-time decision-making, while conventional GPS-based methods face vulnerabilities. Pulsar-inspired timing offers a promising alternative, but extracting reliable timing signals from pulsar data requires advanced denoising techniques due to the complexity and low signal-to-noise ratio of received signals. This paper proposes a deep learning (DL) algorithm—specifically, an autoencoder-for denoising pulsar signals, aiming to simplify signal processing while improving denoising performance. A comprehensive simulation framework is developed, incorporating both mathematical and physical models to generate realistic synthetic data for training. Performance of the DL model is assessed in comparison with conventional methods such as the wavelet transform, using diverse metrics. Experimental results demonstrate that the DL-based approach outperforms conventional methods, highlighting the effectiveness and adaptability of deep learning techniques for pulsar signal denoising and paving the way for practical implementation of pulsar-inspired timing solutions in smart grid synchronization.

Abstract:The increasing frequency of extreme weather events, particularly droughts, poses significant challenges to the resilience and sustainability of rural microgrids. These systems, which integrate renewable energy and water resources to support agricultural operations such as dairy farming and greenhouse cultivation, are especially vulnerable due to resource scarcity and uncertainty. This paper proposes a novel optimization framework that combines quantum computing-based distributionally robust optimization (DRO) with classical optimization techniques to address these challenges. The hybrid quantum-classical DRO approach is designed to manage the complex interdependencies between energy and water resources, ensuring robust performance under uncertainties. The proposed model optimizes resource allocation and balances operational costs with resilience metrics such as renewable energy utilization and water use efficiency. By considering the uncertainties in renewable energy generation, water availability, and drought-induced scarcity, the framework provides a comprehensive solution for rural microgrid operation. The optimization process is demonstrated through case studies of a rural microgrid supporting dairy farms and greenhouses, where it effectively reduces operational costs, improves system efficiency, and enhances resilience.

Abstract:Stochastic and high-power fluctuations of large-scale photovoltaic generations in distribution networks lead to complex power flow variations and voltage violations, posing significant challenges to voltage control. To address these challenges, this paper puts forward a knowledge-data driven centralized-decentralized coordinated four-step voltage control strategy to effectively dispatch heterogeneous voltage regulation devices. Step 1 proposes an optimal power flow model to determine the day-ahead voltage control results by regulating the taps of the on-load tap changer, the number of capacitor banks, and the charging/discharging power of battery energy storage systems, thereby minimizing daily network loss and preventing slow-time-scale voltage violations. Step 2 generates the voltage-regulation dataset through power flow and volt/var optimization calculations, establishing the data foundation for data-driven learning. Step 3 develops an intelligent inverter-based voltage controller by using fuzzy control theory for photovoltaic generations and battery energy storage systems, with voltage regulation knowledge embedded. Furthermore, a data-driven gradient descent learning method is presented for controller parameter optimization, enhancing global voltage regulation performance. Step 4 forms an online decentralized voltage control strategy with optimized voltage controllers to perform effective reactive power control adaptively according to operation states, thereby addressing frequent voltage violations and optimizing network power loss. Simulation results based on the IEEE33-bus system and a large-scale Caracas 141-bus system show that the proposed strategy can effectively maintain bus voltages within a secure range and reduce the network power loss by approximately 49% and 37%, respectively for the two systems, thereby validating its effectiveness and superiority.

Abstract:The integration of distributed generators, combined with the resistive characteristics and low short-circuit capacity (SCC) of feeders, can lead to voltage and power oscillations in distribution networks. This study investigates the dynamic behaviours of such systems, identifies the causes of these oscillations, and proposes two fluctuating power allocation strategies using advanced flexible interconnection devices (FIDs). An FID consists of multiple coordinated voltage source converters (VSCs) and regulates AC voltages to maintain stable system operation. Two representative scenarios are investigated: 1) a flexible interconnected distribution network with normal feeder connections; and 2) a network incorporating low-SCC feeder connections. The proposed strategies perform: 1) coordinated power adjustment and sharing between FID-VSCs and feeders to mitigate active power fluctuations, with each converter controller adjusting its output based on available capacity; and 2) minimizing feeder voltage oscillations and stabilizing system operation through coordinated reactive power support from the FID, while accounting for feeder SCC limits. An FID-enabled 10 kV flexible distribution network with a wind generator is modelled in PSCAD/EMTDC to validate the strategies, demonstrating continuous and steady operation, as well as appropriate power allocation under different conditions. The results show that, the proposed strategies increase converter utilization, improve active power transfer capability, and reduce voltage and power oscillation risks.

Abstract:As an important facility for peak shaving and frequency modulation, the operation safety of pumped storage unit is of great significance for power systems to accommodate new energy power generation. Focusing on stator grounding fault, which have the highest occurrence probability, this paper proposes a fault location method and a multi-solution countermeasure suitable for the winding configuration and parameter characteristics of pumped storage units. Based on the analysis of the fault equivalent circuit, a fault location approach based on the intersection of phasor trajectory is proposed. Combined with a winding potential calculation method based on slot potential analysis, the causes of multiple solutions in fault location results are analyzed and summarized. By introducing injection information or triple harmonic voltage information as auxiliary information, the multi-solution screening is achieved, enabling the determination of a unique fault location. Finally, the effectiveness of the proposed location method is verified by simulation and experimental analysis.

Abstract:This paper addresses the economic dispatch challenge in integrated energy systems (IES) with high renewable energy source (RES) penetration, where existing models often neglect the quantification of RES uncertainty, leading to inefficiencies and instability. This paper proposes a novel information-energy co-optimization framework that integrates generalized information work to quantify RES uncertainty, which is incorporated into a multi-objective economic dispatch model. The framework jointly optimizes energy costs, information work costs, and exergy loss, supported by an enhanced NSGA-III algorithm with dynamic reference point adjustment and TOPSIS-based solution selection. Simulations on a modified 21-bus IES reveal that the proposed model reduces total costs under high RES uncertainty, while achieving a reduction in exergy loss across different scenarios.

Abstract:In HVDC transmission systems, DC magnetic bias in converter transformers can cause the transformer core to saturate, resulting in a substantial amplification of AC/DC harmonic components. This phenomenon prolongs the commutation duration and reduces the commutation margin, thereby significantly increasing the commutation failure risk. While the area-minimum (AMIN) control employed in engineering can calculate the fundamental-frequency commutation margin area in real time and promptly adjust firing angles, this approach neglects the influence of DC magnetic bias on the AMIN control strategy. This oversight limits the effectiveness of AMIN control in mitigating commutation failures. To address this limitation, this study first investigates the impact of DC magnetic bias on AMIN control performance. Subsequently, a critical threshold for single-harmonic distortion rate is defined, and harmonic content boundaries for commutation failure avoidance are analytically derived. Then, a novel commutation failure detection method based on harmonic content boundaries is proposed, along with a modified AMIN control that incorporates harmonic compensation and phase-shift adjustments. Finally, the proposed methodology is validated through simulations using the Three-Gorges-Shanghai DC project model. Comparative analysis against conventional schemes demonstrates that the modified control exhibits superior performance in suppressing commutation failures and mitigating DC current discontinuity during system recovery period.

Abstract:The large-scale integration of renewable energy has intensified the electricity market price fluctuation and encouraged strategic offering behaviors of generation companies (GenCos), including capacity withholding. This paper systematically investigates the load shedding risk driven by both physical outage and capacity withholding. First, a data-driven multi-state reliability model of generators is proposed to quantify the available capacity of power systems. Then, a novel load shedding risk assessment and responsibility allocation framework is proposed to quantify the load shedding risk and the corresponding responsibility of GenCos. Furthermore, to address the curse of dimensionality, a novel physics-informed neural networks (PINNs)-based load shedding risk assessment method is introduced. This approach significantly reduces the computation burden while enabling dynamic load shedding risk assessment and responsibility allocation that account for strategic offering behaviors. Finally, a modified IEEE 30-bus system is developed to validate the effectiveness of the proposed approach.

Abstract:Despite substantial progress in research on the spatial flexibility of fast charging station (FCS) loads, two gaps remain. First, few studies formulate this spatial flexibility within a spatially coupled demand response (SCDR) model, which is a market-oriented model with strong potential to emerge in future electricity markets and to facilitate FCS participation. Second, existing work rarely considers the coordinated scheduling of electric vehicles with fast charging demand (FEVs) and vehicles other than FEVs (OVs). In contrast, this paper explicitly reveals that counter-migration of OVs can offset the travel time impacts caused by FEV migration, thereby enabling imperceptible adjustments of FCS loads. Against this backdrop, this paper develops an FCS load spatial flexibility model formulated as a SCDR model for future electricity market to fully exploit such flexibility. First, the model (incorporating FEV-OV coordination) is built using a “bottom-up” approach, based on the analytical insights into the generation of FCS load spatial flexibility and a base model. Second, the key features of the proposed model are analyzed. Its concise mathematical formulation and clear physical meaning confirm its potential as a newly permittable SCDR model. Finally, an application framework is presented, including an optimal scheduling model for distribution power systems and a rapid disaggregation scheme. Simulation results show that the proposed model performs well in flexibility capacity and cost characterization, and further verify that, under this model, dispatching FCS load spatial flexibility can effectively alleviate local line overload and voltage violations in distribution networks without disrupting traffic.

Abstract:Distributed photovoltaic (PV) systems are characterized by wide and dispersed deployment. However, the spatial distribution, combined with significant power fluctuations caused by meteorological disturbances, poses substantial challenges for accurate power prediction. To address these challenges, a grid-based KACNN-GATransformer distributed PV power prediction framework is proposed. First, a unified geographic grid is constructed based on numerical weather prediction (NWP) grids, enabling grid-based mapping and construction of PV panel positions. Second, the KACNN model is adopted, which utilizes its learnable spline convolution kernels to adaptively extract spatial features of key meteorological factors such as local irradiance and cloud cover, effectively suppressing noise interference. Finally, the GATransformer model is designed, which incorporates a cross-attention mechanism and a dynamic masking strategy into its encoder-decoder architecture. This allows for dynamic coupling of the long-term temporal dependencies of historical power with the driving effect of NWP data, achieving deep fusion of cross-modal spatiotemporal features. Experimental results on datasets containing various typical weather scenarios demonstrate that the proposed model exhibits good robustness in complex meteorological scenarios such as cloudy and rainy weather, with prediction errors significantly lower than those of traditional prediction methods. This study provides effective technical support for reliable dispatching decisions in high-penetration PV grid integration.