Iet Generation Transmission & Distribution最新文献

This paper proposes a comprehensive reliability assessment method for flexible interconnected distribution networks (FIDNs) with soft open point (SOP) incorporation and coordinated multi-device load restoration. The following research work is conducted: first, a reliability function for hybrid modular multi-level converters (MMCs) is developed based on actual topological structures, with sub-module (SM) correlation and redundancy configurations comprehensively considered. Moreover, various SOP ports fault scenarios and their corresponding control strategies for distribution network faults are analysed, and an improved virtual flow method is proposed to satisfy network radiality and connectivity constraints during load restoration. On this basis, a mixed-integer second-order cone programming (MISOCP) model for FIDN load restoration is formulated, aiming to maximise load restoration through multi-device coordinated output. Furthermore, load nodes are classified based on their restorability post-fault, and optimal load restoration is performed under fault scenarios generated by Monte Carlo sampling, with system reliability indices further calculated. Finally, the effectiveness of the proposed method is validated on a modified 37-node system and the modified IEEE 123-node test system.

The fast restoration of the grid infrastructure after a grid failure is essential for a reliable energy supply. In this research, grid restoration by ramp-up of a 400 MW combined cycle power plant, supported by four hydropower generators (100 MW) and three pumps (23 MVA) in a 110-kV island grid in Upper Austria, is investigated. The objective is to test ramp-up feasibility and to develop a simulation model which further is verified by the measurement results from the real-world field tests. Findings include successful ramp-up with different inertia scenarios, frequency dips up to 1.15 Hz, and challenges by connecting inverter-based loads. The study underscores the importance of testing restoration strategies to ensure effective island operation during blackouts and to obtain results for simulation model verification. In addition, the influence of the usage of standard parameters on simulation results, thus insufficient knowledge of the controller parameters of the turbine controller, is analysed.

The paper presents results and experiences from Strømflex, a large-scale demonstration project, which was deployed at Norwegian DSO - Lede for evaluating the potential for activation of flexibility in the distribution network. The objective is to demonstrate how flexibility resources at different types of customers can be realised for the benefit of the DSO. The study focuses on the verification of the actually activated explicit flexibility volumes, practical estimation of baseline levels for the involved customer groups as well as considering consequences of the activations as rebound effect. The study defines the levels of the uncovered realistic flexibility potential, which varies across different customer groups. Finally, the study acknowledges well-functioning and reliability of the installed control technologies and concludes that the explicit activation of flexibility largely worked as intended in the pilot and has therefore a significant up-scaling potential.

With the increasing emphasis on green energy transformation, power systems are evolving into a “double high” structure characterised by a high integration of renewable energy sources and extensive use of power electronics. This transformation leads to more complex system topologies, necessitating improvements in monitoring and control measures. Traditional model-based approaches for identifying power oscillation disturbance sources are increasingly inadequate for the demands of modern power systems. The rapid development of Wide Area Measurement Systems (WAMS) has heightened interest in leveraging system response data for disturbance source localisation. This paper introduces a data-driven numerical method that combines energy functions with logistic regression, enhancing localisation accuracy by utilising power oscillation mechanisms and response data—and specifically improving accuracy by 15–22% over traditional methods. The proposed method identifies potential disturbance sources, ranging from minor random load fluctuations to significant forced power oscillations. A key innovation is the application of logistic regression for the automatic classification and localisation of disturbance sources, reducing the need for manual intervention and addressing the limitations of traditional energy function methods. Validation on WSCC 9-bus, New England 39-bus and 197-bus systems demonstrates 99.5% accuracy for single-source disturbances and 84.5-96.1% for multi-source scenarios, outperforming SVM (98.6%) and LDA (95.4%) while reducing computation time to 0.03s. By quantifying disturbance source localisation in power oscillations, this approach significantly enhances both the accuracy and efficiency of the localisation process.

This paper presents a novel perspective on providing adaptive virtual inertia (AVI), aimed at improving DC voltage stability in Multi-Terminal High Voltage DC (MT-HVDC) grids while simultaneously enhancing frequency response in AC grids. The proposed approach introduces an innovative Virtual Synchronous Generator (VSG) that supplies AVI for the AC systems. Additionally, a new control strategy for the Power Electronics Converters (PECs) that supply the MT-HVDC grid is presented, referred to as dcVSG, to provide AVI for this grid. Utilising both controllers concurrently enables adaptive and simultaneous virtual inertia provision on both DC and AC grids, while effectively leveraging the operational capabilities of the PECs. In this regard, the DC voltage and the AC grid frequency are considered as control parameters. The AVI is dynamically adjusted according to the PEC operating point. Specifically, the calculated maximum AVI is sensitive to the increase and reduction of the control parameter, demonstrating appropriate distinct values in response. This behaviour aims to utilise the PEC's maximum power capacity. The small-signal stability of the proposed system is analysed by focusing on the influence of virtual inertia on overall stability. Also, to assess the stability of the proposed controllers, Lyapunov stability theory, alongside a series of detailed simulation analyses, is conducted utilising the Cigre-DCS3 test grid. The simulation outcomes indicate that the proposed coordinated strategy yields a 20% reduction in DC voltage deviation while also enhancing frequency nadir. Additionally, it achieves over a 60% decrease in the rate of change of voltage (RoCoV) on the DC side and a 68% reduction in the rate of change of frequency (RoCoF), specifically when compared to methods that rely solely on the headroom power of the PEC to deliver maximum virtual inertia.

With advancements in semiconductor technology, switching frequencies of 10–20 kHz, enabled by SiC MOSFETs, are becoming viable for megawatt-scale converters, significantly reducing switching losses and filter size. This highlights SiC MOSFETs' potential in future power conversion. However, careful system design is crucial for stable operation. This paper examines active and passive methods to improve small-signal stability in weak grids across practical switching frequencies achievable by SiC MOSFETs and Si IGBTs. Multi-parallel inverters and various grid scenarios emulate real-world conditions. The findings reveal that while both damping methods enhance stability margins, they exhibit distinct trade-offs. Passive damping, requiring a lower quality factor at lower switching frequencies, results in higher damping losses, while active damping achieves similar stability with minimal losses. Both improve resonance stability but have limited impact on low frequencies. Additionally, results show that combining a phase compensator with active damping improves stability for both low and high-frequency ranges. A summary table presenting the analysis of component costs, power losses and system stability margins for different converter designs was provided, which can assist designers in identifying trade-offs to achieve the optimal design with Si IGBTs and SiC MOSFETs for the targeted application.

The volatility of distributed photovoltaic (PV) and wind turbine (WT) brings great challenge to the real-time dispatching of microgrid. This work aims at solving the problem via an improved approximate dynamic programming (ADP) method. Firstly, a two-stage microgrid dispatching framework is formulated to tackle uncertainty of PV and WT generation with an ADP model which is trained off-line and utilized in real-time dispatching. Secondly, an ambiguity set is proposed to utilize distribution knowledge of renewable generations for the generation of off-line training scenarios. Thirdly, an alternating direction successive projective approximation routine is proposed for the off-line training of ADP model to reduce the impact of initial cost-to-go value function and improve the accuracy of ADP model. Finally, case studies are conducted on the IEEE 37-bus and 123-bus systems to illustrate the effectiveness of the proposed method.

Distribution system state estimation (DSSE) is an essential tool for the effective operation and management of modern distribution systems. A common challenge in DSSE is ensuring accurate estimates despite limited real-time measurements and high pseudo-measurement errors. This paper presents a novel line-wise state estimator (LW-SE) for radial distribution systems, leveraging conic quadratic optimization to transform the non-convex state estimation problem into a convex one. This transformation enhances the accuracy of the state estimation process. Unlike traditional methods, the LW-SE formulation uses line impedances rather than admittances, addressing issues associated with low-impedance branches and leading to more stable power flow representations. Furthermore, the method accommodates diverse types of measurements without requiring paired active and reactive power measurements, their equivalent forms, or phase angle measurements as inputs—while still enabling accurate phase angle estimation. Results of case studies and comparisons with traditional state estimators (T-SE) demonstrate the effectiveness of the LW-SE with accuracy improvement ranging from 60% to 82% in scenarios with low availability of real-time measurements and high errors in pseudo-measurement. In scenarios involving gross measurement errors, the LW-SE consistently delivered lower MAPEs than the weighted least squares (WLS) and weighted least absolute value (WLAV) state estimators, while maintaining computational efficiency. These findings underscore the LW-SE's suitability for modern distribution system applications.

The large-scale integration of renewable distributed generators (DGs) and the increasing frequency of extreme events have heightened the demand for enhanced flexibility and resilience in distribution networks. Energy storage integrated with soft open points (E-SOPs) can improve both flexibility and resilience temporally and spatially. This paper presents a distributionally robust optimisation with a hybrid ambiguity set (HASDRO) method for E-SOPs allocation, aiming to enhance renewable energy consumption and operation efficiency under normal scenarios, while ensuring load supply during extreme events. The proposed hybrid ambiguity set combines a Wasserstein metric-based ambiguity set to capture the probability distributions of DG output and load demand, and a first-order moment-based ambiguity set to represent line outages. A two-stage HASDRO model is then formulated to optimise the planning and operation of E-SOPs, and to minimise total investment and worst-case expected operation costs, including DG curtailment and line loss penalties in normal scenarios as well as load shedding penalties in extreme events. The proposed HASDRO model is reformulated into an equivalent three-level model and solved by the customised column-and-constraint generation algorithm. Finally, the proposed method is validated on a modified IEEE 33-bus system, with the optimal E-SOP configuration comprising an ESS of 1.5 MW / 2.74 MWh and SOPs of 2.1 MVA. The results demonstrate a 43.10% reduction in line losses a 56.15% decrease in DG curtailment in normal scenarios, and a 52.24% reduction in load shedding during extreme events, highlighting the model's effectiveness in enhancing network flexibility and resilience.

The rapid adoption of electric vehicles (EVs) in recent years has led to a surge in power demand, presenting challenges in maintaining grid stability and efficiency. In response, service providers must integrate EVs with renewable energy sources while addressing the intermittent nature of distributed generation (DG) and fluctuating demand. Demand response management (DRM) offers a solution by aligning energy usage with renewable energy availability and optimising grid performance. Modern distribution systems advocate for the prediction of station usage and service availability to estimate charging demand. This research explores the use of a gated recurrent network (GRN) model for scheduling EV charging, with the goal of reducing peak demand. The integration of optimal DRM with DG further enhances the performance. The proposed scheduling algorithm incorporates DG-DRM to predict charging needs and alleviate peak load in the IEEE 33-bus system and the real-time utility network (RTUN)-17 bus test system. Consumer participation in DRM maximises the total social benefit by lowering generation costs and congestion indices. A heuristic GRN model, combined with a probability-based incremental learning algorithm, is introduced to tackle multi-objective optimisation. The algorithm is tested across various scenarios, with EV scheduling carried out in the first phase and DRM with DG parameters optimised in the second. The results show the algorithm's superior performance in achieving the objective function compared to other computational methods.