Iet Generation Transmission & Distribution最新文献

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.

The operational robustness of grid-tied inverters is critically challenged under weak grid conditions characterized by wide variations in grid impedance, while background harmonics in grid voltage induced by nonlinear loads severely degrade current quality. To address these dual challenges, this paper proposes a hybrid control strategy integrating an outer-loop repetitive control (RC) with an inner-loop proportional-phase lag compensation (PPLC) architecture. The proposed approach first establishes an equivalent current loop model and employs the PPLC strategy to stabilize system operation across a broad range of grid impedance fluctuations. Subsequently, an outer-loop RC scheme is systematically designed to enhance harmonic suppression. Key innovations include the implementation of a zero-phase-shift low-pass filter as the inner model parameter in the outer loop, combined with a compensator comprising a second-order low-pass filter, a phase-lead network, and an adaptive gain coefficient. Comprehensive simulations and experimental validations conducted on a semi-physical platform demonstrate that the hybrid strategy effectively maintains system stability under broad grid impedance variations (up to 18mH) and severe background harmonic distortion. The controlled grid current exhibits total harmonic distortion below 2%, satisfying IEEE 1547 standards while achieving superior robustness and enhanced harmonic attenuation. This dual-loop architecture provides a systematic solution for grid-tied inverters operating in challenging weak grid environments with nonlinear load disturbances.

This paper introduces a novel two-stage energy management approach to optimise the operation of multi-microgrid systems in uncertain conditions. The first stage employs a multi-objective optimisation model for day-ahead scheduling, focusing on minimising costs and emissions while maximising social welfare. To generate accurate forecasts for renewable generation and demand, a long short-term memory (LSTM) neural network is employed for time-series forecasting, providing reliable inputs to the optimisation framework. The economic welfare maximisation framework accommodates diverse stakeholder interests. In the second stage, the day-ahead schedule is updated every 5 min based on real-time conditions to mitigate imbalance costs. The proposed approach integrates real-time information, enabling efficient adaptation to changing circumstances. Case studies evaluate the approach, showing a significant 15% reduction in emissions compared to a conventional cost minimisation model. Social welfare is enhanced by approximately 12%. These findings highlight the economic viability of integrating high levels of renewable energy by coordinating multiple microgrids and leveraging distributed energy resources. The paper emphasises the environmental and social benefits of a multi-objective microgrid management strategy within the emerging transactive energy systems paradigm. The proposed two-stage energy management approach offers a robust framework for optimising multi-microgrid systems, contributing to a sustainable and efficient energy future.

Stochastic chronological operation simulation (S-COS) is essential for analysing long-term supply-demand balance in power systems with high penetration of renewable energy. However, conventional methods face significant computational challenges due to inter-temporal constraints and numerous binary variables in multi-scenario annual simulations. This paper presents a novel data-driven, surrogate-assisted approach to accelerate year-round, scenario-based operation simulations. The proposed approach employs a temporal decomposition method to decouple the annual stochastic optimization problem into an inter-day scheduling model and multiple intra-day power dispatch models, which are efficiently solved using a data-driven surrogate model. Case studies on modified six-bus and IEEE 118-bus systems demonstrate the approach's adaptability to various scenarios and its scalability across different network scales. Results show that this approach improves computational efficiency by at least 100 times compared to conventional methods, with even faster performance in larger systems. It also maintains high accuracy, achieving an average annual operating cost error of only 1.35% relative to benchmarks.

High-impedance faults (HIFs) location is an increasingly relevant reliability issue in the power distribution industry. The development of practical and accurate one-terminal HIF-locating methods is vital for reducing long-duration outage restoration time and cost. However, the dependency on the estimation of both fault model parameters and fault current signal can jeopardize the accuracy and practicality of existing one-terminal HIF-locating methods. This paper proposes a one-terminal fault-model-free iterative method based on zero-crossings for locating HIFs in overhead distriFbution networks. Two insights into voltage signal relationships are provided to eliminate the need for estimating fault model parameters and the fault current signal in the HIF-locating process. The first one is based on zero-crossings of the calculated voltage drop signal for estimating two parameters of the voltage signal on the fault point. The other insight is based on the zero-crossing of the voltage signal on the fault point, in which the two parameters were previously estimated, for calculating the fault distance from the kth node. Simulation results on a modified IEEE 34-node test feeder validate the high accuracy and robustness of the proposed method, considering the effect of several factors on fault distance estimation. In addition, the method convergence performance is assessed.