Iet Generation Transmission & Distribution最新文献

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.

Distribution network reconfiguration (DNR) has been extensively studied as a strategy to improve network performance indices such as loss reduction, voltage profile enhancement, and reliability. Despite significant progress, challenges remain—particularly concerning energy imbalances during peak load periods and the need to preserve critical load points while managing load shedding. In this study, a novel graph-based bi-level optimisation model is proposed to address these issues. At the first level, a load flow analysis is performed to determine the optimal network configuration by minimising network losses and voltage deviation. During this stage, only topologies that satisfy voltage convergence and maintain the network's radial condition are retained. In the second level, a graph theory-based search algorithm is employed to determine the optimal placement of two types of switches: disconnector switches (for reducing unsupplied energy and enhancing network reliability) and telecommunication load breaker switches (TCLBS, for shedding non-critical loads during peak demand). This two-level approach ensures that the final solution complies with all operational constraints while effectively addressing the energy imbalance issue. Simulations conducted on an IEEE 33-bus test network demonstrate that the proposed method significantly improves network performance. For instance, in one scenario, energy losses, energy not supplied, and voltage deviation were reduced by approximately 29%, 21%, and 52%, respectively, compared to the initial network conditions. Moreover, the load shedding objective improved by 20%, thereby preserving critical load points. The proposed bi-level optimisation model, which leverages advanced graph-based techniques, offers an efficient and robust solution to the distribution network reconfiguration problem. It not only addresses existing challenges but also provides a promising framework for future research aimed at further enhancing network stability and efficiency.

This paper presents a new statistical approach for reliably detecting power swings and faults in transmission lines that include grid-following (GFL) inverters. The method combines the strengths of the cumulative sum control chart (CUSUM) and entropy analysis (EA), using CUSUM's ability to quickly pick up on signal changes and EA's insight into system complexity. To test the proposed method, simulations were carried out in PSCAD/MATLAB on a modified IEEE 14-bus system with a GFL-type inverter-based resource. The results show that the method can effectively detect both faults and power swings, even in the presence of additive white Gaussian noise (signal-to-noise ratio = 10 dB). It also accurately distinguishes between different transient events, such as load switching, generator trips, and capacitor bank switching, with a 100% success rate in identifying non-fault conditions. Furthermore, it consistently detects faults across a range of fault resistances (0 to 10 Ω) with perfect accuracy. Compared to existing techniques, this approach performs better in systems that integrate GFL inverters and offers a more efficient solution with lower computational requirements for power system protection.

The high voltage DC links are required to provide frequency regulation support to alleviate frequency concerns of multi-area asynchronous power systems. However, the inter-area DC frequency support capabilities lack coordination with the intra-area frequency regulation resources of sub-grids under the existing scheduling mode, which may lead to frequency safety issues after power disturbance. To address this issue, this paper proposes a collaborative scheduling optimisation model, where the DC droop factors are set as scheduling variables and periodically allocated in coordination with the frequency regulation capabilities and power scheduling plan of each sub-area. Several types of power disturbances, including the intra-area disturbances as well as the inter-area DC blocking disturbances, are considered. The RoCoF and frequency deviation of all sub-networks under these disturbance scenarios are constrained in the proposed model. Case studies show that the frequency safety can be guaranteed and the asynchronous power system would benefit from the improved operational flexibility.

With the increasing demand for power transmission, compact, high surge impedance loading (HSIL) high-voltage transmission lines have emerged as a viable solution due to their reduced land acquisition costs and higher power delivery capability. The compactness of a transmission line depends on effective insulation coordination, particularly in determining the phase-to-phase clearance, which is governed by the critical flashover voltage under switching and lightning overvoltage conditions. Traditional methods for phase-to-phase clearance rely on empirical formulas derived from experimental data, which are convenient for conventional high-voltage lines. However, unconventional HSIL lines require a faster and more adaptable evaluation method, as they involve optimized conductor positioning to reduce right-of-way requirements while enhancing natural power loadability. This study presents a simplified numerical approach to determine the minimum phase-to-phase gap, utilizing arc propagation viability curves, and offers an efficient alternative to conventional empirical methods. The proposed method was successfully applied to a 500 kV conventional line as well as 500 and 735 kV unconventional line designs, demonstrating its capability in accurately assessing insulation requirements. Results reveal that the method can support reduced gap clearances while still maintaining reliability, thereby validating its usefulness in optimizing compact transmission line configurations.