Iet Generation Transmission & Distribution最新文献

With the charging stations (CSs) construction and the vehicle-to-grid (V2G) development, electric vehicles (EVs) have become an important load-side controllable resource. Therefore, a V2G power response model based on the prediction, evaluation, and correction of CSs real-time frequency regulation (FR) capability is proposed in this paper. Firstly, a hierarchical control framework for large-scale EVs aggregated to participate in power grid dispatching/FR service is proposed. Secondly, an extreme gradient boosting (XGBoost)-convolutional neural network (CNN)-bidirectional long-term and short-term memory (BiLSTM)-attention prediction model for CSs FR capability is proposed, which combines the advantages of CNN and BiLSTM to strengthen the mining of multi-dimensional features. Meanwhile, a rolling evaluation-correction model for CSs FR capability based on the EV CC–CV charging process is proposed, which improves the evaluation fineness and aggregation fitness. Furthermore, a V2G power response model considering the EV battery loss is established. Finally, the simulation results show that compared with LSTM, XGBoost-CNN-BiLSTM, support vector machine, and other prediction models, the proposed XGBoost-CNN-BiLSTM-attention CSs FR capability prediction model with improvement has a better prediction accuracy. In addition, the V2G power response model can achieve the coordination between the EV users’ charging demands and FR tasks.

The addition of inverter-based generation to the power system complicates protective relaying in several ways: the magnitude of the fault current will be altered based upon the location of the fault relative to the inverter-based resource, and the response to a fault will be different than traditional generation. The relatively low fault current of inverter-based resources presents difficulties in differentiating faults from increased loads. Additionally negative sequence relays may fail in the presence of IBR's due to the negligible negative sequence current produced by the IBR during a fault. This paper presents a novel method to allow the sensitive sub-cycle detection, and determination of the relative location, of asymmetrical ground in systems containing inverter-based resources through phasor analysis of zero sequence current. Least Error Squares Estimation is applied to the fault currents to improve the detection time of these faults, with low computational overheard for hardware implementation. Hardware implementation of the protection scheme on an Intel Cyclone IV-E FPGA is completed and correct operation of the scheme is verified for the raw samples, as well as five and seven sample LES phasor estimations. Hardware testing is compared to the EMT output to confirm the results.

As more renewable energy generators and adjustable loads such as electric vehicles are being connected to the power grids, load modelling of the distribution network becomes more complicated. Therefore, this paper explores a dynamic equivalent modelling method for active distribution network that takes into account electric vehicle charging. First of all the combination of integrated ZIP loads and motors is adopted as an equivalent model for active distribution networks. Subsequently, a four-layer, tri-stage deep reinforcement learning approach is used to solve the relevant key parameters of the proposed equivalent model. The method proposed in this paper fully utilizes the superiority of reinforcement learning in decision making, while the method combines the excellent feature extraction capability of deep learning. The method utilizes measurements obtained at boundary nodes to obtain an active distributed network equivalent model after a series of calculations. At the same time, adjustable loads are identified in detail. On the other hand, this method introduces a prioritized empirical playback mechanism, log-cosh loss function, and adaptive operator to improve the computational efficiency of the method. From the simulation results, the present method is effective.

The operating environment of transformers is getting more complex with the emergence of new energy sources and power electronic devices. This complexity can cause minor internal faults in transformer windings. Under the cumulative effect, minor faults gradually develop into serious faults, resulting in transformer damage. Conventional differential protection systems may have difficulty detecting these glitches and require avoiding the problem of protection false activation caused by inrush currents. This paper proposes a new online monitoring method for transformer windings based on longitudinal branch-circuit conductance to address this issue. First, a unified transformer equivalent circuit is proposed to represent transformers under normal conditions, inrush currents, and internal faults. Then, an online transformer monitoring method based on branch conductance is proposed, which is immune to inrush currents. This method aims to prevent delayed detection of faults during inrush currents, improving sensitivity and response speed, especially for minor turn-to-turn faults hidden in inrush currents. The proposed method also provides higher sensitivity to minor turn-to-turn faults and larger protection margins. Simulation and experimental results validate the effectiveness of this method.

The fractional frequency offshore wind power system (FFOWPS) is a core technology for the long-distance transmission and integration of the large-scale offshore wind power. However, the risk of wide-band oscillation in the FFOWPS has become increasingly prominent due to the inevitable presence of the power electronic devices. The traditional admittance of the frequency converter, which is a crucial component in the FFOWPS, may contain right-half-plane (RHP) poles when operating under various control modes. To address this deficiency, this paper establishes a four-port small-signal admittance model of a back-to-back frequency converter and demonstrates its interconvertibility with the traditional model. On this basis, the wide-band oscillation analysis method for the FFOWPS is proposed, which explicitly describes the influence of the industrial frequency system, the fractional frequency system, and their coupling interactions on system stability. Simulation results in MATLAB/Simulink verify the proposed stability analysis method, highlight stability misjudgements associated with the traditional admittance, and analyse the impact of the coupling terms on system stability.

As the penetration level of renewable energies increases, many inverter-based resources (IBRs) managing algorithms are proposed to control the impact of this penetration on the system. Some of these algorithms are based on a local control without visualizing the status of the whole network and others are based on a centralized control approach that results in dynamic settings without considering the mutual sensitivity among different IBRs. Hence, flexible management of IBRs to be simultaneously controlled either locally or remotely and satisfy technical and fair utilization at IBRs and systems levels becomes essential. The proposed flexible management algorithm suggested a dynamic setting that estimates the mutual sensitivity among IBRs at the system level and assures fairness among different owners. This mutual sensitivity link provides a quantifying measure of IBRs injected power on the status of the whole network. Furthermore, this measure defines dynamic clusters' boundaries that are continuously updated by including the most sensitive IBRs and updating their dynamic settings. This update takes place as system configuration, loads, and available sun-power change in order to satisfy the flexible management algorithm without impacting fairness. The algorithm is applied on a sample DN and imposes compliance with system standard limits within a time-frame of the stand-alone LoRaWAN communication platform.

Accurate and fast disturbance localisation is critical for taking timely controls to prevent power system instability. With the increased complexity of systems, the physical model-based disturbance localisation is challenging to achieve good performance due to model deficiency. Phasor measurement unit (PMU)-based approaches are developed but their performance has been significantly affected by the number of PMUs. To this end, this article proposes a joint data and knowledge-driven disturbance localisation method. A spatiotemporal graph convolutional network is proposed to effectively capture the spatiotemporal dependence with a limited number of PMU measurements. By integrating the physical constraints of disturbance type-topology information and localisation cost characteristics, a composite constraint loss function is proposed that embed physical knowledge into the data-driven method. This leads to the development of the disturbance localisation method and allows quick identification, improved localisation accuracy, and interpretability of the algorithm. Simulation results carried out on the IEEE 39-bus system and IEEE 118-bus system verify the effectiveness and robustness of the proposed method.

Utilizing wind power alongside flexible resources such as power-to-gas technology, gas-fuel generator, demand response (DR) program, grid-enhancing technologies, and carbon capture and storage can help to low carbon operation of integrated electricity-gas systems (IEGSs). Accordingly, this paper proposes a low-carbon economic dispatch model for the IEGS, in which gas-fuel generators, DR, and gas-fuel generator are considered to realize the economic and environmentally friendly operation of these systems. Also, the flexible AC transmission system device as one of the grid-enhancing technologies is innovatively included in IEGSs to guarantee that wind power is deliverable entire the electricity system. Besides, power-to-gas equipped with hydrogen storage is used to absorb the excess wind power to produce CH₄. On the other hand, to capture the inherent flexibility of the gas network, the gas-storing characteristic of pipelines is shown by line pack modelling. To manage uncertainties associated with wind power and DR program, the proposed model is formulated as an IGDT-stochastic problem. For efficient computation purposes, the present work follows the mixed-integer linear programming framework. Different case studies are performed on an integrated test system. Numerical simulation results show that the proposed model leads to reducing the total cost, carbon emissions, and wind curtailment by 29%, 16.4%, and 100%, respectively. It can be seen that the proposed low-carbon ED model is environmentally friendly and has economic benefits.

The diffusion of offshore wind power plants (OWPPs), useful to fulfil renewable diffusion policy targets, implies several technological challenges for integration in power transmission network. The definition of the OWPP connection to the transmission network affects possible exploitation of the assets, involves an evaluation of active power losses and loadability and points out the need for compensation devices, in order to comply with connection rules fixed by the grid operator. This paper proposes a methodology to determine the most suitable electric connection for an OWPP based on high-voltage alternating current (HVAC) cables, for providing indications for transmission system operators. In particular, different HVAC connection schemes for OWPPs are analysed, focusing on new 66 kV standard voltage and high-voltage levels, providing technical insight on transmission cable optimal operation conditions. An economic analysis is carried out by proper estimation of construction costs and production forecast, accounting for active power losses and reliability impact. The most suitable connection solution depending on OWPP size and distance in Italian framework is evaluated by means of synthetic techno-economic indicators, further analysing possible sensitivities of energy price and capacity factor.

Nowadays, power system protection is increasingly important because of the growing number of customers and the pressing need for timely fault resolution and relay operations. This paper addresses the non-linear nature of the objective function in the optimal coordination of directional overcurrent relays (DOCRs) by employing a quadratic Taylor series expansion around an operating point, converting the problem into a quadratically constrained quadratic programming problem, ensuring a global optimal solution with increased computational efficiency. Additionally, the quadratic constraints are converted into second-order cone constraints for compatibility with the CPLEX solver. Using the least square method, the operating point values are determined and further fine-tuned using iterations with the DOCR operation times. The IEEE 3-bus, IEEE 8-bus, and IEEE 14-bus test systems are used to test the method, which shows higher improvement rates in reducing DOCR operation times and enhancing cooperation than conventional and metaheuristic methods. The simulation results verify the numerical superiority of the method in optimizing the protection system's efficiency while obtaining rapid and accurate solutions. The proposed method was tested on IEEE 3-bus, 8-bus, and 14-bus systems, optimizing relay operating times to 0.87, 2.96, and 7.05 s, respectively, demonstrating the method's efficiency over conventional approaches.