IET Energy Systems Integration最新文献

The worldwide electricity network is undergoing a crucial transformation, shifting from traditional synchronous generators to inverter-based renewable energy sources (IRESs). This shift is expected to reduce the grid's available fault level (AFL), potentially impacting grid functionality, particularly during the integration of IRESs into weak grids. This paper examines the challenges associated with weak grids, focusing on the steady-state and dynamic stability of IRES when integrated into these systems. In the steady-state analysis, the effects of AFL, injected power volume, and grid characteristics on the steady-state voltage at the point of interconnection were explored. For dynamic stability, eigenvalue and H₂ norm analyses are used for evaluation. Sensitivity analysis is conducted to assess the impact of these factors on the stability of IRESs connected to weak grids. A detailed case study using the IEEE 39 bus test power system is included to demonstrate our findings, where the steady-state and dynamic stability of IRES connected to the test system are assessed using the proposed methods. The accuracy of these analyses is confirmed by extensive simulation studies on the OPAL-RT real-time digital simulator platform.

Super low frequency electric field measurements are crucial in analysing electromagnetic compatibility, assessing equipment status, and other related fields. Rydberg atom-based super low frequency electric field measurements are performed by observing the Stark shift in the spectrum of the Rydberg state. In a specific range of field strength (E < E_avoid, where E_avoid is the threshold to avoid crossing electric fields), the Rydberg atomic spectrum experiences a quadratic frequency shift in relation to the field strength, with the coefficient being determined by the atomic polarisability α. The authors establish a dynamic equation for the interaction between the external electric field and the atomic system, and present the Stark structure diagram of the Caesium Rydberg atom. The mathematical formulae for α and E_avoid in different Rydberg states are also obtained: α = A × (n*)⁶ + B × (n*)⁷ and E_avoid = C/(n*)⁵ + D/(n*)⁷, where A(B) = 2.2503 × 10⁻⁹(7.49,948 × 10⁻¹¹) and C(D) = 1.68,868 × 10⁸(2.45,991 × 10⁹). The error of α and E_avoid compared with the experimental values does not exceed 8% and is even lower in the low Rydberg states. Accurately calculating the values of α and E_avoid is crucial in incorporating the Rydberg atom quantum coherence effect into super low frequency electric field measurements in new power systems.

During the production process of lithium-ion batteries, there exists a scenario of excessive water inside the battery due to poor water control in the factory environment. In addition, the battery housing may be damaged by corrosion, external vibration etc., which would cause water to enter the battery. To the best of the authors’ knowledge, there is little literature to reveal the influencing mechanism related to the above issue. The effects of excessive water on battery performance and safety were discussed. The results show that when the battery absorbs excessive water, the capacity decreases and the self-discharging rate increases rapidly. The self-heating temperature of the battery shows an increasing trend. The thermal runaway temperature decreases significantly with the time from self-heating to thermal runaway dramatically shortened. The thermal stability of the battery deteriorates throughout the reaction process. This is mainly due to the mechanisms by which the water absorbed in the battery reacts with the electrolyte and the electrode material, resulting in the decrease of the electrolyte conductivity and the corrosion of the electrode material, as well as the thickening of the Solid Electrolyte Interface film and the accumulation of impurities. The findings are of positive significance in demonstrating the quantitative relationship between excessive water and the performance and safety of batteries. Also, it can add to the understanding of the complex scenarios of battery spontaneous failure, which is vital for solving battery self-thermal runaways.

To improve the stability of the hybrid modular multilevel converter (MMC), a simplified dominant mode-based control parameter optimisation method of the hybrid MMC system is proposed. Firstly, in the medium-voltage DC distribution network, the small-signal model of the hybrid MMC is established. Secondly, the influence of a weak AC system on stability is analysed through eigenvalue analysis. Finally, a simplified objective function is designed for eigenvalues of the dominant mode by considering only real parts, and improved small-signal stability can be achieved by control parameters optimisation. The proposed method optimises all control parameters at the same time, which further reduces the number of algorithm iterations. Simulation results show that by the proposed control parameter optimisation method, the hybrid MMC has better transient performance and reduced disturbance under SCR variation, indicating a significantly improved system stability, and the dynamic response time can be reduced.

Increasing penetration of variable renewable generations will diminish system inertia thereby degrading the conventional frequency regulation capability. As a result, maintaining frequency stability will be more and more challenging with traditional approaches. Even though renewable sources integration would jeopardise the grid stability, it also presents several opportunities as well. For example, converter-interfaced generators can bid in real-time electricity markets (RTEM) and provide short-time dispatch to minimise load-generation mismatch. In this paper, an integrated approach that accommodates discrete automatic generation control (AGC) system with a regulation mileage framework and RTEM model to balance generation and consumption is proposed. The RTEM model is assumed to have a five-minute dispatch trading interval which is to some extent comparable to the discrete AGC system. Furthermore, a fractional order PID (FOPID) controller is equipped in the AGC system whose parameters are tuned using a novel metaheuristic-based optimisation called Lichtenberg Algorithm (LA). The proposed framework is tested in a three-area system under several operating conditions to reveal the improvement in the dynamic performance of the system. The objective function is also incorporated with mileage payment that allows a fair compensation rule for all the units.

A distribution transformer's thermal operating conditions can impose a limitation on the Hosting Capacity (HC) of an electrical distribution feeder for PV interconnections in the feeder's low-voltage network. This is undesirable as it curtails PV interconnection of both residential and commercial customers in the secondary networks at a time when there are record numbers of interconnection requests by utilities' customers. The authors analyse the limitations on HC due to transformer loading and degradation considerations. Then, the paper proposes a battery energy storage system (BESS) dispatch strategy that will mitigate the limitation on distribution feeder HC by distribution transformers. Three scenarios of HC were simulated for a test network—HC evaluation without restrictions by the distribution transformer (scenario 1), HC evaluation with restrictions by the distribution transformer (scenario 2), and HC evaluation without restriction by the distribution transformer, and with the implementation of the proposed BESS mitigation strategy (scenario 3). Simulation results show that transformer lifetime is depleted to about 6% of expected lifetime for unrestricted HC in scenario 1. Curtailing the HC by 32% in scenario 2 improves the lifetime to 149% of expected lifetime. Implementing the proposed BESS in scenario 3 improves the transformer lifetime to 127% and increases the HC by 62% above the curtailed value in scenario 2, and by 10% above the original HC in scenario 1. The BESS strategy implementation produced cost savings of 49% and 27% of the transformer cost in scenarios 2 and 3, respectively, due to deferred transformer replacement. Conversely, there is a 1600% replacement cost incurred in scenario 1, which underscores the need for a mitigation strategy. The proposed BESS strategy does not only improve the HC of a distribution feeder but also increases a distribution transformer's lifetime leading to replacement cost savings.

Having correct distribution network topology information is essential for system state estimation, line loss analysis, electricity theft detection and fault location. At present, with continuous deployment of smart sensors, a large amount of monitoring data is collected, which enables refined management for distribution network. A data-driven low voltage (LV) distribution network topology identification method is proposed, which realises transformer-customer pairing and customer phase identification for distribution network with relatively balanced power supplies. Firstly, an integrated similarity coefficient of voltage curve is proposed, which can reflect the neighbourhood relationship within stations while increase the distinction between stations; the K-Nearest Neighbour (KNN) algorithm is used to propagate the service transformer labels to complete transformer-customer association. Then, the influence of power fluctuation on voltage curve is analysed and a dynamic sliding window model is adopted to search for voltage segments with significantly difference among three phase feeders to formulate a voltage time series to identify customer phase. Finally, the results are corrected and verified based on the principle of network power balance. The proposed algorithm is tested in two different real substations in China and Europe and shows high accuracy and robustness especially in distribution network with relatively balanced power supplies.

Wind energy is crucial in the global shift towards a sustainable energy system. Thus, this research innovatively addresses the challenges in wind energy system fault classification and detection, emphasising the integration of robust machine learning methodologies. Our study focuses on enhancing fault management through supervisory control and data acquisition (SCADA) systems, addressing imbalanced data representation issues and error vulnerabilities. A key innovation lies in applying particle swarm optimisation-tuned extreme gradient boosting (XGBoost) on imbalanced SCADA datasets, combining resampled SCADA data with deep learning features produced by deep convolutional neural networks. The novel use of PSO-XGBoost showcases effectiveness in optimising parameters and ensuring model robustness. Furthermore, our research contributes to supervised and unsupervised anomaly detection models using Seasonal-Trend decomposition using locally estimated scatterplot smoothing and PSO-XGBoost, presenting substantial advancements in fault classification and prediction metrics. Overall, the study offers a unique, integrated framework for fault management, demonstrating improved reliability in predictive maintenance architectures. Lastly, it highlights the transformative potential of advanced machine learning in enhancing sustainability within efficient and clean energy production.

Grid-connected voltage source converters (VSCs) have been broadly applied in modern power system. However, instability issues may be triggered by the integration of grid-connected VSCs, jeopardising the operation of the power grid. Conventional stability analysis methods can be utilised to derive system stability margins under nominal conditions. Whereas grid-connected VSCs inevitably operate under multiparameter uncertainty, which may result in overly optimistic or even incorrect estimations of stability margins, thereby posing potential risks to system operation. To address this issue, an interval small-signal stability analysis approach is proposed to investigate the system stability under multiparameter uncertainty. First, the interval state-space model of the grid-connected VSC system is constructed based on interval symbolic linearisation. Furthermore, the interval eigenvalue decomposition is introduced to calculate the interval eigenvalue distribution of the interval state-space model. Eventually, the upper bounds of the real part of the dominant interval eigenvalues are utilised for deriving interval stable parameter regions. Results of Monte Carlo analysis and time-domain simulations of the grid-connected VSC system are utilised to verify the effectiveness of the proposed interval stability analysis method.