IET Energy Systems Integration最新文献

Zinc-bromine flow batteries (ZBFBs) hold promise as energy storage systems for facilitating the efficient utilisation of renewable energy due to their low cost, high energy density, safety features, and long cycle life. However, challenges such as uneven zinc deposition leading to zinc dendrite formation on the negative electrode and parasitic hydrogen evolution reaction during charging pose risks of membrane puncturing and short circuits, thereby limiting energy density improvements. Additionally, the sluggish and non-reversible Br₂/Br⁻ redox reaction hampers battery power density enhancements. Given that electrode properties significantly influence the rate and reversibility of these redox reactions, modifications to the negative and positive electrodes are crucial. While inert carbon-based electrodes are favoured for their corrosion resistance and conductivity, their hydrophobic nature and low electrochemical activity restrain the performance of ZBFBs. The authors present a comprehensive review of recent advancements in both negative and positive electrode modifications in ZBFBs. It also puts forward future research directions aimed at overcoming existing challenges, with the ultimate objective of promoting the efficient utilisation of ZBFBs.

Aiming at the electrical safety problems of lithium-ion battery system due to series arc fault, a finite element simulation model of square battery under series arc fault is established based on the magnetohydro-dynamic equations. The arc voltages at different electrode spacings are analysed, and the electric field distribution, magnetic field distribution, arc temperature and flow velocity around the arc are investigated to determine the maximum values and locations of the electric field strength, magnetic density, arc temperature and flow velocity at the time of the arc fault. Then, on the basis of the series arc experimental platform, determine the value of the arc starting voltage and the critical power supply voltage that produces a stable arc, analyse the evolution of the arc burning time with different loop currents, the evolution of the arc of the separation gap, and then summarise the effect of the disaster on the battery. Finally, the errors of simulation and experimental arc voltage and battery surface temperature are analysed to verify the accuracy of the model.

The economic and low-carbon operation strategy of multi-energy microgrids (MEM) has become an important research topic in smart grids. The operation of MEM is affected by uncertain factors from renewable energy and internal load. To handle uncertainties from both source and load sides, the authors propose a novel worst-case-and-probability uncertainty sets and a novel worst-expectation min-max-max-min two-stage four-level robust optimisation (RO) model considering stepped carbon trading for MEM. This four-level RO model is a more generalised model compared with the traditional two-stage RO and distributionally robust optimisation based models. First, the unsolvable original four-level model is decoupled into a master problem and a sub-problem (SP). Second, Karush–Kuhn–Tucker condition is applied to convert SP into a solvable problem. Third, column and constraint generation (C&CG) algorithm is employed to solve the reformulated four-level RO. Finally, the effectiveness of the proposed model and solution method is verified by case studies. The results indicate that the convergence behaviour of this solution method is excellent. The MEM operator can select appropriate robustness factors and comprehensive norms to control its conservatism level. Besides, MEM could reduce carbon emissions by participating in carbon trading and installing carbon capture devices.

Lithium-ion batteries are widely employed in electric vehicles, power grid energy storage, and other fields. Thermal fault diagnostics for battery packs is crucial to preventing thermal runaway from impairing the safe operation and extended cycle service life of batteries. Therefore, a lithium-ion battery thermal fault diagnosis model based on deep learning algorithms is presented, which includes three parts: autoencoder denoising network, coarse mask generator, and mask precise adjustment. Autoencoder denoising network can reduce data noise during thermal imaging acquisition, improve the anti-interference ability of diagnostic models, and ensure the accuracy of thermal runaway diagnosis. A two-stage diagnostic structure is then formulated by the coarse mask generator and mask precise adjustment, which enable quick identification, categorisation, and localisation of thermal fault battery cells. According to the test results, the segmentation boundary is more distinct and is capable of matching the original image's level. The recognition accuracy of the thermal diagnosis model for faulty batteries is close to 100%. After denoising by the autoencoder, the prediction results improved by 22% compared to non-local mean denoising and by about 32% compared to noisy images.

The health state of lithium-ion batteries is influenced by the operating conditions of energy storage stations and battery characteristics. It is challenging to obtain real-time characterisation parameters like maximum discharge capacity and internal resistance. It is necessary to extract sensitivity indicators from electrical parameters such as voltage, current, and temperature. Utilising the Stanford-MIT Research Institute battery dataset, this paper selects batteries with over 1000 cycles and five distinct charging and discharging strategies as samples. During the daily operation and maintenance of the energy storage station, health indicators are extracted from the voltage, current, and temperature curves within the state of charge range of 20%–80%. The ridge regression method is used to establish the health status estimation model. The gated recurrent unit (GRU) model is leveraged for health state prediction. Simulation results demonstrate that the proposed health indicators effectively assess lithium battery health, the health state estimation errors mean absolute error (MAE) and root mean squared error (RMSE) based on the ridge regression model are within 1.5% and 2%, and the health state prediction errors MAE and RMSE based on GRU model are within 1%. This approach exhibits stability, high accuracy, and strong generalisation capabilities.

Increasing global environmental concerns encourage a continuous reduction in carbon emissions from the shipping industry. It has become an irreversible trend to replace traditional fossil fuels with advanced energy storage technology. However, an improper energy management leads to not only energy waste but also undesired costs and emissions. Accordingly, the authors develop a two-layer shipboard energy management framework. In the initial stage, a shipboard navigation planning problem is formulated that considers battery state estimation and is subsequently solved using particle swarm optimisation to obtain an optimal speed trajectory. To track the scheduled speed, a reinforcement learning method based on a deep Q-Network is proposed in the second stage to realise real-time energy management of the diesel generator and energy storage system. This approach ensures that the state of charge remains within a safe range and that the performance is improved, avoiding excessive discharge from the energy storage systems and further enhancing the efficiency. The numerical results demonstrate the necessity and effectiveness of the proposed method.

The rapid development of electric vehicles (EVs) has made the construction and service capability evaluation of EV charging and battery swapping stations (CBSSs) more important. A comprehensive evaluation indicator system for the service capability evaluation of EV-CBSSs is established, meticulously outlining the critical aspects of operational efficiency, economy, convenience, and reliability, each with multiple indicators for thorough assessment. To address the shortcomings of individual evaluation methods in evaluating the service capability of EV-CBSSs, the game theory-based combination weighting (GTCW) method is adopted, which integrates the advantages of the analytic hierarchy process method, entropy weight method, and grey relation analysis method. Specifically, the weights for each indicator are obtained separately using these three evaluation methods, and then combined using the GTCW method to calculate the final weights. In case studies, the service capability for each EV-CBSS is calculated and compared between these three individual methods and the proposed GTCW method. Simulation results validate that the proposed evaluation indicator system and GTCW method can offer a more comprehensive evaluation of the service capability for EV-CBSSs, providing guiding suggestions for future construction plans.

Electricity system decarbonisation poses several challenges to network stability and supply security, given renewables' intermittency and possible reduction of system inertia. This manuscript presents a novel integrated system framework to determine optimal generation investments for addressing decarbonisation challenges and achieving cost-effective electricity systems while ensuring frequency stability and reserve requirements are met at the operational level in a net-zero system. The novel planning framework is a mixed-integer bilinear programming problem accurately modelling clustered variables for the on/off status of generation units and seconds-timescale frequency requirements at an operational and planning level. The benefits of the decision framework and effects of dispatch decisions in a year are illustrated using the Great Britain case study. The results provide optimal trade-offs and cost-effective investment portfolios for including detailed modelling of unit-commitment and frequency stability constraints versus not including them in the planning model. Making investment decisions for a net-zero electricity system without these constraints can lead to very high system costs due to significant demand curtailment. Although the model's computation burden was increased by these constraints, complexity was managed by formulating them tightly and compactly. Non-convex quadratic nadir constraints were efficiently solvable to global optimality by applying McCormick relaxations and branching techniques in an advanced solver.

It is with great pleasure that the authors introduce this special issue, commemorating the 8th Asia Conference on Power and Electrical Engineering held in Tianjin in 2023. This conference served as a nexus for researchers, practitioners, and industry experts from around the globe to convene and exchange cutting-edge insights, innovative ideas, and transformative advancements in the field of power and electrical engineering. The contributions featured in this special issue represent a diverse array of research endeavours, spanning from fundamental theories to practical applications, all aimed at addressing the myriad challenges and opportunities facing the power and electrical engineering domain. From novel methodologies in renewable energy integration to advancements in smart grid technologies, each article encapsulates the spirit of innovation and collaboration that characterised the conference. This special issue includes scientific investigations on topology modelling and virtual stability analysis methods for distribution networks with high penetration of renewable energy resources, monitoring and situation awareness on grid inertia and power-frequency evolution, novel voltage source converter control schemes, and reviews of low-carbon planning and operation of electricity, hydrogen fuel, and transportation networks.

Inverter-based distributed generators (IBDGs), mainly solar photovoltaic, connected in medium-voltage (MV) networks cause challenges, such as voltage limit violations, for distribution network service providers (DNSPs), and require advanced network management strategies to mitigate these challenges. A theoretical analysis of the voltage limit-induced barrier to IBDG connection and their export limits due to the change in network characteristics is imperative for developing new strategies. The authors formulated a relationship between the network equivalent impedance and the IBDG's connection point in the network and further explored the link between the network equivalent impedance and voltage magnitude due to the IBDG connection point. The authors also assessed the voltage limit-induced barrier to IBDG connections in MV networks and proposed solutions to overcome issues with the dynamic export limit of IBDGs. Four representative Australian MV networks are analysed in DIgSILENT PowerFactory under different scenarios, such as variation in IBDG location and static and dynamic export limits. The authors found that an IBDG connected at the end of the network can achieve better performance in supporting the network voltage. An IBDG with a dynamic export limit can export three times more energy than the static export limit, which benefits both the DNSPs and IBDG owners.