IET Energy Systems Integration最新文献

This research paper introduces a comprehensive formulation for robust dynamic network reconfiguration (NR) of unbalanced active electric distribution networks (DNs). Network reconfiguration is a potent strategy to minimise active power loss in DN as it involves altering network topology through sectionalising (normally closed) and tie-line switches (normally open). However, NR is usually a mixed integer NP-hard non-linear optimisation problem due to the discrete nature of the switches. Hence, including variable injection uncertainties (from generation or load) for an unbalanced active DN with all its attributes further poses a significant challenge in solving NR. The proposed formulation addresses these challenges in a robust optimisation (RO) framework to get a robust topology and power and voltage set points for dispatchable Distributed Generators (DGs). Also, Chance-Constrained robust formulations are proposed to regulate the conservatism of RO. Numerical analyses demonstrate the impact of conservative robust NR on DG set points compared to the non-robust NR method. Tests on a modified unbalanced IEEE 34-bus system and comparison with previous formulations verify the efficacy of the proposed approach, showcasing its effectiveness.

To ensure long and reliable operation of lithium-ion battery storage workstations, accurate, fast, and stable lifetime prediction is crucial. However, due to the complex and interrelated ageing mechanisms of Li-ion batteries, using physical model-based methods for accurate description is challenging. Therefore, building data-driven models based on direct measurement data (voltage, current, capacity, etc.) during battery operation may be a more effective approach. This paper employs a time series analysis of discharge capacity/voltage curves to perform feature predication. The goal is to predict the state of health using a short-term model and the remaining useful life of batteries using a long-term iterative model. The validity of this method is verified using the open-source MIT battery dataset. Comparisons with models reported in the literature demonstrate that this method is generalisable and ensures accuracy across a wider range of predictions.

The consideration of mileage settlement in the frequency regulation market has encouraged fast-acting units, such as converter-interfaced generators (CIG) and electric vehicle stations, to actively participate in load-generation balancing through automatic generation control (AGC). Conventional frequency regulation faces challenges in coping with the growing variability of CIGs and also lacks effective incentives for rapid-responding units. In this context, a bi-level AGC dispatch approach based on a stacked long short-term memory (LSTM)-deep neural network (DNN)-based decoder framework is proposed for a power system comprising diverse CIGs forming a virtual power plant and electric vehicle aggregators. The proposed decoder network is comprised of stacked LSTM and DNN, wherein the cascaded LSTM layers are introduced to accurately capture temporal information from time series input. The inclusion of a dropout mechanism further enhances the model’s generalisability in unforeseen environments. The proposed dispatch framework uses mileage-based compensation criteria to optimally allocate instructions among various participating units with differing regulation characteristics. The performance of the proposed method is analysed by considering packet loss, delay, unexpected generation failure, and denial of service attacks. The evaluation of the proposed approach reveals its superior performance compared to proportionality, particle swarm optimisation, decision tree, and DNN methods.

In this paper, the authors present a methodology for building realistic synthetic test cases to model combined electric and natural gas infrastructure networks. Because these networks are synthetic, that is, fictitious, they are able to be freely shared; hence, they serve to support research and development in the area of coupled-infrastructure analysis with large-scale realistic test cases that do not contain non-public information. We anchor our process for building these synthetic networks in a structural characterisation of actual electric and natural gas networks. Supply and demand nodes are geographically placed, based on a combination of publicly available information from several sources and a clustering-based method to match the right fraction of each node type, taking into account the intersection points between the electric and gas grids. Then the networks are connected with pipelines and transmission lines using a systematic graph construction method, validated against network properties including degree distribution, clustering, graph diameter, and degree of planarity, along with operational validation to ensure the simulated solution is realistic. The methodology is demonstrated by creating and validating a test case with 6717 electric buses and 2451 gas nodes.

In light of the increasing hydrogen permeability in distribution networks as a means to cope with extreme events and improve network resilience, this paper introduces a novel strategy for enhancing power distribution network resilience. It outlines a comprehensive approach that focuses on dispatching hydrogen storage (HS) and hydrogen vehicle (HV) within hydrogen penetrated distribution systems (HPDS), segmenting the strategy into pre-disaster and post-disaster stages. Firstly, in the pre-disaster stage, models for HS and HVs are established to gather operational data and facilitate rapid post-disaster response, alongside a coupled electric grid and road network model for optimising HV routing and dispatch. Subsequently, the post-disaster stage focuses on a scheduling model that aims to minimise load power losses and economic costs, balancing immediate power support with cost-effectiveness through detailed analysis of HS and HV dispatch strategies. Finally, this paper demonstrates the effectiveness of this strategy via a case study, highlighting significant improvements in network resilience and recovery and underscoring the potential of hydrogen technologies in enhancing infrastructure resilience.

In order to guarantee the durability and security of electric vehicles (EV), the ageing estimation of lithium-ion batteries (LIBs) is of great practical significance. Lithium inventory is an important indicator for assessing the LIB ageing process. Incremental capacity (IC), particle swarm optimisation (PSO) and support vector machine (SVM) are proposed to estimate the LIBs lithium inventory. Firstly, the IC curve that reflect the electrochemical reaction is analysed, and the middle peak of IC curve that characterises the material phase transition point is selected to represent the LIB lithium inventory. IC curve is smoothed by the Savitzky–Golay method to eliminate noise. Three features of the charging voltage curve are selected as the LIB health feature, and the correlation between three features and the lithium inventory is analysed by using the grey relation analysis method. Then, the mapping relationship between the lithium inventory and three health features is established based on SVM. PSO is used to optimise SVM kernel and penalty parameters to improve the precision of LIBs lithium inventory estimation. Finally, the proposed method is verified by three ageing experiments of LIBs. The results show that the proposed method can precisely estimate the lithium inventory of different LIBs.

The maritime industry is a significant emitter of greenhouse gases in marine ecosystems, prompting a global shift towards renewable-powered electric vessels, where energy storage is pivotal. The authors examine the potential ramifications of coordinating the charging of Electric Ferries (EFs) on local distribution networks, with Gladstone Marina in Queensland, Australia, serving as a case study. Employing OpenDSS software for power flow analysis, the authors utilise actual load data and simulate a network with four Battery Energy Storage Systems (BESSs) representing proposed charging stations. The authors discuss the impact on bus voltage, load current, and power flow by integrating a storage controller to optimise BESS charging and discharging dynamics. The Dynamic Link Library (DLL) of MATLAB Simulink-based BESS's dynamic model is linked with OpenDSS environment to replicate the actual electric ferry storage. Additionally, a user-written DLL in Python regulates BESS charging and discharging by the storage controller according to load demand and BESS State of Charge for ensuring efficient operation within the network. The power flow results without inclusion of BESSs to the network, referred to as the base case, are used for relative comparison with the results in the coordinated mode. The power flow analysis suggests that bus voltages rise by approximately 1%–1.5%, while load current consumption decreases by around 2%–2.5% compared to the base case with variable load. Selected lines and transformers maintain consistent power flows. Notably, a reduction in total power consumption and losses is observed, particularly under an 80% load demand increase. These findings indicate that the coordinated mode with a storage controller effectively manages BESS charging and discharging according to demand. Moreover, the storage controller ensures system parameters remain within permissible limits. The support of real and reactive power by BESSs during peak hours validates their role as peak shavers for the test network, suggesting that EFs can operate in either Grid to Ferry mode during charging and Ferry to Grid mode during discharging.

Due to the availability of zinc resources, and reduced security risks, aqueous zinc-ion batteries (AZIBs) are potential contenders for next-generation energy storage systems. With the multi-scene application of AZIBs, the temperature adaptation of electrolytes poses a great challenge. However, the aqueous electrolyte is prone to freezing in sub-zero environments, which leads to undesirable problems such as undesirable ion transfer and poor electrode/electrolyte interface, resulting in a sharp deterioration of the electrochemical properties of AZIBs in cold conditions and limited practical use of AZIBs. Antifreeze electrolyte modification strategies have gained popularity as effective ways to optimise the low-temperature behaviour of AZIB. The results of recent studies of electrolyte modification strategies are systematically summarised for low-temperature AZIBs, focusing on the modification methods, principles, and effects achieved. Firstly, the authors describe the mechanism of failure of AZIBs at low temperatures. Subsequently, the modification strategies of antifreeze electrolytes are summarised, including the utilisation of high salt content, the design of organic electrolytes, the adoption of antifreeze electrolyte additives, and the building of hydrogel electrolytes. Finally, the issues faced by electrolytes at low temperatures are further indicated and suggestions are provided for their future development.

Under the background of achieving the dual-carbon goal, the park is a natural experimental field for practicing the dual-carbon goal. A low-carbon optimal scheduling strategy for a multi-agent park-integrated energy system (P-IES) based on the Stackelberg–Nash game is proposed. Firstly, a low-carbon P-IES scheduling model is established, considering the two-stage operation of power-to-gas (P2G) technology and reward–punishment stepwise carbon trading mechanism. Then, a two-layer game optimisation model is proposed with a park-integrated energy system operator (P-IESO) as the leader and multiple low-carbon P-IESs as followers. Among them, the parks are connected with each other through the power trading channels. Then, the Nash equilibrium solution is obtained by the iterative search method, and the uniqueness of the equilibrium solution of the game is proven, so as to determine the optimal pricing strategy of the operator. Finally, a typical industrial park is taken as an example for simulation verification. The example analysis verifies that the model can effectively reduce the system's carbon emissions and improve the park's economic benefits and low-carbon levels, thereby achieving the coordinated development of low-carbon and economic performance in the park.

With an increasing number of lithium-ion battery (LIB) energy storage station being built globally, safety accidents occur frequently. Diagnosing faults accurately and quickly can effectively avoid safe accidents. However, few studies have provided a detailed summary of lithium-ion battery energy storage station fault diagnosis methods. In this paper, an overview of topologies, protection equipment, data acquisition and data transmission systems is firstly presented, which is related to the safety of the LIB energy storage power station. Then, existing fault diagnosis technologies are reviewed in detail. Finally, the future developing trends of fault diagnosis technology are discussed.