IET Energy Systems Integration最新文献

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.

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.

The receiving-end system AC fault of the line-commutated-converter-based high voltage direct current (LCC-HVDC) will lead to commutation failure of the inverter side. During the fault and its recovery, AC transient low voltage and transient overvoltage (TOV) will occur in the sending-end system. The TOV has the risk of triggering the disorderly off-grid of the nearby renewable power generations. Besides, in a serious situation, it will threaten the power system to maintain a secure and steady operation. Therefore, the authors analyse the mechanism involved in the AC transient voltage during the AC fault and the recovery period first. It reveals that the key factor causing the TOV of the sending-end system is the setting of the DC current reference value. Then, a DC current reference value limit method based on the AC TOV sampling value is proposed, which is used to accelerate DC current recovery and suppress the TOV of the sending-end system. Finally, the effectiveness of the designed control method has been confirmed through electromagnetic transient simulations using the CIGRE HVDC benchmark model and a ±800 kV HVDC transmission system model situated in Northwest China.

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.

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.

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.

The trend of global energy systems towards carbon neutrality has led to an escalating interdependency between electricity, hydrogen fuel, and transportation networks. However, the means of surmounting the many challenges confronting the optimal coupling and coordination of electric power, hydrogen fuel, and transportation systems are not sufficiently understood to guide modern infrastructure planning operations. The present work addresses this issue by surveying the extant literature, relevant government policies, and future development trends to evaluate the present state of technology available for coordinating these systems and then determine the most pressing issues that remain to be addressed to facilitate future trends. On the one hand, the users of transportation networks represent flexible consumers of electric power and hydrogen fuel for those connected via devices such as electric vehicles and hydrogen fuel cell vehicles through charging stations and hydrogen refuelling stations. On the other hand, power grids can mitigate the negative effect of random charging behaviours on grid security through time-of-use electricity pricing, while excess renewable energy outputs can be applied to generate hydrogen fuel. The findings of this overview offer support for infrastructure planning and operations. Finally, the most urgent issues requiring further research are summarised.

A supercapacitor-based coordinated synthetic inertia (SCSI) scheme for a voltage source converter-based HVDC (VSC-HVDC)-integrated offshore wind farm (OWF) is proposed. The proposed SCSI allows the OWF to provide a designated inertial response to an onshore grid. Under the SCSI scheme, a supercapacitor is added to the DC side of each wind turbine generator via a bidirectional DC/DC converter, varying its voltage along with the offshore frequency to synthesise the desired inertial response. The HVDC grid side VSC employs a DC voltage/frequency droop control to convey the onshore frequency information to DC voltage without communication. Meanwhile, the wind farm side VSC regulates the offshore frequency to couple with the conveyed onshore frequency, considering voltage drop across the DC cables. An offshore frequency switching algorithm is incorporated to avoid undesired SCSI maloperation under offshore faults. The key parameters of the proposed SCSI are optimised through a small signal stability analysis. The effectiveness of the SCSI scheme is evaluated using a modified IEEE 39-bus test system. The results show that the proposed SCSI scheme can provide required inertial support from WTG-installed supercapacitors to the onshore grid through the VSC-HVDC link, significantly improving the onshore frequency stability.

In order to optimise resource allocation within the province, a two-stage scheduling model for provincial-level power grids, encompassing day-ahead and intra-day stages is proposed. Firstly, a Conditional Generative Adversarial Network is employed to generate scenarios for load and new energy output. Based on the generated scenario set, the model takes into account the uncertainty and permissible error intervals of new energy and load, utilising conditional value at risk to measure the system scheduling risk. In the day-ahead stage, an optimisation model is proposed, considering intra-provincial power purchase demands, with the goal of minimising system operating costs, including risk costs. It optimises day-ahead scheduling and contingency plans to ensure economic efficiency and robustness of the system based on extreme scenarios. During the training phase, the dataset is enhanced using Conditional Generative Adversarial Network and updated daily, improving the training effectiveness of the multi-agent proximal policy optimisation intra-day scheduling model. In the intra-day stage, the intra-day scheduling model utilises ultra-short-term forecasting data as input to generate contingency plans for dispatching reserve units. Experiments conducted on the IEEE 39-node system validate the feasibility and effectiveness of the proposed approach.