IET Energy Systems Integration最新文献

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 frequent occurrence of various extreme weather has a great influence on the normal and stable operation of the distribution network. In order to minimise the large-scale power loss of the distribution network caused by extreme weather, considering that most of the practical disasters are concurrent with various weather types, and each disaster is independent of each other. A unified failure rate calculation model is proposed, which includes line break, short circuit, tower fall, insulator flashover etc., to realise disaster scenario prediction. Secondly, a resilience evaluation index of island rescue based on mobile energy storage system (IR-MESS) is proposed. Thirdly, considering the flexibility of MESS, the pre-disaster scheduling of MESS is carried out according to the predicted disaster scenario. After the disaster occurs, a spatio-temporal optimisation scheduling model based on the rescue state and charge state of MESS is proposed, and a dynamic IR-MESS is formed to provide power supply for important loads in out-of-load areas of the distribution network. Finally, taking the actual ice disaster in northeast China as an example, the results show that the rescue strategy based on IR-MESS proposed in this paper can effectively elevate the resilience of the distribution network.

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.

Integrated electricity-heat system (IEHS) has been paid more and more attention in recent years for its advantage in improving energy efficiency, reducing carbon emissions and increasing renewable energy penetration. To ensure the safety, reliability and economic operation of IEHS, several centralised state estimation (SE) methods for IEHS have been proposed. However, power systems and heat systems often belong to different management entities, and there are industrial barriers such as information privacy, operational differences, and target differences between them, which leads to less applicability of centralised SE methods. In addition, the robustness of existing distributed SE methods for IEHS is not satisfactory. To this end, a distributed robust state estimation (DRSE) model for IEHS based on the alternating direction method of multipliers (ADMM) is proposed. Firstly, by introducing auxiliary state variables and measurements, a robust linear SE model based on weighted least absolute values (WLAV) is proposed. Then, second-order cone constraints composed of auxiliary state variables are added to the SE model, leading a SOCP-based robust SE model. Finally, the ADMM algorithm is used to solve the proposed SOCP-based robust SE model. Simulations demonstrate that the proposed method has higher estimation accuracy in both general and strongly correlated adverse data tests and also can ensure data privacy, good robustness and high estimation accuracy. This indicates that the method proposed has good robustness and solves the problem of weak robustness of existing distributed static state estimation methods.

The emergence of novel factors, such as the energy Internet and electricity supply-side reform within the context of the dual-carbon target (carbon peaking and carbon neutrality), has heightened the uncertainty surrounding electricity consumption (EC). This increased uncertainty poses challenges for accurate long-term EC forecasting. Due to the complexities of feature extraction and the absence of labelled data, conventional supervised learning-based forecasting methods, such as support vector machines (SVM) and long short-term memory networks (LSTM), struggle to predict EC with precision in situations of heightened uncertainty resulting from the interplay of multiple factors. To address this issue, a novel method based on a conditional generative adversarial network (CGAN) is proposed. Initially, the dominant factors influencing future electricity consumption trends through grey correlation degree analysis and the K-L information method are identified. Subsequently, an EC forecast model is introduced based on CGAN, adept at capturing essential factors and the non-linear relationship between EC and exogenous factors. This approach effectively models the uncertainty of EC, accurately approximating the true distribution with only a small dataset. Finally, the proposed method by forecasting China's EC from 2015 to 2020 is validated. The results demonstrate that the authors’ method achieves lower root mean square error and mean absolute percentage error values, specifically 0.177% and 2.39%, respectively, outperforming established advanced methods such as SVM and LSTM.

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.

As the integration of microgrids (MG) and energy storage continues to grow, the need for efficient distributed cooperation between MGs and common energy storage (CES) becomes paramount. A robust optimisation model for the distributed cooperation of MG-CES is presented, taking into account distributed generation under uncertainty. The proposed model follows a two-stage, four-layer ‘min-min-max-min’ structure. In the first stage, the initial layer ‘min’ addresses the distributed cooperation problem between MG and CES, while the second stage employs ‘min-max-min’ to optimise the scheduling of MG. To enhance the solution process and expedite convergence, the authors introduce a column-constrained generation algorithm with alternating iterations of U and D variables (CCG-UD) specifically designed for the three-layer structure in the second stage. This algorithm effectively decouples subproblems, contributing to accelerated solutions. To tackle the convergence challenges posed by the non-convex MG-CES model, the authors integrate the Bregman alternating direction method with multipliers (BADMM) with CCG-UD in the final solution step. Real case tests are conducted using three zone-level MGs to validate the efficacy of the proposed model and methodology. The results demonstrate the practical utility and efficiency of the developed approach in addressing distributed cooperation challenges in microgrid systems with energy storage.

Network operators with significant solar photovoltaic (PV) penetration are having difficulty maintaining grid frequency and voltage within acceptable bounds due to the progressive displacement of synchronous machines. Utility-scale solar PV plants have a huge potential for participation in frequency and voltage regulation since they are linked to the grid through power electronic interfaces with flexible, decoupled control of active and reactive power. A comprehensive control strategy for a utility-scale solar PV plant is proposed to simultaneously participate in frequency and voltage control without the aid of any energy storage. The frequency response is accomplished by maintaining some active power reserves that enable the PV plant to participate in both over- and under-frequency events. The active power of the PV plant is modulated by operating the PV as a virtual synchronous generator (VSG). Unlike the classic notion of VSG, an intelligent fuzzy-based technique is employed to adapt the gains of the VSG controller for improved control performance. Additionally, an adaptive droop-based voltage control mechanism is proposed to control the reactive power reference for the PV plant. The gain of the droop controller is adapted to the varying maximum reactive power capacity of the PV plant. This ensures that the PV system's unused reactive power capability is fully utilised. Through simulation studies, the efficiency of the proposed frequency and voltage control mechanisms is validated under a range of realistic circumstances. The findings show that the suggested control approach can efficiently leverage the PV plants' capacity to handle both frequency and voltage events.

Blockchain-based local energy markets have been proposed in recent years to provide a market platform for local prosumers and consumers to exchange their energy in a secured, transparent and tamper-proof manner. However, there are still some challenges regarding the scalability of blockchain to handle high computational models/algorithms/contracts as this may result in the extension of the block size of the blockchain network and very high gas costs. Also, there is still the problem of transparency as regards General Data Protection Regulation because the full visibility of data in the blockchain may collide with privacy in some settings. A framework is presented that combines the on-chain features of blockchain with trusted execution environments to develop a transparent, tamper-resistant, low operation cost, scalable and resilient hybrid model architecture for local electricity trading. The model architecture was simulated in German community case scenarios for a varying number of prosumers and consumers to show its applicability. The simulation results show that the model was able to solve the scalability problem of blockchain for the local energy market application as the market model is run in a trusted environment where the integrity of the model can be verified by the participants.

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.