IET Energy Systems Integration最新文献

The power system is undergoing a significant shift from fossil fuel-based electricity generation to inverter-based renewable energy resources (IBRs), accelerating the transition towards cleaner energy. This transition, however, introduces new challenges for system stability and control. One of the most critical issues is the decline in frequency stability due to reduced system inertia and damping, particularly in isolated or weakly interconnected power systems such as microgrids. Therefore, novel ancillary services capable of delivering fast and effective frequency support that accounts for the dynamic nature of the modern power system are crucial. In this study, we develop a reinforcement learning (RL)-based control framework to provide fast frequency response (FFR) in a microgrid. The RL-based controller is trained through continuous interaction with a simulated microgrid environment using the soft actor-critic (SAC) algorithm, an advanced off-policy RL technique. To enable efficient RL training, a scalable co-simulation framework with a real-time digital environment is employed, allowing a parallel execution of online RL training and microgrid model simulation. The RL training configuration is deployed on the Cordova, Alaska, benchmark microgrid. A detailed evaluation of the trained RL-based controller demonstrates its ability to deliver efficient and timely frequency support to the microgrid, reducing frequency nadirs by 55.03% and 61.78% in cases with and without under-frequency load shedding (UFLS). Load impact assessments confirm the controller's robustness under varying loading scenarios, and the computational times during training and testing validate its real-time applicability for use in microgrids. Additionally, a practical evaluation of energy storage system (ESS) sizing under a 2-day load profile provides valuable insights into resource considerations for real-world FFR implementation.

This study introduces an optimised capacity configuration for park integrated energy systems (PIES) to boost energy efficiency, ensure power supply reliability and economy, and advance low-carbon operations. The approach integrates seasonal aspects and flexible load participation's impact on renewable energy absorption, using an enhanced K-means clustering algorithm with mixed-integer linear programming. It includes: (1) creating a probability density model from wind and solar data to categorise power generation scenarios across seasons; (2) integrating flexible loads into PIES optimisation, analysing technology combinations and output distributions; (3) developing a model for electricity, heat, and multi-energy coupling to assess cross-seasonal supply-demand matching; (4) establishing an optimisation model for inter-seasonal energy storage considering operational costs. Case studies confirm the benefits of seasonal factors, flexible load participation, energy coupling, storage, and seasonal dispatch on PIES efficiency and economy.

False data injection (FDI) attacks pose a great threat to the safe operation of power grid. By attacking nodes with weak defences, high transmission risks and high returns, attackers can cause more damage to the power grid with limited resources. Therefore, it is of great significance to evaluate these critical nodes for active defence of power grid. This paper presents an evaluation index and defence method of critical information node. Firstly, by establishing an FDI attack model, information system model and attack propagation model, quantitative analysis is carried out on basic indicators, such as attack return, attack success probability, transmission risk, transmission intensity and correlation, between attack return and transmission risk of information nodes under FDI attack scene. According to the attack detected and no attack detected scenes, the basic evaluation indexes were selected, respectively, to establish a comprehensive evaluation index. Finally, based on the comprehensive evaluation index, two defence methods are proposed to improve the power grid's ability to resist FDI attacks, respectively, applicable to detected attacks and undetected attacks. The effectiveness of the proposed evaluation index and defence method is verified by the simulation results of IEEE57 nodes system.

The growing penetration of renewable energy sources and the electrification of transportation have introduced significant challenges in power system operations, including renewable intermittency, forecast uncertainties and increased peak demand. This paper presents an electric vehicle-integrated chance-constrained economic dispatch (EV-Integrated CCED) model, a novel framework that integrates electric vehicles (EVs) as distributed, bidirectional energy storage resources to address these issues. Unlike traditional models, the proposed approach incorporates a distributionally robust optimisation framework to handle uncertainties in renewable generation and net load forecasts, ensuring reliable and cost-efficient operation even under worst-case scenarios. By dynamically scheduling EV charging and discharging activities, the model enhances grid flexibility, optimises renewable energy utilisation and minimises operational costs. Numerical studies on the 8-zone ISO-NE test system demonstrate the model's ability to significantly outperform traditional methods, showcasing its potential for modern power systems transitioning to a clean and electrified energy future.

This study proposes a two-stage methodological framework that simultaneously expedites model training and enhances predictive fidelity in wind power forecasting. In the first stage, input dimensionality is reduced through an initial correlation coefficient screening, followed by an iterative correlation stability analysis that retains only those instances exhibiting robust and persistent associations with the target variable. In the second stage, the hyperparameters of nonlinear autoregressive models with exogenous inputs are optimally calibrated via a grey wolf optimiser employing integer encoding. Empirical assessments conducted on extensive wind speed and wind power time series reveal that the streamlined feature set, coupled with optimised hyperparameters, reduces training time substantially while yielding superior forecasting accuracy relative to conventional baselines. Moreover, the refined wind speed estimates propagate to wind-power predictions, delivering additional performance gains. The findings corroborate the efficacy of integrating rigorous dimensionality reduction with metaheuristic hyperparameter tuning for data-driven wind power forecasting.

To solve the complex scenarios, such as multimodal noise, bad measurement data and sudden load changes, in the power system, the enhanced generalised cross correlation entropy unscented Kalman filter (EnGCCE-UKF) method is proposed in this paper. This method replaces the mean square error (MSE) criterion of the traditional UKF with the generalised cross correlation entropy (GCCE) criterion and obtains the optimal solution by optimising the state estimation cost function, significantly improving the robustness and estimation accuracy in the non-Gaussian noise environment. Considering the interference of bad measurement data on the information matrix, the strong filtering tracking (SFT) theory is further embedded in the GCCE-UKF framework. By dynamically adjusting the information matrix to suppress the influence of abnormal factors, the anti-interference ability of the algorithm is enhanced. This method integrates the state and measurement error into the cost function of the EnGCCE by using the statistical linearisation technique and recursively updates the posterior estimation and covariance matrix with the help of the fixed-point iterative algorithm. Verified through multiscenario experiments and comparative analysis, the proposed method has shown good effectiveness in power systems of three scales.

Due to the excellent thermal storage capacity, electric water heaters (EWHs) have significant scheduling potential in peak-load shaving and accommodating fluctuations of renewable energy. However, traditional scheduling methods for EWHs mainly focus on optimising electrical power while neglecting the shifting flexibility of water-using activities. This paper proposes a scheduling method of aggregate EWHs that considers the shifting flexibility of water-using activities. Based on the thermodynamic analysis of EWHs, the shifting range boundary for aggregate EWHs is obtained by considering the permissible shifting time range of water-using activities of individual EWH. By this way, the scheduling model for the aggregate EWHs considering shifting flexibility of water-using activities is established, achieving joint scheduling of the aggregate water flow-rate and the aggregate electrical power. In addition, a joint decomposition method for aggregate water flow-rate and aggregate electrical power is designed. Finally, the aggregate scheduling results are decomposed into the water flow-rate and electric power of individual EWH, which provides a basis for the response of individual EWH. Case studies validate the effectiveness of the proposed method from three aspects: aggregate modelling, optimal scheduling and scheduling result decomposition of EWHs.

The virtual synchronous generator (VSG) concept is implemented to mimic the properties of synchronous generators and therefore improve the stability of modern power systems. Superconducting magnetic energy storage (SMES) is able to rapidly absorb power in the case of a fault and becomes completely charged due to capacity constraints. When the energy storage is fully charged, it goes offline, and it is unable to respond to any subsequent disturbances in a short span of time. Any type of energy storage, which is regarded as the core of VSG technology, is likewise susceptible to the same issue. To address this, the authors present a novel coordinated active-reactive power control of SMES that operates in VSG mode. This control is accomplished by imposing dynamic saturation limits on reactive power. The proposed technique ensures an improvement in transient stability by utilising the converter that is associated with the SMES as a static synchronous compensator (STATCOM) when the SMES is completely charged. The proposed control approach is evaluated on a modified 68-bus power system and results in an increase of more than 55 ms in critical clearing time (CCT). The proposed technique is demonstrated in real time utilising the OP-4510 Real Time Digital Simulator.

Reliable frequency regulation in low-inertia power grids requires the integration of renewable energy sources and energy storage systems. High levels of renewable penetration reduce system inertia, causing deeper frequency nadirs and raising stability concerns. Grid codes mandate effective inertia emulation to limit frequency deviations and manage tie-line power flows in wind-integrated systems. In low-inertia systems lacking support from renewable sources, frequency nadirs can drop sharply. Incorporating fractional-order controllers enhances inertia emulation, and tuning their parameters using the Rao algorithm achieves faster settling times compared to other metaheuristic approaches. Moreover, challenges such as secondary frequency dips can be alleviated by leveraging grid storage, particularly via electric vehicle clusters. This paper proposes a coordinated control strategy that combines inertia emulation, EV storage and fractional-order controllers for low-inertia power systems. The optimal parameters obtained using the Rao algorithm are validated through real-time testing on the Typhoon HIL 402 emulator.

Accurate frequency modelling of inverter-based resource (IBR)-dominated power systems is crucial for ensuring stable, reliable and resilient operations, particularly given their inherent low-inertia characteristics and fast dynamics that traditional swing equation-based models inadequately capture. This paper explores neural ordinary differential equations (Neural ODEs) as a computationally efficient, data-driven framework for modelling power system frequency dynamics, specifically within microgrids integrating high penetrations of distributed energy resources (DERs). The developed neural ODEs framework incorporates a neural network architecture designed to capture input dynamics. By actively perturbing the system with a known signal, the Python-based neural ODEs framework was trained using measured system states and inputs, without the need for detailed system information. The framework, tested on a model of the Cordova, AK, microgrid, achieved a goodness of fit ranging from 60% to 99% across different state variables and maintained a mean square error in the $��1�0^{�-�6�}�$ p.u. range under square and step excitation signals. The proposed approach demonstrated robustness to measurement noise and initial condition variations while maintaining low computational complexity suitable for real-time power system control applications. Furthermore, transfer learning enabled the neural ODEs model to adapt to the following changes in system topology or generator dispatch, highlighting its effectiveness for dynamic microgrids with frequently evolving configurations and diverse DERs.