IET Energy Systems Integration最新文献

DC distribution networks (DCDNs) possess the advantages of high power efficiency, low operation loss, and favourable control flexibility, and are regarded as an essential form of future power distribution systems. In order to increase the voltage transient performance of DCDNs against disturbances, this paper proposes a methodology for realising the active voltage support control of DCDNs based on virtual inertia coordination. Firstly, the impact of the inertia of DCDNs towards the transient voltage change is analysed, and the active voltage support mechanism considering the variable droop inertia control and virtual DC motor inertia (VDCMI) control is explored. Then, a time sequence coordination strategy based on the voltage grading of DCDNs is developed, and an adaptive inertia coefficient is designed to achieve the inertia adjustment in terms of the voltage sag and recovery processes. Using MATLAB/Simulink, a detailed model of the double-terminal DCDNs is created to check the efficacy of the proposed approach. Different voltage disturbance scenarios are imitated, and the comparative simulations demonstrate that the proposed approach can fully utilise the inertia potential of the DCDNs to suppress the voltage sag and smooth the voltage recovery procedure. The proposed method's validity and feasibility can be well validated.

The cover image is based on the Original Article Investigation of active voltage support control approach of DC distribution networks based on virtual inertia coordination by Lei Chen et al., https://doi.org/10.1049/esi2.12123.

Although the electric vehicle supplied through distributed generators (DGs) is one of the most promising methods to reduce carbon emission and has been widely studied, the accurate current sharing regarding multi-bus DC charging stations considering uncertainties and network attacks is rarely studied. Based on this, a fully distributed current sharing control strategy is presented, which can improve the reliability of system under denial of service (DoS). Firstly, the DC charging station system (DCCSS) with uncertainties is modelled. Primary control is designed to provide stable voltage and inaccurate current sharing. Furthermore, the state-space function considering power coupling among different DC buses is built, which lays the foundation for the design of the following control. Then, the model of DoS attacks is proposed. Based on this, the fully distributed consensus control is proposed to achieve the accurate current sharing for DCCSS under DoS. Meanwhile, a method for solving the control gain without global information is given and proved. Moreover, it can be solved by LMI toolbox. As a comparison, a common control strategy is introduced that only considers the uncertainties. Finally, the feasibility of the proposed method is verified through comparison of simulation results.

The virtual synchronous generator (SG) (VSG) can not only enhance the inertia of the grid, but also introduce the oscillation characteristics of SG, which is easy to interact with the power angle of SG in the grid, and even produce low-frequency oscillation (LFO). The authors first construct a two-machine interconnected power system model containing VSG and traditional SG. The model is linearised to construct the state space equations to obtain the Phillips–Heffron model with VSG. The LFO path of action between VSG and SG is analysed. To reduce the negative damping torque provided by VSG to SG through this path, a virtual power system stabiliser (VPSS) is proposed and the controller parameters are adjusted according to the phase compensation method. Finally, the effectiveness of VPSS is verified by modal analysis and simulation comparison.

Power system dynamic security assessment (DSA) has long been essential for protecting the system from the risk of cascading failures and wide-spread blackouts. The machine learning (ML) based data-driven strategy is promising due to its real-time computation speed and knowledge discovery capacity. However, ML algorithms are found to be vulnerable against well-designed malicious input samples that can lead to wrong outputs. Adversarial attacks are implemented to measure the vulnerability of the trained ML models. Specifically, the targets of attacks are identified by interpretation analysis that the data features with large SHAP values will be assigned with perturbations. The proposed method has the superiority that an instance-based DSA method is established with interpretation of the ML models, where effective adversarial attacks and its mitigation countermeasure are developed by assigning the perturbations on features with high importance. Later, these generated adversarial examples are employed for adversarial training and mitigation. The simulation results present that the model accuracy and robustness vary with the quantity of adversarial examples used, and there is not necessarily a trade-off between these two indicators. Furthermore, the rate of successful attacks increases when a greater bound of perturbations is permitted. By this method, the correlation between model accuracy and robustness can be clearly stated, which will provide considerable assistance in decision making.

In photovoltaic (PV) systems, inverters play a crucial role for supplying electricity to meet the demand while maintaining power quality. For a local load connected to a grid-interfaced photovoltaic (GIPV) system, active and reactive power control is necessary at the distribution level. Thus, the foremost purpose of this article is to get the best optimally designed robust controller for control of active and reactive power. A GIPV system with Improved Arithmetic Optimisation Algorithm (IAOA)-based Super Twisting Sliding Mode Controller (ST-SMC) methodology has been proposed in this article for active and reactive power management. The conventional PI controller in the GIPV system that is most frequently used has considerable undershoot and a long settling period. PI controller tuning parameters were also changed to account for the wide change in the reference pattern. Therefore, STSMC and SMC are used for ensuring robustness against external disturbances. The conventional SMC comes out to have a chattering issue. Furthermore, the proposed IAOA technique is validated through some benchmark functions. The proposed IAOA technique outperforms Particle Swarm Optimisation (PSO), Forensic Based Investigation (FBI), and Traditional Arithmetic Optimisation Algorithm (TAOA) in terms of the number of iterations and accurately achieving optimal solutions for active and reactive power control. The results show that the proposed IAOA-based STSMC technique has an improved performance of settling time and undershoot for active and reactive power control. This article also presents stability analysis and robustness test of the above mentioned controllers to illustrate the effectiveness of each optimally designed controller. A 40 kW GIPV system performance is evaluated using the MATLAB environment, and the results are validated in a real-time simulator platform OPAL-RT 4510.

The application of a semi-definite programming (SDP) approach to the Alternating Current Optimal Power Flow problem has attracted significant attention in recent years. However, the SDP relaxation of optimal power flow (OPF) can be computationally intensive and lead to memory issues when dealing with large-scale power systems. To overcome these challenges, we have developed APD–SDP, an optimisation solver based on a first-order primal–dual algorithm. This framework incorporates various acceleration techniques, such as rescaling, step size decay and reset, adaptive line search, and restart, to improve efficiency. To further speed up computations, we have developed a customised eigenvalue decomposition component by exploiting the 3 × 3 block structure in the dual SDP formulation. Experimental results demonstrate that APD–SDP outperforms other commercial and open-source SDP solvers on large-scale and high-dimensional PGLib-OPF datasets.

Electric vehicles (EVs) are increasingly being valued by countries, but the disorderly charging behaviour of too many EVs poses a huge challenge to the operation of the power grid. First, for EVs, a methodical charging and discharging technique was designed, taking into account the temporal and spatiotemporal characteristics of different EV models, time convenience of owners, and safe operation of power grid. Second, the EV characteristics and safe operation of the grid after EV integration into the grid are presented for vehicle owners to achieve minimal charging station and optimal charging stations selection as well as charging and discharging schemes. Third, simulation calculations and analyses of ordered charging and discharging modes as well as disordered charging modes under various scenarios were performed. This study fully considers the characteristics of different vehicle models and the willingness of users to respond, making the model more realistic. The findings demonstrate that the optimised charging and discharging strategy in this study lowers the cost of charging for vehicle owners, boosts revenue from the charging station and the rate of use of the charging pile, lowers the risk of the safe operation of distribution networks, and effectively relieves pressure on the power grid. While solving the scheduling difficulties of a large number of EVs, it increases the economy between users and the power grid, and improves the safety of power grid operation.

Short-term interval estimation can effectively and precisely quantify the uncertainties of renewable energy, accurately represent the range of fluctuations of uncertain variables in robust optimisation of electricity-heating integrated energy system (EHIES) and it is getting crucial for reliable and flexible operation of renewable dominated new energy systems. The authors present a multivariate data-driven short-term PV power interval prediction model that consists of multiple layers, including one-dimensional convolutional layer, ultra-lightweight subspace attention mechanism (ULSAM), bidirectional long and short-term memory (BiLSTM), quantile regression (QR) and kernel density estimation (KDE). The one-dimensional convolutional layer and ULSAM can extract sequential features and highlight key information from the data; the BiLSTM processes time series data in both directions and conveys historical information; the QR and KDE models generate interval prediction with a given confidence level. Based on the proposed interval estimation, a refined PV uncertainty set can be established and adopted by robust optimal scheduling of EHIES utilising min-max-min algorithm. The simulation results have demonstrated the estimation accuracy and adaptability to various weather scenarios.

In this article, the stability of a synchronous motor generator pair (SMGP) used for improving the inertia of grid-connected renewable energy systems is investigated. The useful operational regime for different sets of system parameters is identified, such as electromagnetic torque, damping co-efficient, and inertia by employing bifurcation analysis to detect stability boundaries. For the first time, the existence of bi-stable regimes for the SMGP with non-linear stability analysis is revealed. The authors' analysis unravels the possibility of the system getting transited to unsafe operation even when the system is in the linearly stable region. The existence of the bistable regime indicates the possibility of the system becoming unstable even when the eigenvalues are in the left half plane. The authors also identify globally stable and globally unstable regimes in the parameter space. The safe operating range of inertia and damping co-efficient values helps in the design of a suitable MGP set that is robust to frequency deviations, even with a low inertia source. With the recommended values of electromagnetic torque, the authors' analysis provides a safe operational regime for power generation from renewable energy sources.

The primary objective of the authors is to design a new robust and improved virtual inertia controller (VIC) for renewable energy dominated inverter interfaced low inertia microgrid (LIMG). Increasing penetration of inertia-less renewable generation in microgrid leads to increased frequency deviation during and after a disturbance. To improve the frequency response of the LIMG, conventional VIC added with different second stage and third stage controllers are proposed in existing works. Higher degree-of-freedom (DOF) PID controller synchronised with fractional-order (FO) operators are used with conventional VIC controllers. These controllers work in addition with conventional VIC and the multi-stage controllers make the system more complex. To reduce the number of controller stages and, subsequently, reduce cost and complexity of the system, a single stage 3DOF-FOPID controller is proposed to mitigate the frequency deviation after a disturbance in a LIMG. Performance of the proposed single stage controller is compared with that of the existing controllers to establish the advantages of the proposed controller. The parameters of the proposed 3DOF-FOPID controller are optimised by Mountain Gazelle Optimsation. The robustness of this controller is also tested for random load fluctuation and renewable power variations in presence of system non-linearities.