Iet Generation Transmission & Distribution最新文献

The grid-forming (GFM) converters connected renewable energy (RE) to the power grid, which can offer inertia and damping support under disturbance, thereby enhancing the stability of power grid in weak-grid conditions. However, in the controller, the active and reactive power are coupled with each other. Thus brings errors into the GFM controller, leading to the inaccuracy of the GFM controller. To address this issue, this paper designs a feedback controller for GFM converters based on exact linearization. First, considering the characteristics of the GFM converter, the simplified model of the GFM converter is built and the mathematical expression of the GFM controller is derived. Then, the diffeomorphism is built to linearize the nonlinear GFM controller. After that, the feedback controller is designed and the parameters are modified to improve the dynamics of the GFM controller. Finally, simulation results are provided to verify the effectiveness of the feedback controller.

This paper develops a model for energy management in low-carbon distribution systems that considers multiple goals: reducing costs, cutting emissions, and keeping the grid reliable. The framework links the distribution system operator (DSO) with prosumers such as buildings equipped with solar panels, batteries, and smart parking lots. Parking lots play an essential role since electric vehicles (EVs) can act as mobile batteries, charging when demand is low and returning power to the grid at peak times. A risk-averse method is applied to handle uncertainty in renewable generation, demand, and market prices rather than relying only on fixed forecasts. The model shows that coordinated interaction between DSOs and prosumers can lower costs and emissions while improving system stability. It also highlights the economic opportunities for prosumer owners and EV participants in transitioning toward low-carbon distribution networks. Over a 24 h horizon, the proposed framework reduced operating costs by 13.9%, emissions by 18.1%, and network losses by approximately 2%.

The commercialization of vehicle-to-grid (V2G) technology is critically hindered by the absence of a cost-effective and user-acceptable incentive mechanism, which limits electric vehicles (EVs) users' willingness to participate in grid dispatch. To address this issue, this paper thoroughly designs the optimal V2G incentive from a policy-making perspective, based on real-world data from Zhejiang Province, China. The methodology involves: 1) defining the lower and upper limits of V2G incentive from user and utility perspectives; 2) categorizing V2G application scenarios (industrial parks, residential communities, public parking lots) and designing each business schemes; 3) constructing optimization models and performing Monte Carlo simulations; 4) conducting a social experiment in Zhejiang Province, China, to modeling user participation willingness based on real data. The quantitative results indicate that the optimal V2G incentives are 0.71 CNY/kWh for industrial parks, 0.77 CNY/kWh for residential communities and 0.32 CNY/kWh for public parking lots. A unified policy incentive is derived as 0.75 CNY/kWh through welfare weighting. Sensitivity analysis reveals that while the power source mix and V2G technology cost have limited impacts, advancements in power battery technology, higher time-of-use tariff differentials and increased subsidies can enhance overall welfare. Furthermore, improved power supply reliability and larger EVs numbers can reduce the optimal V2G incentive, projecting a decline to 0.30 CNY/kWh by 2029.

Ensuring reliable operation of active microgrids with critical loads, such as emergency infrastructure or energy-sensitive industries, under uncertain conditions such as unplanned grid power outages, introduces a significant challenge to electric power system operators. This paper proposes an optimization framework to address the operational complexities of an active microgrid that participates in the day-ahead and real-time electricity markets while prioritizing uninterrupted power supply to mission-critical loads during unplanned utility grid power outages. Given the constraints of the electrical power grid, this paper utilizes a customized stochastic adaptive robust optimization method to handle various uncertainties that such a microgrid faces, including electricity prices, duration and time of unplanned grid power outages, and renewable energy sources output. This customized method enables the microgrid operator to consider unintentional islanding events rationally using a modified uncertainty budget allocation logic. In order to investigate the effectiveness of the proposed management framework, case studies were conducted using an IEEE base case model. Results have shown that, utilizing the proposed method, the microgrid operator is able to reduce total system costs by 27% while lowering the instances of critical load curtailment by 71% compared to the deterministic approach. In addition, results of assessments demonstrate that using the proposed method, the microgrid is able to operate safely under at least 98% of the simulated days, securing the safe power supply for the critical loads using the proposed framework.

Price-based demand response has been widely implemented by load aggregators to guide end-users to optimise power usage patterns. However, a key problem in practical implementation is that the current values of user price elasticity cannot be known before the current price is announced to users. To address this issue, we propose a constrained online convex optimisation (OCO) pricing strategy, which utilises the previous price adjustment and the corresponding user price elasticity to make the current price adjustment, schedule the uncertain load to track the power setpoint and combine energy storage to compensate for the tracking deviations in each round. The proposed OCO approach incorporates adversarial loss functions and adversarial constraints. Notably, these constraints are revealed only after making decisions and can tolerate instantaneous violations, yet they must be satisfied in the long term on average. Besides, dynamic Regret and dynamic Violation are introduced to guarantee the performance of the proposed approach. Finally, step and sinusoidal fluctuations are tested to validate the tracking performance. The findings highlight great application potential of the proposed constrained OCO pricing strategy in EV charging stations.

This article explores the secondary use of power line communication (PLC) technology, specifically broadband power line (BPL), as a diagnostic tool for medium voltage (MV) cable lines. Through experimental measurements, it was confirmed that selected communication parameters of BPL modems exhibit measurable deviations in the presence of partial discharge (PD) activity. Specifically, the parameters considered are throughput, signal-to-noise ratio (SNR), link capacity, bit load estimate (BLE), transmission amplitude map, Tonemap (modulation scheme), and Tonemask. In particular, BPL throughput decreased by approximately 10 Mbps for 100 pC PD and by around 50% for 1,000 pC PD. Since partial discharges are an early indicator of insulation system degradation, their detection supports predictive maintenance and contributes to improving the operational reliability of distribution networks. The results demonstrate that PLC/BPL technology–originally designed for communication and automation of distribution transformer stations (DTS)–can also serve as a cost-effective and readily deployable sensor system for real-time monitoring of cable health, without the need for dedicated diagnostic equipment. Experiments employing both inductive and capacitive couplers confirm the effectiveness of this secondary application in detecting insulation-related anomalies.

Mechanical fault diagnosis of high-voltage circuit breakers (HVCBs) remains challenging due to the complex and nonstationary nature of vibration signals, scarcity of fault samples, and the limited feature-extraction capacity of existing few-shot learning models. To address these challenges, this paper proposes a multi-information fusion diagnostic framework that integrates a Newton-Raphson optimised Transformer with meta-transfer learning (MTL). Specifically, entropy-weighted fusion is introduced to suppress conflicting channels and aggregate multi-directional vibration measurements into an informative representation. To improve training stability and reduce sensitivity to manual trial-and-error under limited data, a Newton–Raphson-based optimiser is employed offline to select key Transformer hyperparameters. For data-scarce and cross-scenario diagnosis, a meta-transfer learning scheme with a lightweight scale-shift adaptation module enables fast adaptation while mitigating overfitting. The proposed framework is validated on a self-developed multimodal vibration acquisition platform and compared with representative baselines. Experimental results show that the proposed approach achieves the highest diagnostic accuracy (98.45%) and F1-score (98.26%) under 5-way 5-shot settings, outperforming conventional baselines by 2.0%–5.5%. The method exhibits strong interpretability and adaptability to variable operating conditions, providing a reliable solution for intelligent mechanical fault diagnosis of HVCBs.

To address the challenge brought by the wind power uncertainty to power system scheduling, this paper proposes a two-stage robust security-constrained unit commitment (RSCUC) model for wind power reserve provision, considering the impact of optimal decisions on the uncertainty set. In the first stage (pre-scheduling stage), the curtailment plan of wind power is made and the wind power reserve is optimised, where the curtailment plan will influence the wind power uncertainty, giving rise to a decision-dependent uncertainty (DDU) set. In the second stage (re-scheduling stage), the thermal unit redispatch and the wind power reserve are allowed to keep the system balanced. The two-stage RSCUC model with DDU can be solved by reformulating DDU into decision-independent uncertainty, and the column-and-constraint generation algorithm is employed. Numerical results verify the effectiveness of the proposed model.

This paper addresses the enhancement of power system stability by incorporating the coordinated operation of Permanent Magnet Synchronous Generator (PMSG) and Unified Power Flow Controller (UPFC). A multi-input backstepping control strategy is designed to improve the power system's transient performance, while its asymptotic stability is rigorously verified through Lyapunov's direct method. The proposed control law acts on both PMSG and UPFC inputs to increase the system's critical clearing time under fault conditions. The full nonlinear dynamics of the PMSG and UPFC are explicitly integrated into the control scheme. To achieve optimal performance, the Particle Swarm Optimisation (PSO) algorithm is employed to fine-tune the gains of the controller. Furthermore, a second-order sliding-mode observer (SOSMO) is implemented to estimate the time derivatives of selected voltage signals. The designed controller also incorporates physical and operational constraints on control inputs, synchronous generator variables, and system currents and voltages. The effectiveness and robustness of the proposed approach are validated through simulations conducted on the standard New England 39-bus test system and also the 118-bus system. Comparative results with a similar system utilising a Doubly-Fed Induction Generator (DFIG) demonstrate that the proposed controller achieves superior transient stability and robustness against parameter uncertainties, as well as variations in fault location and timing.

The massive integration of renewable energy has imposed new challenges on traditional AC power networks, such as excessive short-circuit currents, overload risks of N-1 contingencies, and voltage/frequency instability. With the capabilities of electrical decoupling and flexible power flow control, embedded high-voltage direct current (HVDC) systems have emerged as a key technology for enhancing the security and stability of dense AC grids. Focusing on the topological optimisation of embedded HVDC systems, this paper first analyses the coupling characteristics between HVDC systems and intensive AC grids from four technical dimensions. Then, integrating multi-dimensional stability constraints — power flow under steady-state and N-1 contingency, short-circuit current, voltage and transient stability, a multi-dimensional stability constrained bi-level topology optimisation model is proposed for HVDC systems embedded in dense AC grids, which incorporates upper-level economic optimisation and lower-level stability correction using penalty functions, addressing the limitations of traditional planning in multi-objective coordination and computational efficiency for large-scale grids. Finally, simulation calculations are conducted on a typical 500 kV dense AC transmission network in a provincial region. The results demonstrate the feasibility and scientific validity of the optimisation method in suppressing short-circuit currents, optimising power flow distribution and enhancing the grid stability margin.