Iet Generation Transmission & Distribution最新文献

Distance protection is commonly used as the main and backup protection for outgoing lines of inverter-based resources (IBRs). However, the weak feed-in characteristics and controlled phase angles of IBR output currents make distance protection more vulnerable to fault resistance, resulting in degraded performance. This paper proposes a distance protection scheme capable of fault location based on the coordination of control and protection. First, it is assumed that two linearly independent states exist in the system after a fault. Accordingly, a fault location calculation method is introduced, relying on the relationship among electrical quantities and current distribution coefficients in these two states. Second, a low-voltage ride-through (LVRT) strategy based on control switching is presented, whereby the IBR initially adopts active control to support positive-sequence directional detection and then switches to reactive-priority control to meet grid code (GC) requirements. Through control switching, two fault states are established. Third, to address the timing discrepancy in voltage drop detection between the IBR and relay protection, a coordination method of control and protection is optimized. Finally, a model of IBR outgoing lines is developed in PSCAD/EMTDC and RTDS simulation environments to validate both the coordinated method and the effectiveness of the proposed distance protection scheme.

This paper proposes a multi-stage planning framework for park integrated energy systems (PIES), integrating stochastic optimisation and distributionally robust optimisation (DRO) to address the uncertainties of vehicle-to-grid (V2G) response and photovoltaic (PV) generation. The framework fully leverages the potential of electric vehicles (EVs) stationed in the park during working hours as schedulable energy storage while ensuring their energy needs for post-work commutes. The arrival and departure times of individual EVs, as well as their energy demands for post-work travel, are modelled using Monte Carlo simulation. A price incentive mechanism is introduced to encourage EV owners to participate in scheduling, with explicit consideration of EV battery degradation. Furthermore, a scenario probability-driven DRO method is employed to manage PV generation uncertainty. Simulation results demonstrate that, for long-term planning with steadily increasing demands, the proposed multi-stage approach effectively avoids redundant equipment configuration and enhances economics compared to a one-shot decision. V2G participation significantly reduces equipment investment costs, operational expenses, and carbon emissions. Meanwhile, the DRO planning model achieves an optimal balance between economic efficiency and planning robustness by combining the benefits of stochastic and robust optimisation.

High renewable energy penetration poses significant challenges to supply-demand balancing in transmission networks, making transmission-storage cooperative planning (TSCP) a crucial strategy for mitigating renewable variability. Meanwhile, the carbon reduction potential on the demand side remains insufficiently explored. Coordinating grid-side resource planning with demand response can enhance system flexibility and support deep decarbonization. To address this, this study develops a TSCP model integrated with carbon-aware demand response under a bi-level robust optimization framework. The upper level performs robust TSCP under source–load uncertainty, leveraging battery and hydrogen storage to smooth renewable output fluctuations, while the lower level adjusts user-side electricity consumption in response to price signals to reduce emissions. A dynamic carbon pricing model is proposed based on carbon emission flow analysis, integrated with locational marginal pricing to form a unified electricity-carbon price signal that stimulates low-carbon responsiveness on the demand side. A nested alternating optimization procedure with column-and-constraint generation (NAOP-C&CG) algorithm is developed for an efficient solution. Case studies on the Northwest China HRP-38 system show that the proposed method reduces total system cost by 48.47%, decreases the load shedding rate by 0.92%, and improves renewable energy utilization by 6.6%. Furthermore, it achieves CO₂ emission reductions of 10.82 Mt and 9.4 Mt compared to fixed and conventional ladder-type carbon pricing schemes, respectively.

In a microgrid system with multiple virtual synchronous generators (VSGs), the introduction of inertia makes the power oscillations of each VSG under an active power disturbance more obvious. Therefore, this paper establishes the active power frequency response model of the multi-VSG parallel microgrid system. On this basis, combined with the system topology, phase angle feedforward control is proposed, and the difference between the initial power and the steady power caused by the power oscillations is compensated by the optimal compensation coefficient, which can improve the system power response speed, restrain the power oscillation between units, and retain the inertia response adjustment ability. Finally, simulation experiments are carried out on the Matlab/Simulink platform to verify the correctness of the proposed active power frequency response model and the effectiveness of the proposed method for suppressing power oscillation.

This study investigates the integration of electricity and heat systems using local peer-to-peer energy and flexibility markets to enhance multi-energy system efficiency. In the day-ahead market, prosumers actively trade energy, while system operators manage operational flexibility by dividing it into upward and downward components. Through collaboration, operators and prosumers establish nodal marginal flexibility coefficients, which quantify each prosumer's contribution to system flexibility. These coefficients help operators allocate flexibility more effectively across the network. To address uncertainties and ensure reliability, an hour-ahead flexibility market is introduced. This market allows prosumers to trade flexibility and lease it to network operators via shared multi-energy storage systems, improving real-time adaptability. The alternating direction method of multipliers algorithm facilitates these transactions while preserving prosumer autonomy in decision-making. The framework is validated through three case studies: a 5-bus power grid with a 5-node heating system, a 33-bus grid with a 23-node heating system, and a 289-bus grid linked to a 67-node heating system involving 30 prosumers. Results show reduced operational costs for prosumers and increased profits for those offering flexibility services, demonstrating the framework's effectiveness in enabling decentralized energy exchanges and improving overall market performance.

This paper presents a novel distributed secondary resilience control strategy designed to effectively counteract the instability induced by false data injection (FDI) attacks in islanded microgrids. Initially, precise FDI attack models were developed for various attack locations, and their impacts on the stability of islanded microgrids were examined. Subsequently, a secondary frequency controller was proposed, grounded in the distributed consensus algorithm. Utilizing adaptive control principles, a resilient secondary controller was engineered to mitigate the detrimental effects of FDI attacks on the microgrid's operation. The stability of the proposed controller was theoretically established using the Lyapunov function. The effectiveness of the control method was verified through hardware-in-the-loop simulation, providing a practical and efficient solution for maintaining the stable operation of islanded microgrids under complex attack scenarios.

Accurate and efficient evaluation of losses in modular multilevel converters (MMC) is of great significance for the design and reliable operation of high-voltage direct current transmission systems. In this paper, an efficient online loss evaluation method for MMC is proposed. First, a switching frequency surface is constructed based on the determined modulation and capacitor voltage balancing strategy, which enables the estimation of the switching frequency under any steady-state operating condition. Then, an arm average value model (AAVM) is employed to estimate the upper bounds of the switching frequency and associated switching losses. Moreover, combined with the switching frequency surface, an approximate and efficient estimation of the actual switching losses is achieved, which ensures a balance between computational accuracy and efficiency. In addition, the thermal network model is employed to represent the temperature-dependent characteristics of losses. Considering the impact of losses on simulation results, the energy absorbed by a controlled voltage source in series with the arm is used to characterize the losses. Subsequently, a switching loss injection method based on a decay function is proposed, which mitigates voltage spikes and enhances the effectiveness of loss injection. Finally, an AAVM considering loss injection is developed in PSCAD to verify the computational efficiency and accuracy of the proposed method.

The decarbonisation of the energy sector necessitates effective coordination and control of heterogeneous energy sources, each exhibiting distinct dynamic behaviours and response characteristics. Model-reference control has emerged as a promising strategy to enforce desired system behaviour by aligning it with that of a known model. This paper proposes a unified control framework for both direct current (DC) and alternate current (AC) power grids based on the model-reference principle. The method draws inspiration from robust non-linear control techniques and is designed to address the structural and dynamical disparities between DC and AC systems within a unified control architecture. Comprehensive application case studies are conducted using MATLAB-Simscape environment focussing on representative DC and AC power grids scenarios. The outcomes demonstrate the superior performance of the proposed method in comparison to conventional approaches, thereby validating its efficacy in achieving reliable and coordinated control across diverse grid types.

Nowadays, with the increasing use of distributed energy resources (DERs), distribution networks have transitioned from traditional to active states. The single-phase and unbalanced connection of DERs leads to power quality phenomena such as voltage unbalance (VU). These phenomena disrupt the performance of the distribution network. This paper proposes a two-stage optimisation approach for the optimal operation of equipment in active distribution networks (ADNs), considering the load pattern. In Stage I, system uncertainties are modelled using Latin Hypercube Sampling (LHS), and correlated uncertainties in DERs (e.g., wind turbines (WTs) and photovoltaic (PV)) are addressed via Cholesky decomposition. In Stage II, the optimisation problem is formulated and solved to minimise active power losses (APL), voltage unbalance (VU) and voltage deviation (VD), while meeting operational constraints. The multi-objective problem is converted into a single-objective one using weighted coefficients. MATLAB and OpenDSS are employed for implementation; OpenDSS handles the unbalanced load flow analysis, and Discrete Particle Swarm Optimisation (DPSO) is used to solve the optimisation problem. To demonstrate the superiority of the proposed approach, comparative analyses are conducted with PSO and Harmony Search (HS) algorithms under various loading conditions. The results clearly show that the proposed DPSO-based method outperforms the alternatives in terms of optimisation quality, convergence speed and power quality improvements. The proposed method is applied to the modified IEEE 13-bus and 123-bus networks, and the results confirm that it effectively achieves all problem objectives, ensures constraint satisfaction and maintains robustness and scalability regardless of network size.

The high penetration of renewable energy and increasing load volatility in active distribution networks have led to rapid voltage fluctuations, posing significant challenges to conventional voltage-var control (VVC) strategies. Photovoltaic (PV) inverters offer strong potential for voltage regulation by utilizing their adjustable capacity. However, existing studies largely overlook the need for forecasting this capacity. Additionally, effective VVC based on PV adjustable capacity (PVAC) remains difficult to achieve under uncertainty and communication constraints. Therefore, this paper proposes a multi-timescale VVC framework that integrates PVAC forecasting with voltage regulation based on deep reinforcement learning. First, the PVAC available for VVC is formally defined, and an enhanced Transformer-based model is developed to forecast its probabilistic intervals. Second, a hybrid-action deep reinforcement learning (DRL) algorithm is proposed to coordinate continuous inverter VAR support and discrete tap/capacitor switching. To improve robustness under limited communication, the method incorporates a partially observable Markov decision process (POMDP) formulation and recurrent policy networks. Simulation results demonstrate that the proposed approach provides accurate PVAC interval predictions, offering enhanced robustness compared to point forecasting. Extensive VVC experiments on the IEEE 123-bus feeder confirm improved voltage regulation, reduced power losses, and fewer discrete control actions, even under communication impairments.