Iet Generation Transmission & Distribution最新文献

Power systems must address increasingly severe environmental challenges through an efficient low-carbon transition. However, most current studies considered different single technology to achieve this transition. Research on the interactive mechanisms between different carbon reduction measures remains limited. This paper proposes a capacity expansion planning model for a low-carbon power system that integrates multiple technologies, incorporating two complementary types of carbon reduction measures. Specifically, the technologies are categorized into two categories: direct CO₂ reduction measures (e.g., carbon capture and storage) and indirect CO₂ reduction measures (e.g., flexibility retrofit, energy storage system and wind expansion). Furthermore, a distributionally robust optimization method is developed to address the uncertainty of wind power. The column and constraint generation algorithm is employed to solve the model and derive the optimal planning scheme. Case studies based on the modified IEEE 24-bus system and the IEEE 118-bus system indicate that the proposed method significantly reduces both the system cost and carbon emissions. Additionally, the planning results demonstrate effective synergy between direct and indirect carbon reduction measures.

Voltage Source Converter-based Energy Storage System (VSC-ESS) integration face technological obstacles like active power oscillations during the frequency regulation process for power system frequency management. Such problems are more prominent in parallel VSC-ESS. To address this, this paper introduces a transient electromagnetic power compensation control strategy for parallel VSC-ESS to suppress the overshooting and oscillations during the frequency response. First, a comprehensive investigation of the state-space equations and transfer functions for parallelled VSC-ESS elucidates the influence of key control parameters on both system stability and frequency response characteristics. Then, in order to improve the dynamic response performance, an adaptive inertia control approach is devised. Simulation and experimental results verify the effectiveness and feasibility of the proposed control.

The rapid advancement of smart distribution systems, driven by the integration of distributed energy resources, intelligent switching devices and communication-based control, has considerably increased the complexity of reliability assessment. Traditional evaluation methods, relying on predefined fault cases, iterative simulations, or heuristic techniques, often suffer from scalability challenges and computational burden. To address these limitations, this study presents a generalized, non-iterative analytical framework grounded in graph-theoretic principles. Building upon Lemma 1, which restates the classical reachability property of graph theory, where the n-th power of the adjacency matrix represents node connectivity, the method systematically identifies isolated nodes under diverse network topologies. Building upon this foundation, a fast distribution reliability calculator is developed and linked to each load point, enabling efficient calculation of reliability indices without extensive simulations. Unlike conventional approaches, the proposed framework inherently incorporates realistic modelling of key system elements, such as disconnectors, circuit breakers, remotely operated switches and distributed generation units. Its effectiveness is demonstrated on two networks, including Bus 6 of the 65-bus Roy Billinton test system and a real 778-node feeder located in Khorasan Razavi Province, Iran, where the results confirm superior accuracy and computational efficiency. Overall, the method offers a scalable and robust solution for evaluating the reliability and resilience of modern distribution networks.

Multiterminal offshore high-voltage direct current systems provide a suitable option for efficient and reliable power transfer to shore for future offshore generation. To limit loss of power transmission, protection systems based on DC circuit breakers (DCCBs) are envisioned. In case of grid-following wind turbine generators (WTG), DCCBs require large series reactors to avoid blocking of the offshore converter and subsequent shutdown of the offshore wind farm. By contrast, the potential of grid-forming WTG to ride through converter blocking during DC-side faults has not been investigated yet. Therefore, this paper compares four variations of WTG and HVDC converter control through detailed electromagnetic transient simulations of DC-side fault scenarios. It highlights that grid-forming WTG keep the offshore system in stable operation during temporary blocking of the offshore converter. Moreover, the paper shows that protection requirements can be reduced through the use of smaller DC series reactors. During temporary blocking, DC and offshore AC-fault-ride-through requirements are met but the AC power transfer to the onshore system is interrupted. Therefore, the temporary blocking time has to be chosen to adhere to current grid codes, with the active power return being dependent on the WTG. Already short blocking times of 10 ms can significantly reduce the inductance. However, that requires the fast deblocking to be in accordance with the internal protection mechanisms of the converter.

The growing penetration of renewable energy sources has played a significant role in power markets, necessitating the need to address the stochasticity arising from these sources. This paper introduces a distributionally robust chance-constrained market design to internalise uncertainty and strike a balance between economic efficiency and operational reliability. Three different moment-based ambiguity sets are leveraged to account for wind power uncertainty. We demonstrate that the proposed economic dispatch model under various uncertainty assumptions can be transformed into a second-order cone programming problem. Therefore, the uncertainty-contained electricity prices are accurately derived. In addition, we prove that the suggested pricing mechanism constitutes a robust competitive equilibrium under certain conditions. Finally, the effectiveness of the proposed approach is assessed on the Pennsylvania–New Jersey–Maryland Interconnection 5-bus system and the Institute of Electrical and Electronics Engineers 118-bus test system.

This study introduces a forecasting-driven framework for solving the hybrid dynamic economic emission dispatch (HDEED) problem with uncertain wind and solar generation. Unlike conventional approaches that rely on probabilistic models, this research uses data-driven forecasting techniques to predict the output powers of wind and PV plants and uses them in the multi-objective optimisation algorithm to minimise costs and emissions. An arithmetic optimiser with a sine cosine assisted driving training-based optimisation algorithm (AOASC-DTBO) is presented to solve various single and multi-objective HDEED problems based on fuzzy decision-making and the Pareto dominance concept. The proposed dispatching algorithm is validated using 10-unit, 40-unit and 7-unit IEEE 57 bus systems. The results revealed that the AOASC-DTBO algorithm achieved 8.73% and 4.95% lower fuel costs when compared with the PSO and DTBO algorithms, respectively. In addition, the emissions were 6.47% and 2.25% lower than the BMO and DTBO algorithms, respectively. The work highlights the importance of integrating renewable energy sources (RES) into power systems to achieve cost savings and reduced emissions, while also emphasising the need for efficient dispatch algorithms to ensure grid stability and reliability. The results demonstrate that integrating RES into the power system can yield substantial economic and environmental benefits, achieving cost savings of up to $6,908.034 and reducing emissions by 13,233.691 tonnes per day.

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.