Iet Generation Transmission & Distribution最新文献

This paper introduces an optimized distributed secondary control framework for islanded DC microgrids that simultaneously achieves voltage regulation, economic dispatch, and voltage ripple reduction. The framework integrates consensus-based distributed optimization to compute optimal power outputs and applies particle swarm optimization (PSO) to generate ripple-minimizing reference signals. Both values are directly injected into the secondary control loop to adjust the system's operating point dynamically. This structure enables real-time coordination of multiple control objectives without mutual interference, ensuring that improvements in one objective do not adversely affect the others. Simulation results on a six bus DC microgrid under varying load conditions confirm significant improvements in voltage stability, reduction of generation costs, and effective ripple suppression. The proposed approach offers a scalable, robust solution that balances technical performance with economic and power quality considerations.

The neglect of randomness in electric vehicle (EV) users' charging behaviour and existing charging stations' impacts leads to irrational service range division and capacity allocation for newly added charging stations. To address this, a multi-stage optimal allocation method for newly added electric vehicle charging stations (EVCSs) based on comprehensive service performance is proposed. First, an improved Huff model incorporating charging station attributes, travel time value, and individual user preferences analyses probabilistic charging behaviour to determine service range and charging demand. Second, a comprehensive evaluation method using power utilisation rate, effective service rate, and capacity redundancy is established, with indicator weights determined by an Analytic Hierarchy Process-Entropy Weight (AHP-EW) method integrated into a fuzzy theory-based scoring system. Third, an allocation model integrating comprehensive service evaluation from a combined operator-user economic perspective is formulated. Finally, an Immune Clone Selection–Variable Neighbourhood Search (ICS–VNS) hybrid algorithm is designed for efficient optimisation. Case studies demonstrate that, compared to the existing planning scheme, both the single-station and two-station deployment schemes reduce peak demands at existing busiest charging stations by 16.1% and 27.5%. These improvements are achieved with controllable investment and operational costs, ensuring a balance between enhanced service performance and cost efficiency.

Over the past two decades, there have been frequent large-scale power outages worldwide caused by voltage collapse, resulting in significant economic losses. With the development of technologies such as renewable energy and HVDC (high voltage direct current) transmission, the power system is gradually moving towards a trend of high penetration of renewable energy and a high proportion of power electronic devices. As a result, the mechanism of transient voltage stability is becoming increasingly complex, posing serious challenges to transient voltage stability analysis. Therefore, it is of great importance to quickly and effectively assess and quantify transient voltage stability for the safe and stable operation of the system. This paper first explores the concept of transient voltage stability, and then provides a detailed review and analysis of transient voltage stability practical criteria in various countries and regions, summarizing a more adaptable and practical three-stage transient voltage stability criterion. Additionally, it also classifies and summarizes existing transient voltage stability assessment indices and highlights three key aspects in the future research of the indices.

To address the issue of frequency regulation in AC-islanded microgrids (MGs), this paper introduces a novel approach for adjusting primary controller gains for AC-islanded MGs. The proposed approach uses the linear matrix inequality (LMI) for polytopic linear parameter varying (P-LPV) modelling based on the H_∞ control theory and principal component analysis (PCA) algorithm. The objective is to regulate the MG frequency while considering the nonlinearity and uncertainty of the system. To achieve optimal control gains, the method considers all the system uncertainties in a P-LPV modelling of the system. The PCA algorithm reduces the scheduling parameter region, and the optimal control gains are computed by solving the relevant LMIs defined on the obtained P-LPV model based on H∞ performance and stability achievement. The primary control gains are optimised to minimise the errors between the optimal and actual control signals. Importantly, the suggested method preserves the order and structure of the primary control, making it applicable to implement on digital hardware devices. In addition, the MG is simulated in MATLAB/Simulink, and the simulation results demonstrate the authenticity, effectiveness, and efficiency of the proposed process for MG frequency regulation in the presence of uncertainties, disturbances, nonlinearity, and dynamic changes in MGs.

This paper presents a practical method for allocating inertia and damping in power systems integrated with renewable energy sources (RES). The frequency response model of the power system is first established, with distinct modeling of the dynamics of synchronous generators (SGs) and three types of RES units. A state-space model, which describes the relationship between frequency response and disturbances, is derived through the disturbance allocation process. A fitting method for frequency and power responses is proposed based on modal analysis. Analytical formulas for frequency stability indices (FSIs) and power indices are derived. Furthermore, applying matrix perturbation theory, an iterative optimization method for inertia and damping allocation of RES units is proposed and validated through simulation cases using the modified IEEE 39-bus system, including scenarios with multiple disturbance locations, which demonstrate the effectiveness and flexibility of the proposed approach.

As the integration of renewable energy into power grids, accurately determining the direction of active power flow has become a crucial aspect of grid management. This paper proposes a novel method for detecting the direction of active power flow in single-phase power systems with fast dynamic response and high precision. An algebraic relationship between the power factor, grid voltage, and current is derived through a trigonometric transformation. Then, an adaptive estimator is proposed to estimate both the power factor and the active power flow direction. A Lyapunov-based argument is introduced to demonstrate the accuracy of the estimated power factor and flow direction. Comprehensive experimental results validate the proposed method, emphasising its response time and accuracy in practical applications.

Effective pre- and post-disaster strategies are crucial for mitigating the impacts of extreme events and enhancing the resilience of networked microgrids (NMGs). However, traditional methods often fail to comprehensively and efficiently consider fault scenarios before disasters, and the inefficient challenge of addressing large-scale post-disaster recovery problems necessitate advanced computational approaches. This paper proposes a quantum-assisted resilience enhancement framework for power distribution systems with NMGs, fully accounting for potential failure risks. The main contributions include a two-stage quantum-assisted resilience enhancement framework that integrates quantum amplitude estimation (QAE) for quantifying failure risks, and generating scenarios, the quantum-encoded model establishment, and the proposed quantum surrogate absolute-value Lagrangian relaxation (Q-SAVLR) algorithm for achieving quantum-accelerated parallel optimization. Numerical tests on a modified IEEE system show that our approach reduces computation time by roughly 40%–75% relative to the classical solver, enabling faster repair resource deployment and more efficient resilience optimization for NMGs.

This paper introduces a coordinated state estimation (CSE) approach designed to achieve a unique reference solution for both transmission and distribution systems. The CSE framework additionally addresses the challenges related to balanced bus requirements within distribution system state estimation (DSSE). Specifically, the CSE method models a transmission boundary bus as a reference bus for the execution of DSSE. Furthermore, the methodology constructs a consistency check vector based on the estimated variables of the modelled reference bus, thereby ensuring the coherence of the solution for the entire power system. To manage the substantial complexity of the problem, the CSE approach employs a parallel estimation strategy. This paper validates the CSE technique through a comprehensive analysis of different power transmission and distribution system combinations. The study focuses on the IEEE 14-bus and New England 39-bus systems as transmission networks, alongside the IEEE 13-node and 37-node feeders as distribution networks. Validation is conducted using MATLAB and DIgSILENT PowerFactory software tools.

The term “hosting capacity”, in the context of power systems, was first introduced in March 2004 and has since resulted in a range of applications and research. Network operators commonly use the concept to quantify the ability of their networks to accept new production and consumption. Academic research on hosting capacity took off seriously after 2010 and has resulted in thousands of publications. This paper presents a brief history of the early years of hosting capacity studies, gives an overview of the status of both applications and research, summarises the different methods and types of studies, and correlates all that with the underlying fundamental principles of the hosting capacity concept, as it was introduced in 2004. The main focus of this paper is to review and relate these methods and studies to the fundamental principles. Having a clear understanding of these fundamental principles enables a wide range of applications for hosting capacity studies with detailed methods and models. However, it also requires transparency to ensure a coherent analysis, correct interpretation of the results, and an open discussion between stakeholders. With research on hosting capacity, it is important to refer to the fundamental principles; with applications, it is important to maintain transparency and objectivity.

The rapid integration of renewable energy generation has significantly reduced the flexibility regulation capacity of power systems, necessitating the exploration of adjustable resources on the load side to establish a novel ‘source-load interaction’ balancing mechanism. Air conditioning (AC) load, as a critical demand response resource, has garnered increasing attention. However, existing AC load control strategies are either heavily influenced by user behaviour uncertainty or overly reliant on communication and measurement infrastructure. Moreover, most approaches adopt random switching control methods, which fail to maximise user participation willingness and overlook the dynamic variations in user responsiveness, ultimately limiting their practical effectiveness. To address these challenges, this study proposes a dynamic scheduling model that comprehensively considers the aggregated response potential of AC loads and the characteristics of multiple flexible load types, thereby fully exploiting the coordinated regulation capability of load-side resources. Targeting a low-carbon community scenario (incorporating distributed wind power and residential users), the model is formulated with the dual objectives of maximising wind power accommodation and minimising source-load power deviation. A greedy algorithm is employed to iteratively solve the maximum available response capacity and actual dispatchable quantity of AC loads in each time slot, enabling dynamic updates of potential assessment and scheduling decisions. Case studies validate the effectiveness of the proposed model in enhancing wind power utilisation and optimising load scheduling, providing a feasible solution for source-load coordination in modern power systems.