Iet Generation Transmission & Distribution最新文献

Smart cities consist of various energy systems and services that must be optimally scheduled to improve energy efficiency and reduce operation costs. The smart city layout comprises a power distribution system, a thermal energy system, a water system, and the private and public transportation systems. Additionally, several new technologies such as reconfiguration, regenerative braking energy of the metro, etc. are considered. This study is one of the first to consider all these technologies together in a smart city. The proposed power distribution system is a grid-connected hybrid AC–DC microgrid. The biogeography-based optimization algorithm was utilized to seek the best solution for scheduling micro-turbines, fuel cells, heat pumps, desalination units, energy storage systems, AC–DC converters, purchasing power from the upstream, distributed energy resources, and transferring power amongst electric vehicle parking stations and metro for the next day. Also, the reduced unscented transformation layout was used to capture the system's uncertainty. The suggested layout is implemented on an enhanced IEEE 33-bus test system to show the efficiency of the suggested method. The results show that costs and environmental pollution are reduced. By comparing the proposed smart city with other studies, the efficiency and completeness of the proposed smart city are shown.

This work deals with designing an advanced integral sliding mode controller (AISMC) for residual current compensation (RCC) inverters connected with arc suppression coils to compensate for power distribution networks where the main idea is to alleviate hazardous circumstances caused by electric faults on powerlines. The key advancement in the proposed AISMC over traditional sliding mode controllers is the utilization of an improved exponential reaching law which ensures the faster convergence of the desired control objective that is the fault current compensation in this particular application. An improved exponential reaching law (IERL) used in this work is a combination of the exponential and constant-proportional reaching laws (while existing approaches use constant reaching laws) which assists to minimize the current injection error through the RCC inverter in the steady-state. The integral action in conjunction with the exponential function in the sliding surface, based on which the proposed controller is designed, helps to eliminate the chattering effects in a quickest way that can be evidenced from the settling time and percentage overshoot. The feasibility of the proposed AISMC is theoretically assessed by analyzing the stability using the Lyapunov stability theory. Simulation and processor-in-loop results further justify the theoretical foundation by confirming the desired current injection and compensating voltage and current due to the fault. Finally, results are compared with a TIMSC for demonstrating the superiority of the AISMC.

This paper sets out to develop an efficient probabilistic optimal power flow (POPF) algorithm to assess the influence of wind power on power grid. Given a set of wind data at multiple sites, their marginal distributions are fitted by a newly developed generalized Johnson system, whose parameters are specified by a percentile matching method. The correlation of wind speeds is characterized by a flexible Liouville copula, which allows to model the asymmetric dependence structure. In order to improve the efficiency for solving POPF problem, a lattice sampling method is developed to generate wind samples at multiple sites, and a logistic mixture model is proposed to fit distributions of POPF outputs. Finally, case studies are performed, the generalized Johnson system is compared with Weibull distribution and the original Johnson system for fitting wind samples, Liouville copula is compared against Archimedean copula for modelling correlated wind samples, and lattice sampling method is compared with Sobol sequence and Latin hypercube sampling for solving POPF problem on IEEE 118-bus system, the results indicate the higher accuracy of the proposed methods for recovering the joint cumulative distribution function of correlated wind samples, as well as the higher efficiency for calculating statistical information of POPF outputs.

To accurately detect the early deterioration of electrical insulation in power cables, an energizing system is required for offline partial discharge (PD) measurements. The damped alternating current (DAC) testing system has emerged as an effective tool for inducing PD events. However, the existing systems often suffer from limitations such as signal interference, low sensitivity, and high costs. To address these issues, this study proposes a novel dual-resonance DAC testing system that is simple in structure and cost-effective. The prototype was built for testing 10-kV distribution cables, and its effectiveness was validated through experimental tests on a cable sample. The results show that this approach significantly improves PD detection sensitivity and successfully completes PD localization. This research contributes to the development of more efficient and affordable DAC generation technology for power cable testing applications.

The cost for covering active power losses makes a significant item in transmission system operators (TSO) annual budgets, and still it received limited attention in the existing literature. The focus of accurate power loss forecasting and procurement is of high increase during the past 2 years due to spikes in electricity prices, making the cost of covering the active power losses a dominant factor of TSO operational costs. This paper presents practical aspects of the highly accurate models for transmission loss forecast in the day ahead time frame for the Croatian transmission system. The contributions are two-fold: 1) Practical insights into usable TSO data are provided, filling a critical research gap and a foundational literature review is established on transmission loss forecasting. 2) A novel method utilizing only electricity transit data as input which outperforms existing practices is presented. For this, several algorithms such as gradient boosted decision tree model (XGB), support vector regressors, multiple linear regression and fully connected feedforward artificial neural networks are developed, and implemented and validated on data obtained from the Croatian TSO. The results show that the XGB model outperforms current TSO model by 32% for 4 months of comparison and TSCNET's commercial solution by 25% during a year-long testing period. The developed XGB model is also implemented as a software tool and put into everyday operation with the Croatian TSO.

With the continuous growth of electricity demand, the safe and stable operation of distribution lines is crucial for power transportation. Unmanned aerial vehicle (UAV) inspection has been widely used for the maintenance and repair of distribution lines. Due to the limitations of computational power and endurance, it is difficult for UAVs to independently complete data processing. Combined with mobile edge computing (MEC), this paper proposes a computing offloading strategy based on multi-agent reinforcement learning and double-layer offloading mechanism, which can further utilize the computing power of non-task devices and edge servers. Firstly, three-layer system architecture, named MEC-U-NTDC (MEC-UAV-Non-task Device Cloud), is built. Secondly, double-layer offloading mechanism is designed to comprehensively utilize the computing power of edge servers and neighbouring non-task devices. Finally, a multi-agent algorithm DLMQMIX is proposed to minimize the total cost for UAV inspection. Simulation experiments show that the proposed algorithm can effectively solve the task offloading problem of UAV-aided distribution line inspection, and compared with algorithms such as PSO, GA, and QMIX, it performs better in terms of average delay, system cost, and load balancing, achieving a smaller total system cost.

Transmission line planning is affected by a variety of geographic information factors, and traditional path planning methods are difficult to meet the future intelligent planning needs. Here, based on the geographic information system platform for geographic information data processing, the triangular fuzzy number analytic hierarchy process (TFN-AHP) is utilized for weight analysis to construct the cost layer. Meanwhile, guided mechanism, dimension-reducing mechanism, and avoidance mechanism are proposed to improve the ant colony optimization (ACO) algorithm, to shorten the planning time and improve the route planning effect. Finally, a local area in Guizhou Province is selected for case study and compared with the results of traditional ant colony optimization, and the results prove the effectiveness of the transmission line planning model proposed here.

This study introduces a series of strategies to enhance convergence in the Newton–Raphson power flow method for unbalanced distribution networks. The proposed approach incorporates graph theory, Kron reduction, and current injection techniques. The network components, including transmission conductors, power transformers, power conductors, shunt capacitors/reactors, and demand loads, were merged into the bus impedance matrix. Using two identification processes (based on mismatches for bus voltages and bus power injections) and optimal multipliers (μ), the proposed approach estimates the initial bus voltages by approximating the converged solutions. To verify the effectiveness of the proposed approach, the authors performed a comparative analysis of five IEEE test feeders in various scenarios. The results confirmed the effectiveness of the proposed approach, particularly for unbalanced distribution systems with diverse transformer connections.

Uncertainty analysis deals with the fluctuations and unpredictability of the electrical power generated from renewable resources (RRs), such as solar PV and wind energy systems. This paper gives an insight into various techniques used for the uncertainty analysis and a probabilistic Monte Carlo Simulation is applied for modelling the uncertainties concerned with RRs and electric vehicle (EV) load in the MATLAB platform. The uncertainty associated with the price sensitivity of EV charging and the state of charge of EVs is taken as a prime factor for analysis in the present work. Despite the fluctuations and unpredictability of electricity generation and consumption, the considered system ensures that the total amount of electricity supplied by solar PV, wind and grid matches the total amount of electricity demanded by EV load. Rao-1, Rao-2 and Rao-3 algorithms are applied in this work to optimize the operation cost of charging stations under uncertain conditions and without any uncertainties. The results obtained without uncertainties by Rao algorithms are compared with the existing particle swarm optimisation method. In the presence of uncertainties, Rao-1 and Rao-2 algorithms are compared with Rao-3 and it is found that the Rao-3 algorithm performed better.

Recently, various distributed energy resources are significantly integrated into the modern power systems. This introduction of distributed energy resource-rich systems can cause various power quality issues, due to their uncertainties and capacity variations. Therefore, it is crucial to establish energy balance between generation and demand to improve power system's reliability and stability and to minimize energy costs without sacrificing customers’ comfort or utility. In this regard, power system flexibility concept is highlighted as a robust and cost-effective energy management system, especially on the demand side, to provide consumers’ demands with an acceptable level of power quality. Accordingly, here, a comprehensive review of recent developments in the power system flexibility and demand-side management strategies and demand response programs are provided to include mainly classifications, estimation methods, distributed energy resource modelling approaches, infrastructure requirements, and applications. In addition, current research topics for applying power system flexibility solutions and demand-side management strategies based on modern power system operation are deliberated. Also, prominent challenges, research trends, and future perspectives are discussed. Finally, this review article aims to be an appropriate reference for comprehensive research trends in the power system flexibility concept in general and in demand-side management strategies and demand response programs, specifically.