Hasil untuk "Distribution or transmission of electric power"

Siddhesh Pimpale

The introduction of Smart Electric Vehicles (SEVs) represents an increasingly disruption on automotive area, once integrates advanced computer and communication technologies to highly electrical cars, which come with high performances, environment friendly and user friendly characteristics . But the increasing complexity of SEVs prompted by greater dependence on interconnected systems, autonomous capabilities and electrification, presents new challenges in cybersecurity as well as functional safety. The safety and reliability of such vehicles is paramount, as unsafe or unreliable operation in either case represents an unacceptable risk in terms of the performance of the vehicle and safety of the passenger. This paper investigates the integrated development of cybersecurity and functional safety for SEVs, emphasizing the requirement for the parallel development of these domains as components that are not treated separately. In SEVs, cybersecurity is quite crucial in order to prevent the threats of hacking, data breaches and unauthorized access to vehicle systems. Functional safety ensures that important vehicle functions (braking, steering, battery control, etc.) keep working even if some part fails. This convergence of functional safety issues with cybersecurity issues is becoming more crucial, since a security incident can result in a failure of catastrophic consequences for a functional safety system and, conversely. This paper reports the current state of cybersecurity and functional safety standards for SEVs, highlighting challenges that include the weaknesses of communication networks, the potential security threats of over-the-air updates, and the demand for real-time responsive systems for failure.

Zheng Pan, Liang Che, Chunming Tu

Abstract State estimation is critical for railway power supply systems (RPSSs). Pseudo‐measurement is commonly used in state estimation. However, the fluctuations of renewable generations and railway traction loads in RPSS may introduce data noise, which will jeopardize the accuracy of the generated pseudo‐measurements and thus impact the state estimation. Additionally, when learning the historical measurement data sequences, the traditional pseudo‐measurement model is likely to have overfitting, which will further impact the accuracy of pseudo‐measurements, thereby affecting the accuracy of state estimation. To address these issues, this paper proposes a high‐accuracy pseudo‐measurement‐based state estimation approach for RPSSs. Firstly, a denoising autoencoder‐based method is used to mitigate the impact of data noise on the accuracy of pseudo measurements, and a gated recurrent unit‐based method is used to adaptively learn the historical measurement data sequence, thereby improving the accuracy of pseudo measurements. Next, the pseudo‐measurement weights are obtained by generating pseudo‐measurement variances using the Gaussian mixture model. Finally, the pseudo measurements and real‐time measurements are integrated by weighted least squares to realize the state estimation of RPSS. The effectiveness and accuracy of the proposed method are verified by simulation on a modified IEEE 33‐node system which includes a railway traction substation and renewable generations.

Morteza Shafiekhani, Meysam Qadrdan, Yue Zhou et al.

Abstract Electric power systems are currently undergoing a transformation towards a decentralized paradigm by actively involving prosumers, through the utilization of distributed multi‐energy sources. This research introduces a fully decentralized multi‐community peer‐to‐peer electricity trading mechanism, which integrates iterative auction and pricing methods within local electricity markets. The mechanism classifies peers in all communities on an hourly basis depending on their electricity surplus or deficit, facilitating electricity exchange between sellers and buyers. Moreover, communities engage in energy exchange not only within and between themselves but also with the grid. The proposed mechanism adopts a fully decentralized approach known as the alternating direction method of multipliers. The key advantage of this approach is that it eliminates the need for a supervisory node or the disclosure of private information of the involved parties. Furthermore, this study incorporates the flexibility provided by residential heating systems and energy storage systems into the energy scheduling of some prosumers. Case studies illustrate that the proposed multi‐community peer‐to‐peer electricity trading mechanism effectively enhances local energy balance. Specifically, the proposed mechanism reduces average daily electricity costs for individual prosumers by 63% compared to scenarios where peer‐to‐peer electricity trading is not employed.

Kun Yang, Yulong Deng, Chunyan Li et al.

Abstract Green hydrogen, the cleanest energy carrier, is receiving increased attention in recent years. Transporting hydrogen through a natural gas system (NGS) will significantly promote the use of hydrogen, moreover, hydrogen‐enriched compressed natural gas (HCNG) has great potential for renewable energy accommodation. To solve the problem of altered gas flow caused by hydrogen injection into natural gas networks, an optimized operation model of integrated electricity‐HCNG systems (IEHCNGS) with distributed hydrogen injecting is proposed in this paper. Firstly, a calculating model of hydrogen volume fraction based on minimum square summation and depth‐first search is established to describe the gas flow distribution of NGS accurately. Secondly, a quantitative method of gas supply reliability based on maximum entropy is proposed to ensure the safe operation of the system. Finally, an optimization model of IEHCNGS is established considering the coupling constraints of the integrated system and the reliability of NGS. The case study shows that the hydrogen volume fraction calculation model can correct the heat value of gas in each pipeline in real‐time, the maximum entropy model helps to improve the gas supply reliability of NGS, and the distributed hydrogen injecting mode is more capable of accommodating renewable energy.

Zhen Wang, Hao Pan, Peng Cheng et al.

Abstract This study proposes a frequency coupling suppression strategy for voltage source converters (VSCs) with DC‐link dynamics to address the challenges posed by the asymmetric system structures for system modelling and control design. The strategy introduces a reshaping outer loop into the q‐axis, which eliminates the conjugate voltages and currents induced by the DC‐link dynamics, to achieve decoupling of the DC‐link voltage control (DVC). Therefore, the VSC is modelled as a single‐input single‐output (SISO) admittance model, which provides clear physical insights for the analysis of admittance characteristics. Compared with the admittance matrix, the SISO model reveals the dominance of each control dynamics on VSC admittance in different frequency bands. This insight facilitates the exploration of optimized control strategies to improve the system stability margin. Based on the SISO model, a virtual admittance for compensating the negative resistive effect of the PLL is designed to illustrate the advantages of the proposed method in providing design‐oriented analysis. Experimental results verify the effectiveness of the proposed control strategy.

Mehdi Akbari Moghadam, Sajad Bagheri, Amir Hosein Salemi et al.

Abstract Planning maintenance of medium voltage overhead lines is one of the most important issues in the studies on power distribution network, which will prevent and reduce unwanted interruption. In this paper, long‐term maintenance of medium voltage networks was planned by multi‐objective function, including an extended mixed‐integer linear model to optimize costs, energy not supplied (ENS), and average interruption duration index (SAIDI). In addition, the uncertainty about the annual growth rate of the load, the increase in the cost of goods and services and the increase in the selling price of energy as well as various constraints are all included in the desired objective function, which is one of the main innovations of this paper compared to other published studies. To apply the uncertainties, information gap decision theory (IGDT) has been used, and to solve the objective functions, LP‐Metric method has been used. The proposed method was implemented on the standard 11‐bus RBTS network by MATLAB and GAMS. The results showed that three different long‐term maintenance plans proposed here lead to the optimization of the annual maintenance costs of network, reduction in ENS and interruption, and increase in the reliability of the network based on the uncertainty of each feeder.

Yi Sun, Yiyuan Ding, Minghao Chen et al.

Abstract As an emerging multi‐energy consumption subject, data centres (DCs) are bound to become crucial energy users for integrated energy systems (IES). Therefore, how to fully tap the potential of the collaborative operation between DCs and IES to improve total energy efficiency and economic performance is becoming a pressing need. In this article, the authors research an optimization coordinated by the energy scheduling and information service provision within the scenario of an integrated energy system with a data centre (IES‐DC). The mathematical model of IES‐DC is first established to reveal the energy conversion process of the electricity‐heat‐gas IES and the DC's energy consumption affected by the scale of active IT equipment. For dynamical providing multi‐energy and computing service by coordinating scheduling energy and information equipment, the formulations of IES‐DC scheduling, which is described as a Markov decision process (MDP), are presented, and it is solved by introducing the twin‐delayed deep deterministic policy gradient (TD3), which is a model‐free deep reinforcement learning (DRL) algorithm. Finally, the numerical studies show that compared with benchmarks, the proposed method based on the TD3 algorithm can effectively control the operation of energy conversion equipment and the number of active servers in IES‐DC.

Yang Hong, Sylvain Almonacil, Haik Mardoyan et al.

We demonstrate nonlinearity-free transmission of Terabit/s/lambda PCS-64QAM signals through an HCF-based optical recirculating loop, which yields ~17.4% higher capacity than SMF-based loop under 23-dBm launch power (~13.5 dBm/channel) after 25 loops. Both lab experiment and field trial show HCF exhibits ~1.6-us/km lower latency than SMF.

Łukasz Marcul, Mateusz Brzozowski, Artur Janicki

In this article, we present the results of experiments with finding an efficient radio transmission method for an electric energy measurement system called OneMeter 2.0. This system offers a way of collecting energy usage data from beacons attached to regular, non-smart meters. In our study, we compared several low power wide area network (LPWAN) protocols, out of which we chose the LoRaWAN protocol. We verified the energy consumption of a LoRa-based transmission unit, as well as the transmission range between network nodes in urban conditions. We discovered that LoRaWAN-based transmission was highly energy-efficient and offered decent coverage, even in a difficult, dense urban environment.

A. Umunnakwe, M. Parvania, Hieu Nguyen et al.

Spatio-temporal wildfire risk assessment, power grid resilience, proactive de-energization, stochastic wildfire ignition maps, power system wildfire metrics. Abstract: Wildfires are natural or man-made disasters that continuously threaten portions of the transmission and distribution grid, and thus the stability of the electric grid. This paper presents a two-stage framework for assessing power system-wildfire risk using a data-driven wildfire prediction model. The first stage of the framework estimates the spatio-temporal probability of potential wildfire ignition and propagation using a deep neural network in combination with the wildfire physical spread model. Analysis reveals similar spatial and temporal patterns between the model-predicted wildfire ignition potential and actual wildfire ignition. Motivated by these observations, the second stage assesses the wildfire risk in the power grid operation in terms of potential loss of load by de-energization, through combining geospatial information system (GIS) data of the power grid topology and the stochastic spatio-temporal wildfire model developed in the first stage. The electric power utility applications introduced by the proposed framework are twofold: 1) a spatio-temporal risk model for proactive de-energization against potential power system failure-induced wildfire, and 2) a spatio-temporal spreading model for optimal grid operations against exogenous wildfire. The proposed model, based on real-world dataset, is demonstrated on the IEEE 24-bus test system mapped to a study area in northern California, while the results illustrate the proposed model can achieve the best performance in potential wildfire ignition detection (AUC of 0.995) compared to other baselines, as well as demonstrates the risk-aware operation of the power system enabled by the proposed framework.

Mansour Rafiee, Mahdi Pourgholi, Afshin Shahmohammadi

In this paper, a 3.1kW DC motor is used to simulate the static and dynamic behavior of a horizontal axis wind turbine. The effect of the difference between the wind turbine's inertia and DC motor on the dynamic behavior of the system, torque oscillation due to the effect of the tower shadow and wind shear, and the effect of rotary losses in the mechanical torque of the DC motor are considered. The torque of the DC motor is controlled by the closed-loop PID controller. This closed loop has two feedbacks for the speed and current of the DC motor. The turbine calculations are performed in Matlab/Simulink and the reference signal of the turbine's torque is sent to the chopper using an interface card. Two methods are proposed to estimate the acceleration of the DC motor to improve the compensation of the turbine inertia effect: the Kalman Filter, and the second-order low-pass filter. Adjustable parameters of proposed methods are optimized using the particle swarm optimization algorithm (PSO). A 2.2 kW synchronous generator is coupled with the wind turbine emulator and feeds a constant load. To show the effectiveness of the proposed methods the results are reported. The results indicate that the Kalman filter has a better performance in the compensation of the turbine inertia effect.

Xiuping Su, Yanqing Jia, Hongwei Shi et al.

Abstract The iron core in the circuit breaker of oil dashpot is mainly affected by the electromagnetic force, spring force and oil damping force, which can play the function of anti‐time protection when overload current is excited. At low temperature, the viscosity of the methyl silicone oil damping fluid will change, and the parameters of spring and iron core will also change with temperature. The change of these parameters will lead to the change of the resultant force on the iron core, which will affect the operation time. To analyze this problem, a correlation model is established. Through the measurement of parameters at low temperature, numerical calculation, electromagnetic analysis, simulation analysis and compared with the experimental results, the study found that the viscosity of the damping fluid increases, the iron core is deformed, and the spring stiffness increases at low temperature. The influence of a single factor on the operation characteristics of the tripper is not enough to reflect its overall characteristics, and the comprehensive effect of each factor needs to be considered. After comprehensively considering all factors, the results are closer to the experimental results. The results provide a theoretical basis for the optimization design of the circuit breaker.

Malini Ghosal, Allison M. Campbell, Marcelo A. Elizondo et al.

High penetration of intermittent generation increases uncertainty and variability in balancing reserve needs. New tools are needed to help the balancing authority system operator plan for intraday and intra-hour balance between generation and load. The Grid Reserve and Flexibility Planning tool (GRAF-Plan) helps plan for adequate balancing reserves for future years or seasons for expected wind and solar generation. It also assesses the flexibility of the scheduled generation fleet to meet such requirements. The estimations are based on utilities’ operational practices (e.g., forecasting and time frame of reserve deployment), and it incorporates detailed data from renewable generation and load. Application of the tool in estimating reserve requirements in Central America under different levels of renewable generation (high and low) and for the Western Electricity Coordinating Council (WECC) 2030 Anchor Data Set scenario is discussed.

Zhengxian Chen, Maowei Wang, Conghu Li

With the development of the electric power system, the smart grid has become an important part of the smart city. The rational transmission of electric energy and the guarantee of power supply of the smart grid are very important to smart cities, smart cities can provide better services through smart grids. Among them, predicting and judging city electric power consumption is closely related to electricity supply and regulation, the location of power plants, and the control of electricity transmission losses. Based on big data, this paper establishes a neural network and considers the influence of various nonlinear factors on city electric power consumption. A model is established to realize the prediction of power consumption. Based on the permutation importance test, an evaluation model of the influencing factors of city electric power consumption is constructed to obtain the core characteristic values of city electric power consumption prediction, which can provide an important reference for electric power related industry.

Christoforos Menos-Aikateriniadis, Stavros Sykiotis, Pavlos S. Georgilakis

The rapid growth of decentralized energy resources and especially Electric Vehicles (EV), that are expected to increase sharply over the next decade, will put further stress on existing power distribution networks, increasing the need for higher system reliability and flexibility. In an attempt to avoid unnecessary network investments and to increase the controllability over distribution networks, network operators develop demand response (DR) programs that incentivize end users to shift their consumption in return for financial or other benefits. Artificial intelligence (AI) methods are in the research forefront for residential load scheduling applications, mainly due to their high accuracy, high computational speed and lower dependence on the physical characteristics of the models under development. The aim of this work is to identify households' EV cost-reducing charging policy under a Time-of-Use tariff scheme, with the use of Deep Reinforcement Learning, and more specifically Deep Q-Networks (DQN). A novel end users flexibility potential reward is inferred from historical data analysis, where households with solar power generation have been used to train and test the designed algorithm. The suggested DQN EV charging policy can lead to more than 20% of savings in end users electricity bills.

Rayan El Helou, S. Sivaranjani, D. Kalathil et al.

The electrification of heavy-duty vehicles (HDEVs) is a nascent and rapidly emerging avenue for decarbonization of the transportation sector. In this paper, we examine the impacts of increased vehicle electrification on the power grid infrastructure, with particular focus on HDEVs. We utilize a synthetic representation of the 2000-bus Texas transmission grid, and realistic representations of multiple distribution grids in Travis county, Texas, as well as transit data pertaining to HDEVs, to uncover the consequences of HDEV electrification, and expose the limitations imposed by existing electric grid infrastructure. Our analysis reveals that grid-wide voltage problems that are spatiotemporally correlated with the mobility of HDEVs may occur even at modest penetration levels. In fact, we find that as little as 11% of heavy duty vehicles in Texas charging simultaneously can lead to significant voltage violations on the transmission network that compromise grid reliability. Furthermore, we find that just a few dozen EVs charging simultaneously can lead to voltage violations at the distribution level.

Fiaz Ahmad, Akhtar Rasool, E. Ozsoy et al.

M. Mahdavi, Hassan Haes Alhelou, N. Hatziargyriou et al.

Distribution network is an essential part of electric power system, which however has higher power losses than transmission system. Distribution losses directly affect the operational cost of the system. Therefore, power loss reduction in distribution network is very important for distribution system users and connected customers. One of the commonly used ways for reducing losses is distribution system reconfiguration (DSR). In this process, configuration of distribution network changes by opening and closing sectional and tie switches in order to achieve the lowest level of power losses, while the network has to maintain its radial configuration and nodal voltage limits, and supply all connected loads. The DSR aiming loss reduction is a complex mixed-integer optimization problem with a quadratic term of power losses in the objective function and a set of linear and non-linear constraints. Accordingly, distribution network researchers have dedicated their efforts to developing efficient models and methodologies in order to find optimal solutions for loss reduction via DSR. In this paper, an efficient mathematical model for loss minimization in distribution network reconfiguration considering the system voltage profile is presented. The model can be solved by commercially available solvers. In the paper, the proposed model is applied to several test systems and real distribution networks showing its high efficiency and effectiveness for distribution systems reconfiguration.

Xiao-Ping Zhang, Shao-Hui Tang, Quangsheng Liu et al.

Shota Yumiki, Y. Susuki, Yuta Oshikubo et al.

We develop a system-level design for the provision of Ancillary Service (AS) for control of electric power grids by in-vehicle batteries, suitably applied to Electric Vehicles (EVs) operated in a sharing service. An architecture for cooperation between transportation and energy management systems is introduced that enables us to design an autonomous Vehicle-to-Grid (V2G) for the provision of multi-objective AS: primary frequency control in a transmission grid and voltage amplitude regulation in a distribution grid connected to EVs. The design is based on the ordinary differential equation model of distribution voltage, which has been recently introduced as a new physics-based model, and is utilized in this paper for assessing and regulating the impact of spatiotemporal charging/charging of a large population of EVs to a distribution grid. Effectiveness of the autonomous V2G design is evaluated with numerical simulations of realistic models for transmission and distribution grids with synthetic operation data on EVs in a sharing service. In addition, we present a hardware-in-the-loop test for evaluating its feasibility in a situation where inevitable latency is involved due to power, control, and communication equipments.