Hasil untuk "Distribution or transmission of electric power"

Constance Crozier, Thomas Morstyn, M. Mcculloch

Abstract A rapid increase in the number of electric vehicles is expected in coming years, driven by government incentives and falling battery prices. Charging these vehicles will add significant load to the electricity network, and it is important to understand the impact this will have on both the transmission and distribution level systems, and how smart charging can alleviate it. Here we analyse the effects that charging a large electric vehicle fleet would have on the power network, taking into account the spatial heterogeneity of vehicle use, electricity demand, and network structure. A conditional probability based method is used to model uncontrolled charging demand, and convex optimisation is used to model smart charging. Stochasticity is captured using Monte Carlo simulations. It is shown that for Great Britain’s power system, smart charging can simultaneously eliminate the need for additional generation infrastructure required with 100% electric vehicle adoption, while also reducing the percentage of distribution networks which would require reinforcement from 28% to 9%. Discussion is included as to how far these results can be extended to other power systems.

Bartosz Uniejewski

Accurate day-ahead electricity price forecasts are critical for power system operation and market participation, yet growing renewable penetration and recent crises have caused unprecedented volatility that challenges standard models. This paper revisits variance stabilizing transformations (VSTs) as a preprocessing tool by introducing a novel parametrization of the asinh transformation, systematically analyzing parameter sensitivity and calibration window size, and explicitly testing performance under volatile market regimes. Using data from Germany, Spain, and France over 2015-2024 with two model classes (NARX and LEAR), we show that VSTs substantially reduce forecast errors, with gains of up to 14.6% for LEAR and 8.7% for NARX relative to untransformed benchmarks. The new parametrized asinh consistently outperforms its standard form, while rolling averaging across transformations delivers the most robust improvements, reducing errors by up to 17.7%. Results demonstrate that VSTs are especially valuable in volatile regimes, making them a powerful tool for enhancing electricity price forecasting in today's power markets.

Shaocheng Wu, Linong Wang, Bin Song et al.

The presence of deteriorated insulators in power grids poses a significant threat to the safety of power transmission. The electric field detection method (EFDM) is considered an effective approach for detecting low or zero value insulators on high-voltage transmission lines. In this article, we analyzed the effects of the detection device, measurement angle, and insulator deterioration on the axial electric field value of insulator strings using both test values and simulation values. Our results indicated that the detection device had minimal impact on the normalized values of the axial electric field, whereas the measurement angle affected the values. However, the errors between the test values and the simulation values at the ends of the insulator strings were larger, indicating that the measurement device is better suited for measuring at nonends of the insulator strings. Based on our findings, we used the detection device to measure the electric field of insulator strings on a 220-kV transmission line. The results of the measurement showed that the electric field detector could accurately measure the axial electric field of the insulator string and detect deteriorated insulators, thereby providing a more effective measurement method. Our research thus confirms the feasibility of the EFDM in actual power transmission lines.

Franko Pandžić, Ivan Sudić, Tomislav Capuder et al.

Abstract 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.

Mohammad Ebrahim Hajiabadi, Mahdi Samadi, Mohammad Hassan Nikkhah et al.

Abstract One of the primary challenges faced by generation companies (GenCos), which operate multiple generation units within the electricity market, is the determination of the optimal bid price for these units to maximize profit. This paper proposes a novel approach to ascertain the optimal bid price direction for GenCos by leveraging the gradient vector of the profit function within the constraints of the electricity market. First, the Jacobian matrix of unit profits is computed using the electricity market structural decomposition method. This matrix highlights how the profit of generation units is affected by market input parameters, including the bid prices of the units. Then, the gradient vector of the GenCos' profit function and the optimal bid price direction are derived from the Jacobian matrix. The methodology is applied to a 24‐bus IEEE network, with results validated against those from a simulation method to confirm the efficacy of the proposed approach. The simulation results show that the highest and lowest profit changes with a step increase of 0.1$/MWh are observed for GenCo 4 and GenCo 6 with values of 60.28 and 2.20 $/h, respectively. The proposed approach can be effective in the changes of bid direction of the units of a GenCo to achieve the highest possible profit.

Yangwu Shen, Feifan Shen, Heping Jin et al.

Abstract Natural disasters would destroy power grids and lead to blackouts. To enhance resilience of distribution systems, the sequential load restoration strategy can be adopted to restore outage portions using a sequence of control actions, such as switch on/off, load pickup, distributed energy resource dispatch etc. However, the traditional strategy may be unable to restore the distribution system in extreme weather events due to random sequential contingencies during the restoration process. To address this issue, this paper proposes a distributionally robust sequential load restoration strategy to determine restoration actions. Firstly, a novel multi‐time period and multi‐zone contingency occurrence uncertainty set is constructed to model spatial and temporal nature of sequential line contingencies caused by natural disasters. Then, a distributionally robust load restoration model considering uncertain line contingency probability distribution is formulated to maximize the expected restored load amount with respect to the worst‐case line contingency probability distribution. Case studies were carried out on the modified IEEE 123‐node system. Simulation results show that the proposed distributionally robust sequential load restoration strategy can produce a more resilient load restoration strategy against random sequential contingencies. Moreover, as compared with the conventional robust restoration strategy, the proposed strategy yields a less conservative restoration solution.

Lionel Leroy Sonfack, René Kuate‐Fochie, Andrew Muluh Fombu et al.

Abstract This manuscript proposes a robust excitation control strategy for synchronous generators using backstepping theory and an artificial neural network with a radial basis function to improve power system performance during disturbances and parametric uncertainties. The artificial neural network is used to estimate unmeasurable quantities and unknown internal parameters of a recursive backstepping control. Lyapunov theory is used to carry out the stability analysis and to deduce the online adaptation laws of artificial neural network parameters (weights, centres and widths). To validate the performance of this approach, simulations are performed on an IEEE 9 bus multi‐machine power system. Different test results, compared with those of an existing non‐linear adaptive controller, confirm the high robustness of the proposed method against disturbances and uncertainties.

Yuanshi Zhang, Wenyan Qian, Jun Shao et al.

Active power sharing and voltage regulation are two of the major control challenges in the operation of the voltage source converter based multi-terminal high-voltage DC (VSC-MTDC) system when integrating large-scale offshore wind farms (OWFs). This paper proposes two novel adaptive voltage reference based droop control methods to regulate pilot DC voltage and share the power burden autonomously. The proposed Method I utilizes DC grid lossy model with the local voltage droop control strategy, while the proposed Method II adopts a modified pilot voltage droop control (MPVDC) to avoid the large errors caused by the DC grid lossless model. Dynamic simulations of a five-terminal MTDC grid are carried out using MATLAB/Simulink SimPowerSystems /Specialized Technology to verify the proposed autonomous control methods under various types of disturbance and contingency. In addition, comparative study is implemented to demonstrate the advantages of the proposed methods.

G. Gómez-Ramírez, Rebeca Solís-Ortega, Luis Alberto Ross-Lépiz

This paper presents a methodology for assessing the impact of electric vehicles (EVs) on the power transmission grid of the Costa Rica Power System. The methodology considers penetration scenarios, user preferences, charging habits, and expected fleet growth. Using ETAP software, the study simulates power flow, demand behavior, and voltage levels in the presence of high penetration of electric vehicles. The analysis covers a 15-year horizon and focuses on voltage and demand profiles in 2025, 2030, and 2040. The results indicate a decline in voltage profiles that reaches dangerous levels after 2030, primarily in the distribution grid, and an increase in demand by Image 1 for 2040 in the most severe scenario. The analysis also reveals several key findings (a) the identification of problems in the electrical infrastructure starting in 2030 and a major insufficiency in accommodating the increase in EVs by 2040; (b) the need to evaluate stability in transmission grids considering loadability and voltage; (c) the necessity of investing in electrical infrastructure, driven by public policies, to meet future energy requirements and strengthen transmission networks; (d) the significance of accounting for both EV growth and electric infrastructure improvements in system analysis; and (e) the anticipation that the system's performance will fall within the extreme demand values presented in the analysis. The study emphasizes the importance of considering a broader range of scenarios and variability in parameters, especially user charging behaviors, to enable decision-makers to plan for the challenges and opportunities associated with the widespread adoption of EVs in a country's power grid.

Mohammad Tayyab, Adil Sarwar, Shadab Murshid et al.

Abstract In recent times, multilevel inverters (MLIs) have become very popular for commercial and industrial applications. Here, an eleven‐level inverter and its power flow control are presented. The presented topology has a lesser component count than other existing topologies, thus reducing the devices and overall cost of the inverter. This inverter comprises six bidirectional switches, two DC sources, one four‐quadrant switch, and two capacitors for the voltage divider circuit. The conduction modes and corresponding switching states of the presented eleven‐level inverter are shown in detail. Further, the apparent power control of the presented inverter under grid‐connected operation is discussed, which provides simultaneous active and reactive power control over the power injected into the grid. Switching and conduction losses are calculated for 3 and 6 kVA grid injected power at 0.8 power factor lagging. The obtained results show that the total harmonic distortion (THD) of the inverter output voltage and grid current is 12.10% and 0.23%, respectively, under 6 kVA power transfer conditions. The real‐time analysis is also carried out for 3 and 6 kVA power transfer conditions for the presented eleven‐level inverter to validate the active and reactive power flow control.

Jan Klusacek, Jiri Drapela, Roberto Langella

The presence of power generating plants owned by prosumers may lead to unbalanced bidirectional energy flows at the points of connection to the relevant distribution systems. This will impact future energy communities, where appropriate metering within the community is a crucial issue for billing purposes. This paper shows that the current metrics for active energy measurement and the registration of three-phase revenue meters may fail to fairly charge unbalanced prosumers for their use of the distribution system as an inherent phase-to-phase balancer. On the other hand, it is proven here that adopting metrics based on positive sequence power/energy measurement would lead to more fair billing within the community. A comparative study was performed using a simplified but realistic model of a distribution system feeding two prosumers (i.e., an archetype of an energy community). First, representative case studies were considered. Then, a more realistic simulation of a single day of operation was conducted. The main contribution of the paper is a detailed and systematic comparison of the methods used for measuring and sorting energy into registers in revenue meters to support the ongoing discussions about fair metering within future energy communities.

Xiangwu Yan, Chen Shao, Jiaoxin Jia et al.

Abstract With the rapid development of active distribution networks, the “petal”‐type distribution network has become the mainstream power supply structure. Power control methods for active distribution networks should be further studied to ensure the safe and reliable power supply of a distribution system. An electromagnetic rotary power flow controller (RPFC) is a feasible solution for controlling power in active distribution networks. However, when testing the effectiveness of the PQ decoupled control method for RPFC based on instantaneous reactive power theory, difficulties were encountered with the synchronous control of the rotor position angle of two rotating‐phase transformers, and the accuracy of power control was unsatisfactory. Given this condition, PQ control is improved in three ways. First, the system's periodic oscillation problem is solved via variable speed control. Second, the servo motor‐rotary phase‐shifting transformer synchronous rotation scheme, which reduces power control error and improves stability, is designed. Third, the overshoot phenomenon in power control is improved using the variable‐domain fuzzy proportional‐integral adaptive method. Experimental results show that the proposed advanced control scheme exhibits good dynamic and static performance in power control scenarios and achieves effective improvement in RPFC.

Xuan Deng, Yufan Hu, Yiyang Jia et al.

Abstract The social economy is growing rapidly, and the power grid load demand is increasing. To maintain the stability of the power grid, it is crucial to achieve accurate and rapid power system stability assessment. In the actual operation of the power network, data loss is an unavoidable situation. However, most of the data‐driven models currently used assume that the input data is complete, which has obvious limitations in real‐world applications. This paper suggests an IVS‐GAN model to assess power system stability using incomplete phasor measurement unit measurement data with random loss. The proposed method combines the super‐resolution perception technology based on generative adversarial network (GAN) with a time‐series signal classification model. The generator adopts a 1D U‐Net network and uses convolutional layers to complete and recover missing data. The discriminator adopts a new gated recurrent unit–attention architecture proposed here to better extract voltage temporal variation features on key buses. The result of this paper is that the stability evaluation method outperforms other algorithms in high voltage data loss rates on the New England 10‐machine 39‐bus system.

Manijeh Alipour, Omid Alizadeh-Mousavi

Renewable energy productions and electrification of mobility are promising solutions to reduce greenhouse gas emissions. Their effective integration in a power grid encounters several challenges. The uncertain nature of renewable energy productions as well as stochastic consumptions of electric vehicles introduce remarkable intermittency to a distribution grid and results in bi-uncertain characteristics of both supply and demand sides. One way to verify the secure grid operation within acceptable voltage and loading levels is to assess its required flexibility considering possible boundaries of uncertain variables. In this paper, first a comprehensive linear model of distribution grid considering all pertaining constraints is presented. Then, a flexibility estimation technique is proposed based on the feasibility study of the uncertain space of photovoltaic power productions and load containing electric vehicles. The proposed methodology uses grid monitoring data to determine grid state and to model uncertain parameters. The model is applied on a real low voltage (LV) system equipped with grid monitoring devices.

Jochen Stiasny, Baosen Zhang, Spyros Chatzivasileiadis

The dynamic behaviour of a power system can be described by a system of differential-algebraic equations. Time-domain simulations are used to simulate the evolution of these dynamics. They often require the use of small time step sizes and therefore become computationally expensive. To accelerate these simulations, we propose a simulator - PINNSim - that allows to take significantly larger time steps. It is based on Physics-Informed Neural Networks (PINNs) for the solution of the dynamics of single components in the power system. To resolve their interaction we employ a scalable root-finding algorithm. We demonstrate PINNSim on a 9-bus system and show the increased time step size compared to a trapezoidal integration rule. We discuss key characteristics of PINNSim and important steps for developing PINNSim into a fully fledged simulator. As such, it could offer the opportunity for significantly increasing time step sizes and thereby accelerating time-domain simulations.

Xiaojun Zhao, Yingrui Liu, X. Chai et al.

H. Saboori, S. Jadid

Abstract Today, knowledge of battery energy storage systems (BESSs) has experienced a rapid growth resulting to the numerous grid applications. The utility-scale batteries assembled in containers can be transported in the grid. Despite numerous benefits, this feature has been overlooked. In previous studies, battery movement is modeled based on a specific transfer method, such as a truck or train. Accordingly, by changing the method of transporting the battery, the problem should be re-modeled and also it is not possible to schedule the battery movements by combining two transfer methods. In this context, this paper proposes a new battery movement scheduling in the distribution networks. To this end, optimal charging or discharging power in addition to the bus location will be determined for any time period of operation. In the proposed model, only distance between buses is important and how the battery is transferred is not important. accordingly, battery transfer may be performed using one transmission method, such as a truck or a combination of two methods (truck and train). Reactive power contribution by the battery, power losses and bus voltages of the network are also counted by maintaining linear structure of the model. This guarantees practical application of the formulation for the real-life distribution grids. Results of implementing the model on a test system indicate distinct superiority of the mobile BESS with respect to the stationary installations.

Yijing Liu, T. Overbye, Wenbo Wang et al.

This paper investigates the impacts of electric vehicles (EVs) on power system frequency regulation based on an open-source transmission-and-distribution (T&D) dynamic co-simulation framework. The development of an EV dynamic model based on an Western Electricity Coordinating Council dynamic model is introduced first, then the T&D dynamic co-simulation platform is described. The advantage of the overall platform is that distributed energy resources, such as distributed photo-voltaics and EVs, are modeled explicitly in both transmission and distribution simulators for frequency and voltage dynamics, respectively. The case studies simulate the frequency responses (i.e., primary and/or secondary) of the EVs after the system is exposed to an N-1 contingency, such as a generation trip. Various EV frequency regulation participation strategies are also investigated to study their impacts on system frequency response. The studies shows that EVs have the potential capability to provide effective frequency regulation services.

Kun Liu, Feng Gao

Abstract With the breaking down of information barriers between energy flow with manufacturing flow, the coordination between them is an effective way to relieve the dual pressure from energy and environment industrial integrated energy system. The authors develop a scenario‐based coordination model for energy flow and manufacturing flow to make full use of the flexibilities of energy supply and production process to reduce energy cost. To capture the flexibility in the energy flow, the authors enhance an electricity‐steam‐product gas–gas storage coupling energy flow model considering multi‐uncertainties. The authors also develop a batch process model to formulate the flexibility in the production process. Based on the batch energy consumption constraints, the energy flow model and the batch process model are integrated as a coordination model. To ensure the feasibility of hard budget constraints under all possible random realisations, we add all‐scenario‐feasibility robust constraints which are infinite‐dimensional constraints into the model. To solve the model, a vertex scenario set based on the characteristics of convex optimisation is constructed to equivalently convert infinite‐dimensional constraints to finite‐dimensional constraints. In this way, the coordination model is transformed to a mixed integer linear programming and can be solved using CPLEX. Finally, numerical test based on a real iron and steel plant is analysed. The results show that coordination between energy with manufacturing flow is effective to reduce the energy cost and carbon emission. Compare with only optimising energy flow, the coordination model can reduce total cost about 221.6 thousand RMB and 304.44t coal every day.

Pascal A. Schirmer, Iosif Mporas

Non-Intrusive Load Monitoring aims to extract the energy consumption of individual electrical appliances through disaggregation of the total power consumption as measured by a single smart meter in a household. Although when data from the same household are used to train a disaggregation model the device disaggregation accuracy is quite high (80% - 95%), depending on the number of devices, the use of pre-trained disaggregation models in new households in most cases results in a significant reduction of disaggregation accuracy. In this article we propose a transferability approach for Non-Intrusive Load Monitoring using fractional calculus and normalized Karhunen Loeve Expansion based spectrograms followed by a Convolutional Neural Network in order to generate device characteristic features that do not change significantly across different households. The performance of the proposed methodology was evaluated using two publicly available datasets, namely REDD and REFIT. The proposed transferability approach improves the Mean Absolute Error by 13.1% when compared to other transfer learning approaches for energy disaggregation.