Hasil untuk "Distribution or transmission of electric power"

Carson W Taylor Neal J Balu Dominic Maratukulam

Ramon Abritta, Gabriel M. G. Guerreiro, Alexey Pavlov et al.

The generator side of inverter-based resources (IBRs) interfaced with back-to-back converters is electrically decoupled from the grid. During electrical faults, grid-following IBRs behave as voltage-dependent current sources due to grid code requirements and converter control. This paper presents Newton-Raphson formulations for estimating short-circuit currents from IBRs using novel power flow modeling approaches to address voltage-dependent current sources. The work shows how to represent dq0 current injections, which are typically implemented in control architectures based on phase-locked loops. The power flow formulations are adapted to capture grid codes with different characteristics, such as the injection of negative-sequence currents during unbalanced faults. Comparisons against a field-validated electromagnetic transients (EMT) model reveal mean absolute errors of less than 0.5% for the proposed approach when estimating steady-state short-circuit currents from type IV wind turbine generators under grid-following control.

Kisung Park, Joonyoung Lee, A. Das et al.

Sajjad Maleki, Shijie Pan, Subhash Lakshminarayana et al.

The growing penetration of IoT devices in power grids despite its benefits, raises cybersecurity concerns. In particular, load-altering attacks (LAAs) targeting high-wattage IoT-controllable load devices pose serious risks to grid stability and disrupt electricity markets. This paper provides a comprehensive review of LAAs, highlighting the threat model, analyzing their impact on transmission and distribution networks, and the electricity market dynamics. We also review the detection and localization schemes for LAAs that employ either model-based or data-driven approaches, with some hybrid methods combining the strengths of both. Additionally, mitigation techniques are examined, focusing on both preventive measures, designed to thwart attack execution, and reactive methods, which aim to optimize responses to ongoing attacks. We look into the application of each study and highlight potential streams for future research.

H. Taraghi Nazloo, R. Babazadeh, M. Varmazyar

With increasing electricity demand, conventional centralized power generation systems encounter numerous challenges, including transmission and distribution losses, limited capacity, and high operational costs. In response, distributed energy systems have emerged as a promising solution by enabling electricity generation in close proximity to consumption points. These systems leverage renewable energy sources and minimize energy losses during transmission, presenting a more sustainable and efficient alternative. By utilizing diverse energy sources such as solar thermal panels, photovoltaic systems, geothermal energy, distributed energy systems enhance overall efficiency, and reduce power losses during transmission as well as greenhouse gas emissions. This research endeavor presents a novel approach employing mixed-integer linear programming to optimize distributed energy systems. The proposed model facilitates the determination of optimal dimensions of technologies, including combined heat and power systems, boilers, electric chillers, and absorption chillers, while simultaneously minimizing total costs and greenhouse gas emissions and adhering to real-world constraints. The findings of this study are validated through a real-world numerical example, confirming the model’s efficiency in configuring and planning distributed energy systems optimally, thereby enhancing their operational performance.

Tan Nhat Pham, Rakibuzzaman Shah, Nima Amjady et al.

Abstract Wildfires, which can cause significant damage to power systems, are mostly inevitable and unpredictable. Fire danger indexes, such as the Forest Fire Danger Index (FFDI) and the Canadian Fire Weather Index (FWI), measure the potential wildfire danger at a given time and location. Thus, by predicting these fire danger indexes in advance, power system operators can obtain valuable insight into the potential wildfire risks and can better be prepared to tackle the wildfires. However, due to dependency on weather conditions, these indexes usually have volatile time series, which make their prediction complex. Taking these facts into account, this paper, unlike previous approaches that predict fire danger indexes based on climatological models, develops a machine learning‐based forecast process to predict these indexes using the relevant weather data and past performance. To do this, first, a volatility analysis approach is presented to analyse the volatility level of the time series data of a fire danger index. Afterwards, an effective machine learning‐based forecast methodology using a new deep feature selection model is proposed to predict fire danger indexes. The developed forecast methodology is tested on the real‐world data of FFDI and FWI and is compared with several popular alternative methods reported in the literature.

Yuxiong Huang, Xuanman Rong, Gengfeng Li et al.

Abstract Distributed energy resources (DERs) provide flexible load restoration strategies, which can effectively enhance the resilience of active distribution systems (ADSs). Whereas, the widespread DERs in ADSs complicates the supply‐demand relationship and make the system resilience difficult to access. Therefore, this paper proposes a simulation‐based resilience assessment algorithm of ADSs considering the microgrid formation based on grid‐edge DERs. Microgrid formation is used to depict the resilience gain of grid‐edge DERs on ADSs. Specifically, a resilience assessment framework for ADSs is firstly proposed, where the uncertainty of component state and supply‐demand is modelled based on probability statistics. Then the mixed integer linear programming is used to search for optimal load restoration strategies with microgrid formation. On this basis, a set of resilience indices are defined to quantitatively analyse the resilience of ADSs, and a resilience assessment algorithm with uncertainty scenario generation is proposed to obtain these indices. Furthermore, extensive numerical results based on a modified IEEE 123‐bus feeder validate the effectiveness of the proposed method.

Satoru Miyazaki, Takashi Kuraishi, Tsuguhiro Takahashi

Abstract In recent years, the “asset management” or “managing assets” technique has been expected to support the rationalization of the maintenance and operation of electric power equipment, especially as a countermeasure for aging equipment. For this purpose, the development of diagnostic methods for aging, remaining life, and faults is necessary. Furthermore, it is important to accumulate case studies applying such diagnostic methods to real equipment and the analysing benefits of the applications of the diagnostic methods. In this report, frequency response analysis and a partial discharge measurement are applied to a shunt reactor suspected of occurrence of partial discharge. Results of the diagnostic methods suggest the breakage of the earth bar connecting the iron‐core blocks and the occurrence of the partial discharge at the iron core. From these diagnostic findings, the shunt‐reactor operator decided to postpone the replacement for 2 years, compared with the case where no diagnostic methods were applied. In this report, the benefits of deciding the postponement is also evaluated.

Avishan Bagherinezhad, Mahnoosh Alizadeh, Masood Parvania

The adoption of autonomous electric vehicles (AEVs) offers an opportunity to decarbonize the transportation sector while eliminating the human errors in driving accidents. However, adopting AEVs may impose challenges to the operation of power distribution systems to ensure the availability of power for charging a growing number of AEVs at different times and locations. This paper takes an opportunistic look at this problem and develops a rolling horizon model for coordinating the operation of electric autonomous ride-hailing systems with power distribution systems. The proposed model incorporates the most recent real-time information and the future expected value of energy level, spatial and temporal location of AEV fleet, traffic data, and passenger demand. Using this data, the proposed model adopts a rolling horizon approach to optimize the routing of AEVs to serve spatio-temporal passenger demand across the transportation network, while optimizing the time and location of AEVs charging to ensure the availability of energy to serve the passenger demand, and satisfying the operational constraints of the power distribution system. The proposed model is implemented on a test transportation system, coupled with the IEEE 33-bus test power distribution system. The numerical results demonstrate the capability of the proposed model in ensuring the reliability and quality of service for both electric autonomous ride-hailing and power distribution systems.

Rodrigo Rozenblit Tiferes, Giovanni Manassero, Eduardo Lorenzetti Pellini et al.

This paper presents a novel pilot protection algorithm for alternating current (ac) transmission lines that relies on biweight midcorrelation (BWMC) coefficients between the terminals’ currents. The proposed solution was compared with recent and similar literature alternatives, and the results indicate that it is both effective in detecting faults of any type and that, as it is median-based, unlike the techniques used so far, the BWMC-based algorithm may be advantageous over existing methods as it presents higher robustness to not incorrectly trip on external faults, mainly under the critical conditions of CT saturation, noisy measurements, and sample misalignment, typically associated with outliers. The algorithm also proved effective against simultaneous multi-faults, weak infeed, power swing, multiple cases on different lines, and faults with time-varying transition resistance. The BWMC-based solution was finally experimentally tested in real-time with a hardware-in-the-loop system containing a CMC-256 set, an IEC 61850-based IED that embeds the algorithm’s logic, and current measurements recorded during actual faults on existing 500 kV lines from the Brazilian interconnected power system. All analyses indicate that this new algorithm can be a rapid, reliable, and robust solution.

Zhenyu Wang, Zhao Xu, Donglian Qi et al.

Abstract This paper presents an approximate power flow model‐based forecasting‐aided state estimation estimator for power distribution networks subject to naive forecasting methods and nonlinear filtering processes. To this end, this estimator designs a voltage perturbation vector around the priori‐determined nominal value as the dynamic state variable, which enables more detailed depictions of voltage changes. Then, a state transition model incorporating nodal power variation is derived from the approximate power injection model. The constant state transition matrix working on power variations only consists of nodal impedance, which reduces the extensive parameter tuning effort when facing different estimation tasks. Furthermore, an approximate branch power flow observation equation is proposed to improve the filtering efficiency. The observation matrix with branch admittance information presents the linear filtering relationship between power flow measurements and forecasted states, omitting the complex iterative updates of the Jacobian matrix for nonlinear measurements. Finally, the overall estimated voltage state at each time sample is entirely obtained by combining the filtered voltage perturbation vector with the priori‐determined nominal value. Numerical simulation comparisons on a symmetric balanced 56‐node distribution system verify the performance of the proposed estimator in terms of accuracy and robustness under normal and abnormal conditions.

Xuzhuo Wang, Guoan Yan, Zhengshuo Li

A low-observable distribution system has insufficient measurements for conventional weighted least square state estimators. Matrix completion state estimators have been suggested, but their computational times could be prohibitive. To resolve this problem, a novel and efficient power-flow-embedded projection conic matrix completion method customized for low-observable distribution systems is proposed in this letter. This method can yield more accurate state estimations (2-fold improvement) in a much shorter time (5% or less) than other methods. Case studies on different-scale systems have demonstrated the efficacy of the proposed method when applied to low-observable distribution system state estimation problems.

Yang Li

This chapter addresses the increasing vulnerability of coastal regions to typhoons and the consequent power outages, emphasizing the critical role of power transmission systems in disaster resilience. It introduces a framework for assessing and enhancing the resilience of these systems against typhoon impacts. The approach integrates a hybrid-driven model for system failure analysis and resilience assessment, employing both data-driven and model-driven techniques. It includes a unique method to identify system vulnerabilities and optimal strategies for resilience enhancement, considering cost-effectiveness. The efficacy of this method is demonstrated through simulations on the IEEE RTS-79 system under realistic typhoon scenarios, showcasing its potential to guide planners in making informed decisions for disaster resilience.

Adeline Guéret, Wolf-Peter Schill, Carlos Gaete-Morales

Electrifying the car fleet is a major strategy for mitigating emissions in the transport sector. As electrification cannot solve all negative externalities associated with cars, reducing the size of the car fleet would be beneficial. Electric carsharing could allow to reconcile current car usage habits with a smaller fleet, but this may reduce the potential of electric cars to align their grid interactions with variable renewable electricity generation. We investigate how electric carsharing may impact the power sector, combining three methods: sequence clustering of car travel diaries, generation of synthetic electric vehicle time series, and power sector modelling. We show that switching to electric carsharing only moderately increases power sector costs, less than 110 euros per substituted car in our main setting. This effect is largest with bidirectional charging. We conclude that the power sector interactions of shared electric car fleets could still be aligned with variable renewable electricity generation.

Mohammad Nazaralilou, Behrouz Tousi, Mohammad Farhadi‐Kangarlu

Abstract Evaluating the positive impacts of private investor (PI) contributions in improving distribution network (DN) technical, environmental and economic features are considered in the recent studies. In this paper photovoltaic investor (PVI) participates in improving technical constraints of DN. DN operator (DNO) is the owner of wind turbine (WT). The main functions are formed as benefit functions. Benefits which are obtained by improving technical constraints (impacts of contributing PVI and WTs in improving technical constraints) are written in the benefit functions. Technical constraints include: voltage deviation (VD), active and reactive power loss. By designating weight with high value to each of technical constraints, genetic algorithm (GA) solves the optimization problem. Different scenarios are considered in the siting and sizing problem. Furthermore, comprehensive study is done by defining benefit contribution parameter (ψ). Changing the value of ψ from 0 to 100%, gives the best interaction between PVI and DN operator (DNO). Since the presented optimal siting and sizing problem is formed as multi criteria, by applying multi‐attribute decision making approach based on technique for order preference by similarity to ideal solution (TOPSIS), the optimal contribution of DNO and PVI is determined. Obtained results from simulation reveal that in a case without sharing benefit between DNO and PVI, the PVI could not receive benefits from project (all of solutions are negative value) but in the case with benefit sharing, the project has attractiveness for the PVI (it is illustrated as a positive benefit values for PVI). The optimum solution is received by making a trade‐off between DNO and PVI benefits. The optimum values are 2.287E6 and 2.201E6, respectively.

Wen Wang, Keli Gao, Zhibing Li et al.

Abstract Following the design demands of the protective switch used in ±800 kV hybrid UHVDC system, a plasma‐triggered‐based protective gas switch (PTGS) is developed by performing the comparative study of its dynamic performance by considering the arc dynamic behaviour induced by the transverse magnetic field (TMF)‐based electrode used in the PTGS. The PTGS‐based electrode, as the core component, is designed with a helical‐slotted structure, the TMF could drive the arc between moving from the electrode centre to the electrode edge and then rotate rapidly along the edge. A 3D magnetohydrodynamic model (MHD) was established to computationally evaluate the effects of electrode structure variation on arc rotation characteristics. The model was verified by comparing the calculation results with experiments performed on the designed PTGS prototype. A comprehensive evaluation approach was also proposed to support the comparative analysis of the arc dynamic behaviour and estimate the arc‐controlling capability of the designed electrode to further assess the dynamic performance of the designed PTGS. This approach could quantitatively estimate the arc behaviour from a spatial scale, the variation of characterised parameters such as temperature associated with arc behaviour is concerned as well, which reasonably supports the electrode optimum design and the PTGS.

Reza Talebi, Mohammad Hassan Moradi

Power transformers are one of the main components of the power grids. Several main and back up protection relays are provided to protect them against different fault types. Restricted Earth Fault (REF) protection relay is used as main protection to protect transformer against in-zone phase-to-ground faults. This protection has high accuracy to detect earth faults inside the transformer protection zone. Operational experiences show that in some cases and during external high current out-zone faults which leads to current transformers (CTs) core saturation, REF relay operates incorrectly and cause unwanted trip. Relay setting base and operation curve methods to solve the problem have been devised, but the problem has not yet been completely solved. In this article, different fault types such as symmetric/asymmetric, single/multiple phases, low/high current are simulated. Then, causes of REF protection maloperation and commercial relays settings and curves are studied. The optimal setting for the relay is extracted and its effectiveness in different fault types validated by simulating real data with PSCAD and MATLAB software.

Anindya Ray, Kaushik Rajashekara

Subsea oil and gas (O&G) exploration demands significantly high power to supply the electrical loads for extraction and pumping of the oil and gas. The energy demand is usually met by fossil fuel combustion-based platform generation, which releases a substantial volume of greenhouse gases including carbon dioxide (CO₂) and methane into the atmosphere. The severity of the resulting adverse environmental impact has increased the focus on more sustainable and environment-friendly power processing for deepwater O&G production. The most feasible way toward sustainable power processing lies in the complete electrification of subsea systems. This paper aims to dive deep into the technology trends that enable an all-electric subsea grid and the real-world challenges that hinder the proliferation of these technologies. Two main enabling technologies are the transmission of electrical power from the onshore electrical grid to the subsea petroleum installations or the integration of offshore renewable energy sources to form a microgrid to power the platform-based and subsea loads. This paper reviews the feasible power generation sources for interconnection with subsea oil installations. Next, this interconnection’s possible power transmission and distribution architectures are presented, including auxiliary power processing systems like subsea electric heating. As the electrical fault is one of the major challenges for DC systems, the fault protection topologies for the subsea HVDC architectures are also reviewed. A brief discussion and comparison of the reviewed technologies are presented. Finally, the critical findings are summarized in the conclusion section.

Mahdiyeh Ehsanbakhsh, Mohammad Sadegh Sepasian

Abstract Soft Open Points (SOPs) are power electronic devices and emerging flexible interconnections. The active and reactive power flow flexible controllability of SOP facilitates to improve the performance characteristics of the networks. To evaluate the contribution of SOPs to operational characteristics improvement, instead of a fixed number and an optimized configuration of SOPs is necessary. In addition to optimal siting and sizing of SOPs in distribution networks, considering the smart switching capability of SOPs compared to conventional switching through tie switches is important. This issue affects the decision of simultaneous investment for these types of equipment. Existing studies have not considered the coordinated presence of SOPs and candidate tie switches in a planning scheme for allocating them simultaneously. Here, a stochastic scenario‐based planning model is proposed to determine the optimal sites and sizes of SOPs and the allocation of tie switches simultaneously while obtaining the optimal operation strategy of these devices during the network reconfiguration process. The model is formulated using the second‐order cone programming approach. The optimization problem is solved considering renewable power production scenarios and different loading conditions. Finally, the proposed model is conducted on the IEEE 33‐bus and 69‐bus systems, and the results are presented.

Naime Ahmadi, Yacine Chakhchoukh, Hideaki Ishii

The impact of false data injection (FDI) attacks on static state estimation of power systems has been actively studied in the past decade. In this paper, we consider an estimation method that first decomposes the system into islands and then implements robust regression estimators at the island level as well as the system level. We carry out an analysis to establish its advantages in terms of state estimation accuracy and attack detections. In particular, we focus on highly adversarial cases where the attacker can attack both the measurement vector and the regressor matrix and attempts to manipulate the states to targeted values. Our estimation approach employs a system decomposition method capable to generate islands small in their sizes and applies the robust estimation method of least trimmed squares. We make comparisons with methods using other decompositions and other robust estimators. To this end, we analyze the structure of the system topology and measurements and perform extensive simulations using the IEEE 14- and 118-bus systems. Furthermore, we investigate robustness improvement when phasor measurement units (PMUs) are available and hybrid state estimation can be employed.