Dynamic Line Rating Research Articles

An ADMM approach for unit commitment with considering dynamic line rating

To enhance the transmission capabilities in power system scheduling, this paper develops a unit commitment model that incorporates dynamic transmission line capacities and proposes an efficient solving algorithm. A multi-scenario unit commitment model that integrates dynamic transmission line capacities is introduced, using quantile regression to construct a data-driven capacity increase model based on historical environmental data. The model is solved using Lagrangian relaxation and the Alternating Direction Method of Multipliers (ADMM) to decouple dynamic constraints, allowing the dual problem to be decomposed into sub-problems and solved iteratively. The proposed model and algorithm are validated using the IEEE-118 and IEEE-300 test cases, demonstrating their effectiveness in handling dynamic transmission line capacities and improving scheduling performance.The approach provides a robust and flexible solution for power system scheduling, enhancing reliability and economic efficiency.

The Role of AC Resistance of Bare Stranded Conductors for Developing Dynamic Line Rating Approaches

Overhead transmission line conductors are usually helically stranded. The current-carrying section is made of aluminum and/or aluminum alloys. Several factors affect their electrical resistance, such as the conductivity of the conductor material, the cross-sectional area, the lay length of the different layers of aluminum, and the presence of a steel core used to increase the mechanical strength of the conductor. The direct current (DC) and alternating current (AC) resistances per unit length of stranded conductors are different due to the effect of the eddy currents. In steel-reinforced conductors, there are other effects, such as the transformer effect due to the magnetization of the steel core, which make the AC resistance dependent on the current. Operating temperature also has an important effect on electrical resistance. Resistive losses are the main source of heating in transmission line conductors, so their temperature rise is highly dominated by such power losses, making it critical to know the value of the AC resistance per unit length when applying dynamic line rating (DLR) methods. They are of great interest especially in congested lines, as by applying DLR approaches it is possible to utilize the full line capacity of the line. This paper highlights the difficulty of accurately calculating the electrical resistance of helically stranded conductors, especially those with a magnetic core, and the importance of accurate measurements for the development of conductor models and DLR approaches.

Dynamic Line Rating for Congestion Management in Distribution Network

The growth of the demand because of the electrification of the heating system and the popularization of the use of electric vehicles added to an increase on the renewable energies generation connected to the distribution level, are responsible for an increase in the use of distribution network. This may lead to more congestions at this level that up to the date, are solved with direct switching actions which implies a negative economic impact and consequences for consumers and producers. Congestions can be solved also with market-based demand response which need the system to have flexibility to be applied effectively. On transmission network the usage of dynamic line rating (DLR) is more extended but on the distribution level, it is not common. This paper aims to show the use of DLR to provide flexibility to the distribution network, easing congestion management.

Calculating a conductor’s temperature-related sag as the limiting value for a Dynamic Line Rating

The ampacity — a term that refers to the maximum current in amperes — of existing overhead lines can be increased using Dynamic Line Rating (DLR) systems. Based on ambient online conditions and electrical power flow measurements, together with a forecast of future ambient conditions, the ampacity of overhead lines can be dynamically adjusted. In essence, the ampacity of an overhead line is determined by the operating temperature of the conductor that still guarantees the required safe distances to the objects under the overhead line. This paper presents a calculation algorithm for the sag of a conductor that addresses an unequal temperature of the conductor along the line as a response to an expected problem in the future, when increasing numbers of DLR sensors will be installed on or even near overhead lines. A novel approach to the sag-temperature calculation useful for a DLR is developed based on the linearization of cable equations. As a result, the computational costs are greatly reduced, which is an important factor when the DLR system includes a large number of overhead lines and processes them simultaneously. The presented methods are open and can be adapted for an arbitrary DLR system.

Reliability assessment of dynamic line rating methods based on conductor temperature estimation

Dynamic line rating techniques are increasingly prevalent in the computation of ampacity for overhead transmission lines. Some of these methods, commonly employed by system operators, rely on real-time measurements to calculate conductor temperature, either directly or indirectly. Despite their widespread usage, the susceptibility of these techniques to measurement errors has been largely disregarded in prior research. In this regard, this paper includes a groundbreaking reliability analysis addressing the impact of measurement errors on various dynamic line rating techniques in overhead lines, based on conductor temperature estimation. Two case studies are presented, incorporating typical errors from simulations. The first case study adopts a standard 24-hour profile for the variables involved in the conductor ampacity calculation. In the second study, Monte Carlo simulations are conducted, in which meteorological variables are randomly assigned with five load levels to evaluate the current’s influence on measurement noise sensitivity. The resulting ampacity errors across all methods considered are remarkably high, with mean values exceeding 150% in specific scenarios. This unequivocally demonstrates that the commonly accepted levels of measurement accuracy in these widely used rating techniques yield estimations falling far below acceptable reliability standards.

Optimizing High-Voltage Direct Current Transmission Corridors: Dynamic Thermal Line Rating for Enhanced Renewable Generation and Greenhouse Gas Emission Reductions

Recently, significant attention has been paid to the large-scale use of renewable energy through high-voltage direct current (HVDC) because of its economic feasibility. At the same time, the growing demand for electricity and the increasing penetration of renewable energy sources have prompted the electric power industry to explore methods to optimize the use of the existing grid infrastructure. Dynamic thermal line rating (DTLR) is a technique that allows transmission lines to operate at their maximum capacity, considering their real-time operating conditions. The majority of existing research on this topic has focused predominantly on employing DTLR in alternating current systems and exploring their applications. This study presents a novel approach by applying DTLR to HVDC transmission corridors, with the aim of maximizing the utilization of their capacity and facilitating increased integration of renewable energy. The performance of the proposed approach is evaluated by conducting a case study for an HVDC transmission line in Alberta, Canada. On average, the mean increase in ampacity above the static rating is 64% during winter and 34% during summer. This additional capacity can be used to integrate wind energy, replacing coal-fired generation. This leads to a significant reduction in greenhouse gas emissions, also quantified in this contribution.

Analyzing the role of emissivity in stranded conductors for overhead power lines

Emissivity and absorptivity are important parameters in overhead power line conductors that depend on the conductor surface condition, so their values change as the conductors age. They have an important effect on their thermal behavior, so it is important to know their value when designing dynamic line rating (DLR) strategies. DLR methods are becoming increasingly important, especially in areas with congested power lines, as they allow the full capacity of the lines to be utilized by dynamically determining their maximum allowable currents without exceeding the safe temperature limits of the conductors, thus ensuring that they operate within safe limits. However, measuring emissivity and absorptivity is non-trivial and requires complex tests that cannot be performed in the field. Using simulations based on a realistic thermoelectric model and experimental tests performed on three types of transmission line conductors in a high-current laboratory, this paper shows the importance and effect of emissivity on conductor temperature. It also presents a method for measuring the emissivity value based on adjusting the emissivity and absorptivity values in the thermoelectric model to match the experimental values. The results presented show that aged conductors allow an increase in the maximum current carrying capacity compared to new conductors in the order of 5–14 %, depending on the conductor type considered and the operating conditions.

Interactive demand response and dynamic thermal line rating for minimizing the wind power spillage and carbon emissions

Spilling has already occurred as a result of rising the penetration of intermittent renewable generation, and it is anticipated that the level of renewable energy curtailment will continue to soar. This leads to an increase in operating costs, CO2 emissions, and not good utilization of renewable energy resources. A bi-level multi-objective optimization model is proposed in this paper to reduce wind power spillage (WPS) based on demand response (DR) and dynamic thermal line rating (DTLR). In the upper level, multiple objectives will be satisfied based on the optimal allocation and time of DR programs considering DTLR obtained in the lower level. The minimization of WPS, load shedding, power losses, and CO2 emissions are the objectives of this level. While the lower-level aims to maximize social welfare under different scenarios and overall system constraints. Under the uncertainty of the wind power and load demand, a collection of lower-level problems that represent the market clearing conditions is used to constrain the upper-level. The effectiveness of the proposed algorithm is examined on a modified two-area IEEE 24-bus test system. Results depict that the suggested bi-level model enables considerable reductions in the WPS by up to 32.7 %. Also, there is an enhancement in load shedding, power losses, and CO2 emissions by 28.93 %, 23.07 %, and 13.9 % respectively. Finally, the social welfare increased by up to 36.6 %.

Feasible operation region estimation of virtual power plant considering heterogeneity and uncertainty of distributed energy resources

The concept of the virtual power plant (VPP) has attracted extensive attention due to its distinguished capability of integrating various types of distributed energy resources (DERs). Defining the feasible operation region (FOR) is a crucial prerequisite for VPP's participation in the provision of power system services. However, considering the heterogeneity and uncertainty of DERs, it is always a challenging task to calculate the accurate FOR of VPP. In this paper, we propose a novel internal resource aggregation approach to estimate the FOR of VPP. The design of the proposed aggregation model consists of two stages. In the first stage, all the operational constraints of various DERs at the same node are expressed with a unified polytope form, and then these constraints are aggregated into one single polytope operation constraint by the computational geometry method. In the second stage, based on the aggregated polytope operation constraint at each node, combined with the dynamic line rating (DLR) and network constraints, a data-driven distributionally robust FOR estimation problem is formulated to determine the boundaries of the FOR. Numerical experiments have been conducted to validate the feasibility and superiority of the proposed approach and its applicability in handling multi-period FOR estimation.

Data-driven learning-based classification model for mitigating false data injection attacks on dynamic line rating systems

The increasing need to explore electric power grid expansion technologies like dynamic line rating (DLR) systems and their dependence on real-time weather data for system planning necessitates research into false data injection attacks (FDIA) on cyber-physical power systems (CPS). This study aims to develop a robust machine learning model to mitigate FDIA in DLR systems, focusing on statistical data processing, feature ranking, selection, training, validation and evaluation. It synthesises z-score and other statistical analyses with minimum redundancy maximum relevance (MR-MR) feature ranking and selection algorithm to improve model performance and generalisation of binary generalised linear model logistic regression (BGLM-LR) and other machine learning classification algorithms. The resulting models formed with BGLM-LR, Gaussian naïve Bayes (GNB), linear support vector machine (LSVM), wide neural network (WNN), and decision tree (DT) were trained and tested with 10-year hourly DLR history data features. The evaluation of the models on unseen data revealed enhanced validation and testing accuracies after the MR-MR feature ranking and selection. BGLM-LR, GNB, LSVM, and WNN showed promising performance for mitigating FDIA. However, the study identifies DT’s limitations as overfitting and lacking generalisation in FDIA mitigation. z-score-MR-MR-BGLM-LR and z-score-MR-MR-LSVM models exhibited outstanding performances with zero false negative rates highlighting the significance of feature ranking and selection. Still, the z-score-MR-MR-BGLM-LR combination exhibits the highest marginal improvement from training to testing, the lowest training and validation time and a perfect area under curve (AUC) of the receiver operating characteristics making it the best choice in mitigating FDIA when computational resources are limited.

Analyzing flexibility options for microgrid management from economical operational and environmental perspectives

Power grids have undergone a massive transformation due to increasing number of renewable/non renewable distributed generators, storage technologies and electrical vehicles. This paper focuses on the flexible energy management of grid connected microgrid (MG) in order to analyze the effects of the flexibility options such as electric vehicle with demand response (EV-DR), battery storage system (BSS), hydrogen storage system (HSS) and dynamic line rating (DLR) in presence of renewable sources. In addition, microturbine (MT), fuel cell (FC), biomass (BIO), geothermal (GEO), and diesel engine (DIE) whose output is controllable are also considered as flexible sources. In this paper, optimum operation of grid-connected MG is modeled as a mixed-integer linear programming problem considering uncertainties of wind turbine (WT) and photovoltaic (PV) sources. A day-ahead forecasting of irradiance and wind speed are performed with Decision Tree Regression (DTR) and Long Short Term Memory (LSTM) algorithms for sensitive calculation of PV and WT output. The cost function merges operating costs including fuel, operation & maintains (O&M), depletion costs with emission cost of CO2, SO2, and NOx. Obtained numerical results show that total cost of the MG is reduced by 9.40% and emission cost is diminished by 5.59% with inclusion of all considered flexibility options. Further, load factor is improved from 0.7932 to 0.8236 with 100% flexibility condition for MG.

Optimal transformer loss of life management in day-ahead scheduling of wind-rich power systems at the presence of dynamic ratings

Integration of wind power generations within today's power has faced power grid operators with challenges such as variable power generation and congestion issues. To settle these problems, the power system operators can make use of smart-grid technologies, one of which is dynamic rating of power equipment. In this paper, optimal day-ahead scheduling of wind-rich power systems is implemented at the presence of dynamic line rating (DLR) and dynamic transformer rating (DTR). Employing DTR brings about higher loading of transformer endangering its lifetime as costly instrument; hence, the thermal modeling of the transformer has been incorporated into the proposed model of this paper to avoid transformer's loss of life (LOL). All the constraints of the day-ahead AC optimal power flow have been considered to compose an optimization model which is programmed in the GAMS and optimized by appropriate solvers. Several experiments have been accomplished on the IEEE 24-bus system where the simulation results clearly demonstrate efficacy of the conducted approach.

A Novel Approach to Dynamic Line Rating Calculation

In order to adjust the electric power network to today’s trends—such as the charging of electric vehicles, design of the Internal Electricity Market (IEM), headway of renewable energy sources, etc.—the demand for flexible network solutions is increasingly significant. Dynamic line rating (DLR) technology offers a flexible solution for the uprating of overhead lines. Moreover, the DLR methodology can be used in a wider system approach in order to establish a line management system, including functions such as ice prevention subsystems or the real-time tracking of clearances. Therefore, the extension possibilities and operational features of DLR-based systems have been extensively researched in the last few years, which is also confirmed by the high number of pilot projects funded for research and development on the topic of DLR. The key question during the implementation of a DLR system is the obtainable operational safety of such a system. This mainly depends on the installation places of the field equipment and the accuracy of the line rating calculation models. The aim of this article is the comparison of the so far available international line rating calculation models and also to propose another way for the determination of the real-time line rating. Moreover, laboratory measurements and case studies are presented for the confirmation of the proposed model’s reliability.

Coordinated integration of wind energy in microgrids: A dual strategy approach leveraging dynamic thermal line rating and electric vehicle scheduling

As the world shifts towards sustainable energy, integrating wind energy as a key renewable resource is crucial, despite the challenge of its intermittent output. This paper proposes an integrated approach to enhance wind energy utilization within microgrids, introducing a novel synergy of Dynamic Thermal Line Rating (DTLR) and Electric Vehicle (EV) scheduling. This synergy dynamically adjusts power transmission capacities and harnesses EVs as mobile energy storage units. Notably, case study simulations reveal that compared to other approaches, our proposed solution reduces total operational costs by 24.6% (STLR only) and 11.6% (STLR+EV), and increases wind power absorption by 41.7% (STLR only) and 12.1% (STLR+EV). This marks a significant stride towards sustainable microgrid operation amidst renewable energy integration challenges.

Dynamic line rating for grid transfer capability optimization in Malaysia

<div align="center"><span>This paper details a case study on the implementation of dynamic line rating (DLR) to enhance the ampacity rating of Malaysia’s grid. Utilizing heat balance equations endorsed by the Institute of Electrical and Electronics Engineering (IEEE 738) and the International Council on Large Electric Systems (CIGRE technical brochure 601), the ampacity rating of a Zebra-type aluminum cable steel reinforced (ACSR) conductor on a 275 kV transmission line has been assessed. Real-time weather conditions and conductor temperatures, measured hourly by the DLR sensor over the course of a year, were incorporated into the ampacity calculation to determine the available margin. The weather parameters were analyzed based on the monsoon seasons. A comparative analysis between various methods outlined in the standards and the estimated ampacity rating derived from both standards is presented. According to both standards, the findings indicate that DLR surpasses static line rating (SLR), highlighting the presence of untapped ampacity for grid optimization. Remarkably, CIGRE TB 601 exhibits a higher ampacity rating margin than the IEEE 738 standard, with a percentage difference of 16.20%. The study concludes that the conductor is underutilized and proposes optimization through the integration of real-time weather conditions data into the heat balance equations.</span></div>

Optimal management of power networks using a dynamic line rating approach

Due to the stochastic nature of wind, the wind power integration into the power system poses serious challenges to the transmissionsystem operators(TSO). The impact of large amounts of windenergygeneration onto the power system may congest some of the transmissionlines that transport it to the (sometimes)distant consumption centres. Since the occurrence of wind not only contributes to the loading of theconnecting electric line, but also increases the line capacity, via convective cooling of the cables, a dynamic line rating (DLR)analysis computes a more realistic set of values for the line capacity,thusit can be a cost-effective solution toalleviatesomeoverhead line congestion problems.This work presentsan operational tool for the DLR analysis of power networks,allowingthe optimal integration of renewable energy sources,especially wherepotentially congested linesmay exist. The tool was appliedto a real case studyusing forecast meteorological data, and the results achieved were compared with those obtained by using the Portuguese TSOmethodfor assessingthe transmission capacity of the lines. For high wind speed conditions,results show anoticeable increase on the cable’s convective cooling assessed by the DLR analysis. This assessmentleadsto a noticeable rise on the cables’capacitiesthat overcame the congestion associated with the high injection levels of wind power generationin the power grid.

Energy storage system coordinated with phase-shifting transformer and dynamic rating equipment for optimal operation of wind-rich smart power networks

Emergence of flexibility devices into smart power systems can assist the power system operators in making effective and economical decisions for the power system scheduling. These devices include energy storage system (ESS), phase-shifting transformer (PST), dynamic transformer rating (DTR), and dynamic line rating (DLR). In this paper, an approach is proposed for optimal day-ahead scheduling of power system using coordinated operation of ESS, PST, DTR, and DLR units under high wind power penetration situation. The aim is to minimize operational, load shedding, and wind power curtailment costs by regarding all the problem constraints. All of the model's relations as well as AC power flow equations have been convexified in order to construct a mixed-integer quadratically-constrained programming (MIQCP) model whose solution will obtain global optimum results. The developed model is implemented in GAMS and applied to the IEEE 24-bus system under various case studies, and the obtained results are investigated thoroughly. By coordinated operation of the employed flexibility devices, the loading of transformers and lines reach 159 % and 246 % compared to their nominal rating without violation of temperature limitations. Through releasing the capacity of lines and transformers, the total scheduling cost is reduced by about 52 % compared to traditional operation. Also, the whole energy of wind farms is utilized without any curtailment or constraints violation.

Optimizing Grid With Dynamic Line Rating of Conductors: A Comprehensive Review

Optimizing Grid With Dynamic Line Rating of Conductors: A Comprehensive Review

On static vs. dynamic line ratings in renewable energy zones

Scaling-up Variable Renewable Energy will face critical bottlenecks vis-a-vis requisite transmission hosting capacity. Network developments must navigate the complexity of encroaching on private land, risk disturbing sites of cultural significance, compete with other environmental (i.e. biodiversity) objectives, and endure backlash from directly affected communities. Transmission is costly and post-pandemic supply-chain constraints are sending equipment costs higher. Given time and cost risks, existing transmission networks and successful augmentations need to function at their outer operating envelope. In this article, a Renewable Energy Zone (REZ) is examined by comparing static, seaonal and real-time dynamic line ratings. Historically, seasonal line ratings in the Queensland region of Australia's National Electricity Market reflected the still, hot conditions that characterised critical event maximum demand days. Widespread take-up rates of rooftop solar PV has shifted maximum (grid-supplied) demand to the late-afternoon when wind speeds are rising, which also provides thermal cooling to transmission lines. Optimisation modelling suggests a shift from static to dynamic line ratings for a reference 275 kV radial REZ in Queensland can increase wind hosting capacity from ∼1700 MW to >2800 MW with limited change in the asset base. Dynamically adjusting Frequency Control Ancillary Services further increases VRE hosting capacity.

Optimal energy base planning method with integrated industry high-consumption demand response and dynamic line rating

Optimal energy base planning method with integrated industry high-consumption demand response and dynamic line rating