Dynamic Line Rating Research Articles

A novel comprehensive optimal PMU placement considering practical issues in design and implementation of a wide-area measurement system

• Several WAMS-based applications are considered in the OPP problem. • Concept of the line-wise observability is presented. • Observability of multi-TSO power systems in the OPP problem is investigated • The proposed OPP model is originally a MILP one and its global solution can be easily found. Since phasor measurement units (PMUs) are increasingly installed in power systems, many research works have investigated the optimal PMU placement (OPP) problem with the sake of maximizing observability over the power grid, while minimizing the cost of purchasing PMUs. However, there are still some practical challenges that have not been considered in the previous research studies, and are needed to be addressed for a real wide-area measurement system (WAMS) implementation. In this paper, a comprehensive OPP model is proposed which employs a set of basic and novel constraints for a WAMS design. In this regard, the line-wise observability concept is proposed for the first time to consider the requirements of observability for a range of WAMS-based applications such as restoration management, model validation of power system components, and dynamic line rating (DLR) monitoring. In addition, observability constraints for multi-transmission system operator (multi-TSO) power systems that are operated by different legal entities are provided, which can considerably affect the OPP result. The proposed model is formulated based on a mixed-integer linear programming (MILP) formulation which enables the optimization problem to be solved easily and effectively by existing commercial optimization tools, without any need for linearization. The performance of the proposed OPP model is first verified through simulation results on an 86-bus real power system. Then, simulations are applied to IEEE 118-bus and 300-bus test systems, to certify the performance of the proposed model in comparison with other OPPs in the literature.

A dagging‐based deep learning framework for transmission line flexibility assessment

AbstractUncertainty in renewable energy generation, energy consumption, and electricity prices, as well as transmission congestion, pose a number of problems in modern power grids, necessitating stability on the supply, grid, and demand sides. Grid‐side stability can be achieved by dynamic line rating (DLR) forecasting, which reliably predicts the overall current carrying potential of overhead transmission lines. Long short‐term memory proved beneficiary in this field, owing to its ability to learn highly variable and uncertain data. To empower this network to tackle the non‐stationary nature of meteorological parameters, a novel machine learning (ML) architecture based on Dagging technique is proposed and tested on the data collected from a 400 kV overhead transmission line. Simulation results corroborate that the proposed Dagging‐based stacked LSTM can successfully handle the non‐stationary issue and outperform the decomposition‐based technique, as the state‐of‐the‐art algorithm, for various forecasting horizons. The results confirm the generalizability of models with an application in forecasting DLR over the line without utilizing additional sensors and communication networks. Moreover, the proposed model is compared to several ML architectures, including support vector machines (SVM), random forest (RF), and multi‐layer perceptron (MLP) in a comprehensive benchmark study. The introduced algorithm outperforms MLP by 3.4%, RF by 9.4%, and SVM by 6.7% in terms of average prediction accuracy.

Deep Reinforcement Learning-Based Operation of Transmission Battery Storage with Dynamic Thermal Line Rating

It is well known that dynamic thermal line rating has the potential to use power transmission infrastructure more effectively by allowing higher currents when lines are cooler; however, it is not commonly implemented. Some of the barriers to implementation can be mitigated using modern battery energy storage systems. This paper proposes a combination of dynamic thermal line rating and battery use through the application of deep reinforcement learning. In particular, several algorithms based on deep deterministic policy gradient and soft actor critic are examined, in both single- and multi-agent settings. The selected algorithms are used to control battery energy storage systems in a 6-bus test grid. The effects of load and transmissible power forecasting on the convergence of those algorithms are also examined. The soft actor critic algorithm performs best, followed by deep deterministic policy gradient, and their multi-agent versions in the same order. One-step forecasting of the load and ampacity does not provide any significant benefit for predicting battery action.

Developing a novel zonal congestion management based on demand response programs considering dynamic transmission ratings

Congestion is one of the main obstacles to fair competition in today’s electricity markets. In this regard, a set of rules are legislated to create a fair competitive environment and ensure adequate control over consumers to maintain an acceptable level of reliability with maximum economic efficiency. In this paper, using power tracing techniques and sensitivity analysis, a new dynamic zonal congestion management (ZCM) model based on the demand response program (DRP) is developed. The congestion control zones are determined by an introduced congestion index (CI) including some candidates with both high effectiveness in congestion alleviation (the sensitivity analysis) and high responsibility in the congestion creation (the power tracing technique). Then, through the intelligent implementation of DRP in the zone, a conceptual framework with a comprehensive solution method for congestion alleviation is proposed. Throughout the proposed partitioning based DRP, the optimal amount of load decrease and incentives are determined considering economic issues. In power system operation, contingency analysis such as generator and line outages and uncertainty of lines capacities are inevitable, so they will be taken into account in our proposed methodology. The capacity fluctuates a lot due to the environmental conditions and it will be shown through a comparative study that dynamic line rating (DLR) has a significant impact on results. Using the proposed method, the system is simplified and power system operators can put their focus on the candidate zone than the whole network and alleviate the congestion effectively in the least possible time. The proposed methodology is applied on IEEE 118-bus test system and the results show the effectiveness of the proposed model.

Optimizing PV-Hosting Capacity with the Integrated Employment of Dynamic Line Rating and Voltage Regulation

A record amount of renewable energy has been added to global electricity generation in recent years. Among the renewable energy sources, solar photovoltaic (PV) is the most popular energy source integrated into low voltage distribution networks. However, the voltage limits and current-carrying capacity of the conductors become a barrier to maximizing the PV-hosting capacity in low voltage distribution networks. This paper presents an optimization approach to maximize the PV-hosting capacity in order to fully utilize the existing low voltage distribution network assets. To achieve the maximum PV-hosting capacity of the network, a novel method based on the dynamic line rating of the low voltage distribution network, the coordinated operation of voltage control methods and the PV re-phasing technique was introduced and validated using a case study. The results show that the proposed methodology can enhance the PV-hosting capacity by 53.5% when compared to existing practices.

DLR related model development and performance analysis in the framework of FLEXITRANSTORE

This paper demonstrates a practical implementation of a dynamic line rating (DLR) system and an effective ice prediction and prevention method using line monitoring sensors within the FLEXITRANSTORE international project framework. The presented anti-icing system operated continuously for two years. As a part of the project, 4 line monitoring sensors and 2 weather stations were installed on that section of the power line where icing is most likely to occur. These sensors worked continuously and reliably during the demonstration period. The sensor measurements were supplemented with the load value from the SCADA system and weather forecasts, with the help of which, in addition to the real-time line rating and sag-clearance analysis, a predetermined line rating could also be determined. The investigated system also included an ice prediction subsystem as well as a sag-calculation-based ice detection subsystem. Based on the comparison of the measured and calculated parameters, it can be said that both the operation of the sensors and the quality of the data provided by them were satisfactory. In the case of conductor temperature calculation, the developed mathematical models, which can be applied independently, provided information to the system operator with almost similar accuracy than sensors’ measurements. During the observed period, the ice prediction and detection subsystem were also tested and functioned correctly. As an overview of the project, it can be concluded that an efficient DLR-sensor-based anti-icing system is possible to be realized on the high voltage distribution power grid.

Risk assessment criteria for utilizing dynamic line rating in presence of electric vehicles uncertainty

Dynamic line rating (DLR) is a grid enhancing technology to enable a more effective use of transmission capacity of existing infrastructure. The growth in load consumption along with a high integration of electric vehicles (EV) highlights the potential of DLR utilization for reducing the congestion costs and overloading risks. Selecting the proper lines for DLR implementation is necessary to exploit optimally the benefits of DLR. In this paper, we propose risk assessment criteria to select proper lines for DLR implementation to minimize the system operation costs and the risk of overloading caused by high EV integration. A stochastic method is introduced to model the uncertain behavior of EV in charging stations. Furthermore, we analyze the impact of inherent uncertainties in DLR by comparing different DLR percentages. The benefits of using DLR in different percentages are then quantified in terms of supply and interruption costs. The results show improvements in system supply cost, system reliability, and operation risks.

Energy Harvesting Methods for Transmission Lines: A Comprehensive Review

Humanity faces important challenges concerning the optimal use, security, and availability of energy systems, particularly electrical power systems and transmission lines. In this context, data-driven predictive maintenance plans make it possible to increase the safety, stability, reliability, and availability of electrical power systems. In contrast, strategies such as dynamic line rating (DLR) make it possible to optimize the use of power lines. However, these approaches require developing monitoring plans based on acquiring electrical data in real-time using different types of wireless sensors placed in strategic locations. Due to the specific conditions of the transmission lines, e.g., high electric and magnetic fields, this a challenging problem, aggravated by the harsh outdoor environments where power lines are built. Such sensors must also incorporate an energy harvesting (EH) unit that supplies the necessary electronics. Therefore, the EH unit plays a key role, so when designing such electronic systems, care must be taken to select the most suitable EH technology, which is currently evolving rapidly. This work reviews and analyzes the state-of-the-art technology for EH focused on transmission lines, as it is an area with enormous potential for expansion. In addition to recent advances, it also discusses the research needs and challenges that need to be addressed. Despite the importance of this topic, there is still much to investigate, as this area is still in its infancy. Although EH systems for transmission lines are reviewed, many other applications could potentially benefit from introducing wireless sensors with EH capabilities, such as power transformers, distribution switches, or low- and medium-voltage power lines, among others.

Dynamic-Line-Rating-Based Robust Corrective Dispatch Against Load Redistribution Attacks With Unknown Objectives

Load redistribution (LR) attacks have proven to be hard-detectable and damaging, which require effective corrective schemes to mitigate the impact on power grid operations. Traditional game-theoretic methods and corrective dispatches employing static line rating (SLR) have been studied for attack mitigation based on specific attack objectives but have high dispatch cost and limited performance of attack mitigation. This is because the power transfer capacity of the existing transmission network is underestimated with SLR, and in practical operations, the specific objective of the adversary is not available to the defender, which would introduce uncertainties to the design of corrective schemes. As such, this article incorporates the dynamic line rating (DLR) technology, which enhances the power transfer capability of the existing network, to develop the cost-effective corrective dispatch for mitigating LR attacks with unknown objectives. Specifically, a DLR-based robust corrective (DRC) dispatch model is presented, which guarantees the system security as well as the economic performance. A methodology utilizing the robust counterpart technique and column constraint generation (CCG) algorithm is proposed to solve the dispatch model in a decomposition framework. Case studies based on the IEEE 14- and 118-bus systems verify the performance of the proposed DRC dispatch in enhancing the cyber–physical security of power grids.

On-Line Core Losses Determination in ACSR Conductors for DLR Applications.

Dynamic line rating (DLR) is a method that focuses on dynamically determining the maximum allowable current of power lines, while ensuring they operate within safe limits. DLR needs to monitor the temperature and current of the line in real-time, as well as the weather variables in the surroundings of the power line. DLR approaches also require determining the AC resistance of the power line conductors, which is a key parameter that enables it to determine Joule and core losses. This paper presents an approach for an on-line alternating current (AC) resistance estimation of aluminum conductor steel-reinforced (ACSR) conductors to determine the DLR capability of such conductors from real-time conductor and meteorological parameter measurements. For this purpose, conductors with one, two and three layers of aluminum strands are analyzed in detail. Based on the experimental results presented in this paper, two possible approaches are proposed.

Benefit analysis in power generation dispatch using dynamic line ratings on transmission lines

This paper analyzes the different ampacity limits used in power transmission lines, static line ratings (SLR) and dynamic line ratings (DLR). This paper seeks to quantify the economic benefits that are obtained and how much the wind generation increases based on different line ampacities. With information from monitoring systems and weather stations, ampacity predictions are made for use in the market and operational phases of the power system. Different simulations are carried out on the IEEE 24-bus system, using different ampacity values calculated based on considered risk levels. The results show the economic benefits of using DLR on the system lines.

Hierarchical Extreme Learning Machine Enabled Dynamic Line Rating Forecasting

Dynamic line rating (DLR) is a technology introduced to rectify an overhead line's current carrying capacity based on climatic conditions. Reducing congestion costs, increasing the penetration of renewable energy, and network stability are the most important benefits of this modification. The DLR forecasting is a significant issue and can be very useful in network management decisions for real-time and day-ahead. So far, however, very few studies have talked about the literature on day-ahead DLR forecasting. This article develops various DLR forecasting models based on historical meteorological data and machine learning techniques. Accordingly, multilayer perceptron, group method data handling, support vector regression, back-propagation neural network, extreme learning machine (ELM), and hierarchical ELM (H-ELM) techniques compete for short-term DLR forecasting of two 400 kV overhead transmission lines, installed in the Khaf and Ghadamgah regions in Iran. The meteorological parameters such as air temperature, wind speed, wind direction, and solar radiation in the studied areas are utilized as input variables for selected methods. Different evaluation indicators assessments the forecasting results of each technique. Finally, evaluations emphasize the effectiveness and capability of the H-ELM method with the lowest forecasting error values and the highest correlation coefficient value compared to other methods. Experiments exposed the DLR forecasting models' generalizability at various points of the line without the extension of monitoring devices and communication infrastructures.

Maximizing wind energy utilization in smart power systems using a flexible network-constrained unit commitment through dynamic lines and transformers rating

Wind power generations as renewable and sustainable energies are widely integrated into electric power systems due to their technical, environmental, and economic advantages. This integration affects the power system operation and planning studies. Network-constrained unit commitment (NCUC) is one of important operation studies of electric power systems in which the aim is to schedule the generating units for the next 24-h period with the minimum operating cost considering the constraints of generating units and transmission network. One of the essential constraints influencing the results of NCUC is thermal rating of lines and transformers. In most of the previous studies, static ratings have been used for this equipment. The static ratings are conservative values leading to non-optimal use of real capacity of lines and transformers and increasing the system's operational costs. This issue is highlighted when there exist wind energy generations in system. A new NCUC problem is addressed in this paper that considers dynamic line rating (DLR) and dynamic transformer rating (DTR) in the presence of wind power generations. The proposed approach has been implemented on the 24-bus IEEE-RTS system in the GAMS environment in different experiments. The simulation results demonstrate effectiveness of the conducted model in reducing the operational costs and maximizing the usage of wind energy.

Influence of the wind variability on the calculation of dynamic line rating

This paper presents a novel technique to calculate the ampacity of an overhead transmission line in real time, considering the dynamic evolution of the conductor temperature. In case this method cannot be applied due to lack of adequate information, a correction is proposed for the maximum current, based on Monte Carlo simulations assuming unfavorable external conditions, and validated with real data from a weather station. This technique might be used to avoid the temporary violation of the minimum electrical clearance in transmission lines.

A Secure Dispatch Strategy Subject to the Uncertainties of DLR and Wind Power

The dynamic line rating (DLR) is recognized as an effective method to improve the integration of renewable energies, such as wind power by enhancing the flexibility of the system. The principle of DLR is to increase the transmission capacity of a transmission line according to the forecasted weather data. However, the uncertainties of DLR and wind power caused by the forecasting errors might bring in some severe consequences to the power system, such as line overloads. In this paper, we propose a tri-level preventive dispatch model to deal with this issue. To reduce the conservativeness of the solution, we employ the Density-Based Spatial Clustering of Application with Noise (DBSCAN) method to identify and remove the extreme scenarios of DLR. And a decomposition method is proposed to solve the tri-level model.The numerical simulations on several IEEE bus systems demonstrate the effectiveness of the proposed model.

Advanced prevention against icing on high voltage power lines

Historical meteorological data indicates, that our weather is becoming more and more extreme. For the electrical utility operators (Distribution System Operators - DSOs and Transmission System Operators - TSOs), these changes arise in new operation challenges that need to be addressed. For example, frequent icing phenomenon affects all the components of the power line by a significant mechanical overload: it endangers the conductors, the insulators and the towers, as well. The result is often fatal and beside serious failures, it effects on operators’ decisions. These not only endanger the reliability of electrical grids by the loss of a power line for weeks or even months, but in general, the safety in the surroundings of the power line. As technology advances, we will be able to collected, analyses and predict very large databases in the field of meteorology and electrical engineering. The ability of processing mentioned data, combined with know-how results in the capacity to operate power lines at their thermal limits during different ambient parameters. This technology called Dynamic Line Rating (DLR) – is not only a great way to increase the transmission capacity of a given line, but can also be effectively used to prevent, or even solve icing-related issues. Higher currents result in higher Joule-heats, that consequently heat the conductors. If limits can be reached or approached, icing can be prevented. If prevention is not possible, detection and removal of ice layer is necessary. The proper handling of this icing issues, requires advanced algorithms (expert systems) and reliable measuring equipment. The combination and synchronization between algorithms, weather service and measuring equipment is the key of the successful operation. An EU H2020 financed project called FLEXITRANSTORE has just been launched to develop a cross-country co-operation, with objective to improve anti-icing and de-icing solutions. To establish and analyse different solutions, the project includes several universities, TSOs and DSOs. To solve mentioned icing issues Budapest University of Technology and Economics’ (BME) developed an advanced neural-network based algorithm which use OTLM system. It is planned to install and demonstrate the capabilities of this new technology on the DSOs grid (Electro Ljubljana - ELJ). Besides the introduction of DLR and icing, this paper also focuses on the preparation/organisation of co-operation between different companies and universities.

Minimum Dinamik İletim Hat Derecelendirmesi Kullanarak Güç Sistemin Güvenilirliğini Artırma

Due to increase in electricity demand and renewable resources penetration, power transmission infrastructure is utilized close to its technical limits. Dynamic line rating is one of the new practices in power transmission management by which existing transmission lines can be more efficiently utilized. In the literature, dynamic rating is usually applied to all lines of the power system which is not desirable as it is risky to simultaneously monitor a high number of transmission lines. In this paper we propose to apply dynamic rating on few and most suitable lines to effectively increase the system N–K reliability. We develop a robust min-max-min three-layer optimization model to find minimum number of lines for dynamic rating. We use duality theory and convert the three-layer problem into a two-layer min-max problem and then we develop a benders decomposition framework to solve the two-layer problem. We use two test power systems to demonstrate our solution approach and analyze the results.

Deep learning-based cyber resilient dynamic line rating forecasting

Increased integration of renewable energy resources into the grid may create new difficulties for ensuring a sustainable power grid which drives electric utilities to use a number of cost-effective techniques such as Dynamic line rating (DLR) that enable them to run power networks more efficiently and reliably. DLR forecasting is a technique devised to accurately forecast the maximum current carrying capacity of overhead transmission lines. DLR offers many advantages, including increased renewable energy penetration without system reinforcement, improved grid dependability, and lower congestion costs. So far, many solutions have been proposed for DLR forecasting, which, despite estimating the exact capacity of the DLR, have some problems, such as installing multiple sensors and measurement devices and communication networks with precise calibration, and also neglect cyberattacks which may lead to operators making inappropriate operational choices. To address these issues, in this paper, a novel hybrid deep learning-based DLR forecasting approach called the autoencoder bidirectional long short-term memory (AE-BiLSTM) is efficiently and precisely developed. Several scenarios were developed to test the robustness and accuracy of the proposed methodology using real-world data with and without cyber-attacks. Detection of cyber-attack is done based on the increase in the least square errors of forecasting models. Then, the carefully designed hybrid AE-BiLSTM method reconstructs the falsified measurement data and provides reliable DLR forecasting. Also, a comparative study is carried out. The numerical results demonstrated that the proposed hybrid approach can significantly provide acceptable performance even under cyber-attacks and forecast DLR values ​​with the least possible error, outperforming the existing conventional and deep learning-based techniques.

Coordinated investment in wind‐rich regions using dynamic line rating, energy storage and distributed static series compensation to facilitate congestion management

Wind farms installed at wind-rich regions often located in remote areas increase overloads in the weak adjacent transmission network. As a result of this congestion, extra transmission capacity is required to avoid wind energy or load curtailment that can be achieved by applying some modifications. New line construction and line uprating have been recognised as conventional solutions for the transmission capacity increase. Meanwhile, in the last decade, these solutions have brought several serious issues for the network operators, forcing them to evaluate and install different types of assets based on state-of-the-art technology in order to harvest the existing lines at the maximum possible level. An investment model is developed to relieve the congestion, including dynamic line rating (DLR), distributed static series compensation and energy storage systems (ESS), with a simplified unit commitment implementation. The resulting mixed-integer linear model is then solved with the classic Benders decomposition. Installations of DLR and distributed static series compensator (DSSC) on the same line are formulated, with techno-economic analysis performed for different asset combinations on the IEEE RTS 24 bus system, for high wind energy share scenarios. Wind curtailment levels are studied for different DLR/DSSC approaches, including the impact of practical implementation requirements. Based upon additional interlinking constraints, the robustness of investment decisions for independent DLR and DSSC investments is compared against approaches where DLR and DSSC investments are permitted on the same line. The investment based on sequential incorporation of the unit commitment in the study is also investigated to reflect the importance of the integration of the unit scheduling in the model for high renewable systems.

Transmission Line Overload Risk Assessment Considering Dynamic Line Rating Mechanism in a High-Wind-Penetrated Power System: A Data-Driven Approach

The dynamic line rating (DLR) can be an effective mechanism that reduces wind curtailment induced by line congestion considering its unique correlation to wind speed. However, the calculation of the DLR depends on accurate environmental parameters, which can be challenging in look-ahead decision-making applications. Consequently, transmission line overload risk (TLOR) can exist. In this paper, we propose a two-stage TLOR assessment model for high-wind-penetrated power systems considering the DLR. An overload risk index along with the risk-averse line capacity bound (RAB) for each line can be obtained from the proposed model. Meanwhile, a computationally efficient data-driven approach that combines convex-hull and box-like modeling methods is established to characterize the uncertainty during the decision-making process. The proposed model is solved by a modified column-and-constraint generation (C&CG) algorithm, and its effectiveness and scalability are illustrated by multiple simulation experiments.