Probabilistic real-time thermal rating forecasting for overhead lines by conditionally heteroscedastic auto-regressive models

Conventional approaches to forecasting of real-time thermal ratings (RTTRs) provide only single point estimates with no indication of the size or distribution of possible errors. This paper describes weather based methods to estimate probabilistic RTTR forecasts for overhead lines which can be used by a system operator within a chosen risk policy with respect to probability of a rating being exceeded. Predictive centres of weather conditions are estimated as a sum of residuals predicted by a suitable auto-regressive model and temporal trends fitted by Fourier series. Conditional heteroscedasticity of the predictive distribution is modelled as a linear function of recent changes in residuals within one hour for air temperature and wind speed or concentration of recent wind direction observations within two hours. A technique of minimum continuous ranked probability score estimation is used to estimate predictive distributions. Numerous RTTRs for a particular span are generated by a combination of the Monte Carlo method where weather inputs are randomly sampled from the modelled predictive distributions at a particular future moment and a thermal model of overhead conductors. Kernel density estimation is then used to smooth and estimate the percentiles of RTTR forecasts which are then compared with actual ratings and discussed alongside practical issues around use of RTTR forecasts

Overhead line ampacity forecasting and a methodology for assessing risk and line capacity utilization

This paper proposes a methodology for overhead line ampacity forecasting that enables empirical probabilistic forecasts to be made up to one day ahead, which is useful for grid scheduling and operation. The proposed method is based on the statistical adaptation of weather forecasts to the line-span scale and aims to produce reliable forecasts that allow the selection of a low risk of overheating overhead conductors by TSOs and DSOs. Moreover, a methodology for the evaluation of probabilistic forecasts and line capacity utilization is also proposed.This work was supported by the Ministerio de Economia, Industria y Competitividad, under the Project DPI2016-77215-R(AEI/FEDER, UE), and by the University of the Basque Country UPV/EHU (ELEKTRIKER research group GIU20/034)

Machine Learning Tool for Transmission Capacity Forecasting of Overhead Lines based on Distributed Weather Data

Die Erhöhung des Anteils intermittierender erneuerbarer Energiequellen im elektrischen Energiesystem ist eine Herausforderung für die Netzbetreiber. Ein Beispiel ist die Zunahme der Nord-Süd Übertragung von Windenergie in Deutschland, die zu einer Erhöhung der Engpässe in den Freileitungen führt und sich direkt in den Stromkosten der Endverbraucher niederschlägt. Neben dem Ausbau neuer Freileitungen ist ein witterungsabhängiger Freileitungsbetrieb eine Lösung, um die aktuelle Auslastung des Systems zu verbessern. Aus der Analyse in einer Probeleitung in Deutschland wurde gezeigt, dass einen Zuwachs von ca. 28% der Stromtragfähigkeit eine Reduzierung der Kosten für Engpassmaßnahmen um ca. 55% bedeuten kann. Dieser Vorteil kann nur vom Netzbetreiber wahrgenommen werden, wenn eine Belastbarkeitsprognose für die Stromerzeugunsgplanung der konventionellen Kraftwerke zur Verfügung steht. Das in dieser Dissertation vorgestellte System prognostiziert die Belastbarkeit von Freileitungen für 48 Stunden, mit einer Verbesserung der Prognosegenauigkeit im Vergleich zum Stand-der-Technik von 6,13% in Durchschnitt. Der Ansatz passt die meteorologischen Vorhersagen an die lokale Wettersituation entlang der Leitung an. Diese Anpassungen sind aufgrund von Veränderungen der Topographie entlang der Leitungstrasse und Windschatten der umliegenden Bäume notwendig, da durch die meteorologischen Modelle diese nicht beschrieben werden können. Außerdem ist das in dieser Dissertation entwickelte Modell in der Lage die Genauigkeitsabweichungen der Wettervorhersage zwischen Tag und Nacht abzugleichen, was vorteilhaft für die Strombelastbarkeitsprognose ist. Die Zuverlässigkeit und deswegen auch die Effizienz des Stromerzeugungsplans für den nächsten 48 Stunden wurde um 10% gegenüber dem Stand der Technik erhöht. Außerdem wurde in Rahmen dieser Arbeit ein Verfahren für die Positionierung der Wetterstationen entwickelt, um die wichtigsten Stellen entlang der Leitung abzudecken und gleichzeitig die Anzahl der Wetterstationen zu minimieren. Wird ein verteiltes Sensornetzwerk in ganz Deutschland umgesetzt, wird die Einsparung von Redispatchingkosten eine Kapitalrendite von ungefähr drei Jahren bedeuten. Die Durchführung einer transienten Analyse ist im entwickelten System ebenfalls möglich, um Engpassfälle für einige Minuten zu lösen, ohne die maximale Leitertemperatur zu erreichen. Dieses Dokument versucht, die Vorteile der Freileitungsmonitoringssysteme zu verdeutlichen und stellt eine Lösung zur Unterstützung eines flexiblen elektrischen Netzes vor, die für eine erfolgreiche Energiewende erforderlich ist

Improvement of safety operating conditions in overhead conductors based on ampacity modeling using artificial neural networks

Thermal ratings are usually considered for planning the operating conditions for overhead lines and are usually obtained with static parameters. These conditions can be improved using dynamic ratings based on the region weather forecasts, and this improvement can be ever higher when a local prediction is performed at the point where the line is located. In this work, a model based on artificial neural networks techniques is applied to predict the ampacity property of a transmission overhead line, in order to adjust and optimize the operation point of the grid under safety conditions. These predictions are calculated for a time horizon of 24 hours and are validated with actual conditions of a real overhead line monitored by sensors. With the conclusion that applying the selected model, the operational security of the conductor can be improved, passing from a 17.82% of overheating conditions to only a 3.91%.This work is financially supported by the Ministerio de Economía, Industria y Competitividad, Spain, under the project DPI2016-77215-R (AEI/FEDER, UE)

Forecasting Models for Integration of Large-Scale Renewable Energy Generation to Electric Power Systems

Amid growing concerns about climate change and non-renewable energy sources deple¬tion, vari¬able renewable energy sources (VRESs) are considered as a feasible substitute for conventional environment-polluting fossil fuel-based power plants. Furthermore, the transition towards clean power systems requires additional transmission capacity. Dynamic thermal line rating (DTLR) is being considered as a potential solution to enhance the current transmission line capacity and omit/postpone transmission system expansion planning, while DTLR is highly dependent on weather variations. With increasing the accommodation of VRESs and application of DTLR, fluctuations and variations thereof impose severe and unprecedented challenges on power systems operation. Therefore, short-term forecasting of large-scale VERSs and DTLR play a crucial role in the electric power system op¬eration problems. To this end, this thesis devotes on developing forecasting models for two large-scale VRESs types (i.e., wind and tidal) and DTLR. Deterministic prediction can be employed for a variety of power system operation problems solved by deterministic optimization. Also, the outcomes of deterministic prediction can be employed for conditional probabilistic prediction, which can be used for modeling uncertainty, used in power system operation problems with robust optimization, chance-constrained optimization, etc. By virtue of the importance of deterministic prediction, deterministic prediction models are developed. Prevalently, time-frequency decomposition approaches are adapted to decompose the wind power time series (TS) into several less non-stationary and non-linear components, which can be predicted more precisely. However, in addition to non-stationarity and nonlinearity, wind power TS demonstrates chaotic characteristics, which reduces the predictability of the wind power TS. In this regard, a wind power generation prediction model based on considering the chaosity of the wind power generation TS is addressed. The model consists of a novel TS decomposition approach, named multi-scale singular spectrum analysis (MSSSA), and least squares support vector machines (LSSVMs). Furthermore, deterministic tidal TS prediction model is developed. In the proposed prediction model, a variant of empirical mode decomposition (EMD), which alleviates the issues associated with EMD. To further improve the prediction accuracy, the impact of different components of wind power TS with different frequencies (scales) in the spatiotemporal modeling of the wind farm is assessed. Consequently, a multiscale spatiotemporal wind power prediction is developed, using information theory-based feature selection, wavelet decomposition, and LSSVM. Power system operation problems with robust optimization and interval optimization require prediction intervals (PIs) to model the uncertainty of renewables. The advanced PI models are mainly based on non-differentiable and non-convex cost functions, which make the use of heuristic optimization for tuning a large number of unknown parameters of the prediction models inevitable. However, heuristic optimization suffers from several issues (e.g., being trapped in local optima, irreproducibility, etc.). To this end, a new wind power PI (WPPI) model, based on a bi-level optimization structure, is put forward. In the proposed WPPI, the main unknown parameters of the prediction model are globally tuned based on optimizing a convex and differentiable cost function. In line with solving the non-differentiability and non-convexity of PI formulation, an asymmetrically adaptive quantile regression (AAQR) which benefits from a linear formulation is proposed for tidal uncertainty modeling. In the prevalent QR-based PI models, for a specified reliability level, the probabilities of the quantiles are selected symmetrically with respect the median probability. However, it is found that asymmetrical and adaptive selection of quantiles with respect to median can provide more efficient PIs. To make the formulation of AAQR linear, extreme learning machine (ELM) is adapted as the prediction engine. Prevalently, the parameters of activation functions in ELM are selected randomly; while different sets of random values might result in dissimilar prediction accuracy. To this end, a heuristic optimization is devised to tune the parameters of the activation functions. Also, to enhance the accuracy of probabilistic DTLR, consideration of latent variables in DTLR prediction is assessed. It is observed that convective cooling rate can provide informative features for DTLR prediction. Also, to address the high dimensional feature space in DTLR, a DTR prediction based on deep learning and consideration of latent variables is put forward. Numerical results of this thesis are provided based on realistic data. The simulations confirm the superiority of the proposed models in comparison to traditional benchmark models, as well as the state-of-the-art models

Power System Operation Planning Considering Dynamic Line Rating Uncertainty

The restructuring of power systems and wider introduction of renewable energy sources in the recent years is placing a greater stress on the transmission system. Yet, transmission system is paramount for the reliable, secure and economic operation of power systems. However, modern transmission systems often have insufficient capacity, leading to bottlenecks, congestions and spillage of renewable energy, while their expansion is generally expensive, complicated and time consuming. As an alternative to the transmission expansion, dynamic line rating technologies allows to utilize latent capacity of transmission lines through the use of measurements or forecasts of weather parameters. However, as the forecasts of the weather parameters are inherently uncertain, the estimates of transmission capacity also become uncertain, and must be addressed accordingly. This thesis investigates the impacts of dynamic line rating forecast uncertainty in power system operational planning problems. Thus, the thesis aims at developing mathematical models for the management of such uncertainty to ensure secure and effective operation of power systems. In order to achieve the above objective, firstly, stochastic models for the dynamic line rating are developed that allow to consider thermal dynamics of the conductor in the presence of uncertain weather forecasts. The models are entirely data-based and provide a risk-averse method of controlling conductor temperature in operational planning problems. Furthermore, the models allow to control both the probability of occurrence and the magnitude of the thermal overloading. Secondly, an analysis of uncertain factors and their interactions in power system operational planning is performed using the coherent risk measure framework. Additionally, a novel modelling approach for the uncertain renewable energy sources in operational planning problems is proposed. Then, coherent reformulations of uncertain constraints are developed and integrated into day-ahead unit commitment problem. Finally, the benefits of managing risk in operational planning problems using coherent risk measures are demonstrated in comprehensive case studies