Probabilistic weather forecasting for dynamic line rating studies

This paper aims to describe methods to determine short term probabilistic forecasts of weather conditions experienced at overhead lines (OHLs) in order to predict percentiles of dynamic line ratings of OHLs which can be used by a system operator within a chosen risk policy with respect to probability of a rating being exceeded. Predictive probability distributions of air temperature, wind speed and direction are assumed to be normal, truncated normal and von Mises respectively. Predictive centres are estimated as a sum of residuals predicted by a univariate auto-regressive model or a vector auto-regressive model and temporal trends fitted by a 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. Parameters of the probabilistic models are determined to minimize the average value of continuous ranked probability score which is a summary indicator to assess performance of probabilistic models. The conditionally heteroscedastic models are shown to have appropriate sharpness and better calibration than the respective homoscedastic models

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)

Decomposition-based stacked bagging boosting ensemble for dynamic line rating forecasting

Effective exploitation of overhead transmission lines needs reliable and precise dynamic line rating forecasting. High-accuracy dynamic line rating forecasting, in particular, is an important short-term method for coping with grid congestion, enhancing grid stability, and accommodating high renewable energy penetration. Due to the non-stationarity and stochasticity of the meteorological variables, a single model is often not sufficient to accurately predict the dynamic line rating. Herein, a new stacked bagging boosting ensemble is developed based on multivariate empirical mode decomposition to overcome single models' restrictions and increase the dynamic line rating forecasting performance. The developed ensemble is utilized on the data gathered from a 400 kV aluminum conductor steel-reinforced overhead power line with a length of 32.85 Km between Ghadamgah and Binalood wind farms, located in the northeast of Iran. The simulation results substantiate that the proposed ensemble can capture meteorological variables' non-linear characteristics, yielding more accurate yet to noisy data forecasts than single forecasting models

Flexitranstore

This open access book comprises 10 high-level papers on research and innovation within the Flexitranstore Project that were presented at the FLEXITRANSTORE special session organized as part of the 21st International Symposium on High Voltage Engineering. FLEXITRANSTORE (An Integrated Platform for Increased FLEXIbility in smart TRANSmission grids with STORage Entities and large penetration of Renewable Energy Sources) aims to contribute to the development of a pan-European transmission network with high flexibility and high interconnection levels. This will facilitate the transformation of the current energy production mix by hosting an increasing share of renewable energy sources. Novel smart grid technologies, control and storage methods, and new market approaches will be developed, installed, demonstrated, and tested introducing flexibility to the European power system. FLEXITRANSTORE is developing a next-generation Flexible Energy Grid (FEG) that will be integrated into the European Internal Energy Market (IEM) through the valorization of flexibility services. This FEG addresses the capabilities of a power system to maintain continuous service in the face of rapid and large swings in supply or demand. As such, a wholesale market infrastructure and new business models within this integrated FEG must be upgraded for network players, and offer incentives for new ones to join, while at the same time demonstrating new business perspectives for cross-border resource management and energy trading

Power and Energy Student Summit 2019: 9 – 11 July 2019 Otto von Guericke University Magdeburg ; Conference Program

The book includes a short description of the conference program of the "Power and Energy Student Summit 2019". The conference, which is orgaized for students in the area of electric power systems, covers topics such as renewable energy, high voltage technology, grid control and network planning, power quality, HVDC and FACTS as well as protection technology. Besides the overview of the conference venue, activites and the time schedule, the book includes all papers presented at the conference

Copernicus Cal/Val Solution - D2.4 - Systematic Ground-Based Measurements

This document aims to map different existing ground-based and air-borne instrumented Cal/Val sites and networks acquiring measurements in a systematic manner, in Europe and worldwide. It does not include all available Cal/Val networks but only those that we interviewed or had enough information available online to include in this report. To meet the needs of satellite Cal/Val, measurements one must adhere to the definition for a Fiducial Reference Measurement (FRM)(Giuseppe Zibordi et al. 2014) and to the principles of the Quality Assurance framework for Earth Observation (QA4EO 2010). The scope of this document is not to evaluate the quality or maturity of the networks/sites that were being interviewed. It only maps the current situation and serves as an input for a later stage of the project. The completed questionnaires that we used to collect the data assembled in this report are not added directly to the document but will be available for project partners for next stage analyses

Techniques for Managing Grid Vulnerability and Assessing Structure

As power systems increasingly rely on renewable power sources, generation fluctuations play a greater role in operation. These unpredictable changes shift the system operating point, potentially causing transmission lines to overheat and sag. Any attempt to anticipate line thermal constraint violations due to renewable generation shifts must address the temporal nature of temperature dynamics, as well as changing ambient conditions. An algorithm for assessing vulnerability in an operating environment should also have solution guarantees, and scale well to large systems. A method for quantifying and responding to system vulnerability to renewable generation fluctuations is presented. In contrast to existing methods, the proposed temporal framework captures system changes and line temperature dynamics over time. The non-convex quadratically constrained quadratic program (QCQP) associated with this temporal framework may be reliably solved via a proposed series of transformations. Case studies demonstrate the method's effectiveness for anticipating line temperature constraint violations due to small shifts in renewable generation. The method is also useful for quickly identifying optimal generator dispatch adjustments for cooling an overheated line, making it well-suited for use in power system operation. Development and testing of the temporal deviation scanning method involves time series data and system structure. Time series data are widely available, but publicly available data are often synthesized. Well-known time series analysis techniques are used to assess whether given data are realistic. Bounds from signal processing literature are used to identify, characterize, and isolate the quantization noise that exists in many commonly-used electric load profile datasets. Just as straightforward time series analysis can detect unrealistic data and quantization noise, so graph theory may be employed to identify unrealistic features of transmission networks. A small set of unweighted graph metrics is used on a large set of test networks to reveal unrealistic connectivity patterns in transmission grids. These structural anomalies often arise due to network reduction, and are shown to exist in multiple publicly available test networks. The aforementioned study of system structure suggested a means of improving the performance of algorithms that solve the semidefinite relaxation of the optimal power flow problem (SDP OPF). It is well known that SDP OPF performance improves when the semidefinite constraint is decomposed along the lines of the maximal cliques of the underlying network graph. Further improvement is possible by merging some cliques together, trading off between the number of decomposed constraints and their sizes. Potential for improvement over the existing greedy clique merge algorithm is shown. A comparison of clique merge algorithms demonstrates that approximate problem size may not be the most important consideration when merging cliques. The last subject of interest is the ubiquitous load-tap-changing (LTC) transformer, which regulates voltage in response to changes in generation and load. Unpredictable and significant changes in wind cause LTCs to tap more frequently, reducing their lifetimes. While voltage regulation at renewable sites can resolve this issue for nearby sub-transmission LTCs, upstream transmission-level LTCs must then tap more to offset the reactive power flows that result. A simple test network is used to illustrate this trade-off between transmission LTC and sub-transmission LTC tap operations as a function of wind-farm voltage regulation and device setpoints. The trade-off calls for more nuanced voltage regulation policies that balance tap operations between LTCs.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155266/1/kersulis_1.pd

Quantifying the benefits and risks of real-time thermal ratings in electrical networks

PhD ThesisReal-Time Thermal Rating (RTTR) is a technology that allows the rating of electrical conductors to be estimated using real-time, local weather conditions. In many cases this leads to an increased rating with respect to conventional approaches. It also identifies some instances in which the conventional, static, rating is greater than the true rating, and is therefore potentially unsafe. The work in this thesis comprises methodologies to improve the planning and implementation of RTTR. Techniques commonly employed in the wind energy industry have been modified for use with RTTR. Computational wind simulations were employed to allow the identification of determining conductor spans, to inform network designers of the rating potential of different conductor routes, to estimate the additional wind energy that could be accommodated through the enhanced line rating and to allow informed placement of the monitoring equipment required to implement RTTR. Furthermore, the wind simulation data were also used to allow more accurate estimation of conductor ratings during operation. Probabilistic methods have been devised to estimate the level of additional load that could be accommodated through RTTR, and quantify the risk in doing so. Finally, a method has been developed to calculate the benefit RTTR can provide to system wide reliability. State sampling and sequential Monte Carlo simulations were used to evaluate the probabilistic functions associated with the ratings, the load and failures on both the existing network and the RTTR system itself. These methods combine to address fundamental barriers to the wide scale adoption and implementation of RTTR. The majority of existing research has focussed on improving technical solutions, which are of little benefit if it is not possible to quantify the benefits of RTTR before it is implemented. This work allows quantification not only of those benefits, but of the associated risks and uncertainties as well

Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation

An economic development model analyzes the adoption of alternative strategy capable of leveraging the economy, based essentially on RES. The combination of wind turbine, PV installation with new technology battery energy storage, DSM network and RES forecasting algorithms maximizes RES integration in isolated islands. An innovative model of power system (PS) imbalances is presented, which aims to capture various features of the stochastic behavior of imbalances and to reduce in average reserve requirements and PS risk. Deep learning techniques for medium-term wind speed and solar irradiance forecasting are presented, using for first time a specific cloud index. Scalability-replicability of the FLEXITRANSTORE technology innovations integrates hardware-software solutions in all areas of the transmission system and the wholesale markets, promoting increased RES. A deep learning and GIS approach are combined for the optimal positioning of wave energy converters. An innovative methodology to hybridize battery-based energy storage using supercapacitors for smoother power profile, a new control scheme and battery degradation mechanism and their economic viability are presented. An innovative module-level photovoltaic (PV) architecture in parallel configuration is introduced maximizing power extraction under partial shading. A new method for detecting demagnetization faults in axial flux permanent magnet synchronous wind generators is presented. The stochastic operating temperature (OT) optimization integrated with Markov Chain simulation ascertains a more accurate OT for guiding the coal gasification practice