Power flow simulation of DC railway power supply systems with regenerative braking

The energy efficiency of a railway electrification system can be improved by the recovery of regenerative braking energy which is converted from the mechanical energy of braking trains. In a direct current (DC) railway power supply system, the regenerated energy which would otherwise be dissipated as heat in braking resistors may be consumed by surrounding accelerating trains, stored by energy storage systems, or fed back to upstream alternative current (AC) sides via reversible substations (RSS). It is necessary to evaluate the benefits related to energy savings achieved by the installation of RSS due to the high cost of initial investment. This paper models DC railway power supply systems in Simulink to simulate power flows within the systems in different scenarios with or without the deployment of RSS. Pantograph voltages of trains and power exchange between AC and DC sides are analysed to illustrate the effectiveness of the developed models and the limits on the braking energy recovery

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

Comparison of ARIMA and ANN models used in electricity price forecasting for power market

In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper introduces the models of autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) which are applied to the price forecasts for up to 3 steps ahead in the UK electricity market. The half hourly data of historical prices are obtained from UK Reference Price Data from March 22nd to July 14th 2010 and the predictions are derived from a sliding training window with a length of 8 weeks. The ARIMA with various AR and MA orders and the ANN with different numbers of delays and neurons have been established and compared in terms of the root mean square errors (RMSEs) of price forecasts. The experimental results illustrate that the ARIMA (4,1,2) model gives greater improvement over persistence than the ANN (20 neurons, 4 delays) model

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

Wind forecasting using kriging and vector auto-regressive models for dynamic line rating studies

This paper aims to describe methods to forecast wind speeds experienced around overhead lines (OHLs) in order to predict the wind cooling effect and thus the dynamic line ratings (DLRs) of OHLs. The wind speed at a particular OHL span is forecast through a kriging interpolation between the wind speed predictions produced by a vector auto-regressive (VAR) model for a limited number of weather stations at which observations have been obtained. A temporal de-trending method is used to ensure the stationarity of de-trended data from which model parameters are determined. A spatial de-trending method is adopted in a kriging model. The results show that the kriging model performs better than the inverse distance weighting (IDW) method and that the spatial de-trending makes the main contribution to the accuracy of interpolation. Furthermore, the VAR forecasting model is shown to give greater improvement over persistence than a simple auto-regressive (AR) model

A short-term electricity price forecasting scheme for power market

Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent to contribute more efforts on developing appropriate price forecasting scheme to maximize their profits. This paper introduces a time series method developed by Box-Jenkins that applies autoregressive integrated moving average (ARIMA) model to address a best-fitted time-domain model based on a time series of historical price data. Using the model’s parameters determined from the stationarized time series of prices, the price forecasts in UK electricity market for 1 step ahead are estimated in the next day and the next week. The most suitable models are selected for them separately after comparing their prediction outcomes. The data of historical prices are obtained from UK three-month Reference Price Data from April 1st to July 7th 2010

Optimization of isolation and purification of total flavonoids from Ardisia mamillata Hance roots using macroporous resins, and determination of their antioxidant activity

Purpose: To isolate, purify and determine the antioxidant property of total flavonoids from the roots of Ardisia mamillata, so as to provide a  theoretical basis for development of natural antioxidants.Methods: Macroporous resin was used to optimize the isolation and  purification of total flavonoids, taking adsorption rate and resolution rate as evaluation indices. The antioxidant property of the purified total flavonoids was determined using 1,1-diphenyl-2-picrylhydrazyl radical 2,2-diphenyl-1-(2,4,6- trinitrophenyl)hydrazyl (DPPH) radical scavenging activity.Results: The best conditions for separation and purification of total  flavonoids from Ardisia mamillata roots were: use of ADS-7 resin, loading total flavonoid concentration of 0.8896 mg/mL, loading buffer flow rate of 1.5 mL/min, loading buffer pH of 4.48, elution ethanol concentration of 60 %, and flow rate of 2.5 mL/min. Under these conditions, the degree of purification of total flavonoids of Ardisia mamillata root was 76.43 ± 0.36 %, adsorption rate was 96.52 ± 0.19 %, while resolution rate was 99.31 ± 0.27 %. When the concentration of the purified total flavonoids was 4.0 mg/mL, its DPPH radical scavenging activity was stronger than that of the standard, butylated hydroxytoluene (BHT), but lower than that of vitamin C.Conclusion: ADS-7 resin is the best macroporous resin for the purification of total flavonoids from the radix of Ardisia mamillata Hance, under the  optimized conditions. The purified total flavonoids of Ardisia mamillata root have stronger DPPH radical scavenging ability than the standard, BHT.Keywords: Szechwan raspberry root, Flavonoids, Macroporous adsorption resin, ADS-7 resin, Purification, Antioxidan

Utilisation of energy storage to improve distributed generation connections and network operation on Shetland Islands

The Northern Isles New Energy Solutions (NINES) project on the Shetland Islands seeks to trial the application of alternative solutions, including demand side management and battery energy storage to increase the integration of renewable generation and smooth the demand curve. As part of the NINES project, a 1MW, 3MWh Battery Energy Storage System (BESS) has been installed in the Shetland network and initially operated by an Active Network Management (ANM) system and then brought under the manual scheduling. The main objective was to reduce peak demands to be met by conventional generation and also to increase the demand at off-peak times which may provide additional headroom for non-firm distributed generation, i.e. ANM Controlled Generation (ACG). This paper aims to present experiences and findings from the NINES project regarding the BESS’s operation, utilisation and efficiency (energy losses). Furthermore, the constraint rules that limit the ACG export are discussed alongside practical issues around charging the BESS in response to the ACG curtailment

Enhanced Weather Modelling for Dynamic Line Rating

Conventional approaches to dynamic line rating (DLR) forecasting provide single point estimates with no indication of the distribution of possible errors. Furthermore, most research related to DLR forecasting deals only with continuous or steady-state ratings while less attention has been given to short-term or transient-state ratings. This thesis describes (a) weather-based models to estimate probabilistic forecasts of steady-state DLRs for up to three 10-minutes time steps ahead for a particular span and a complete overhead line (OHL) and also (b) a fast-computational weather-based approach to probabilistic forecasting of transient-state DLRs for a particular span for time horizons of 10, 20 and 30 minutes. The percentiles of DLR forecasts can be used by a system operator within a chosen risk policy informed by the probability of a rating being exceeded. The thesis first develops time series forecasting models for different weather variables that impact on line rating (i.e. air temperature, wind speed, wind direction and solar radiation) at weather stations that are installed along the route of 132kV OHLs in North Wales. Predictive centres of weather variables are modelled as a sum of residuals predicted by a suitable auto-regressive process 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 wind direction observations within the most recent two hours. A technique of minimum continuous ranked probability score estimation is employed to determine predictive distributions of the measured weather variables. Then the thesis uses Monte Carlo simulation to generate a large number of random weather samples from the modelled predictive distributions which are paired to have rank correlations similar to those among their recent observations. The probabilistic steady-state DLR forecasts for a particular span in proximity to a weather station are estimated from the random weather samples combined with a maximum allowable conductor temperature using a thermal model of the conductors (i.e. a steady-state heat balance equation). For a complete OHL, possible weather predictions at each span are inferred from random weather samples at stations by using suitable spatial interpolation models; the steady-state DLR forecast of the OHL is then identified as the minimum DLR among all spans for each generated scenario. Using an enhanced analytical method which evolves from a non-steady-state heat balance equation to track the transient-state conductor temperature, the transient-state DLR forecast for a particular span is calculated as that which increases the conductor temperature from an initial value to the maximum allowable limit for a particular future time period (i.e. in this study, 10, 20 and 30 minutes) under each set of random weather samples. The calibration of probabilistic DLR forecasts estimated from independent or correlated random weather samples are then examined to determine which approaches are most suited to estimation of DLRs at the lower end of a predictive distribution consistent with a system operator’s risk policy. The potential use of DLR forecasting is then evaluated through estimating the degree to which wind generation curtailment for various assumed installed capacities at a wind farm that is connected to the 132kV network in North Wales can be alleviated through using the lower percentiles of steady-state DLR forecasts in place of the SLRs for each 132kV OHL

Transient-state real-time thermal rating forecasting for overhead lines by an enhanced analytical method

The majority of published approaches to real-time thermal rating (RTTR) deal with continuous or steady-state ratings for overhead lines. Less attention has been given to short-term or transient-state RTTRs, partly due to the increased computation time required. This paper describes a fast-computational approach to providing a transient-state RTTR in the form of percentiles based on the predictive distributions modelled for the measured weather variables that are combined with Monte Carlo simulation. An analytical method developed in IEEE Standard 738 calculates the transient-state conductor temperature after a step change in line current only and additionally requires the conductor to be in thermal equilibrium before the step occurs. The IEEE analytical method is enhanced here through inference of an equivalent steady-state initial line current from the initial conductor temperature and weather conditions over a specified time period. Numerous transient-state RTTR forecasts for a particular span are estimated via weather inputs randomly sampled from predictive distributions for a number of time steps ahead combined with the secant method to find the transient-state RTTR. Along with an enhanced analytical method, this yields a maximum allowable conductor temperature for a specified time period under each set of weather samples. The percentiles of transient-state RTTR forecasts are then determined from their sampled values using kernel density estimation. The approach developed here considers variations in weather forecasts at each 10-min time step