Short-term wind prediction using an ensemble of particle swarm optimised FIR filters

Due to the large and increasing penetration of wind power around the world, accurate power production forecasts are required to manage power systems and wind power plants. In this paper we propose an ensemble of particle swarm optimised filtering technique for 1-hour-ahead prediction of hourly mean wind speed and direction. The performance of the new method is assessed by testing it on data from 13 locations around the UK where it performs comparably to linear techniques but is able to provide significant improvement at a subset of locations

Spatio-temporal prediction of wind fields

Short-term wind and wind power forecasts are required for the reliable and economic operation of power systems with significant wind power penetration. This thesis presents new statistical techniques for producing forecasts at multiple locations using spatiotemporal information. Forecast horizons of up to 6 hours are considered for which statistical methods outperform physical models in general. Several methods for producing hourly wind speed and direction forecasts from 1 to 6 hours ahead are presented in addition to a method for producing five-minute-ahead probabilistic wind power forecasts. The former have applications in areas such as energy trading and defining reserve requirements, and the latter in power system balancing and wind farm control. Spatio-temporal information is captured by vector autoregressive (VAR) models that incorporate wind direction by modelling the wind time series using complex numbers. In a further development, the VAR coefficients are replaced with coefficient functions in order to capture the dependence of the predictor on external variables, such as the time of year or wind direction. The complex-valued approach is found to produce accurate speed predictions, and the conditional predictors offer improved performance with little additional computational cost. Two non-linear algorithms have been developed for wind forecasting. In the first, the predictor is derived from an ensemble of particle swarm optimised candidate solutions. This approach is low cost and requires very little training data but fails to capitalise on spatial information. The second approach uses kernelised forms of popular linear algorithms which are shown to produce more accurate forecasts than their linear equivalents for multi-step-ahead prediction. Finally, very-short-term wind power forecasting is considered. Five-minute-ahead parametric probabilistic forecasts are produced by modelling the predictive distribution as logit-normal and forecasting its parameters using a sparse-VAR (sVAR) approach. Development of the sVAR is motivated by the desire to produce forecasts on a large spatial scale, i.e. hundreds of locations, which is critical during periods of high instantaneous wind penetration.Short-term wind and wind power forecasts are required for the reliable and economic operation of power systems with significant wind power penetration. This thesis presents new statistical techniques for producing forecasts at multiple locations using spatiotemporal information. Forecast horizons of up to 6 hours are considered for which statistical methods outperform physical models in general. Several methods for producing hourly wind speed and direction forecasts from 1 to 6 hours ahead are presented in addition to a method for producing five-minute-ahead probabilistic wind power forecasts. The former have applications in areas such as energy trading and defining reserve requirements, and the latter in power system balancing and wind farm control. Spatio-temporal information is captured by vector autoregressive (VAR) models that incorporate wind direction by modelling the wind time series using complex numbers. In a further development, the VAR coefficients are replaced with coefficient functions in order to capture the dependence of the predictor on external variables, such as the time of year or wind direction. The complex-valued approach is found to produce accurate speed predictions, and the conditional predictors offer improved performance with little additional computational cost. Two non-linear algorithms have been developed for wind forecasting. In the first, the predictor is derived from an ensemble of particle swarm optimised candidate solutions. This approach is low cost and requires very little training data but fails to capitalise on spatial information. The second approach uses kernelised forms of popular linear algorithms which are shown to produce more accurate forecasts than their linear equivalents for multi-step-ahead prediction. Finally, very-short-term wind power forecasting is considered. Five-minute-ahead parametric probabilistic forecasts are produced by modelling the predictive distribution as logit-normal and forecasting its parameters using a sparse-VAR (sVAR) approach. Development of the sVAR is motivated by the desire to produce forecasts on a large spatial scale, i.e. hundreds of locations, which is critical during periods of high instantaneous wind penetration

Very-short-term probabilistic wind power forecasts by sparse vector autoregression

A spatio-temporal method for producing very-short-term parametric probabilistic wind power forecasts at a large number of locations is presented. Smart grids containing tens, or hundreds, of wind generators require skilled very-short-term forecasts to operate effectively, and spatial information is highly desirable. In addition, probabilistic forecasts are widely regarded as necessary for optimal power system management as they quantify the uncertainty associated with point forecasts. Here we work within a parametric framework based on the logit-normal distribution and forecast its parameters. The location parameter for multiple wind farms is modelled as a vector-valued spatio-temporal process, and the scale parameter is tracked by modified exponential smoothing. A state-of-the-art technique for fitting sparse vector autoregressive models is employed to model the location parameter and demonstrates numerical advantages over conventional vector autoregressive models. The proposed method is tested on a dataset of 5 minute mean wind power generation at 22 wind farms in Australia. 5-minute-ahead forecasts are produced and evaluated in terms of point and probabilistic forecast skill scores and calibration. Conventional autoregressive and vector autoregressive models serve as benchmarks

A cyclo-stationary complex multichannel wiener filter for the prediction of wind speed and direction

This paper develops a linear predictor for application to wind speed and direction forecasting in time and across different sites. The wind speed and direction are modelled via the magnitude and phase of a complex-valued time-series. A multichannel adaptive filter is set to predict this signal, based on its past values and the spatio-temporal correlation between wind signals measured at numerous geographical locations. The time-varying nature of the underlying system and the annual cycle of seasons motivates the development of a cyclo-stationary Wiener filter, which is tested on hourly mean wind speed and direction data from 13 weather stations across the UK, and shown to provide an improvement over both stationary Wiener filtering and a recent auto-regressive approach

Kernel methods for short-term spatio-temporal wind prediction

Two nonlinear methods for producing short-term spatio-temporal wind speed forecast are presented. From the relatively new class of kernel methods, a kernel least mean squares algorithm and kernel recursive least squares algorithm are introduced and used to produce 1 to 6 hour-ahead predictions of wind speed at six locations in the Netherlands. The performance of the proposed methods are compared to their linear equivalents, as well as the autoregressive, vector autoregressive and persistence time series models. The kernel recursive least squares algorithm is shown to offer significant improvement over all benchmarks, particularly for longer forecast horizons. Both proposed algorithms exhibit desirable numerical properties and are ripe for further development

A review of probabilistic methods for defining reserve requirements

In this paper we examine potential improvements in how load and generation forecast uncertainty is captured when setting reserve levels in power systems with significant renewable generation penetration and discuss the merit of proposed new methods in this area. One important difference between methods is whether reserves are defined based on the marginal distribution of forecast errors, as calculated from historic data, or whether the conditional distribution, specific to the time at which reserves are being scheduled, is used. This paper is a review of published current practice in markets which are at the leading edge of this problem, summarizing their experiences, and aligning it with academic modeling work. We conclude that the ultimate goal for all markets expected to manage high levels of renewable generation should be a reserve setting mechanism which utilizes the best understanding of meteorological uncertainties combined with traditional models of uncertainty arising from forced outages

Wind prediction enhancement by exploiting data non-stationarity

The short term forecasting of wind speed and direction has previously been improved by adopting a cyclo-stationary multichannel linear prediction approach which incorporat ed seasonal cycles into the estimation of statistics. This pap er expands previous analysis by also incorporating diurnal va ri- ation and time-dependent window lengths. Based on a large data set from the UK’s Met Office, we demonstrate the impact of this proposed approach

Short-term forecasting of wind speed and direction exploiting data non-stationarity

This paper explores how the accuracy of short-term prediction of wind speed and direction can be enhanced by considering the diurnal variation of the wind. The wind speed and direction are modelled as the magnitude and phase of a complex-valued time series. The prediction is performed by a multichannel filter using the spatio-temporal correlation between measurements at different geographical locations and the past values of the target site. A multichannel complex-valued non-stationary prediction Wiener filter is proposed that takes into account both the seasonal and diurnal variation of the wind. Using hourly wind speed and direction measurements from over 22 Met Office weather stations distributed across the UK, we demonstrate that there can be a benefit for predicting one hour ahead when taking into account the diurnal and seasonal cyclo-stationary nature of the wind

Screening and techno-economic assessment of biomass-based power generation with CCS technologies to meet 2050 CO2 targets

Biomass-based power generation combined with CO2 capture and storage (Biopower CCS) currently represents one of the few practical and economic means of removing large quantities of CO2 from the atmosphere, and the only approach that involves the generation of electricity at the same time. We present the results of the Techno-Economic Study of Biomass to Power with CO2 capture (TESBiC) project, that entailed desk-based review and analysis, process engineering, optimisation as well as primary data collection from some of the leading pilot demonstration plants. From the perspective of being able to deploy Biopower CCS by 2050, twenty-eight Biopower CCS technology combinations involving combustion or gasification of biomass (either dedicated or co-fired with coal) together with pre-, oxy- or post-combustion CO2 capture were identified and assessed. In addition to the capital and operating costs, techno-economic characteristics such as electrical efficiencies (LHV% basis), Levelised Cost of Electricity (LCOE), costs of CO2 captured and CO2 avoided were modelled over time assuming technology improvements from today to 2050. Many of the Biopower CCS technologies gave relatively similar techno-economic results when analysed at the same scale, with the plant scale (MWe) observed to be the principal driver of CAPEX (£/MWe) and the cofiring % (i.e. the weighted feedstock cost) a key driver of LCOE. The data collected during the TESBiC project also highlighted the lack of financial incentives for generation of electricity with negative CO2 emissions

Spatio-temporal prediction of wind speed and direction by continuous directional regime

This paper proposes a statistical method for 1-6 hour-ahead prediction of hourly mean wind speed and direction to better forecast the power produced by wind turbines, an increasingly important component of power system operation. The wind speed and direction are modelled via the magnitude and phase of a complex vector containing measurements from multiple geographic locations. The predictor is derived from the spatio-temporal covariance which is estimated at regular time intervals from a subset of the available training data, the wind direction of which lies within a sliding range of angles centred on the most recent measurement of wind direction. This is a generalisation of regime-switching type approaches which train separate predictors for a few fixed regimes. The new predictor is tested on the Hydra dataset of wind across the Netherlands and compared to persistence and a cyclo-stationary Wiener filter, a state-of-the-art spatial predictor of wind speed and direction. Results show that the proposed technique is able to predict the wind vector more accurately than these benchmarks on dataset containing 4 to 27 sites, with greater accuracy for larger datasets