Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. Apart from introduction and references the paper is organized as follows. The section 2 presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learning-based algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting

Effects of a winter forage crop rotation on CO2 fluxes at a managed grassland

Temperate grasslands have the potential to sequester carbon, helping to mitigate rising atmospheric CO2 concentrations. The ability of grasslands to absorb CO2 is influenced by site elevation, soil type, management practices, climate and climatic variability. There is a need for long-term observations and field experiments to quantify the effects of the key drivers of management and climate variability. This paper presents over 4 years of eddy covariance measurements of CO2 flux over a managed temperate grassland site in south-east Ireland. For the first 2 years the entire study area was under grass. During the second 2 years a winter forage crop was grown over part of the site. The site was found to have a net uptake of CO2 during all years. However, the magnitude of the CO2 uptake varied considerably from year to year, with a maximum net uptake of 1.32 kg CO2 m−2 in 2004, a year with no winter forage crop. Net uptakes were much lower in the 2 years of mixed grass and kale cultivation, but detailed analysis of the measurement footprint and statistical comparisons showed that this was not due to the introduction of the forage rotation. For a short period following sowing of the forage crop, daytime CO2 uptake was less than that of the area under grass, but over subsequent months daytime CO2 uptake of the kale areas recovered strongly and exceeded that of the grass areas. The net effect over the year following kale planting is close to CO2-neutral

Short duration rainfall extremes in Ireland: influence of climatic variability

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System operational costs reduction with non-conventional reactive power sources

Wind energy installations are increasing in power systems worldwide and wind generation capacity tends to be located some distance from load centers. A conflict may arise at times of high wind generation when it becomes necessary to curtail wind energy in order to maintain conventional generators on-line for the provision of voltage control support at load centers. Using the island of Ireland as a case study and presenting commercially available reactive power support devices as possible solutions to the voltage control problems in urban areas, this paper explores the reduction in total generation costs resulting from the relaxation of the operational constraints requiring conventional generators to be kept on-line near load centers for reactive power support. The paper shows that by 2020 there will be possible savings of 87€m per annum and a reduction in wind curtailment of more than a percentage point if measures are taken to relax these constraints

Quantifying the value of improved wind energy forecasts in a pool-based electricity market

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Cost savings from relaxation of operational constraints on a power system with high wind penetration

Wind energy is predominantly a nonsynchronous generation source. Large-scale integration of wind generation with existing electricity systems, therefore, presents challenges in maintaining system frequency stability and local voltage stability. Transmission system operators have implemented system operational constraints (SOCs) in order to maintain stability with high wind generation, but imposition of these constraints results in higher operating costs. A mixed integer programming tool was used to simulate generator dispatch in order to assess the impact of various SOCs on generation costs. Interleaved day-ahead scheduling and real-time dispatch models were developed to allow accurate representation of forced outages and wind forecast errors, and were applied to the proposed Irish power system of 2020 with a wind penetration of 32%. Savings of at least 7.8% in generation costs and reductions in wind curtailment of 50% were identified when the most influential SOCs were relaxed. The results also illustrate the need to relax local SOCs together with the system-wide nonsynchronous penetration limit SOC, as savings from increasing the nonsynchronous limit beyond 70% were restricted without relaxation of local SOCs. The methodology and results allow for quantification of the costs of SOCs, allowing the optimal upgrade path for generation and transmission infrastructure to be determined

How much wind energy will be curtailed on the 2020 Irish power system?

This paper describes a model of the 2020 Irish electricity system which was developed and solved in a mixed integer programming, unit commitment and economic dispatch tool called PLEXOS. The model includes all generators on the island of Ireland, a simplified representation of the neighbouring British system including proposed wind capacity and interconnectors between the two systems. The level of wind curtailment is determined under varying levels of three influencing factors. The first factor is the amount of offshore wind, the second is the allowed limit of system non-synchronous penetration (SNSP) and the third is inclusion or exclusion of transmission constraints. A binding constraint, resulting from the 2020 EU renewable energy targets, is that 37% of generation comes from wind. When the SNSP limit was increased from 60% to 75% there was a reduction in wind curtailment from 14% to 7%, with a further reduction when the proportion of wind capacity installed offshore was increased. Wind curtailment in the range of SNSP limit of 70-100% is influenced primarily by the inclusion of transmission constraints. Large changes in the dispatch of conventional generators were also evident due to the imposition of SNSP limits and transmission constraints

Persistence of low wind speed conditions and implications for wind power variability

As the penetration of wind generation increases on power systems throughout the world, the effects of wind variability on power systems are of increasing concern. This study focuses on sustained occurrences of low wind speeds over durations ranging from 1 h to 20 days. Such events have major implications for the variability of energy yields from wind farms. This, in turn, influences the accuracy of wind resource assessment. The frequency analysis techniques commonly used to study wind variability cannot represent the autocorrelation properties of wind speeds and thus provide no information on the probabilities of occurrence of such sustained, low wind events. We present two complementary methods for assessing wind variability, runs analysis and intensity–duration–frequency analysis, both with emphasis on characterising the occurrence of continuous, extended periods (up to several days) of low wind speeds. Multi-annual time series of hourly wind speeds from meteorological stations in Ireland are analysed with both techniques. Sustained 20-day periods corresponding to extremely low levels of wind generation are found to have return periods of around 10 years in coastal areas. Persistent, widespread low wind speed conditions across the entire country are found to occur only rarely

Sizing battery energy storage systems: using multi-objective optimisation to overcome the investment scale problem of annual worth

The financial objective, when sizing a Battery Energy Storage System (BESS) for installation in a microgrid, is to maximise the difference between discounted BESS benefits and discounted BESS costs. This may be described as maximising Annual Worth (AW). However, one drawback of sizing microgrid BESS using AW is that the scale of investment is not taken into consideration. This can lead to unrealistic BESS sizes. This paper presents two multi-objective optimisation (MOO) models to account for the scale of investment required in sizing BESS. The first model, Paired Comparison, utilises two objective functions: Daily Worth (DW), which maximises daily benefit cost differences a BESS installation provides a microgrid; and Daily Cost (DC), which minimises the daily cost of a BESS installation. The second model, called Rating Method, uses the objective functions DW and Daily Benefit-Cost Ratio (DBCR), the latter of which maximises the relative measure of BESS benefit and BESS cost. Both models are solved for a test microgrid system under three different scenarios using Compromise Programming (CP). For system designers who rank objective functions by importance, the Rating Method is the appropriate approach, whereas system designers who rank objective functions by absolute values should use Paired Comparison

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