Paul Leahy

Electric Vehicles and energy storage — a case study on Ireland

Renewable energy is generally accepted as an important component of future electricity grids. In late 2008, the Government of the Republic of Ireland set a target of 10% of all vehicles in its transport fleet be powered by electricity by 2020. This paper examines the potential contributions Electric Vehicles (EVs) can make to facilitate increased electricity generation from variable renewable sources such as wind generation in the Republic of Ireland. It also presents an overview of the technical and economic issues associated with this target.

Wind power forecasting & prediction methods

Globally on-shore wind power has seen considerable growth in all grid systems. In the coming decade off-shore wind power is also expected to expand rapidly. Wind power is variable and intermittent over various time scales because it is weather dependent. Therefore wind power integration into traditional grids needs additional power system and electricity market planning and management for system balancing. This extra system balancing means that there is additional system costs associated with wind power assimilation. Wind power forecasting and prediction methods are used by system operators to plan unit commitment, scheduling and dispatch and by electricity traders and wind farm owners to maximize profit. Accurate wind power forecasting and prediction has numerous challenges. This paper presents a study of the existing and possible future methods used in wind power forecasting and prediction for both on-shore and off-shore wind farms.

Wind energy integration and the Ireland-Wales interconnector

In a small electricity grid such as the all Ireland system of Northern Ireland and the Republic of Ireland, managing with the variability of high levels of wind power generation will be crucial to ensuring the economic success of wind energy generation and the overall stability of the electricity system. Storage and interconnection are frequently proposed to manage this variability and total flexibility of the interconnector is assumed. This paper examines how market and meteorological effects could constrain interconnector operation. Currently an interconnector between the Republic of Ireland and Wales officially referred to as the East West Interconnector is in planning. The split between energy transfer and reserve provision through the interconnector will be dictated by energy prices and relative value of reserve services. The levels of wind power generation in each connected region may limit the mutual support expected from interconnection. In this regard wind forecasting and wind correlations in the connected regions are extremely relevant and are discussed in this paper.

Medieval Irish chronicles reveal persistent volcanic forcing of severe winter cold events, 431?1649 CE

Explosive volcanism resulting in stratospheric injection of sulfate aerosol is a major driver of regional to global climatic variability on interannual and longer timescales. However, much of our knowledge of the climatic impact of volcanism derives from the limited number of eruptions that have occurred in the modern period during which meteorological instrumental records are available. We present a uniquely long historical record of severe short-term cold events from Irish chronicles, 431‐1649 CE, and test the association between cold event occurrence and explosive volcanism. Thirty eight (79%) of 48 volcanic events identified in the sulfate deposition record of the Greenland Ice Sheet Project 2 ice-core correspond to 37 (54%) of 69 cold events in this 1219 year period. We show this association to be statistically significant at the 99.7% confidence level, revealing both the consistency of response to explosive volcanism for Ireland’s climatically sensitive Northeast Atlantic location and the large proportional contribution of volcanism to historic cold event frequencies here. Our results expose, moreover, the extent to which volcanism has impacted winter-season climate for the region, and can help to further resolve the complex spatial patterns of Northern Hemisphere winter-season cooling versus warming after major eruptions.

The value of accuracy in wind energy forecasts

Wind generation of electricity is both variable and unpredictable in its nature. Wind energy forecasting is a means of addressing both of these issues. This study quantifies the effects of the accuracy of wind energy forecasts on the operation of the Irish electricity system of 2020. Through combined use of day-ahead scheduling and real-time simulations, it was found that there are notable savings to be made and that changes occur in the dispatch of generator technology types with improvements in wind forecast accuracy. There are potential savings of on average 0.27% of total generation cost for every percentage point decrease in the normalised mean absolute error of wind forecasts. This is mainly resulting from less frequent use of high-cost OCGTs on the system due to the day-ahead schedule becoming a more accurate representation of actual generation as wind forecast accuracy improves. There is also a slight reduction in wind curtailment with increased accuracy of the day-ahead unit commitment schedule.

H-MODES ON COMPASS-D WITH HIGH-POWER ECRH

Recent high-power electron-cyclotron resonance heating (ECRH) H-mode experiments on COMPASS-D have used fundamental high-power ECRH to access high densities. Within the operating range set by the H-mode density threshold and ECRH accessibility, H-mode power thresholds are significantly higher than expected from ITER scalings, and show a contrary dependence on density. Energy confinement is somewhat lower than that suggested by the present ITER scalings, but correction for incomplete RF absorption may be needed. Complex H-mode and ELM behaviour is observed.

Electric vehicles and displaced gaseous emissions

Electric vehicles (EV) do not emit tailpipe exhaust fumes in the same manner as internal combustion engine vehicles. Optimal benefits can only be achieved, if EVS are deployed effectively, so that the tailpipe emissions are not substituted by additional emissions in the electricity sector. This paper examines the potential contributions that Plug in Hybrid Electric Vehicles can make in reducing carbon dioxide. The paper presents the results of the generation expansion model for Northern Ireland and the Republic of Ireland built using the dynamic programming based long term generation expansion planning tool called the Wien Automatic System Planning IV tool. The model optimizes power dispatch using hourly electricity demand curves for each year up to 2020, while incorporating generator characteristics and certain operational requirements such as energy not served and loss of load probability while satisfying constraints on environmental emissions, fuel availability and generator operational and maintenance costs. In order to simulate the effect of PHEV, two distinct charging scenarios are applied based on a peak tariff and an off peak tariff. The importance and influence of the charging regime on the amount of energy used and gaseous emissions displaced is determined and discussed.

Fast charged-coupled device spectrometry using zoom-wavelength optics

Fast charge-coupled device (CCD) detector arrays placed at the output of visible spectrometers are used for multichord Doppler shift analyses on the COMPASS-D and START tokamaks. Unequal magnification in the horizontal and vertical axes allows for optimal matching of throughput and spectral resolution at the CCD detector. This involves cylindrical lenses in an anamorphic mounting. Optical acuity is preserved over a very wide range of wavelengths (220 nm→700 nm) by separate repositioning of all the optical elements which is accomplished by the use of zoom mechanisms. This facilitates rapid changes of wavelength allowing edge and core observations depending on the location of the emitting impurity ions. Changes to the ion temperature and velocity are recorded using 20 chords simultaneously with typical accuracies of Δvi<1 km s−1 and ΔTi/Ti<10% with a time resolution of <1 ms.

Comparison of linear, Kalman filter and neural network downscaling of wind speeds from numerical weather prediction

This paper describes a refinement of wind speed prediction methods in order to enhance their accuracy for wind energy applications. Specifically, techniques used to downscale raw forecasts from numerical weather prediction models are investigated. Wind speed measurements from several surface meteorological stations are used to test the downscaling process. While classical downscaling methods require large sets of historical data in order to be trained, the Kalman filter has the potential to rapidly estimate the bias that needs to be added to the raw forecasts in order to provide the best fit possible to local observations. In this paper, the Kalman filter technique is applied, and its performance is compared with classical linear and simple artificial neural network downscaling methods. It is shown that while the levels of prediction accuracy attainable are similar to classical techniques, the amount of data required to parameterise the Kalman filter is much less than for other techniques.

Offshore wind resource estimation using wave buoy data

In the coming decade installed offshore wind capacity is expected to expand rapidly. This will be both technically and economically challenging. Precise wind resource assessment is one of the more imminent challenges. It is more difficult to assess wind power offshore than onshore due to the paucity of representative wind speed data. Offshore site-specific data is less accessible and is far more costly to collect. However, offshore wind speed data collected from sources such as wave buoys, remote sensing from satellites, national weather ships, and coastal meteorological stations and met masts on barges and platforms may be extrapolated to assess offshore wind power. This study attempts to determine the usefulness of pre-existing offshore wind speed measurements in resource assessment, and presents the results of wind resource estimation in the Atlantic Ocean and in the Irish Sea using data from two offshore meteorological buoys.