Estimating the impact of wind generation and wind forecast errors on energy prices and costs in Ireland

This paper studies the impact of wind generation on system costs and prices in Ireland. The need to mitigate climate change, achieve renewables energy targets, and use renewable sources of energy means that many countries are considering greater levels of wind generation in their power generation mix. The overall impact of wind generation on system costs and performance has only been studied recently, and often with limited actual data from power systems with increased wind penetration. The paper uses a unique dataset of half-hourly system demand, generation, wind forecast generation, and actual wind generation, along with Irish system marginal price (SMP) data from 2008 to autumn 2012. An econometric time-series model of SMP as a function of forecast and realized demand and wind generation is formed. The costs of balancing and system constraints are included in the cost of ‘uplift’, and thus the total cost of a variety of factors is included in our estimates for Ireland. Our results suggest that each 1% increase in wind generation reduces SMP in Ireland by about 0.06%, while each 1% wind forecast error increases SMP about 0.02%. In absolute terms, though, at the mean the impact of wind forecast errors is small, or about 0.4€cent/MWh-wind generated. However, the impact per MWh forecast error is about €1

Detecting abnormalities in the Brent crude oil commodities and derivatives pricing complex

Recent rapidly rising and volatile energy commodities prices and financial price manipulation scandals have brought the pricing mechanisms of crude oil derivatives to the fore of both popular press and policy initiatives. Among the most important of such commodities is Brent Crude. Brent Crude and its complex of derivative products make Brent Crude potentially more opaque and thus susceptible to price manipulation than other commodities. In spite of the importance of Brent to the world economy and world energy prices, and its complex of derivative pricing, relatively little work has been done to explore the potential for, and evidence of, price manipulation in the Brent Crude complex. This paper seeks to address this lack by proposing a method to test whether price squeezes have occurred in Brent Crude. This paper builds on previous work which proposed an a priori test for evidence of manipulation and the theory of storage. Previous work (Barrera-Rey and Seymour 1996) posited that the very close-to-delivery end of the forward curve for Brent should not be simultaneously in contango and backwardation, while other work (Geman and Smith 2012) proposed using an econometric prediction and a model based on the theory of storage to detect manipulation in commodity markets. Our work builds on these approaches by developing a more detailed model of calendar spreads in the Brent Crude complex. In Brent, a particular area of potential manipulation is from the relatively illiquid and more opaque physical OTC forward market (where prices are ‘assessed’ by Platts during a short ‘window’ of time) and the more liquid ICE futures market. Our model relates prompt ICE futures calendar spreads to prompt-over-dated OTC forward spreads. The model then tests whether the a priori indicators of manipulation as suggested by Barrera-Rey and Seymour are statistically consistent with the process which drives spreads historically. We find that in most all cases, the indicated period of manipulation is statistically different. We further investigate whether other factors, such as liquidity (volume and open interest) or world oil market conditions (using WTI spreads) or other forward market conditions could be driving our results. The statistical difference is found to be invariant to the inclusion of these other explanatory variables. We conclude that the evidence is consistent with the hypothesis of price manipulation and that the test provides a model and method for detecting such cases

Detecting abnormalities in the Brent crude oil commodities and derivatives pricing complex

Recent rapidly rising and volatile energy commodities prices and financial price manipulation scandals have brought the pricing mechanisms of crude oil derivatives to the fore of both popular press and policy initiatives. Among the most important of such commodities is Brent Crude. Brent Crude and its complex of derivative products make Brent Crude potentially more opaque and thus susceptible to price manipulation than other commodities. In spite of the importance of Brent to the world economy and world energy prices, and its complex of derivative pricing, relatively little work has been done to explore the potential for, and evidence of, price manipulation in the Brent Crude complex. This paper seeks to address this lack by proposing a method to test whether price squeezes have occurred in Brent Crude. This paper builds on previous work which proposed an a priori test for evidence of manipulation and the theory of storage. Previous work (Barrera-Rey and Seymour 1996) posited that the very close-to-delivery end of the forward curve for Brent should not be simultaneously in contango and backwardation, while other work (Geman and Smith 2012) proposed using an econometric prediction and a model based on the theory of storage to detect manipulation in commodity markets. Our work builds on these approaches by developing a more detailed model of calendar spreads in the Brent Crude complex. In Brent, a particular area of potential manipulation is from the relatively illiquid and more opaque physical OTC forward market (where prices are ‘assessed’ by Platts during a short ‘window’ of time) and the more liquid ICE futures market. Our model relates prompt ICE futures calendar spreads to prompt-over-dated OTC forward spreads. The model then tests whether the a priori indicators of manipulation as suggested by Barrera-Rey and Seymour are statistically consistent with the process which drives spreads historically. We find that in most all cases, the indicated period of manipulation is statistically different. We further investigate whether other factors, such as liquidity (volume and open interest) or world oil market conditions (using WTI spreads) or other forward market conditions could be driving our results. The statistical difference is found to be invariant to the inclusion of these other explanatory variables. We conclude that the evidence is consistent with the hypothesis of price manipulation and that the test provides a model and method for detecting such cases

Estimating the impact of wind generation and wind forecast errors on energy prices and costs in Ireland

This paper studies the impact of wind generation on system costs and prices in Ireland. The need to mitigate climate change, achieve renewables energy targets, and use renewable sources of energy means that many countries are considering greater levels of wind generation in their power generation mix. The overall impact of wind generation on system costs and performance has only been studied recently, and often with limited actual data from power systems with increased wind penetration. The paper uses a unique dataset of half-hourly system demand, generation, wind forecast generation, and actual wind generation, along with Irish system marginal price (SMP) data from 2008 to autumn 2012. An econometric time-series model of SMP as a function of forecast and realized demand and wind generation is formed. The costs of balancing and system constraints are included in the cost of ‘uplift’, and thus the total cost of a variety of factors is included in our estimates for Ireland. Our results suggest that each 1% increase in wind generation reduces SMP in Ireland by about 0.06%, while each 1% wind forecast error increases SMP about 0.02%. In absolute terms, though, at the mean the impact of wind forecast errors is small, or about 0.4€cent/MWh-wind generated. However, the impact per MWh forecast error is about €1

Estimating the impact of household energy savings measures for Ireland using the BER database

This paper estimates the impacts of various household energy saving measures in Ireland. The analysis develops a matrix of household energy use and types based on the Building Energy Rating (BER) system and the major dwelling and fuel-use types. It explicitly models the difference between using the BER data versus Census data. The analysis uses data from the BER, SEAI, and CSO, and other public sources. Modelling work estimated the distribution of energy ratings for the national sample of households from the BER sample, with the results indicating that the worst energy-rated households were about seven per cent underrepresented in the BER sample. The system, data, and modelled predictions were then used to perform some preliminary cost benefit analysis on energy savings measures versus costs in terms of the overall housing stock in Ireland. The results and model were used by SEAI to inform them of values and options in their energy savings policy work. Indicative results suggest that accounting for underrepresentation of the most inefficient housing units and policies aimed at the lower efficiency units may have the highest ratio of costs over benefits

Valuing Assets Using Real Options: An Application to Deregulated Electricity Markets

This paper provides a detailed case of the application of real options valuation techniques to value a contract for the use of a power generation facility. The authors' aim is not primarily to offer a valuation "recipe" for a specific type of asset, but to show how the real options framework can be actually made to work in a variety of situations. 2005 Morgan Stanley.

Racing Demands of Off-Road Triathlon: A Case Study of a National Champion Masters Triathlete

(1) Background: This report examines the unique demands of off-road triathlon (XT) by presenting physiological, field, and race data from a national champion off-road triathlete using several years of laboratory and field data to detail training and race intensity. (2) Methods: Laboratory and field data were collected when the athlete was at near peak fitness and included oxygen consumption (VO2), heart rate (HR), power output (W), and blood lactate (BLC) during cycling and running, while HR, cycling W, and running metrics were obtained from training and race data files over a period of seven years. Intensity was described using % HR max zones (Z) 1 < 75%, 2 = 75–87%, and Zone 3 > 87%, and W. An ordinary least squares analysis was used to model differences between event types. (3) Results: Weather conditions were not different across events. XT events had twice the elevation change (p < 0.01) and two-three times greater anaerobic work capacity (W’) (p < 0.001) than road triathlon (ROAD), but similar HR intensity profiles (max, avg, and zones); both events are predominately performed at >Z2 or higher intensity. Championship XT events were longer (p < 0.01), with higher kJ expenditure (p < 0.001). Ordinary Least Squares (OLS) modelling suggested three variables were strongly related (R2 = 0.84; p < 0.0001) to cycling performance: event type (XT vs ROAD), total meters climbed, and total bike duration. Championship XT runs were slower than either regional (p < 0.05) or ROAD (p < 0.01) runs, but HR intensity profiles similar. OLS modelling indicates that slower running is linked to either greater total bike kJ expenditure (R2 = 0.57; p < 0.001), or total meters gained (R2 = 0.52; p < 0.001). Race simulation data support these findings but failed to produce meaningful differences in running. Conclusions: XT race demands are unique and mirror mountain bike (MTB) and trail running demands. XT athletes must be mindful of developing anaerobic fitness, technical ability, and aerobic fitness, all of which contribute to off-road cycling economy. It is unclear whether XT cycling affects subsequent running performance different from ROAD cycling

Patterns of State Productivity Growth in the U.S. Farm Sector: Linking State and Aggregate Models

This article focuses on agricultural productivity growth at both sector and state levels. It does so in a way that preserves the economic integrity of national and state production accounts. A model accounting for interstate transactions in farm goods links sectorwide and state-specific measures of total factor productivity growth. An interesting conclusion is that the smooth, persistently positive trend typically observed for farm sector productivity growth masks considerable variation across states and regions. The results also indicate that farm sector productivity growth is wholly a function of productivity trends in the individual states. Interstate shifts in production activity and resource reallocations have had little impact. Copyright 1999, Oxford University Press.