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| 1 |
ID:
112897
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2012.
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| Summary/Abstract |
This paper investigates the economic implications of disruptions of one to ninety days to the supply of natural gas in Ireland. We assess the impact of a hypothetical gas supply disruption in both winter and summer in 2008 (with observed market characteristics) and in 2020 (with projected market characteristics). The cost of a natural gas outage includes the cost of natural gas being unavailable for heating and other purposes in the industrial and commercial sectors, lost consumer surplus in the residential sector, the cost of lost electricity in all sectors and lost VAT on the sale of gas and electricity. Ireland generates much of its electricity from natural gas and the loss of this electricity accounts for the majority of the cost of a natural gas outage. Losing gas-fired electricity would cost 0.1-1.0 billion euro per day, depending on the time to the week, the time of year and rationing. Industry should be rationed before households to minimise economic losses, but current emergency protocols do the opposite. If gas-fired electricity is unavailable for three months, the economic loss could be up to 80 billion euro, about half of Gross Domestic Product. Losing gas for heating too would add up to approximately 8 billion euro in economic losses. We also discuss some options to increase Ireland's security of supply, and find that the cost is a small fraction of the avoided maximum damage.
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| 2 |
ID:
097274
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2010.
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| Summary/Abstract |
This paper examines household energy use and appliance ownership in Ireland. Logit regression analyses on a large micro-dataset reveal how household characteristics can help explain the ownership of energy using appliances. Using OLS regression models, we explore the factors affecting residential energy demand conditional on appliance ownership. Results suggest that the methods of space and water heating employed by a household are even more important than electrical appliances in explaining domestic energy usage. However, the stock of appliances must be included in such models so that results will not be biased. The methods employed in this paper can be easily adopted for studies of household energy use in other countries where household expenditure survey data are available.
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| 3 |
ID:
103446
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2011.
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| Summary/Abstract |
This paper estimates the value of short term lost load in the all island electricity market, which includes the Republic of Ireland and Northern Ireland. The value of lost load (VoLL) is the average willingness of electricity consumers to pay to avoid an additional period without power. VoLL is also known as the value of security of electricity supply and is inferred using a production function approach. Detailed electricity use data for the Republic of Ireland allows us to estimate the value of lost load by time of day, time of week and type of user. We find that the value of lost load is highest in the residential sector in both the Republic of Ireland and Northern Ireland. Our results can be used to advise policy decisions in the case of supply outages and to encourage optimum supply security. In the context of this study short term is taken to be a matter of hours rather than days or weeks.
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| 4 |
ID:
125521
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2013.
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| Summary/Abstract |
We estimate the gross margin that is earned from the supply of electricity to households in Ireland. Using half hourly electricity demand data, the system marginal price (also called the wholesale price) and the retail price of electricity, we analyse how the gross margin varies across customers with different characteristics. The wholesale price varies throughout the day, thus, the time at which electricity is used affects the gross margin. The main factor in determining gross margin, however, is the level of demand.
The highest gross margins are earned from supplying customers that have the following characteristics: being aged between 46 and 55, having a household income of at least €75,000 per annum, being self-employed, having a third-level education, having a professional or managerial occupation, living in a household with seven or more people, living in a detached house, having at least five bedrooms or being a mortgage holder.
An OLS regression shows that gross margin is partly explained by the energy conservation measures which are present in a household; the number of household members; the number of bedrooms; age; occupation; and accommodation-type.
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