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| 1 |
ID:
112920
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| Publication |
2012.
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| Summary/Abstract |
This study quantifies the effects of aggregating electric load over various combinations (Aggregation Groupings) of the 10 Federal Energy Regulatory Commission (FERC) regions in the contiguous U.S. Generator capacity capital cost savings, load energy shift operating cost savings, reserve requirement cost savings, and transmission costs due to aggregation were calculated for each Aggregation Grouping. Eight scenarios of Aggregation Groupings over the U.S. were formed to estimate overall system cost. Transmission costs outweighed cost savings due to aggregation for all scenarios and nearly all Aggregation Groupings. East-west transmission layouts had the highest overall cost, and interconnecting ERCOT to adjacent FERC Regions resulted in increased costs, both due to limited existing transmission capacity. This study found little economic benefit of aggregating electric load alone (e.g., without aggregating renewable generators simultaneously), except in the West and Northwest U.S. If aggregation of load alone is desired, small, regional consolidations yield the lowest overall cost. This study neither examines nor precludes benefits of interconnecting geographically-dispersed renewable generators with load. It also does not consider effects from sub-hourly load variability, fuel diversity and price uncertainty, energy price differences due to congestion, or uncertainty due to forecasting errors; thus, results are valid only for the assumptions made.
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| 2 |
ID:
098608
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| Publication |
2010.
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| Summary/Abstract |
Wind power is expected to be the major element of renewable electricity generation in Great Britain (GB) by 2020 with a capacity of around 30 GW. The potential impact of a large amount of wind generation on the GB gas network was investigated using a combined gas and electricity network model. The varying nature of gas and electric power flows, network support facilities such as gas storage and compressors, and the power ramping characteristics of various power plants were considered. Three case studies were modelled, one case uses the existing network and the other two make use of a hypothesised network in 2020 with two distinct levels of wind generation representing low and high wind periods. The simulation results show that a large penetration of wind generation will influence the electricity generation mix as the wind power varies. Gas-fired generation is used to compensate for wind variability. This will cause increased flows and compressor power consumption on the gas network. Linepack depletion during low wind periods was shown to limit the ability of the gas network to fully supply gas-fired generators.
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| 3 |
ID:
125738
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| Publication |
2013.
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| Summary/Abstract |
A Monte Carlo model of the combined GB gas and electricity network was developed to determine the reliability of the energy infrastructure. The model integrates the gas and electricity network into a single sequential Monte Carlo simulation. The model minimises the combined costs of the gas and electricity network, these include gas supplies, gas storage operation and electricity generation. The Monte Carlo model calculates reliability indices such as loss of load probability and expected energy unserved for the combined gas and electricity network. The intention of this tool is to facilitate reliability analysis of integrated energy systems. Applications of this tool are demonstrated through a case study that quantifies the impact on the reliability of the GB gas and electricity network given uncertainties such as wind variability, gas supply availability and outages to energy infrastructure assets. Analysis is performed over a typical midwinter week on a hypothesised GB gas and electricity network in 2020 that meets European renewable energy targets. The efficacy of doubling GB gas storage capacity on the reliability of the energy system is assessed. The results highlight the value of greater gas storage facilities in enhancing the reliability of the GB energy system given various energy uncertainties.
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