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| 1 |
ID:
117339
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| Publication |
2013.
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| Summary/Abstract |
This paper employs a novel Monte-Carlo based generation portfolio assessment tool to explore the implications of increasing wind penetration and carbon prices within future electricity generation portfolios under considerable uncertainty. This tool combines optimal generation mix techniques with Monte Carlo simulation and portfolio analysis methods to determine expected overall generation costs, associated cost uncertainty and expected CO2 emissions for different possible generation portfolios. A case study of an electricity industry with coal, Combined Cycle Gas Turbines (CCGT), Open Cycle Gas Turbines (OCGT) and wind generation options that faces uncertain future fossil-fuel prices, carbon pricing, electricity demand and plant construction costs is presented to illustrate some of the key issues associated with growing wind penetrations. The case study uses half-hourly demand and wind generation data from South Eastern Australia, and regional estimates of new-build plant costs and characteristics. Results suggest that although wind generation generally increases overall industry costs, it reduces associated cost uncertainties and CO2 emissions. However, there are some cases in which wind generation can reduce the overall costs of generation portfolios. The extent to which wind penetration affects industry expected costs and uncertainties depends on the level of carbon price and the conventional technology mix in the portfolios.
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| 2 |
ID:
111344
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| Publication |
2012.
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| Summary/Abstract |
This paper presents a novel decision-support tool for assessing future generation portfolios in an increasingly uncertain electricity industry. The tool combines optimal generation mix concepts with Monte Carlo simulation and portfolio analysis techniques to determine expected overall industry costs, associated cost uncertainty, and expected CO2 emissions for different generation portfolio mixes. The tool can incorporate complex and correlated probability distributions for estimated future fossil-fuel costs, carbon prices, plant investment costs, and demand, including price elasticity impacts. The intent of this tool is to facilitate risk-weighted generation investment and associated policy decision-making given uncertainties facing the electricity industry. Applications of this tool are demonstrated through a case study of an electricity industry with coal, CCGT, and OCGT facing future uncertainties. Results highlight some significant generation investment challenges, including the impacts of uncertain and correlated carbon and fossil-fuel prices, the role of future demand changes in response to electricity prices, and the impact of construction cost uncertainties on capital intensive generation. The tool can incorporate virtually any type of input probability distribution, and support sophisticated risk assessments of different portfolios, including downside economic risks. It can also assess portfolios against multi-criterion objectives such as greenhouse emissions as well as overall industry costs.
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| 3 |
ID:
137659
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| Summary/Abstract |
A generation portfolio modelling was employed to assess the expected costs, cost risk and emissions of different generation portfolios in the Australian National Electricity Market (NEM) under highly uncertain gas prices, carbon pricing policy and electricity demand. Outcomes were modelled for 396 possible generation portfolios, each with 10,000 simulations of possible fuel and carbon prices and electricity demands. In 2030, the lowest expected cost generation portfolio includes 60% renewable energy. Increasing the renewable proportion to 75% slightly increased expected cost (by $0.2/MWh), but significantly decreased the standard deviation of cost (representing the cost risk). Increasing the renewable proportion from the present 15% to 75% by 2030 is found to decrease expected wholesale electricity costs by $17/MWh. Fossil-fuel intensive portfolios have substantial cost risk associated with high uncertainty in future gas and carbon prices. Renewables can effectively mitigate cost risk associated with gas and carbon price uncertainty. This is found to be robust to a wide range of carbon pricing assumptions. This modelling suggests that policy mechanisms to promote an increase in renewable generation towards a level of 75% by 2030 would minimise costs to consumers, and mitigate the risk of extreme electricity prices due to uncertain gas and carbon prices.
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