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ID:
177113
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| Summary/Abstract |
Although numerous studies have examined the economic benefits of demand response programs, the environmental impacts of such programs have been relatively underexplored. This study assesses the impact of demand resource bidding on the wholesale energy market and the environment, based on three years of high temporal-resolution data from Korea. In this demand resource bidding program, successful bidders were paid the system marginal price for reducing their electrical load at a given hour, which in turn reduced the generation of power from various technologies. This investigation of how carbon dioxide and particulate matter emissions from existing power systems changed with the introduction of the demand bidding program finds that the program altered the system operator's electricity generation portfolio and marginally abated carbon dioxide and particulate matter emissions from the power sector. It also shows that the environmental impact of the program varied over the course of the day and the year. The modest but statistically significant environmental impact of the demand resource bidding program points to the importance of including electricity demand resources in the discussion and development of energy and environmental policies for the power sector.
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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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