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A Stochastic Model for the Measurement of Electricity Outage Costs

Abraham Grosfeld-Nir and Asher Tishler

Year: 1993
Volume: Volume 14
Number: Number 2
DOI: 10.5547/ISSN0195-6574-EJ-Vol14-No2-8
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Abstract:
The measurement of customer outage costs has recently become an important subject of research for the electric utilities. This paper uses a stochastic dynamic model as the starting point in developing a market-based method for the evaluation of outage costs. Specifically, the model postulates that once an electricity outage occurs, all production activity stops. Full production is resumed once the electricity outage is over. This process repeats itself indefinitely. The business customer maximizes his expected discounted profits (the expected value of the firm), taking into account his limited ability to respond to repeated random electricity outages. The model is applied to 11 industrial branches in Israel. The estimates exhibit a large variation across branches.



Incorporating Investment Uncertainty into Greenhouse Policy Models

John R. Birge and Charles H. Rosa

Year: 1996
Volume: Volume17
Number: Number 1
DOI: 10.5547/ISSN0195-6574-EJ-Vol17-No1-5
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Abstract:
Greenhouse gas policy decisions require comprehensive understanding of atmospheric, economic, and social impacts. Many studies have considered the effects of atmospheric uncertainty in global warming, but economic uncertainties, have received Less analysis. We consider a key component of economic uncertainty: the return on investments in new technologies. Using a mathematical! programming model, we show that ignoring uncertainty in technology investment policy may lead to decreases as great as 2 percent in overall expected economic activity in the U.S. with even higher losses in possible future scenarios. These results indicate that both federal and private technology investment policies should be based on models explicitly incorporating uncertainty.



Carbon Capture Retrofits and the Cost of Regulatory Uncertainty

Peter S. Reinelt and David W. Keith

Year: 2007
Volume: Volume 28
Number: Number 4
DOI: 10.5547/ISSN0195-6574-EJ-Vol28-No4-5
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Abstract:
Power generation firms confront impending replacement of an aging coal-fired fleet in a business environment characterized by volatile natural gas prices and uncertain carbon regulation. We develop a stochastic dynamic programming model of firm investment decisions that minimizes the expected present value of future power generation costs under uncertain natural gas and carbon prices. We explore the implications of regulatory uncertainty on generation technology choice and the optimal timing of investment, and assess the implications of these choices for regulators. We find that interaction of regulatory uncertainty with irreversible investment always raises the social cost of carbon abatement. Further, the social cost of regulatory uncertainty is strongly dependent on the relative competitiveness of IGCC plants, for which the cost of later carbon capture retrofits is comparatively small, and on the firm�s ability to use investments in natural gas generation as a transitional strategy to manage carbon regulation uncertainty. Without highly competitive IGCC or low gas prices, regulatory uncertainty can increase the expected social cost of reducing emissions by 40 to 60%.



Stochastic Modeling of Natural Gas Infrastructure Development in Europe under Demand Uncertainty

Marte Fodstad, Ruud Egging, Kjetil Midthun, and Asgeir Tomasgard

Year: 2016
Volume: Volume 37
Number: Sustainable Infrastructure Development and Cross-Border Coordination
DOI: https://doi.org/10.5547/01956574.37.SI3.mfod
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Abstract:
We present an analysis of the optimal development of natural gas infrastructure in Europe based on the scenario studies of Holz and von Hirschhausen (2013). We use a stochastic mixed integer quadratic model to analyze the impact of uncertainty about future natural gas consumption in Europe on optimal investments in pipelines. Our data is based on results from the PRIMES model of natural gas demand and technology scenarios discussed in Knopf et al. (2013). We present a comparison between the results from the stochastic model and the expected value model, as well as an analysis of the individual scenarios. We also performed sensitivity analyses on the probabilities of the future scenarios. Comparison of the results from the stochastic model to those of a deterministic expected value model reveals a negligible Value of the Stochastic Solution. We do, however, find structurally different infrastructure solutions in the stochastic and the deterministic models. Regarding infrastructure expansions, we find that 1) the largest pipeline investments will be towards Asia, 2) there is a trend towards a larger gas supply from Africa to Europe, and 3) within Europe, eastward connections will be strengthened. Our main finding using the stochastic approach is that there is limited option value in delaying investments in natural gas infrastructure, until more information is available regarding policy and technology in 2020, due to the low costs of overcapacity.



Investment vs. Refurbishment: Examining Capacity Payment Mechanisms Using Stochastic Mixed Complementarity Problems

Muireann A. Lynch and Mel T. Devine

Year: 2017
Volume: Volume 38
Number: Number 2
DOI: 10.5547/01956574.38.2.mlyn
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Abstract:
Capacity remuneration mechanisms exist in many electricity markets. Capacity mechanism designs do not explicitly consider the effects of refurbishment of existing generation units in order to increase their reliability. This paper presents a stochastic mixed complementarity problem to examine the impact of refurbishment on electricity prices and generation investment. Capacity payments are found to increase reliability when refurbishment is not possible, while capacity payments and reliability options yield similar results when refurbishment is possible. Final costs to consumers are similar under the two mechanisms with the exception of the initial case of overcapacity.





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