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Outage-capacity tradeoff for smart grid with renewables
Yang K., Walid A. ACM SIGMETRICS Performance Evaluation Review41 (3):80-82,2013.Type:Article
Date Reviewed: Apr 30 2015

As the role of renewable energy sources such as photovoltaic systems and wind turbines increases in smart grids, it is essential to “determine the maximum volatility a given power grid system can sustain.” This paper aims to optimize required distributed renewable energy sources (DRES) by studying the relationship between “the capacity of energy storage devices” and the quality in terms of outage probability and supply uncertainty.

This paper derives an optimization function for the minimal cost of DRES while the generations of the DRES satisfy the demand with a given condition. The condition is that when a number of DRES in a power grid are forced into an outage due to an unanticipated breakdown, that is, a degree of protection (DoP) α is given, the power grid is still reliable deterministically. Furthermore, when even more DRES than the DoP are forced into an outage, the system is feasible probabilistically. In a probabilistically feasible system, the system can estimate the risk of outage when it controls the amount of investment for a facility such as energy storage capacity.

This paper studies the relationship between the outage probability and the minimum energy storage capacity, with the assumption that DRES “are independent with each other.” As a result, it shows that “the outage probability can be driven down to as small as 1e-8 if the relative energy storage capacity is only 0.25 percent.” This paper also briefly presents how the correlation among DRES generation significantly affects the required storage capacity.

The main contribution of this paper is that it provides mathematical foundations for the efficient scheduling and maintenance of DRES by analyzing outage probability. However, as DRES are apt to be correlated with each other because they are affected by a common factor--that is, weather--a practical solution will be made after further research on correlation is performed.

Reviewer:  Seon Yeong Han Review #: CR143404 (1507-0593)
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