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The Global Ring Network for Advanced Application Development (GLORIAD) is a facility that provides scientists around the world with advanced networking tools that improve communications and data exchange, enabling active, daily collaboration on common problems. GLORIAD provides large-scale applications support, communication services, large-scale data transport, access to unique scientific facilities, including Grid environments, and specialized network-based tools and technologies for diverse communities of scientists, engineers, and other researcher domains. Currently, GLORIAD has undertaken an expansion plan directed both at building a 10-Gbps hybrid backbone to interconnect national science and engineering networks in the USA, Russia, China, Canada, Korea, and The Netherlands and at creating a switching infrastructure at layer 2 to serve special communities, unique applications with particularly heavy service requirements, and to provide an infrastructure for network research and experimentation. The layer 2 switching infrastructure will eventually evolve into a similar infrastructure for switching layer 1 lightpaths. The GLORIAD network includes special Points of Presence (PoPs) in Chicago, Amsterdam, Moscow, Novosibirsk, Khabarovsk, Beijing, Hong Kong, Hong Kong, and Daejon (South Korea). At layer 1, the transport core of is supported by Multiservice Provisioning Systems (MSPPs). GLORIAD s supporters include the US National Science Foundation, a consortium of science organizations and ministries in Russia, the Chinese Academy of Sciences, the Ministry of Science and Technology of Korea, the Canadian CANARIE network, and The Netherlands SURFnet organization along with some telecommunications services donated by the VSNL telecommunications company. GLORIAD s US home is at the Joint Institute for Computational Science of the University of Tennessee and Oak Ridge National Laboratory (www.gloriad.org).

Step 4 If the comparison of Step 3 is favorable, accept the dispatch from Step 2 as the least-cost solution Otherwise, use the comparison of Step 3 to determine a new estimate of system lambda and return to Step 2 There are several points to remark upon Remarks The strength of the method is that it allows one to solve the maximization problem independently for each unit as opposed to solving the entire problem for each unit and each constraint This follows the common wisdom that we alluded to at the beginning of the section There are numerous ways for accomplishing Step 1 For example, an experienced planner can present a reasonable estimate based on intuition Alternatively, one can use the previous day s actual dispatch costs as an initial point There are different ways to update the estimate.

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To do so requires a quantitative estimate of the relation between changes in output and changes in the system lambda The better one can quantify this, the quicker one can converge to a solution In practice one never arrives at a solution that perfectly matches load Instead, the comparison in Step 4 is usually done with respect to a tolerance band around the load One accepts a solution provided that the output lies within the tolerance band One excellent question is whether or not the algorithm does indeed converge to a solution Unfortunately, this is not the case One reason that the solution may not converge is that the dispatch may be discontinuous in the system lambda: A small change in the system lambda leads to a large change in the total system dispatch.

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Consider, for example, the case in which a system has many peaking units with similar operating costs As the system lambda increases above the cost of these similar units, they all dispatch It may not be possible to balance supply with load by using the system lambda alone when the load requires some, but not all, of the peaking units Nevertheless, in a regulated utility environment the algorithm provides a practical result that benchmarks well against other solution methods and can be appropriately adjusted by a dispatcher The problems with convergence, however, do point to dif culties with competitive market mechanisms Another interesting phenomenon that may occur is the nonuniqueness of the solution Indeed, for the more complex problems posed in subsequent sections, there may be more than one set of system lambdas that produce the same dispatch.

This is not of consequence to the day-ahead dispatcher: one still arrives at a least-cost dispatch However, it is problematic when using the system lambda to develop market forecasts EXAMPLE The Simple Problem of Section 43.

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