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Energy density: a measure of energy stored per volume in Wh/m3 It can be used to give an approximation of the energy storage volume for a given application Physical ef ciency: how much power is stored in a given volume and mass It is usually considered in batteries for transportation applications, in which industry may accept degradation in electrical ef ciency in return for good physical ef ciency Electrical ef ciency: the percentage of power put into a unit that is available to be withdrawn; an important parameter for DG applications It is measured by the energy capable of being converted into work A unit with 90% ef ciency returns 9 kWh of energy for every 10 kWh put into storage Recharge rate: the rate at which power can be pushed for storage A storage system might take 10 hours to deplete but 14 hours to re ll.

Electrical energy storage has long been considered a critical technology. At the beginning of the twentieth century, electrochemical batteries were used to power telephones and telegraphs, and enormous, heavy ywheels were common in rotating generators to smooth out load oscillations. Electric vehicles were more common than gasoline-powered vehicles. Batteries were important in such transportation applications because they could store energy to give reasonable transportation autonomy to vehicles. Even longer ago, water dams were used to store potential energy. Today, energy storage systems play the important role of unifying, distributing, and augmenting the capabilities of alternative and renewable energy-distributed generating systems. The average central electrical generation, transmission, and distribution system is large enough not to be affected by residential and commercial load changes. Figure 11.1 illustrates a typical household peak day load curve and an individual load. On the other hand, distributed generation (DG) is exposed to the uctuations of individual loads; it does not experience the averaging effect seen by large generators. For DG-based industrial applications, base load can be considerably higher, and it is possible that a local generator can operate cost-effectively. Energy storage systems capable of smoothing out load uctuations, making up for seasonal variations

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if ( n.good()) { while (! n.eof()) { n.getline(buffer,sizeof(buffer)/sizeof(buffer[0])); istrstream str(buffer); //Get data str >> dataBuffer; y.push_back(sqrt(atof(dataBuffer))); } } else { cout << File not good! << \n ; } n.close(); GARCH11(y); }

Speci c gravity: a dimensionless unit de ned as the ratio of density of a material to the density of water at a speci ed temperature It can be expressed as SG r rH2 O.

BulkResponse findAssociations (java.util.Collection findQualifiers, java.lang.String sourceObjectId, java.lang.String targetObjectId, java.lang.Collection associationTypes)

(5.17)

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where f x (0, t ) = ln P x (0, t ) ln A(0, t ) B(0, t )x(0) + = t t t (B(0, t ) t )(a2 r 2 / 2) 2 B(0, t )2 4a a2 B(0, t ) + x(0) = t t 2 2 2 a r / 2 B(0, t ) B(0, t ) B(0, t ) = + x(0) 1 + B(0, t ) 2 t t t 2a a a2 r 2 / 2 a2

Roaming User (Callee)

Sat/C6-39

After a path has been successfully calculated, it is set up using RSVP-TE.2 As discussed in previous sections, the path is specified at the LSP head end in the Explicit Route Object (ERO). However,

For efficient execution, the xargs command is one of the best commands that Linux/UNIX offers. xargs has two very valuable functions that we will discuss here.

The discussion so far has focused on de ning and planning the project, acquiring and preparing the data, and performing the analysis. The results from any analysis then need to be translated into tangible actions that impact the organization, as described at the start of the project. Any report resulting from the analysis should make its case and present the evidence clearly. Including the user of the report as an interested party to the analysis will help ensure that the results are readily understandable and usable by the nal recipient. One effective method of deploying the solution is to incorporate the analysis within existing systems, such as ERP or CRM systems, that are routinely used by the targeted end-users. Examples include using scores relating to products speci c customers are likely to buy within a CRM system or using an insurance risk model within online insurance purchasing systems to provide instant insurance quotes. Integrating any externally developed models into the end-user system may require adoption of appropriate standards such as Object Linking and Embedding, Database for Data Mining (Data Mining OLE DB) which is an application programming interface for relational databases (described in Netz et al., 2001), Java Data Mining application programming interface standard (JSR-73 API; discussed in Hornick et al., 2006), and Predictive Model Markup Language (PMML; also reviewed in Hornick et al., 2006). In addition, the models may need to be integrated with current systems that are able to extract data from the current database and build the models automatically. Other issues to consider when planning a deployment include:

TDMA interface modules (DSI/DNI units)

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