11
Power Demand Cartridge Example

This chapter explains the power demand sample data cartridge that is included with the Oracle8i Data Cartridge Software Development Kit (SDK). The power demand cartridge includes a user-defined object type, extensible indexing, and optimization. This chapter covers the following topics:

• "Modeling the Application", including the technical and business scenario

• "Queries and Extensible Indexing", describing kinds of queries that benefit from domain indexes

• "Creating the Domain Index", explaining how the index and related structures for the example were created.

• "Creating the Extensible Optimizer Methods" on page , explaining how the methods for the extensible optimizer were created.

• "Testing the Domain Index", explaining how to test the domain index and see if it is causing more efficient execution of queries than would occur without an index

• "Using Time Series with the Power Demand Cartridge", showing how to define Time Series objects and run time series queries with power demand data

• "Using Spatial with the Power Demand Cartridge", showing how to define Spatial objects and run spatial queries with power demand data

This chapter does not explain in detail the concepts related to the features illustrated. For information about extensible indexing, see Chapter 7, "Building Domain Indexes". For information about extensible query optimization, see Chapter 8, "Query Optimization". For information about cartridge services, see Chapter 9, "Using Cartridge Services"].

This chapter divides the example into segments and provides commentary. The entire cartridge definition is available online in the following location:

...directory-path?.../tkqxpwr.sql   [location and name TBS]

Feature Requirements

A power utility, Power-To-The-People, develops a sophisticated model to decide how to deploy its resources. The region served by the utility is represented by a grid laid over a geographic area.

This region may be surrounded by other regions some of whose power needs are supplied by other utilities. As pictured above, every region is composed of geographic quadrants referred to as "cells" on a 10x10 grid. There are a number of ways of identifying cells -- by spatial coordinates (longitude/latitude), by a matrix numbering (1,1; 1,2;...), and by numbering them sequentially:

Figure 11-1 Regional Grid Cells in Numbered Sequence

Within the area represented by each cell, the power used by consumers in that area is recorded each hour. For example, the power demand readings for a particular hour might be represented by Table 11-2 (cells here represented on a matrix):

Table 11-1 Sample Power Demand Readings for an Hour

	1	2	3	4	5	6	7	8	9	10
1	23	21	25	23	24	25	27	32	31	30
2	33	32	31	33	34	32	23	22	21	34
3	45	44	43	33	44	43	42	41	45	46
4	44	45	45	43	42	26	19	44	33	43
5	45	44	43	42	41	44	45	46	47	44
6	43	45	98	55	54	43	44	33	34	44
7	33	45	44	43	33	44	34	55	46	34
8	87	34	33	32	31	34	35	38	33	39
9	30	40	43	42	33	43	34	32	34	46
10	43	42	34	12	43	45	48	45	43	32

The power stations also receives reports from two other sources:

• Sensors on the ground provide temperature readings for every cell

  By analyzing the correlation between historical power demand from cells and the temperature readings for those regions, the utility is able to determine with a close approximation what the demand will be, given specific temperatures.

• Satellite cameras provide images regarding current conditions that are converted into grayscale images that match the grid:

Figure 11-2 Grayscale Representation of Satellite Image

These images are designed so that 'lighter is colder'. The image above shows a cold front moving into the region from the south-west. By correlating the data provided by the grayscale images with temperature readings taken at the same time, the utility has been able to determine what the power demand is given weather conditions viewed from the stratosphere.

The reason that this is important is that a crucial part of this modeling has to do with noting the rapidity and degree of change in the incoming reports as weather changes and power is deployed. The following diagram shows same cold front at a second recording:

Figure 11-3 Grayscale Representation of Weather Conditions at Second Recording

By analyzing the extent and speed of the cold front, the utility is able to project what the conditions are likely to be in the short and medium term:

Figure 11-4 Grayscale Representation of Conditions as Projected

By combing this data about these conditions, and other anomalous situations (such as the failure of a substation) the utility must be able to organize the most optimal deployment of its resources. The following drawing reflects the distribution of substations across the region:

Figure 11-5 Distribution of Power Stations Across the Region

The distribution of power stations means that the utility can redirect its deployment of electricity to the areas of greatest need. The following figure gives a pictorial representation of the overlap between three stations:

Figure 11-6 Areas Served by Three Power Stations

Depending on fluctuating requirements, the utility must be able to decide how to deploy its resources, and even whether to purchase power from a neighboring utility in the event of shortfall.