CHAPTER3FORECAST

In addition the forecast identifies areas with growth

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Unformatted text preview: use load profiles are divided into classes to group the different load profiles in classes that are more homogeneous. 52 The spatial load forecast addresses the questions of how much, where and when. Geographical maps are used to indicate the location (where) of the expected loads, or increase in new supply points. The service territory is divided into smaller areas. The smaller areas can be small square areas or irregularly shaped and sized to determine the expected loads on feeders. Willis mentions that short-range planning must at least look as far as equipment lead times (manufacturing and commissioning times). Long-range planning looks beyond the lead times in order to assure that the short-time range decisions provide lasting value. Short -range planning decisions are concerned that system additions are made in time, and long-range planning is concerned that the short-range planning decisions have lasting investment and performance values. The long-range plans look whether the load growth in the area will continue after additions to the network have been made, and whether more network additions, such as substations will be required to meet the load growth in the area. Interesting statement by Willis is that forecasting is part of the planning process. It is a decision-making tool, and its value is ultimately judged by its contribution to that process’s success, not by how closely it comes to predicting the eventual future load level. “If the forecast leads the planner to make correct decisions, then it has no error from a practical standpoint. If it leads to wrong decisions, then it is a poor forecast, regardless of what statistical evaluation of its ability to predict future load might say”. The load curve for residential peaks looks sharp (needle effect) and erratic. This is because of the switching-on of equipment such as stoves, heaters, geysers, etc. If a number of those curves are combined, the load curve becomes smoother. Peak load per customer drops as more customers are added to a group. This tendency of observed peak load per customer dropping and the size of the customer group increasing is termed “coincidence” and is measured by the coincidence factor. 53 coincidence factor = (observed peak for the group) ∑ (individual (3.2.7) peaks) Some loads are sensitive to temperature and humidity. Sensitivity analysis can highlight the impact, but it is, however, maybe not that important for the loads at system peak. Normally the country experiences severe cold conditions. The importance here is whether it is a mild or a severe winter that really impact on the peaks at time of system peak. All growth in an area is due to: 1) New customers connected, or existing customers increasing their supply 2) New uses for electricity, existing customers may add new appliances, or replace existing equipment with improved devices that require more power. Two load forecast techniques are used for spatial electrical forecasts. Trending involves extrapolating past load growth into the future, or simulation, which involves modelling the process of load growth itself. The simulation process works well for high spatial resolution - when the region studied is divided into very small areas. Trending is most suited to “large area” forecasting. The simulation process starts by distinguishing customers by class, based on various residential, commercial, and industrial classes and subclasses. Simulation attempts to reproduce the process of load growth to forecast where, when, and how many loads will develop. It also identifies some of the reasons behind the load gro wth. The land use-based simulation concept can be applied on a grid or a polygon (irregularly shaped and sized small areas) basis. Polygon-based modelling is 54 done more for feeders. A grid basis assures uniform resolution of analysis everywhere. Simulation methods have proven themselves to be quite good at forecasting vacant area growth. Incremental growth on a small area basis is not wellhandled by simulation based methods. Maps are used to identify the area to be forecasted, and each sub-area locating a class is marked with a different color. Different maps are prepared for each year to identify the growth in the area and the individual growth for each class. Based on this information, the following is calculated: 1) customers / square metres 2) kVA / customers Then the kVA per small area is determined as: kVA = kVA / customers (3.2.8) customers / square metre The kVA is aggregated from one level to another by simulation software programs, until it is at the leve l that models the distribution substations. Much more is involved in spatial load forecasting as what is summarised above. [44 – 45] Similar to the electrical spatial load forecasts is an article on spatial analysis of crime using GIS-based data. This serves as a proactive tool that can anticipate or provide early warning of criminal patterns so that they may be prevented. In the fifth chapter the assumption is made that criminal activity can be modelled as a chaotic sy...
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This document was uploaded on 03/04/2014.

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