Planning for the future expansion of a power system involves determining both the capacities and the locations of future components; namely, generation facilities, transmission/ sub-transmission/distribution lines and/or cables and various substations. As we will see, later on, in this book, this requires forecasting the future loads with geographic details (locations and magnitudes). In power system context, this topic is addressed as spatial load forecasting.
Suppose a power system operator is going to use STLF results for secure operation of the system. Obviously, the exact details of small area loads is not of interest, but ihe s more interested in knowing the possible loads of substations. This type of forecasting is readily handled by existing STLF methods.
Moving towards LTLF, we even may not know the details of the locations and the capacities of the future substations. Instead, we have to predict, initially, the small area loads (locations and magnitudes) in order to plan (location, capacity and possible loading) for the future substations
Spatial load forecasting is accomplished by dividing utility system into a number of small areas and forecasting the load of each. In some cases, the small areas used may be irregular in shape or size, corresponding to the service areas assigned to particular delivery system components such as substations or feeders. A simple choice is to use a gird of square cells that covers the region to be studied.
In fact, we have to use the forecast methods discussed previously to estimate for small area loads. Once the load of each cell is predicted, the electric load of the system (or a larger geographical area) can be predicted.
An important aspect of electric load is that cells (small areas) do not simultaneously demand their peak powers. The coincidence factor defined as the ratio of peak system load to the sum of small areas peak loads is, normally in the range of 0.3– 0.7.
Constraints in spatial load forecasting:
We earlier discussed about the long-term load driving parameters. For instance, GDP and population rate were mentioned there as two affecting parameters. Now, if we focus on a small area, is it really possible to predict the above two parameters for a small area?
Moreover what happens to load predication based on various classes of customers.
