SUBJECT : POWER DISTRIBUTION AND UTILIZATION (PRESENTATION) INSTRUCTOR:KASHIF MEHMOOD

2.3 MAXIMUM DIVERSIFIED DEMAND Definition : Maximum of the sum of demands imposed by group of loads over a particular period. Arvidson developed a method of estimating DT loads in residential areas by the diversified demand methods which takes into account the diversity of similar and non-coincidence loads of the peaks of different type of load. To take into account the non-coincidence of the peaks of different types of loads, Arvidson introduced the Hourly variation factor that is “The ratio of the demand of a particular type of load coincident with the group maximum demand to the maximum demand of a particular type of load. Hourly variation curves for various type of household appliances. Shown in fig 2.13

F IG SHOWS A NUMBER OF CURVES FOR VARIOUS TYPES OF HOUSEHOLD APPLIANCES TO DETERMINE THE AVERAGE MAXIMUM DIVERSIFIED DEMAND PER CUSTOMER IN KILOWATTS PER LOAD. I N FIG 2.13 EACH CURVE REPRESENT A 100% SATURATION LEVEL FOR A SPECIFIC DEMAND A=clothes dryer B=Off peak water heater C=water heater D=range E=lighting and miscellaneous appliances F=0.5hp room coolers G= Off peak water heater H=Oil burner Fig 2.13

S TEPS T O A PPLY A RVIDSON M ETHOD Determine the total number of appliances by multiplying the total number of customers by the pu saturation. Read the corresponding diversified demand per customer. Determine the maximum demand multiplying the demand found in step 2 by total number of appliances. Finally determine the contribution of that type load to the group maximum demand by multiplying the resultant value from step 3 by the corresponding hourly variation factor found To apply Arvidson method to determine the maximum diversified demand for a given saturation level and appliance the following steps are suggested.

B OX -J ENKINS M ETHODOLOGY It is a method that uses a stochastic time series to forecast future load demands. It is a popular method for short term (5year or less) The Box-Jenkins Methodology is an iterative procedure by which a stochastic model is constructed. The process start from the most simple structure with the least number of parameters and develops into as complex structure as necessary to obtain as adequate model.

2.4 L OAD F ORECASTING Forecasting refers to the prediction of the load behavior for the future Demand forecast To determine capacity of generation transmission and distribution required o Energy Forecast To determine the type of generation facilities required Forecast helps to minimize the operating cost, electric supplier will use forecasted load to control the number of running units

T YPES OF F ORECASTING

F ACTORS FOR FORECASTS Weather influence Electric load has an obvious correlation to weather. The most important variables responsible in load changes are: Dry and wet bulb, temperature, Dew point, Humidity etc Time factors In the forecasting model, we should also consider time factors such as: The day of the week The hour of the day Holidays Customer classes Electric utilities usually serve different types of customers such as residential, commercial, and industrial.

2.4.2 S MALL A REA L OAD F ORECASTING In this type of forecasting the utility service area is divided into a set of small areas and the future load growth in each area is forecasted. Most forecasting methods are based on trending or land use.(detail in section 2.5) Most modern forecasting methods work with a uniform grid of small area that cover the utility service area but more traditional approach was to forecast growth on substation or feeder by feeder.

Final goal is to project change in density of peak demand on locality basis. According to Willis, small area growth is not smooth, continuous process from year to year. Growth in small areas is intense for several years then drops to very low level while high growth suddenly begins in other areas. Small area growth is characterized as Gompertz or S curve i.e. The beginning represents a slow, deliberate but accelerating start, while the end represents a deceleration as the work runs out

2.4.3 S PATIAL L OAD F ORECASTING The methodology for a spatial forecast links consumer and usage growth patterns based on available resources to demand. The strength of this technique lies in the granularity of the geographic projections. Spatial forecast is extremely useful for distribution and transmission system planners who are concerned about load growth at this level because of the facilities that they will recommend to serve it. The forecast growth of any one area should be based on assessment data of that area and also on the neighboring areas.

The best available trending method in terms of accuracy is Load Trend Coupled (LTC) extrapolation a modified form of markov regression. In which the peak load history up to several hundred small areas are extrapolated in a single computation The influence of one area trend on other is found by pattern recognition as a function of past trends and locations. Only peak load histories, substation and location of substation is required as input.

Heating, lighting, using peak day load curves on a 15- min demand period basis.Base spatial data includes multispectral satellite imagery of the region, used for land use identification and mapping purposes customer/billing/rate class data end use load curve and load research surveys and metered load curve readings by substation throughout the system. There are 2 inputs that control the forecast I. Utility system –wide rate and marketing forecast II. Optional set of scenario descriptors that allow the user to change future conditions to answer “what if “questions.

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