Determination of efficient pricing periods using SAS prepared by Voytek Grus for SAS user group, Halifax April 30, 2010

Pricing in Electric Industry is regulated UARB –balancing act –evidence A complex process with heavy inputs from technical and social fields. Guided by established pricing principles –Cost Based but with varying levels of granularity –Fair –Efficient Pricing Signals: no wasteful usage –Simple/transparent

An example of a TOU tariff from NSPI

Methodology 1.Identify patterns in hourly costs or consumption data or … –Seasonal (monthly patterns) –Weekly (daily patterns) –Daily (hourly patterns) 2.Using established and recognized statistical methods (to produce convincing evidence) –Multiple Regression Analysis of Categorical Variables –Cluster Analysis –ANOVA 3.Balance statistical outcomes with social and engineering considerations –Simplicity of the price design (relatively few pricing periods) –Technical considerations behind load shifting equipment (ETS)

Highlights of SAS Application Quality data stored maintained databases Extensive use of modular approach –Nested SAS programs –Use of macro variables to control Analyzed time periods Periodicity of time series Variable mix Variable types Intense use of graphical tools Automated storage of results Vast richness of statistical procedures to choose from

Highlights: Modular approach to programming /********************************************************************** ****************************************************/ /*** Step 6.0 ANOVA Analysis ***/ %let suffix=_scaled;/* _scaled log */ %let critanova1=calendar_year;/* Y_period calendar_year */ %let critanova2=season daytype hourtype; /* season daytype hourtype*/ %let anovaeffects=season|daytype|hourtype; %include 'C:\datain\PROGRAMMS\SAS\Presentations\Rating Periods\MC Rating Periods 4.0 ANOVA Analysis.sas'; /* */

Highlights: Regression Analysis using proc reg Building blocks 1.Centrally controlled dummy variable definition and selection (macros) 2.SAS data programming step to generate dummy variables (nested if then else) 3.Regression Analysis using (proc reg) Use of restriction and test statements Storage of results 4.Graphical display (proc gplot)

Highlights: Intense use of graphical tools (proc gplot)

Cluster Analysis: 2 step process 1proc cluster data=monthly outtree=TreeNSR1 method=single ccc pseudo print=24; by &crit0; var nsr_mean; id month; run; goptions vsize=8in htext=3pct htitle=6pct; axis1 order=(0 to 1 by 0.2); title1 "Dendrogram for Cluster Analysis by &crit using Ave. NSR kW Demand"; 2 proc tree data=TreeNSR1 nclusters=3 graphics vaxis=axis1; by &crit0; height _rsq_; id month; run;

Highlights: Cluster Analysis using proc cluster and proc tree

Highlights: Analysis of Variance Repetitive use of proc GLM to test for two and three way layouts with unequal number of observations Dovetailed with box plot graphs using proc boxplot

Questions?