Browsing by Author "Hussain, Mohsan"

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    Master Thesis
    Determination of time-of-use prices in electricity markets using clustering analyses
    (Kadir Has Üniversitesi, 2016) Hussain, Mohsan; Çelebi, Emre
    in this thesis a clustering analysis to determine the blocks (clusters) of hours for time-of-use (TOU) pricing scheme is proposed and different clustering algorithms are compared using different measures i.e. change in overall revenue mean absolute percent error and adjusted coefficient of determination (R2) from multiple linear regression analyses. Hourly electricity price and demand (load) data for two seasons (winter and summer) from Pennsylvania-New Jersey-Maryland (PJM) wholesale electricity market for 2014-2015 is used and based on detailed descriptive analyses and observations three blocks of hours (off-peak mid-peak and on-peak) are presented. in R software two clustering algorithms (agglomerative hierarchical and k-means) are employed and several clusters for summer and winter weekday hours are formed. The average of the hourly electricity prices in the same cluster for off-peak mid-peak and on-peak hours determines the TOU pricing scheme (hours in each cluster and prices for each clusters). These prices are compared to real-time pricing (RTP) rates in terms of change in overall revenue collected (price*load) and mean absolute percent error with respect to RTP rates. Finally in order to measure the significance of the TOU price and the demand relationship multiple linear regression analyses are performed. in the regression models dependent variable is the TOU price (or logarithm of it) and independent variables are the average load (or logarithm of it) of the TOU block of hours lagged TOU price and lagged TOU average load as well as categorical variables for off-peak mid-peak and on-peak hours for each TOU pricing scheme. Using Minitab software different regression models are built and adjusted R2 significance of regression coefficients and the significance of the overall model are computed. The significant models (with 95% confidence) are reported and the TOU clusters with higher adjusted R2 values are determined. Moreover in order to measure the autocorrelation effect Durbin-Watson statistics for each significant regression model are calculated and positive correlation among dependent and independent variables are reported. These analyses can be used by electricity market retailers distribution companies as well as regulatory bodies in determining TOU time blocks (clusters) and prices.