Paper Info
Title
Decomposition Of Aggregate Electricity Demand Into The Seasonal-Thermal Components For Demand-Side Management Applications In "Smart Grids"
Abstract
Aggregate active and reactive power demands measured at 84 Scottish medium-voltage (MV) buses are used in this paper for the correlation and regression analysis, aimed at demand profiling and load decomposition. Demand profiles are presented with respect to the long-term seasonal variations, medium-term weekly and short-term diurnal cycles, allowing for the characterisation and presentation of load behaviour at different time-scales. The linear relationships between active and reactive power demands, temperature and power factor variations are quantified using regression analysis, on a per-hour of the day basis, as well as using a sliding-window regression approach for estimating relative coefficients within a seasonal moving window. The paper presents three different approaches for the decomposition of aggregate network demand into the temperature-dependent loads (i.e. thermal heating and cooling loads) and temperature-independent loads, providing important basic information for the application of the "smart grid" functionalities, such as demand-side management, or balancing of variable energy flows from renewable generation.
Year
DOI
Venue
2016
10.1007/978-3-319-50947-1_11
DATA ANALYTICS FOR RENEWABLE ENERGY INTEGRATION (DARE 2016)
Keywords
DocType
Volume
Load decomposition and profiling, Smart grids, Demand-side management, Temperature-demand dependencies, Correlation and regression analysis, Sliding-window data analysis, Power-factor analysis
Conference
10097
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Andreas Paisios100.34
Sasa Z. Djokic210.69