Title :
Cost-of-Service Segmentation of Energy Consumers
Author :
Albert, Adrian ; Rajagopal, Ram
Author_Institution :
Dept. of Electr. Eng., Stanford Univ., Stanford, CA, USA
Abstract :
Uncertainty in consumption is a key challenge at energy utility companies, which are faced with balancing highly stochastic demand with increasingly volatile supply characterized by significant penetration rates of intermittent renewable sources. This paper proposes a methodology to quantify uncertainty in consumption that highlights the dependence of the cost-of-service with volatility in demand. We use a large and rich dataset of consumption time series to provide evidence that there is a substantial degree of high-level structure in the statistics of consumption across users which may be partially explained by certain characteristics of the users. To uncover this structure, we propose a new technique for extracting typical statistical signatures of consumption-energy demand distributions (EDDs)-that is based on clustering distributions using a fast, approximated algorithm. We next study the factors influencing the choice of consumption signature and identify certain types of appliances and behaviors related to appliance operation that are most predictive. Finally, we comment on how structure in consumption statistics may be used to target residential energy efficiency programs to achieve greatest impact in curtailing cost of service.
Keywords :
approximation theory; demand side management; domestic appliances; energy conservation; power consumption; power markets; renewable energy sources; statistical analysis; stochastic processes; EDD; appliance operation; approximated algorithm; clustering distributions; cost-of-service segmentation; energy consumers; energy demand distribution; energy utility companies; intermittent renewable sources; penetration rates; power consumption uncertainty; residential energy efficiency programs; statistical consumption signature; stochastic demand balancing; volatile demand; Clustering algorithms; Computational modeling; Energy consumption; Home appliances; Time series analysis; Uncertainty; Segmentation; service cost; smart meter data;
Journal_Title :
Power Systems, IEEE Transactions on
DOI :
10.1109/TPWRS.2014.2312721