Abstract:
This research work is based on the Demand Side Management (DSM) of the residential
load of Pakistan through peak shaving using a Battery Energy Storage System (BESS).
The secondary data set used in this study is the PRECON data set that is composed of
per-minute electric power usage of 42 houses of different demographics in Lahore,
Pakistan. The in-depth analysis of the PRECON data set is made based on
demographics and also through K-Means clustering of metadata. A distribution
network synthesis is performed in MATLAB to design unstructured communities
consisting of a user-specified number of households and feeders. DSM is done at
household and community levels in System Advisor Model (SAM). At the household
and unstructured community level, DSM is also done through an automated algorithm
that determines peaks and valleys from the load curve of a given house for battery
scheduling. At the structured community level, a third-party distributor, other than the
consumer and utility, is supposed to buy electricity from the utility at off-peak hours
and provide it during peak hours at a cost lower than the official peak-hour cost. The
original data is classified into three different scales, representing three structured
communities, through K-means clustering and feeder synthesis is done through
uniform distribution in a Monte-Carlo simulation. After feeder synthesis, peak shaving
is done using BESS for each feeder by performing a techno-economic feasibility
analysis in SAM. The results of this research work show that it is economically and
technically beneficial to install the BESS at the community level than at the household
level. For individual households, the DSM using an automated algorithm resulted in
76% of houses with 2.5 to 29% better load factors. At the structured community level,
this work is equally valid to profit the distributor, reduce the electricity billing costs
and improve the load factor from 1 to 7%.
