Abstract:
Supplying peak energy demand in a reliable, cost-effective manner is a critical
focus for utilities internationally. Recently, electricity systems have been examined
worldwide for their environmental impact including climate change, depletion of
resources through the continued use of fossil fuels, and the consistently rising cost of
electricity to customers due to the investment required for upgrading infrastructure to
provide power during periods of peak demand. All these issues could be avoided if
residential customers changed their demand patterns. This research focuses on Demand
Side Response (changes to the time of electricity use), rather than on electricity demand
reduction. One of the most common demand-side management programs consists of
time-of-use (TOU) tariffs, where consumers are charged differently depending on the
time of the day when they make use of energy services. This tariff system has the
potential to reduce energy demand during peak hours, which will decrease costs and
carbon dioxide emissions across the electricity system. This will allow more efficient
use of existing electricity generation and network capacity. Also, this system would
reduce the need for investment in new capacity and minimize the use of less efficient
generation plants. In this work, an optimal tariff model is proposed and evaluated using
the Smart Meter Analytics Platform (SMAP) solution. In addition, the impact of this
optimal system will be assessed on a dataset of residential users in a Lebanese town
where smart meters are installed, in terms of changes in electricity demand profile,
price savings, and carbon footprint reduction.
