  In this paper, we consider a realistic and meaningful scenario in the context
of smart grids where an electricity retailer serves three different types of
customers, i.e., customers with an optimal home energy management system
embedded in their smart meters (C-HEMS), customers with only smart meters
(C-SM), and customers without smart meters (C-NONE). The main objective of this
paper is to support the retailer to make optimal day-ahead dynamic pricing
decisions in such a mixed customer pool. To this end, we propose a two-level
decision-making framework where the retailer acting as upper-level agent
firstly announces its electricity prices of next 24 hours and customers acting
as lower-level agents subsequently schedule their energy usages accordingly.
For the lower level problem, we model the price responsiveness of different
customers according to their unique characteristics. For the upper level
problem, we optimize the dynamic prices for the retailer to maximize its profit
subject to realistic market constraints. The above two-level model is tackled
by genetic algorithms (GA) based distributed optimization methods while its
feasibility and effectiveness are confirmed via simulation results.
