  This article addresses the residential energy cost optimization problem in
smart grid. To date, most of the previous research only consider a partial
aspect of the cost optimization problem. As a result, they fail to analyze
scenarios when the interconnected components along with their properties have
to be considered simultaneously. The proposed model combines these partial
models into a single unified cost optimization model. Therefore, it is able to
analyze scenarios which are closer to practical implementation. Furthermore, it
is useful to analyze the behavior of a population (e.g., smart buildings, smart
cities, etc.) and properties of the components for specific scenarios (e.g.,
the impact of aggregate storage capacity, etc.). It allows energy trading in
microgrid which introduces a cost fairness problem. It ensures Pareto
optimality among the households which guarantees that no household will be
worse off to improve the cost of others. Results show that it can maintain the
user preferences and can react to a demand response program by rescheduling the
household loads and sources. Finally, the paper addresses the challenge of the
computational complexity of the proposed model, showing that solution time
increases exponentially with the problem size and proposes possible approaches
to solve this.
