Price-Controlled Energy Management of Smart Homes for Maximizing Profit of a GENCO

In this paper, price-controlled energy management is investigated in a bi-level optimization framework, that is, energy scheduling problem of smart homes (SHs) and generation scheduling and unit commitment (UC) problems of a generation company (GENCO). SHs as the responsive customers (respect to the energy management) include a variety of sources such as photovoltaic (PV) panels, diesel generator, and battery as an energy storage. In addition, SHs are able to transact electricity with the GENCO through the power system. In this paper, the goal of GENCO is to design an optimal energy management scheme (optimal price of electricity) to maximize its daily profit. Herein, each SH reacts to the energy management scheme and reschedules its energy resources to minimize its daily operation cost. In this paper, a scenario-based stochastic optimization approach is applied in the energy scheduling problem of an SH to address the variability and uncertainty issues of the PV panels. Also, a combination of genetic algorithm (GA) and linear programming is applied as the optimization tool for the energy scheduling problem of an SH. Moreover, lambda-iteration economic dispatch and GA techniques are applied to solve the generation scheduling and UC problems of the GENCO, respectively. The numerical study demonstrates that in order to reach the maximum profit of GENCO, the energy management must be optimally designed and implemented; otherwise, the energy management scheme may result in detriment. Moreover, it is shown that each SH is able to get benefit from the energy management scheme and minimize its daily operation cost.