Optimal dynamic pricing for an electricity retailer in the price-responsive environment of smart grid

The main purpose of this study is to support a retail electric provider (REP) to make the best day-ahead dynamic pricing decisions in a realistic scenario. These decisions are made with the aim of maximizing the profit achieved by the REP under the assumption that mixed types of customers with different behaviors in the electricity market are considered. While some of the customers have installed smart meters with an embedded home energy management system (HEMS) in their home, others do not participate in the demand response (DR) programs. For this purpose, a bi-level hybrid demand modeling framework is proposed. It firstly uses an optimal energy management algorithm with bill minimization in order to model the behavior of customers with smart meters. Then, using a customers? behavior learning machine (CBLM), the behavior of other groups without smart meters is modeled. Therefore, the proposed hybrid model cannot only schedule usage of home appliances to the interests of customers with smart meters but can also be used to understand electricity usage behavior of customers without smart meters. The proposed model includes a stacked auto-encoder (SAE), one of the deep learning (DL) methods suitable for real-valued inputs, and adaptive neuro-fuzzy inference system (ANFIS). Based on the established hybrid demand model for all customers, a profit maximization algorithm is developed in order to achieve optimal prices for the REP under relevant market constraints. The results of the case studies confirm the applicability and effectiveness of the proposed model.