## 1 Introduction  
The increasing costs of energy and environmental contamination are major concerns in today's world [1]. As most of the power plants rely on fossil fuel resources for electricity production, they are depleting at a fast rate. Moreover, the increased utilization of fossil fuels is resulting in global warming, which is a challenging issue to deal with. These seminal drivers have led to the belief that the need for public and private decision-makers to transition toward green and sustainable energy is inexorable. The integration of renewable energy resources (RERs), especially solar and wind, is a handy option for generating green electricity with reduced global warming effects [2; 3]. These factors lead us to the concept of a smart grid, which is a digital, bidirectional network of distributed generators. Due to installed sensors, it has the capabilities of self-monitoring, self-healing, remote control, and pervasive control [4; 5]. A smart grid offers opportunities for maximum energy savings by organizing energy management systems (EMSs) and their functions. It relies on intelligent control devices to establish an efficient communication between the consumer and utility provider. Mutual coordination between electric grids, utility operators, and smart homes is the main point that allows a smart grid to work accurately. A major part of electrical energy is being utilized for serving residential communities. According to Lior [6], 30-40% of the overall supplied energy is used in residential houses. Therefore, energy management in residential homes plays an important role in reducing the consumption of electrical energy supplied by the grid to diminish the peak load on grids. With the improvement in control and communication methodologies, home energy management systems (HEMSs) are playing an important role in reducing electricity consumption in residential buildings. An optimal collaboration between smart homes and the utility provider due to HEMS decreases electricity consumption cost [7]. Moreover, an optimum collaboration between smart homes and a smart grid saves 10-30% of electrical energy consumed from the grid [8]. Electricity providing utilities introduced different dynamic tariff structures to encourage the electricity customers to decrease their consumption in high-tariff time slots [9; 10]. Based on this information, a smart grid utilizes supply-side management (SSM) and demand-side management (DSM) tools for energy conservation. The optimization activities covered in SSM are implemented regarding the generation, transmission, and distribution sides of the energy chain, while in DSM, the consumer side load management is performed based on the demand response. The electricity consumption charges may be reduced by shifting household appliances from a peak tariff time to an off-peak tariff time. The users can get maximum benefits in terms of a decrease in electricity consumption charges, reduction in the peak-to-average ratio (PAR) and avoiding energy blackouts due to two features of DSM, namely the demand response (DR) and load management (LM) [11]. DR programs play a significant role in a smart grid operation by scheduling household appliances from away from high-tariff time slots to low-tariff time slots according to time-based electricity tariffs, which are explained in References [9; 12]. DR programs are further classified into two types, i.e., incentive-based (IB) and price-based (PB) DR programs. Figure 1 represents the classification of DR programs. In IB DR programs, the utility wirelessly shifts home appliances to an OFF state by sending a short notice to consumers whenever a peak demand occurs in any time slot [13]. Using the PB DR programs, the electricity utilities encourage the electricity users to adopt efficient scheduling strategies such that they can minimize their electrical energy consumption and reduce their electricity consumption costs [14]. This paper focuses on PB DR programs with the integration of RERs, and energy storage system (ESS), and a smart meter at each home.