## 2 Related Work  
SG introduces a new vision of the upcoming future energy systems with advanced communication, sensing, controlling, transmission and distribution technologies for providing cost-effective and uninterrupted energy supply in a smart manner [10,11]. Demand-side management (DSM) and DR are two major components of SG, which provide assistance in the implementation of energy management programs in different areas, like: electric market energy management, the industrial sector and, especially, the residential sector. DSM or energy demand management can be defined as the modification of consumer's energy consumption profile by using various methods, such as incentive-based DR mechanisms and improvement in lifestyle through education. Controlling the energy demand and flow can provide benefits by reducing the peaks to stabilize the grid and increasing the users monetary benefits. The DSM mechanism plays an important role in the electricity market for energy management [12,13]. Different DSM algorithms are used in the literature [14, 15, 16]. Most of these techniques are based on specified systems, and others are not practically implementable due to the large number of independent devices [17]. In [18], the authors use a decision support tool (DST) for household appliance scheduling in the TOU pricing environment. A dynamic scheduling system (DSS) is utilized to schedule the appliances and energy consumption based on the historical data of appliances [19], while in [20], the authors used a separate system to handle the load of a large number of customers by formulating the problem as a multi-objective optimization problem. There is also a large number of centralized optimization techniques and algorithms in the literature. Each technique has been designed for different aims and goals. Heuristic-based particle swarm optimization (PSO) and stochastic-based robust techniques for optimization are used for DSM [21, 22, 23]. In order to maximize the user comfort and cost reduction, the PSO algorithm is used for building energy management [24]. Two versions, constant weight PSO and dynamic weight PSO, have been proposed and tested, which effectively save energy by keeping the maximum comfort level. Generally, centralized optimization techniques can give the best global optimum solution. However, sometimes, there might be scalability issues, and great computational power is required while designing for large buildings. Decentralized techniques map a large number of distributed systems and multiple agents to obtain final solution(s) in a natural way. Here, optimization has been done locally, and information exchange between distributed systems is done via agents. In [25], the authors propose a game-based approach to balance the overall load and peak-to-average ratio (PAR) in a neighbourhood environment. All of the players in the game are involved to find the optimal energy consumption schedules and share the information with other neighbours. A similar Stackelberg game-based approach is used to reduce the generation, as well as the electricity cost. Here, the game is played with different utility companies and end users, where each struggles for maximum profit [26]. In [27], a congestion game (non-cooperative game) is utilized to control electricity demand in a dynamic pricing (DP) environment. The coordination signal-based decentralized approach is used to solve the decomposed demand response optimization problem [28]. A combined centralize and decentralized three-step technique is used for the scheduling of electric vehicle (EV) charging based on the DR signal [29]. A well-known DR approach that considers user comfort for the minimization of electricity cost is attractive among residential users. In the literature, a load prioritization mechanism has been used, where a high priority load can be served first [30]. On the other hand, users can also set appliance priorities and preferences in the form of a matrix to meet their comfort level [31].