# Demand-Response Control in Smart Grids
## 2 State of the Art  
Demand-response methods are essential for the efficient operation of smart grids. They allow for the management of electricity demand in order to reduce peak load and improve system reliability. The smart grid necessitates the development of efficient demand-response methods. To do so, DR deployment is critical. The DR is divided into many categories, and each category has its own benefits and drawbacks. We categorize the DR control into three fundamental groups [15]. In the first class, the DR programs aim to minimize power consumption. This category is classified into two groups: time-based and incentive-based programs [16]. In the first subgroup, which is a time-based program (known as a price-based program), customers are given time-varying pricing that is proposed by the power utility based on the energy cost in several slots of time [17]. In the second subgroup, which is an incentive-based program, consumers must pay fixed or time-varying payments to decrease their electricity consumption during peaktime, but they are also subjected to certain limits or penalized for not contributing to the allocation [7]. The second class contains task-scheduling and energy-management-based demand-response systems (known as energy scheduling DR methods). The fundamental aspect of task-scheduling DR is the possibility of choosing the required charge-activation time, which can be relocated at times of peak demand [18]. DR strategies, focusing on energy management, achieve varying power usage during times of high demand by lowering the consumed power of specified loads [19]. Finally, demand-response methods are divided into centralized and distributed categories based on the communication model. In the centralized category, decisions for load activation or load scheduling are managed by the energy utility based on the data sent by the consumers. In this category, each customer contributes to the solution on their method, without having to worry about the other group members' participation. For example, a centralized solution is used to determine scheduling decisions [20] and control appliances [21], buildings [2], PHEV charging stations [22], and the application of DR in microgrids [19]. In the distributed approach, the key contribution of the power utility is the delivery of prices that depend on the total system load; customers may work together to reduce the total load [23]. By inhibiting central authority from gathering data for decision-making, this decentralized control ensures scalability as well as consumer privacy. To ensure effective control mechanisms in DR, the distributed nature of the Internet has encouraged several researchers to develop effective control techniques for smart-grid systems. There are papers in which the authors present a distributed DR strategy that is based on the tariff of congestion in the control of Internet traffic [24], a distributed DR approach that was used with a voltage control strategy [25], a frequency-sensitive demand-response method [26], and distributed algorithms to coordinate and monitor demand response and distributed power resources [27]. There have been several different energy scheduling systems considered in the past [28; 29; 30; 31]. This study is based on the results presented in [32]. The aim of this study was to develop a completely fair energy-scheduling mechanism for a smart, distributed grid system. The proposed mechanism was able to provide fairness in terms of energy scheduling and also improved the overall efficiency of the system. Our fair energy-scheduling method is executed in two phases. During the first phase, the energy is equally distributed among all consumers. During the second phase, the Vickrey auction model is used to schedule the remaining energy among those consumers whose energy requirements were not met during the first phase. All consumers are handled equally, making it a non-priority-based famine-free method. There is a variety of auction types that can be categorized based on some factors [33].