## 2 Demand-Side Management Classifications and Their Roles in Cost Minimization
### Cost Minimization by Employing Efficient DSM Schemes
#### 2.2.1 Game Theoretic and Real-Time Pricing Approaches in Cost Minimization
The consumers have the choice of whether to sign the contract or not. If the consumers agree with the scheduler contracts, then they are rewarded in two different ways, i.e., either the aggregator will serve the consumers with electricity consumption at a lower price or they will be awarded an extra amount of energy during off-peak hours. An evolutionary algorithm based on day ahead DSM was proposed by the author of [118]. The objective of the proposed framework is to shave the demand during peak hours and to minimize the generation cost. Cost Minimization Using DSM Scheme Based on RTP  
Generally, consumer demand is higher and more critical in peak hours. In order to ensure the supply of energy to the consumers in peak hours, the utility and generation companies run peaker power plants, and therefore, charge higher prices per kWh to the consumers. Moreover, by operating the peaker power plants, the peak to average power ratio (PAPR) increases, which, in turn, affects the reliability of the energy supply. Each consumer's electricity price is based on his/her load and the increase in the demanded load can lead to an increased electricity price. To manage the consumer  
Figure 7: Game theory-based DSM scheme. demand, there are a lot of research articles that are based on real-time pricing with the objective of minimizing the electricity cost. In [98], the authors presented a DSM scheme consisting of an efficient scheduling model based on real-time pricing (RTP). The central controller installed at consumer's premises gets the RTP from the supplier end and schedules or shifts the high energy household devices through the home gateway into off-peak hours. The proposed model reduces the energy cost as well as the PAPR which leads to system stability. In [99], the authors explored an incentive-based demand response algorithm to shave the demand during peak hours. The proposed algorithm can be applied both for essential and flexible consumer loads. Furthermore, the scheme is divided into two control levels, i.e., the first control level is used to manage and improve the feeder voltage profile within a certain range and the second control level is used to minimize the peak demand and prevent the transformer from overloading. The proposed scheme minimizes consumers the demand during peak hours and considers the consumers' priorities and satisfaction and also improves the feeder voltage profile. A day ahead scheduling algorithm was provided by the authors of [144; 145] in order to minimize the energy costs. A DSM scheme based on mixed integer multi-time scale stochastic optimization was proposed by [PERSON] et al. in [119]. In this framework, the authors first considered the consumer's satisfaction by maximizing the power production, and then focused on energy consumption and cost of energy consumed and finally, suggested an efficient control strategy to minimize the peak demand. The authors of [43] designed an autonomous DSM scheme to minimize the energy cost. The authors used game theory to formulate the consumption scheduling game with the following objective:  
\[\min\sum_{t=1}^{T}C_{t}\left(\sum_{n\in N}\sum_{a\in A_{n}}X_{n,a}^{t}\right) \tag{3}\]  
where \(C_{t}\left(\sum_{n\in N}\sum_{a\in A_{n}}X_{n,a}^{t}\right)\) is the cost function and \(X_{n,a}^{t}\) is the energy consumed by appliance \(a\) of consumer \(n\) at time \(t\) hours. In addition, the authors in [75] minimized the demand during peak hours by shifting the high power rating appliances to off-peak hours that resulted in the minimization of PAPR. The PAPR minimization problem was formulated as follows:  
\[\min_{X_{n,i}\in X_{n}}\frac{\max\left(\sum_{t\in N}\sum_{a\in A_{n}}X_{n,a}^{ t}\right)}{\frac{1}{T}\left(\sum_{n\in N}\sum_{a\in A_{n}}X_{n,a}^{t}\right)} \tag{4}\]  
where \(\max\left(\sum_{n\in N}\sum_{a\in A_{n}}X_{n,a}^{t}\right)\) is the demand during peak hours and \(\frac{1}{T}\left(\sum_{n\in N}\sum_{a\in A_{n}}X_{n,a}^{t}\right)\) is the average demand of consumer \(n\).