## 2 Demand-Side Management Classifications and Their Roles in Cost Minimization
### Unpredictable Nature of DERs and the Impact on DSM
To produce a reliable system and to flatten the load curve, the controller at each consumer's end is in contact with the neighbor consumers. The communication between neighbors is asynchronous and is based on distribution coordination scheme. The controller is operating on multi-objective optimization algorithm where the cost of consumers and the flattening of the load curve are formulated as follows:  
\[\min z_{c}=\sum_{t\in T}C^{t}P^{t}\triangle t, \tag{9}\]  
\[\min z_{s}=\frac{\sum_{t\in T}S^{t}P^{t}\triangle t}{T} \tag{10}\]  
where \(z_{c}\) is considered the primary objective variable which represents the energy cost at time \(t\), while \(z_{s}\) denotes the load curve function which is considered the secondary objective. \(P^{t}\) is the power consumed by consumers for interval \(\triangle t\) with expenses of \(C^{t}\), and \(S^{t}\) represents the aggregated load that can be computed by the interaction of the neighbors through a controller installed at their premises. Equation (10) is also named the load stability objective equation. Another scheduling model was discussed by the authors of [96], where the consumers' energy consumption was divided into two different parts: deterministic dynamic consumption and stochastic consumption. The authors maximized the DER generation by an formulating energy scheduling problem using Gaussian approximation and the rolling horizon optimization technique. The authors in [88] presented another home energy management system in which storage devices are installed at the consumer's premises. The storage devices are jointly accessible for both suppliers and for consumers. Therefore, the proposed system is beneficial both for suppliers as well as for consumers. The consumers can reduce their energy bills by shifting the peak demand to off-peak hours, where they are charged a low price per kWh which minimize the demand during peak hours. Moreover, due to the reduction in demand during peak hours, the generation cost is also minimized. **Conclusions of the section:** In this section, we introduced the DERs, the integration of DERs, PAPR minimization using DERs, cost minimization using DERs, and the unpredictable nature of DERs. Moreover, we also discussed that how these DERs can help in achieving various performance metrics of the given DSM model like PAPR and cost minimization. In addition, we also summarized the unpredictable nature of DERs and their impacts on the DSM schemes. In this section, we summarized that DERs can help the DSM model in cost minimization and PAPR reduction very efficiently, but due to the uncertain nature of DERs, it can not continuously help the DSM model. Therefore, we identified that DER availability forecasting and frequent adjustment of the DSM model according to the uncertainty associated with DERs need to be considered in the upcoming DSM models. Moreover, attention is needed to motivate the consumers to increase the installed DERs and also to discover various methods for the optimal placement of DERs which would result in the minimization of line losses.