## 9 Discussion and Findings  
During the systematic review of the papers as a part of the literature survey, several research gaps were identified in the present research scenario, as well as implementations in various projects across the research domain. Some of the key findings identified during the survey include:  
* Most of the research papers addressed DSM formulation in the EV scenario by incorporating bidirectional power flow, but the uncertainty in demand and supply forecasting leads to inefficient control over power flow. * The limited participation of DGs, mainly on the distribution level, restrains the individual customers, and they cannot directly participate in ancillary services and energy markets [183; 184]. Clustered DGs must be able to collectively participate in the formation and maintenance of such groups in the proper sizing and architecture, which should be scalable in future implementations. * The clustering of uncoordinated DGs, which generally operate in a decentralized setup among different utility operators, seems challenging. It is necessary to implement a proper service-oriented architecture to group together the operation and participa \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multirow{2}{*}{**Refs.**} & **Optimization Algorithm** & **DR Programs Used** & \multirow{2}{*}{**Objective Function**} & \multirow{2}{*}{**Constraints**} & \multirow{2}{*}{**Decision Variables**} \\ \hline
[176] & \(\bullet\) & VF & \(\bullet\) & Minimization of EV charging costs & \(\bullet\) & ESS charging rate constraints & \(\bullet\) & ESS availability \\ \hline
[177] & \(\bullet\) & RTP & \(\bullet\) & Minimization of electricity costs & \(\bullet\) & Consumer comfort limits & \(\bullet\) & ESS availability \\ \hline
[178] & Convex optimization & \(\bullet\) & PS & \(\bullet\) & Minimization of total electric energy costs & \(\bullet\) & Power balance limits & \(\bullet\) & Number of available EVs \\ \hline
[179] & \(\bullet\) & RTP & \(\bullet\) & Minimization of electricity cost for the consumer & \(\bullet\) & Charge/discharge power limits & \(\bullet\) & Number of available EVs \\ \hline
[180] & Quadratic programming & \(\bullet\) & PS & \(\bullet\) & Maximization of vehicle’s fuel economy & \(\bullet\) & Power flow limits SoC limits & \(\bullet\) & Cost function \\ \hline
[181] & Non-intrusive load extracting (NLE) algorithm & \(\bullet\) & LS & \(\bullet\) & Minimization of the daily cluster charging costs of EVs & \(\bullet\) & Power balance limits & \(\bullet\) & EV charging power Availability of EVs \\ \hline
[182] & Monte Carlo-based risk-averse scheduling optimization & \(\bullet\) & ToU RTP & \(\bullet\) & Maximization of profits & \(\bullet\) & SoC limits Charging period limits & \(\bullet\) & Electricity tariff \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._tion of different DG aggregating companies to make DG-DSM integration into the commercial markets more profitable and easier to implement on a technical front. * The drive cycle of the EV owners, on an individual basis, has not been taken into consideration on an end-user level. The optimization of charging and discharging can be improved, to a great extent, with the personalized scheduling of EV and ESS charge/discharge operations based on the user's comfort and usage cycle. * ICT technologies are currently implemented mainly on the transmission system operator (TSO) and DSO levels. They need to be integrated directly into the end-user location with a two-way communication channel to ensure more engaging and detailed EV and ESS charge scheduling operations. The EV and ESS can provide personalized data collected during diagnostic and data collection schedules to supply the EV aggregator with proper charge schedule data. This will allow the EV aggregator to optimally dispatch loads based on detailed SoC, SoH, BESS capacity, and drive cycle condition data. * The customer's security and privacy are prioritized in the public domain.