## 10 Future Research Direction  
This literature survey carefully examined the current research and the advances in the domain of EV-based DSM, and after thoughtful discussion, based on the identified research gaps, some valuable suggestions regarding future research directions and prospective areas of research are suggested:  
* DG integration on a system-wide scale can be a beneficial front for the maximum utilization of intelligent loads and appliances to participate in DSM, with EVs being smart energy hubs concerning energy dispatch and storage [188,189].
* Data collection and data handling for relevant information extraction and calculation should be prioritized in the future since information gathering and processing have a significant influence on performance.
* Hybrid incentive-based and tariff-based financial models can be formulated for the optimization of load control features, such as the DSM response speed, the duration of the program, advanced alert and notification systems, geolocation sensitivity-based analysis, and real-time load monitoring rates [190,191,192].
* Meta-heuristic-based optimization can be hybridized, or newer, more efficient heuristic algorithms can be used for better computation in the scheduling of DSM operations. PSO, GA, wavelet transform-modified ANN, adaptive FL, support vector machine computation, and autoregressive moving average value integration with models can be implemented to obtain higher load forecast accuracy considering the regulation of loads, dispatch, scheduling, and the unit commitment problems of smart grids [193,194].
* K-map algorithms, fuzzy constrained algorithms, self-reorganizing maps, multilevel hierarchy-based clustering techniques, artificial bee colony (ABC) optimization, and an ACO can be implemented for the extraction of crucial information from aggregated load consumption profiles and in the classification of various load types in intelligent distribution systems [195].
* EV DSM models need to be more comprehensive in their operation for better practical implementation, i.e., varying charging rates, standards implemented on EVCS premises, standardized BESS swapping station methodologies, and the active participation of EVs in overall market trading and ancillary service support scenarios. More research needs to be focused on obtaining an optimized tradeoff between the performance of the system and computational complexity.
* Through data-mining and decision-making processes, diverse and hybrid optimization techniques, such as game theory and Bayesian probability theory, among others, should be explored further for internal energy dispatch, external market participation, risk evaluation, information and strategy coordination, and bidding strategy.
* The practical and easy implementation of the management of charging demand during peak/off-peak usage periods, with price-sensitive scheduling, is an excellent prospect for DSM aggregators. With large-scale EV integration into smart grids, it is a very feasible research direction to be focused upon, with an emphasis on EV charging strategies based on price response and price elasticity dynamics [196].
* Climate-based EV-DSM scheduling should be researched further, as it would affect RES generation to a large extent, and forecasting-based scheduling could help the RES to be dispatched more efficiently based on meteorological data [197].
* There is a severe lack of datasets necessary for training machine learning and deep learning models. Only five well-known EV charge scheduling datasets are available in the open research domain for researchers [198, 199, 200, 201, 202]. Other datasets that have been developed are available to commercial companies. More machine learning models and bio-inspired optimization techniques need to be developed to represent varying architectures and geographical locations [203].
* Big-data analysis should be emphasized to establish appropriate information to improve the perception of the energy market to bring compatibility, universality, and competitiveness.