## 7 Future Trends in Next-Generation Smart Grids (SGs) After the Integration of EV Market
### Application of Artificial Intelligence (AI) to SG Technology for EV Market  
[PERSON] et al. [89] and [PERSON] [90] explored the application of big data and deep learning (DL) in SG applications to predict solar and wind power generation, as well as load demand, facilitating more informed decisions for power generation optimization. A similar survey was carried out by [PERSON] et al. [129], focusing on EV charging issues. Given the intermittent nature of renewable energy sources, optimizing EV charging is crucial to balance the load distribution on the SG while ensuring that no EV owner is left without power. [PERSON] et al. [77] conducted a simulation-based study on EV charging scenarios for dynamic wireless charging (DWC) lanes in Dubai and Sharjah, employing various mixed-integer non-linear programming optimization algorithms to enhance load balancing and prevent overloads. Their approach resulted in a 44% reduction in PAER compared with using a shortest-distance algorithm for assigning EVs to charging stations. [PERSON] et al. [81] investigated the application of AI algorithms for 5G-powered SG applications. The high data speeds of 5G networks are critical for supporting massive IoT (mIoT), which is necessary for transmitting the substantial data required by AI algorithms [130]. These algorithms enable effective load forecasting, real-time fault management, and grid stability assessment. [PERSON] et al. [80] addressed how AI could stabilize the SG amidst volatile power generation from renewable energy sources. Greeneum [62], a decentralized P2P blockchain system for green energy, uses AI algorithms to reward carbon-neutral businesses and blockchain for smart contract generation and sharing. AI algorithms are also utilized as third-party platforms for predicting energy prices; [PERSON] et al. [88] recently researched predicting Bitcoin values using time-series data and applying two popular ML algorithms: support vector machine (SVM) and linear regression (LR). [PERSON] and [PERSON] [82] and [PERSON] et al. [83] employed game theory approaches for optimizing energy trading. One controversial aspect of blockchain in SG applications is the computationally intensive consensus mechanism, such as PoW, which consumes significant power at miner nodes. [PERSON] et al. [131] proposed a novel green PoW (GPoW) mechanism that uses ML and Markov decision processes to reduce the number of miners competing based on their historical win/lose records, thereby decreasing the energy consumption of the consensus mechanism. Oscillatory Load Behavior on SG Technology Due to Selfish User-Incentivized EV Transportation Schedules  
The gradual rise in the EV market, including private EV users as well as EV-based public transport options (buses and taxis), will present important design and implementation challenges for SG management as consumers strategize their transportation schedules according to beneficial tariffs for EV charging, putting strain on existing DSM algorithms [132; 133; 134; 135; 136; 137]. Also, their charging demands would be dependent on their driving style and available time for partial/complete charging [138; 139; 140]. ML algorithms [141] could particularly assist EV owners in selfishly optimizing EV distance range and battery management according to user preference for a particular charging station on their route. For example, EV owners could choose to charge from charging stations located at their offices during the day as compared with their homes according to their work schedules and tariff variation according to place-of-use and ToU. As a result, this would result in spikes in tariff rates in central business district (CBD) areas as compared with residential suburbs due to increased demand during specific times of day. An increase in the tariff rate due to increased demand in CBD areas would prompt EV users to opt for selling surplus energy at CBD locations instead of buying and choosing an alternative charging option at some sub-urban location or en route to their homes.