###### Abstract
The simulation demonstrated the proposed approach's ability and proficiency to prevent unanticipated changes. In another study, the researchers simulated eight system control modes to evaluate a proposed V2H concept [34]. The main operating modes consisted of an in-house battery charging mode where the PV-generated power is used simultaneously for in-house battery charging and load power. The in-house battery discharge mode uses the stored power of the in-house battery to satisfy the load demand. The Nissan Leaf charging mode involves charging the Nissan Leaf using the excess power remaining after the in-house battery is charged and the load demand is satisfied. The Nissan Leaf discharge mode includes using the Nissan Leaf to provide the power to satisfy the demand when the load demand is high, and the generated PV power and state-of-charge (SOC) of the in-house batteries are low. The in-house battery protection mode prevents the degenerative discharge condition of the in-house battery, and the system isolation mode prevents system failure. The excess power handling mode manages the excess generated power, and finally, the night-time charging mode charging the Nissan Leaf should be prepared for the next day. Researchers simulated an optimized strategy for the operations and facilities of a residential energy system [35]. The authors set up several scenarios and investigated the optimized strategy with different PV panel installation locations, automotive usage patterns, and energy demand characteristics. For houses with extended EV absence times, the results demonstrate that combining a V2H system with a stationary battery (SB) is more cost-effective. Thus, when designing a household energy system, it is advisable to evaluate numerous battery alternatives to determine whether a V2H system should be placed alone or in conjunction with a stationary battery. The above studies slightly deviate from the uncertainties. Thus, efficient uncertainty prediction models are required to improve decision-making for cost reduction and DR optimization. For instance, the RnEs can predict the amount of energy produced during the day and the household energy demand when each production source and electricity consumption profile are known. For energy prediction, household energy consumption and production values are used to predict how much energy is consumed and produced by appliances. Researchers argued that EVs as energy resources can create ancillary service markets in SGs by providing ancillary services [36]. Therefore, the grid-integrated-vehicle (GIV) technology is appropriate for investing in EVs for ancillary service markets. Furthermore, it is vital to consider individual consumption behaviour when deciding optimal operational EV points for increasing user profit and resource effectiveness. However, a mechanism that considers individual consumption behaviour has not been created. Thus, a proposed V2H aggregator uses the in-vehicle batteries of EVs to enable individuals to participate in a regulated market. The results indicated that the proposed V2H aggregator could predict the power required for the grid. Studies have been conducted in which the researchers present a partially implemented software to control the energy exchange in V2H technology [37]. They implemented various basic algorithms to satisfy the load demand. The software can receive requests, such as XML documents from several control sources, that include long- and short-term plans from smart homes or EV viewpoints. In addition, the model includes an energy flow control unit (EFCU) as a system for bidirectional EV connection within the smart home. Conventionally, EFCUs are operated manually to start and stop the charging and discharging mechanism. In contrast, the optimized model enables the automatic control of the charging and discharging mechanism based on the predicted long and short-term plans using any communication technology, e.g., WiFi or Bluetooth.