**4. Discussion**

The majority of the selected studies mention the need for decentralized DR management in IoV and smart grids, which will primarily promote privacy and security, and then will efficiently incorporate the increasing number of electric vehicles. Likewise, the need for a blockchain framework that offers optimized managemen<sup>t</sup> and coordination of EV charging is another similarity identified in the literature. The latter is further supported from the common belief that blockchain technology could provide security and privacy of the drivers' data and the exchange of information. In addition, many of the studies highlight that focus should be given to human behavior and preferences, creating EV profiles, which will help the managemen<sup>t</sup> of the IoV-assisted smart grids and also emphasize different social aspects. Furthermore, another similarity revealed that real-time energy demand is not sufficiently analyzed and explored, considering also the randomness of the EVs events and the unexpected events that may occur during the everyday life.

Finally, the majority of the studies highlight that the advantages of blockchain technology within the IoV-assisted smart grids are numerous, although there is a lack of incentivization schemes that provide relevant rewards to prosumers in order to participate in such schemes. The similarities are depicted in Table 4.


**Table 4.** Similarities and differences among the selected studies.

Furthermore, the SLR reveled some differences (Table 4). The first difference derives from Y.C. Tsao [40], in which the sustainable microgrid design problem is addressed by leveraging blockchain technology to provide real-time-based demand response programs. The study, though, is not coupled with IoV or EVs, although the optimization approach that is proposed is evaluated, making the authors believe that it could be also applied in the area of IoV. Additionally, compared with the rest of the selected studies that are focused on price-based incentives, N. Karandikar [50] focused on non-fungible tokens as a mean for the incentivization of the users. On the contrary, B. Prapadevi [45] reviewed four important themes, such as electric load forecasting, state estimation, energy theft detection and energy sharing and trading, trying to illustrate the need of deep learning solutions in smart grids and demand response. Similarly, A. Kumari [56] reported that current DR managemen<sup>t</sup> solutions are not adequate in terms of peak loads reduction, consumer comfort and data security issues, and proposed a data analytics scheme for security-aware management. The proposed scheme used blockchain to maintain the grid stability and reduce peak energy consumption.

The discussion presented above led to the following observations, as those are illustrated in Figure 6.

**Figure 6.** Systematic literature review observations.

**Observation 1—EVs as a distributed power backup:** EVs can be used as a distributed backup power for the grid, storing electricity during the low period and providing electricity to the power grid during peak period. EVs are not only charged from the grid, but they can also discharge electricity towards that through V2G technology. Hence, EVs may be considered as a distributed backup power, enabling electricity storage during low demand periods, while providing electricity back to the grid during peak periods.

**Observation 2—Demand Response Problem due to energy surplus or deficit:** Energy surplus or deficit may threaten the security of the energy supply and demand, leading to a demand response problem. The latter is becoming worse considering the randomness of the EV events, which may lead to energy components' overload and culminating with power outages or service disruptions, leading to the so-called demand response problem.

**Observation 3—Optimal schedule of EVs' charging and discharging:** It is challenging to optimally schedule the charging/discharging behavior of EVs to achieve energy balance, considering the instability of EV demand during specific time periods and/or locations. Specific areas of the IoV-assisted smart grid may increase the demand during

specific time periods and/or locations. Thus, it is a challenge to optimally schedule the charging/discharging behavior of EVs in order to achieve energy balance in the grid.

**Observation 4—Need for a charging coordination mechanism:** Existing pricing coordination techniques have a number of flaws since they rely on a single entity, which might be an untrustworthy third party who is not always truthful when scheduling charging requests. Furthermore, private information about EV owners (such as driving patterns and profiles) could be revealed. To tackle the DR problem in IoV-assisted smart grids, a decentralized, transparent, and privacy-preserving charging coordination mechanism is required.

**Observation 5—Blockchain incentives should be considered:** Most of the studies consider blockchain technology to ensure the privacy of the EVs, although few of them consider incentive mechanisms to encourage EV drivers to participate in blockchain-enabled DR through optimal scheduling. Existing charging coordination mechanisms suffer from several limitations, e.g., they rely on a single entity, which may be an untrusted party that is not always honest in scheduling charging requests. In most of the selected studies, it was observed that the researchers considered blockchain technology to ensure the privacy of EV owners. However, not many of them considered incentive mechanisms to encourage EV drivers to participate in this kind of blockchain-enabled DR framework. There are a couple of studies, though, that state that the provision of incentives to the participants (e.g., EV drivers, energy providers and households) will be the key to exploit blockchain technology within smart grids and IoV.

**Observation 6—EV profiling for energy demand planning:** EV profiling should be considered to perform an alignment of EV charging and driver mobility demand towards optimizing electricity demand forecasting and planning. Forecasting the electricity price plays a significant role in reducing energy costs. Moreover, energy demand forecasting helps to maintain the balance between electricity demand and supply in the IoV-assisted smart grid. As a consequence, to achieve the optimization of electricity demand forecasting and planning, EV profiling should be considered.

As discussed in earlier sections, IoV is now under strain as a result of substantial changes in the production and development of EVs. Indeed, the growing malfunctions in power generation need the development of new paradigms. Demand response is an approach in which EV customers actively alter their consumption in response to grid demands. Thus, energy management, which allows the optimal use of constrained energy resources, is required for the establishment of a smart, green and sustainable smart grid. However, the widespread use of unpredictable and uncoordinated EVs creates problems. To balance load and supply, a large number of centralized generators and energy storage devices should be placed, resulting in a considerable CAPEX and operational expense OPEX. Another option is to investigate the rapid spread of DR, which may be used in smart cities to allow energy users to proactively change how and when they use (or create) energy based on the cost (or reward). Because IoV is a participatory data exchange and storage platform, the underlying information exchange system has to be safe, transparent and immutable in order to accomplish the desired objectives. In this regard, the use of blockchain as a system platform for addressing the IoV's demands was investigated. IoV applications enabled by blockchain are thought to offer a variety of desirable features, such as decentralization, security, transparency, immutability and automation, due to their decentralized and immutable nature.

Even though the current studies have several similarities, there are still open research challenges that need to be further investigated. The identified challenges are described below among with some suggestions for further investigation.

Research Challenges and Suggestions 1:


• Description: The existing charging coordination mechanisms suffer from their relation to a single entity (e.g., the charging coordinator), which can reveal private information about the owners of the EVs (e.g., patterns and drivers' profiles). Thus, the integration of blockchain in the IoV should guarantee the privacy of all participants and the security of the exchanged information.

Research Challenges and Suggestions 2:


Research Challenges and Suggestions 3:


Research Challenges and Suggestions 4:

