*2.3. Report the Review*

The majority of the studies were journal articles and high-quality conference papers, although some book chapters were also analyzed. A clear depiction of the types of publications identified is presented in Figure 2, while their distribution within the time range of the review is presented in Figure 3. Moreover, their publishers are also presented in Figure 4. From the primary studies (i.e., cluster of 106), the most interesting and relevant ones were selected (excluding the documents in the form of a survey or literature review, which were also considered separately) to highlight the main current research trends and the gaps that have ye<sup>t</sup> to be filled. We selected studies only considering if the paper's argumen<sup>t</sup> was built on an appropriate base of theory and concepts, and if the evaluation results were clearly presented and appropriately analyzed. The selected studies were 20 papers and are presented in Appendix A.

**Figure 2.** Distribution of the identified studies based on their type.

The following features were highlighted: problem statement, proposed solution/objectives and outcomes/limitations.

Evaluating the selected studies in terms of research knowledge, it appears that current studies partially tackle DRM designed for IoV. There were studies that were separately studied and solved some of the IoV issues, although none succeed in tackling all of them holistically, providing a systematic literature review or focusing on how blockchain could be incorporated into DR managemen<sup>t</sup> schemes designed for IoV. The aforementioned statement is supported by Table 3, where it can be seen that no work was identified to employ blockchain-based privacy, DRM, V2V/V2G energy trading, charging scheduling, incentivization schemes and EV profiles. Table 3 summarizes and compares the main features observed in the literature review.

**Figure 3.** Distribution of the identified studies within the review's time range.

**Figure 4.** Publishers for the identified primary studies.



### **3. Current Perspectives and Research Efforts on Blockchain-Enabled IoV**

This section summarizes the research findings derived from the systematic literature review, focusing on the perspectives and research efforts around the demand response managemen<sup>t</sup> in the IoV, taking into consideration the application of blockchain technology.

### *3.1. P2P Trading and Management in Energy Blockchain*

There are several studies that propose the use of blockchain technology in P2P energy trading [57]. For instance, in [44], a game-theoretic approach for a demand side management model that incorporates a localized PBFT-CB was proposed. The study incorporated the interaction between sellers and buyers, considering the Stackelberg game and noncooperative static games. Additionally, Brooklyn microgrid was one of the first applied engineering programs of energy blockchain [58]. The project is based on blockchain P2P energy trading without the intermediation of a third-party energy supplier. The Brooklyn microgrid demonstrates that blockchain may be utilized in real-world P2P energy transactions. Moreover, Q. Duan [59] proposed an optimal scheduling and managemen<sup>t</sup> smart city scheme, within the safe framework of blockchain. To do so, Q. Duan presented an enhanced directed acyclic graph strategy to increase the security of data transactions inside a smart city, as well as a security layer based on blockchain to prevent cyber hacking. Additionally, the LO3 Energy company introduced an energy supply scheme to the closest neighbors based on P2P trading [60]. Lastly, the current literature dictates future distributed ledger implementations and mechanisms and revealed that blockchain is an important part of P2P energy trading [52,61–63].

### *3.2. Blockchain-Based Demand Response Programs and Optimization Models*

Demand response has been recognized as an important tool for managing supply and demand in electrical grids [64]. When there is an electrical deficit, DR becomes an effective alternative for absorbing the energy gap and managing power utilization [47]. Significant initiatives in blockchain-based demand response programs and optimization models are presented below.

To handle demand response in a V2G context, a P2P energy trading mechanism between EVs and network operators was proposed by S. Aggarwal [39], in an attempt to overcome smart grid imbalances and to control the ever-growing energy demands from EVs. Moreover, Z. Guo [47] presented a blockchain-enabled DR scheme with an incentive pricing model. First, the authors proposed a blockchain-enabled DR framework to promote the secure implementation of DR, while then they also designed a dual-incentive mechanism, based on the Stackelberg game model, to successfully implement blockchain demand response management. Furthermore, in the area of IoV, there are also several studies that address the DR problem and propose optimization solutions. For instance, Z. Zhou [54] proposed a consortium blockchain-enabled secure energy trading framework for EVs with a moderate cost, using a contract theory-based incentive mechanism to incentivize more EVs to participate in DR. The proposed optimization scheme falls into the category of difference of convex programing and is solved by using the iterative convex– concave procedure algorithm. Likewise, T. Zhang [41] incorporated a blockchain-based cryptocurrency component, with which the system can incentivize users with monetary and non-monetary means in a flat-rate manner.

In recent years, the implementation of DR programs in smart grids has drawn a lot of academic attention. A taxonomy of these research endeavors is depicted in Figure 5, which was generated from the outcomes of the current SLR. This categorization is based on the DR procedure's control mechanism, customer motives to lower or move their expectations and the DR decision variable.

DR systems have two types of control mechanisms: centralized and distributed. In the centralized mode, consumers connect directly with the electricity network without engaging with one another. In the distributed mode, user interactions feed the network with information about overall usage [65].

**Figure 5.** Demand response program taxonomy.

The motivations offered to producers and consumers to decrease their energy usage are classified in the second category of DR schemes. These motivations are divided into two categories:


The main feature of task scheduling DR is control over the desired load's activation time, which may be moved to peak-demand periods [71,72]. The energy-managementbased DR solutions accomplish different power usage during peak-demand hours by decreasing the power consumption of certain loads [73,74].

### *3.3. Electric Vehicles Charging Scheduling Using Blockchain*

To study the consequences of increased EV load and charging mechanisms, the accurate modeling of EV charging profiles is necessary [75]. The size and topology of the energy grid, the number and size of EVs, the mode, time and location of charging as well as the daily driving distance influence the above-mentioned charging profiles. As a consequence, the charging profiles of the drivers are increasingly coupled with charging schedules.

N. Guo et al. [76] proposed a centralized control architecture to handle the modeling and managemen<sup>t</sup> of EV charging by reducing peak demand and increasing the number of EVs charged concurrently. A common finding in numerous studies on EV battery chargers is that EV battery workloads are commonly thought of as a static, with the actual system behavior of the batteries throughout the charging process being overlooked. In order to tackle the latter, Y. Wu [77] emphasized that a bi-directional energy flow is conceivable. EV batteries may be utilized in the grid in the manner of any other energy storage device, with the additional perk of mobility. The owners of EVs would be able to participate in energy market trading, recharge batteries when energy is cheap and discharge if the smart grid rewards them for their excess energy. This type of energy exchange and negotiation can allow the network to regulate demand (e.g., peak shaving) or offer additional storage in the case of excess renewable energy generation. Consumers will be able to pick where, when and which EV to charge, reducing the strain of the grid.

Using the adaptable EV charging flow, C. Lazaroiu [42] developed a model for smart charging of EVs, in which a software agen<sup>t</sup> selects whether it should load a unit, in what sequence or whether it is better to sell energy to the market. The concept is based on blockchain technology, which makes interactions reliable and traceable, with the goal of decreasing or eliminating intermediaries in energy trade and lowering anxiety.

Furthermore, given the growing popularity of EVs and their unpredictable dynamic nature in terms of charging and route patterns, EV load might be difficult for energy distribution operators and utilities to manage [41]. Thus, T. Zhang proposed SMERCOIN, which is a real-time solution that integrates the concepts of priority and cryptocurrencies to encourage EV owners to charge on a renewable energy-friendly timeframe. Customers with a longer history of utilizing renewable energy are given priority in the system, which uses a rating system. By including cryptocurrency, the system may encourage users using both monetary and non-monetary techniques in a flat-rate manner. Similarly, Z. Zhou [54] developed a distributed, privacy-preserved and incentive-compatible DR mechanism for IoV. In more detail, the authors suggested a low-cost consortium blockchain-enabled secure energy trade platform EVs, as well as an incentive system based on contract theory in order to encourage more EVs to join the DR program.

Furthermore, a dependable solution is required to meet the future energy demands of urban and industrial customers, while also supporting the charging and discharging needs of EVs. Therefore, some research articles have been presented, studying the energy trading in the IoV for demand response management. For example, S. Aggarwal [39] proposed a blockchain-based secure energy trading scheme for demand response managemen<sup>t</sup> between EVs and the service providers, while a double auction mechanism is proposed between EVs and SPs to maximizes social welfare with privacy preservation. Similar examples can also be found in [12,46], in which the authors analyzed the energy in IoVassisted smart cities, employing blockchain capabilities in order to select the most suitable charging station without sharing private information, and to balance the spatio-temporal dynamic demands of computing resource.
