# Blockchain Technology Applied in IoV Demand Response Management: A Systematic Literature Review
## 3 Current Perspectives and Research Efforts on Blockchain-Enabled IoV
### 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 [PERSON] [39], in an attempt to overcome smart grid imbalances and to control the ever-growing energy demands from EVs. Moreover, [PERSON] [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, [PERSON] [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, [PERSON] [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].  
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:  
* Time-based DR: In the time-based DR, consumers are provided time-varying pricing depending on the cost across various time periods.
* Incentive-based DR: Customers in incentive-based DR schemes are offered fixed or time-varying payments to encourage them to reduce their electricity usage during times of system stress [66; 67; 68], but they are also subject to specific constraints or are penalized if they do not participate in the program.
* Finally, DR systems that utilize the decision variable to identify task-scheduling and energy-management based DR schemes are classified into the third group [69; 70].  
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-management-based DR solutions accomplish different power usage during peak-demand hours by decreasing the power consumption of certain loads [73; 74].