Blockchain and Double AuctionBased Trustful EVs Energy Trading Scheme for Optimum Pricing
Abstract
:1. Introduction
 Most conventional energy trading schemes have focused on the security aspect of the EVs while neglecting the optimal payoff between them;
 As per the literature, researchers have addressed the security and privacy issues of EVs energy trading schemes. As a result, authors in [12,24,25,26] investigated the blockchainbased energy trading scheme to strengthen the security and transparency of the EVs communication. However, they have ignored the price optimality aspect of EVs along with other factors such as profit for consumers, computation time, and data storage computation. Moreover, authors of [27] have adopted a double auction mechanism for optimality, but they did not consider the truthfulness and individual rationality of the EVs;
 Therefore, a blockchain and IPFSenabled energy trading scheme is a plausible solution to overcome the price optimality and trust issues of the EVs based on the trustful double auction mechanism. Furthermore, the IPFS and 5G wireless technology ensures the highly reliable, scalable, and efficient data communication between EVs in energy trading.
 We propose a blockchainbased secure and truthful energy trading scheme for optimal payoff between EVs, i.e., prosumers and consumers;
 We employ a P2P IPFS protocol with the blockchain to provide a low cost data storage for EV energy trading;
 We formulate a truthful double auction mechanism to enable the optimized payoff for EVs in energy trading due to its truthfulness and individual rationality;
 Finally, the simulation of the proposed energy trading scheme has been presented in terms of various aspects such as convergence, profit for consumers, computation time, and data storage cost computation.
2. Materials and Methods
3. Related Works
4. System Model and Problem Formulation
4.1. System Model
4.1.1. Prosumer Layer
4.1.2. Blockchain and Auction Layer
 Blockchainbased secure data storage algorithmIn Algorithm 1, the two entities, i.e., prosumer ${\alpha}_{r}$ and consumer ${\beta}_{o}$, are considered along with their data $D{t}_{{\alpha}_{r}}$ and $D{t}_{{\beta}_{o}}$ needed to be stored in the IPFS beforehand certified by the registering authority ${\theta}^{Ra}$. After obtaining the legitimate certificate from ${\theta}^{Ra}$, EVs can store their energy trading data in the IPFS that further generates the corresponding hash keys for them. Furthermore, public key cryptography associated with the publicprivate key pair for the EVs can be used to ensure authenticity and transparency in the energy trading based on the blockchain network [38]. The complete procedure of blockchainbased secure data storage for p and q number of EVs, i.e., prosumer and consumer, can be computed in terms of the time complexity of O(p) and O(q).
Algorithm 1 Blockchainbased algorithm to perform secure data transactions for EVs 
Input: ${\alpha}_{r},{\beta}_{o},IPF{S}^{has{h}_{k}},D{t}_{{\alpha}_{r}},D{t}_{{\beta}_{o}},{\theta}^{Ra}$ Output: Add data transactions to the blockchain

4.1.3. Consumer Layer
4.2. Problem Formulation
5. The Proposed Scheme: Truthful Double Auction
5.1. Truthful and Individual Rationality Characteristics of the Double Auction Mechanism
 If ${f}_{{\alpha}_{r}}$ < ${t}_{{\alpha}_{r}}$, then prosumers trade energy with the bid value less than its true value. However, prosumers bidding with the energy price less than their true value can deviate them from the energy trading due to the incurred loss;
 If ${f}_{{\alpha}_{r}}$ > ${t}_{{\alpha}_{r}}$, then prosumers trade energy with the bid value greater than its true value, which seems to be a beneficial choice for them. However, consumers may not be willing to participate in the energy trading due to the high energy prices for EVs.
 Similarly, if ${f}_{{\beta}_{o}}$ < ${t}_{{\beta}_{o}}$ and ${f}_{{\beta}_{o}}$ > ${t}_{{\beta}_{o}}$, then, based on the first case, consumers trade energy with the bid value less than its true value and, in the second case, consumers trade energy with the bid value greater than its true value. However, the first case is not beneficial for prosumers due to the willingness of consumer to trade energy with less number of Volt coins. Alternatively, the second case is not favorable for consumers due to the higher bidding value (more number of Volt coins) in the energy trading scheme.
5.2. Truthful Double Auction Mechanism Algorithm for EVs Optimal Payoff
Algorithm 2 The truthful double auction mechanism between EVs for optimal payoff 
Input: ${\xi}_{{\beta}_{o}},{\iota}_{\Theta},{\Xi}_{{\beta}_{o}},{\Lambda}_{\Theta},E{u}^{d}$ Output: ${\pi}_{{\alpha}_{r},{\beta}_{o}}$

6. Performance Evaluation
6.1. Convergence
6.2. Profit for Consumers
6.3. Computation Time
6.4. Blockchain Analysis
 Node Commit Latency—In this subsection, we discuss an important metric, the node commit latency of the proposed scheme. The node commit latency is the elapsed time when a transaction is proposed, and it is finalized in the block with the consensus validation. Figure 6 presents the results. Thus, the node commit latency is directly proportional to the consensus mechanism employed in the system, and the value of miner difficulty ${M}_{D}$. We compare the results with Kumari et al., which uses a ProofofStake (PoS) consensus. Let us consider that q EV transactions are finalized, with a difficulty ${M}_{D}$. Thus, in the PoS consensus, the finalized blocks ${B}_{final}$ satisfy the condition as follows:$$\begin{array}{c}\hfill {B}_{final}={B}_{final}\le \frac{bloc{k}_{i}\times ag{e}_{i}}{{M}_{D}}\end{array}$$
 Collusion Attack Scenario—Next, we present the importance of blockchain in mitigating the collusion attacks. Figure 7 presents the results. In the scheme, we discuss a truthful double auction between p prosumers and c consumers. We consider a scenario where the k among the p prosumers can collude to decrease the energy level ${\zeta}_{{\alpha}_{r}}$, which would lead to price inflation for consumers. This situation would not be solved by an auction pricing mechanism as it would only determine a faulty optimal payoff condition. Instead, once the energy units are fixed by p, the details of energy price per unit are also fixed, and then the auction starts. As the details are stored in the blockchain, the colluding parties’ higher bids would not be considered by other nodes during validation.Intuitively, auction fairness in the real sense is defined as the trading condition where no payoff condition brings a price loss to the buyer and seller nodes. In the proposed double auction mechanism, considering n nodes in the network, we assume that, if the offered price ${o}_{p}$ to a particular seller node s is less than a designated base (nodal price) ${n}_{p}$, then an additional compensation amount c is also paid to s. Thus, the seller node’s auction fairness (selling price) condition is depicted as follows:$$\begin{array}{c}\hfill \begin{array}{cc}\hfill {f}_{p}^{s}& ={o}_{p}+c,\phantom{\rule{14.22636pt}{0ex}}if\phantom{\rule{14.22636pt}{0ex}}{o}_{p}<{n}_{p}\hfill \\ \hfill {f}_{p}^{s}& ={o}_{p},\phantom{\rule{28.45274pt}{0ex}}if\phantom{\rule{14.22636pt}{0ex}}{o}_{p}\ge {n}_{p}\hfill \end{array}\end{array}$$$$\begin{array}{c}\hfill \begin{array}{cc}\hfill {f}_{p}^{b}& ={c}_{p}c,\phantom{\rule{14.22636pt}{0ex}}if\phantom{\rule{14.22636pt}{0ex}}{o}_{p}\ge {n}_{p}\hfill \\ \hfill {f}_{p}^{b}& ={c}_{p},\phantom{\rule{28.45274pt}{0ex}}if\phantom{\rule{14.22636pt}{0ex}}{o}_{p}\le {n}_{p}\hfill \end{array}\end{array}$$We compare our approach to Aujla et al. [47], which proposes a Stackelberg game formation for energy trading among EVs and CS. In the figure, a collusion indicator of $0.3$ indicates that, out of the total participating entities (p prosumers and c consumers), we assume that 30% of the population is not fair, or have made parties with others. In the consensus formation, we consider a sharded PoS, which elects a miner based on a reputation score R.In any event E, out of total users, we consider the R value for each node between 0 and 1, i.e., $0<R<1$, and is defined as $\frac{{B}_{Final}}{{T}_{blocks}}$, where ${B}_{Final}$ is the final verified blocks, and ${T}_{blocks}$ is the total number of blocks. As ${B}_{Final}$ increases during the auction phase, the fair energy allocation decreases in a nonBC approach, whereas, in the proposed scheme, we see a gradual drop in auction fairness. At $0.5$ collusion scenario (50% dishonest entities), the fairness indicator is $0.68$ in our scheme, which means that still 68% of the traded transactions are fair, compared to $0.41$ in the nonBC approach. The reason is trivial: once the prices are stored, they cannot be altered, and the sharded PoS elects a miner based on a high value of R, and, thus, there are less chances for the miner to be biased. This leads to fair block proposals in most auction events.
 Energy consumption by EVs Lastly, we simulate the energy consumption by EVs against consensus approaches like ProofofWork (PoW), and PoS. Figure 8 depicts the results. As shared PoS has a low transactional finality time, thus less time is required to form the collective voting decision to add the next block. However, in both PoW and PoS, the network requires multiple message confirmations to finalize the block. In PoW, we consider t miners in the ecosystem; then, the average expected time $PoW\left[{T}_{EAvg}\right]$ to finalize a block is presented as follows [48]:$$\begin{array}{c}\hfill PoW\left[{T}_{EAvg}\right]=\frac{TD}{{\sum}_{i=1}^{t}{P}_{i}}.\end{array}$$$$\begin{array}{c}\hfill PoS\left[{T}_{EAvg}\right]=\frac{TD}{{\sum}_{i=1}^{t}stak{e}_{i}\times lif{e}_{i}}.\end{array}$$$$\begin{array}{c}\hfill {T}_{del}={M}_{c}\times {T}_{EAvg}\end{array}$$
6.5. Data Storage Cost Computation
7. Conclusions and Future Scope
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Author  Year  Objective  Blockchain Platform  Pros  Cons 

Ashfaq et al. [32]  2020  Presented a consortium blockchainbased secure energy trading for EVs  Yes  Optimized charging and execution cost, resource efficient  Ignorance of price optimality and data storage cost 
Kumari et al. [33]  2020  Designed a smart contractbased energy trading scheme for smart grid  Yes  Improved storage cost, low latency  No consideration of optimal pricing 
Chen et al. [34]  2021  Discussed a blockchainbased trusted energy trading by adopting an optimization scheme  Yes  Enhanced scalability and computation time  Need to focus on security against cyber, data spoofing, and DoS attacks 
Chung et al. [14]  2021  Designed a energy trading and demand response framework for EVs using smart grid  No  Reduced charging cost, optimized revenue  Should focus on security and privacy issues 
Bhattacharya et al. [12]  2021  Presented a blockchainbased EVs energy trading platform for vehicletoanything system  Yes  Improved optimized, communication, and computation costs  Need to provide security against malicious attacks 
Chen et al. [36]  2022  Investigated a robust blockchainbased dispatch framework  Yes  Highly robust  Security issues against malicious attack, no discussion on optimality 
Chen et al. [35]  2022  Proposed a blockchainbased energy trading framework for an optimal solution  Yes  Optimum solution, less complex  Optimal payoff and computation time is ignored 
Thim Kim et al. [10]  2022  Discussed an energy trading incentive mechanism between EVs and mobile CSs  No  Enhanced computational efficiency  Need to focus on optimality and computation time 
The proposed system  2022  Proposed a blockchain and double auctionbased trustful EV energy trading scheme for optimum pricing  Yes  Highly secure, efficient, and optimized payoff   
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Kakkar, R.; Gupta, R.; Agrawal, S.; Bhattacharya, P.; Tanwar, S.; Raboaca, M.S.; Alqahtani, F.; Tolba, A. Blockchain and Double AuctionBased Trustful EVs Energy Trading Scheme for Optimum Pricing. Mathematics 2022, 10, 2748. https://doi.org/10.3390/math10152748
Kakkar R, Gupta R, Agrawal S, Bhattacharya P, Tanwar S, Raboaca MS, Alqahtani F, Tolba A. Blockchain and Double AuctionBased Trustful EVs Energy Trading Scheme for Optimum Pricing. Mathematics. 2022; 10(15):2748. https://doi.org/10.3390/math10152748
Chicago/Turabian StyleKakkar, Riya, Rajesh Gupta, Smita Agrawal, Pronaya Bhattacharya, Sudeep Tanwar, Maria Simona Raboaca, Fayez Alqahtani, and Amr Tolba. 2022. "Blockchain and Double AuctionBased Trustful EVs Energy Trading Scheme for Optimum Pricing" Mathematics 10, no. 15: 2748. https://doi.org/10.3390/math10152748