A Novel Power Market Mechanism Based on Blockchain for Electric Vehicle Charging Stations
Abstract
:1. Introduction
- (1)
- This paper proposes a novel double auction mechanism in the day-ahead market (DAM), where EV owners fully consider both the bidding price and quantity of energy involved. This mechanism greatly promotes the energy exchange between buyers and sellers.
- (2)
- The charging system operator (CSO) satisfies the EVs’ demand in DAM and the real-time market (RTM). In DAM, CSO will sign a smart contract with those who unsuccessfully match in the double auction, optimizing revenue, social benefits, and participant satisfaction. In RTM, CSO will check the trading status and record cheated behavior in blockchain.
- (3)
- Blockchain-based energy trading is proposed to ensure fairness and validity in trading and prevent swindling act.
2. Framework of Trading Mechanism
2.1. Charging Token Based on Blockchain
2.2. Charging Token Based on Blockchain
2.3. Trading Process
- (a)
- In the double auction mechanism, EVs that are willing to take part in energy trading hand in an entrance fee and submit their bidding information, including trading role (buyer or seller), bidding quantity and price, and their trading time. It is worth noting that, when multiple participants offer the same bidding price, the credit degree is used as a secondary indicator to analyze the ranking sequence of participants in the auction. After clearing results, the EVs that fail to match, will go to the next step.
- (b)
- In the smart match mechanism between CSO and EVs, CSO dispatches the EVs that are willing to trade but fail to match. In this step, the objective is to minimize the operation cost of CSO and maximize the satisfaction of EVs and social welfare. The EVs that fail to match will go to RTM. CSO will submit all the trading contract made in DAM to blockchain before 6 h in the beginning of RTM.
- (c)
- In RTM, in every hour, CSO will check the status of trading based on the contract made in DAM and record the trading result in blockchain. CSO is responsible for satisfying the demand of EVs in the charging station. If there is a contract violation, the compensation and punishment mechanism will be conducted automatically. It should be pointed out that violators not only need to submit the penalty, but also their credit degree will be reduced and uploaded to the blockchain, which is very unfavorable in the subsequent transactions. If EV users trade successfully in RTM, the entrance fee will be returned to them. After trading in RTM, the trading record will be updated in blockchain. EV users can get information and cash with CSO.
3. Optimal Bidding Strategy in Double Auction Mechanism
3.1. Optimal Bidding Strategy for Buyer
3.2. Optimal Bidding Strategy for Seller
4. Smart Match Mechanism
4.1. Objective Function
4.2. Constraints
- (a)
- Physical constraints:
- (b)
- Trading constraints:
5. Results and Discussion
5.1. Double Auction Mechanism
5.2. Smart Match Mechanism
5.3. Analysis of Global Indicators in Cases 1 and 2
5.4. Sensitivity Analysis
5.5. Application of Blockchain
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Nomenclature
Variables and Functions: | |
Rank expectation of buyer i | |
Rank expectation of seller j | |
Evaluation price of buyer i and seller j | |
Bidding price of buyer i and seller j | |
Bidding quantity of buyer i and seller j | |
Probability function | |
Probability function for bidding of buyer and seller | |
Optimal bidding price of buyer i and seller j | |
Mathematical expectation | |
Bidding strategy function | |
Original trading status of buyer i and seller j | |
Original trading quantity of buyer i and seller j | |
Bidding price of buyer i and seller j in double auction | |
Trading status of buyer i and seller j in double auction | |
Objective function of charging system operator | |
Operational cost | |
Satisfaction of EV users | |
Social welfare | |
Price that charging system operator provides to buyer i and seller j | |
Satisfaction of buyer i and seller j | |
Trading status of charging system operator with buyer i /seller j | |
Net power of charging station | |
Trading status of buyer i and seller j in smart match mechanism | |
Quantity shifted of buyer i and seller j in smart match mechanism | |
Auxiliary variable | |
Bidding strategy function | |
Profit of buyer i and seller j | |
Cost of buyer i without trading and with trading | |
Revenue of seller j without trading and with trading | |
Constants and Sets: | |
Breakpoint in double auction mechanism | |
The value of the breakpoint on the x axis | |
Maximum bidding price and evaluation price of seller | |
Number of buyer and seller | |
Number of time slot | |
Price offered to buyer i and seller j in real-time market | |
Weight value in objective function | |
Maximum quantity shifted in smart match mechanism | |
Market shares of charging station | |
Forecasted demand in distribution system | |
Big positive constant |
Appendix A
Appendix B
Appendix C
Buyers | Sellers | |||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6.6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4.4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1.1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1.1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1.1 | 0 | 0 | 0 | 0 |
10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2.2 | 0 | 0 | 1.1 | 0 | 0 | 0 | 0 | 0 |
11 | 0 | 0 | 0 | 0 | 1.1 | 0 | 1.1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
12 | 0 | 3.3 | 1.1 | 1.1 | 0 | 0 | 1.1 | 1.1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2.2 | 0 | 0 |
13 | 0 | 0 | 0 | 6.6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3.3 | 0 | 0 | 0 | 0 |
14 | 0 | 2.2 | 0 | 5.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1.1 | 0 | 0 | 0 | 0 | 0 |
15 | 3.3 | 2.2 | 0 | 0 | 0 | 0 | 0 | 1.1 | 0 | 0 | 0 | 0 | 0 | 0 | 1.1 | 0 | 0 | 0 | 0 | 1.1 |
16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1.1 | 3.3 | 2.2 | 0 | 0 | 0 | 1.1 | 0 | 0 | 0 | 1.1 |
17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2.2 | 0 | 1.1 | 0 | 6.6 | 0 | 2.2 | 0 | 0 | 3.3 | 0 | 0 | 0 |
18 | 0 | 1.1 | 0 | 0 | 0 | 2.2 | 0 | 0 | 0 | 0 | 0 | 1.1 | 0 | 1.1 | 2.2 | 0 | 0 | 0 | 0 | 2.2 |
19 | 1.1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6.6 | 4.4 | 1.1 | 4.4 | 0 | 0 | 0 |
20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1.1 | 6.6 | 0 | 0 | 0 | 3.3 | 2.2 | 0 | 0 | 6.6 | 0 | 2.2 | 1.1 |
21 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4.4 | 0 | 0 | 0 | 2.2 | 0 | 0 | 0 | 2.2 | 0 | 0 | 6.6 |
22 | 0 | 0 | 0 | 0 | 0 | 1.1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6.6 |
23 | 0 | 0 | 0 | 0 | 0 | 0 | 2.2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4.4 |
24 | 0 | 0 | 0 | 0 | 0 | 5.5 | 1.1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
References
- Ferrero, E.; Alessandrini, S.; Balanzino, A. Impact of the electric vehicles on the air pollution from a highway. Appl. Energy 2016, 169, 450–459. [Google Scholar]
- Guidelines for the Development of Electric Vehicles Charging Infrastructure. Last Updated: 23 October 2019. Available online: https://www.iea.org/policies/2695-guidelines-for-the-development-of-electric-vehicles-charging-infrastructure (accessed on 23 December 2020).
- Ma, Z.; Callaway, D.; Hiskens, I. Decentralized charging control for large populations of plug-in electric vehicles: Application of the Nash certainty equivalence principle. In Proceedings of the 2010 IEEE International Conference on Control Applications, Yokohama, Japan, 8–10 September 2010; pp. 191–195. [Google Scholar]
- Du, J.; Ouyang, D. Progress of Chinese electric vehicles industrialization in 2015: A review. Appl. Energy 2017, 188, 529–546. [Google Scholar]
- Palmer, K.; Tate, J.E.; Wadud, Z.; Nellthorp, J. Total cost of ownership and market share for hybrid and electric vehicles in the UK, US and Japan. Appl. Energy 2018, 209, 108–119. [Google Scholar]
- De Melo, H.N.; Trovão, J.P.F.; Pereirinha, P.G.; Jorge, H.M.; Antunes, C.H. A controllable bidirectional battery charger for electric vehicles with vehicle-to-grid capability. IEEE Trans. Veh. Technol. 2018, 67, 114–123. [Google Scholar]
- Wu, Y.; Ravey, A.; Chrenko, D.; Miraoui, A. Demand side energy management of EV charging stations by approximate dynamic programming. Energy Convers. Manag. 2019, 196, 878–890. [Google Scholar]
- Tang, W.; Bi, S.; Zhang, Y.J.; Yuan, X. Joint routing and charging scheduling optimizations for smart-grid enabled electric vehicle networks. In Proceedings of the IEEE 85th Vehicular Technology Conference (VTC Spring), Sydney, Australia, 4–7 July 2017; pp. 1–5. [Google Scholar]
- Junming, R.; Wang, H.; Wei, Y.; Liu, Y.; Tsang, K.F.; Lai, L.L.; Chung, L.C. A novel genetic algorithm-based emergent electric vehicle charging scheduling scheme. In Proceedings of the IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society, Lisbon, Portugal, 14–17 October 2019; pp. 4289–4292. [Google Scholar]
- Mou, X.; Zhao, R.; Gladwin, D.T. Vehicle to vehicle charging (V2V) bases on wireless power transfer technology. In Proceedings of the IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, USA, 21–23 October 2018; pp. 4862–4867. [Google Scholar]
- Lai, C.S.; Locatelli, G.; Pimm, A.; Tao, Y.; Li, X.; Lai, L.L. A financial model for lithium-ion storage in a photovoltaic and biogas energy system. Appl. Energy 2019, 251, 1–16. [Google Scholar]
- Zhong, W.; Xie, K.; Liu, Y.; Yang, C.; Xie, S. Topology-aware vehicle-to-grid energy trading for active distribution systems. IEEE. Trans. Smart. Grid. 2019, 10, 2137–2147. [Google Scholar]
- Turker, H.; Bacha, S. Optimal minimization of plug-in electric vehicle charging cost with vehicle-to-home and vehicle-to-grid concepts. IEEE Trans. Veh. Technol. 2018, 67, 10281–10292. [Google Scholar]
- Shin, H.; Baldick, R. Plug-in electric vehicle to home (V2H) operation under a grid outage. IEEE. Trans. Smart. Grid. 2017, 8, 2032–2041. [Google Scholar]
- Yu, Y.; Chen, S.; Luo, Z. Residential microgrids energy trading with plug-in electric vehicle battery via stochastic games. IEEE. Access 2019, 7, 174507–174516. [Google Scholar]
- Liu, H.; Qi, J.; Wang, J.; Li, P.; Li, C.; Wei, H. EV dispatch control for supplementary frequency regulation considering the expectation of EV owners. IEEE Trans. Smart. Grid. 2018, 9, 3763–3772. [Google Scholar]
- Pearre, N.S.; Swan, L.G. Electric vehicle charging to support renewable energy integration in a capacity constrained electricity grid. Energy Convers. Manag. 2016, 109, 130–139. [Google Scholar]
- Shafie-khah, M.; Heydarian-Forushani, E.; Golshan, M.E.H. Optimal trading of plug-in electric vehicle aggregation agents in a market environment for sustainability. Appl. Energy 2016, 162, 601–612. [Google Scholar]
- Feng, K.; Zhong, Y.; Hong, B.; Wu, X.; Lai, C.S.; Bai, C. The impact of plug-in electric vehicles on distribution network. In Proceedings of the 2020 IEEE Smart Cities Conference, Online, 28 September–1 October 2020; pp. 1–7. [Google Scholar]
- Wang, M.; Ismail, M.; Zhang, R.; Shen, X.; Serpedin, E.; Qaraqe, K. Spatio-temporal coordinated V2V energy swapping strategy for mobile PEVs. IEEE Trans. Smart. Grid. 2018, 9, 1566–1579. [Google Scholar]
- Hu, Z.; Zhan, K.; Zhang, H.; Song, Y. Pricing mechanisms design for guiding electric vehicle charging to fill load valley. Appl. Energy 2016, 178, 155–163. [Google Scholar]
- Moon, S.K.; Kim, J.O. Balanced charging strategies for electric vehicles on power systems. Appl. Energy 2017, 189, 44–54. [Google Scholar]
- Huang, J.; Wang, D.; Wu, R.; Lai, C.S.; Xie, C.; Zhao, Z.; Lai, L.L. Optimal operation of smart buildings with stochastic connection of electric vehicles. In Proceedings of the 2020 IEEE International Smart Cities Conference (ISC2), Piscataway, NJ, USA, 28 September–1 October 2020; pp. 1–7. [Google Scholar]
- Yan, D.; Li, T.; Ma, C.; Lai, L.L.; Tsang, K.F. Cost effective energy management of home energy system with photovoltaic-battery and electric vehicle. In Proceedings of the IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, Singapore, 18–21 October 2020; pp. 3611–3616. [Google Scholar]
- Lai, C.S.; McCulloch, M.D. Sizing of stand-alone solar PV and storage system with anaerobic digestion biogas power plants. IEEE Trans. Ind. Electron. 2017, 64, 2112–2121. [Google Scholar]
- Lai, C.S.; Li, X.; Locatelli, G.; Lai, L.L. Cost benefit analysis and data analytics for renewable energy and electrical energy storage. In Proceedings of the 11th IET International Conference on Advances in Power System Control, Operation and Management (APSCOM 2018), Hong Kong, China, 11–15 November 2018; pp. 1–3. [Google Scholar]
- Kang, J.; Yu, R.; Huang, X. Enabling localized peer-to-peer electricity trading among plug-in hybrid electric vehicles using consortium blockchains. IEEE Trans. Ind. Inform. 2017, 13, 3154–3164. [Google Scholar]
- Eid, C.; Codani, P.; Perez, Y.; Reneses, J.; Hakvoort, R. Managing electric flexibility from distributed energy resources: A review of incentives for market design. Renew. Sust. Energy Rev. 2016, 64, 237–247. [Google Scholar]
- Lam, L.K.; Ko, K.T.; Tung, H.Y.; Tung, H.C.; Sham, N.Y.; Tsang, K.F.; Lai, L.L. Advanced metering infrastructure for electric vehicle charging. Smart Grid Renew. Energy 2011, 2, 312–323. [Google Scholar]
- Shum, C.; Lau, W.H.; Lam, K.L.; He, Y.; Chung, H.; Tse, N.C.F.; Tsang, K.F.; Lai, L.L. The development of a smart grid co-simulation platform and case study on Vehicle-to-Grid voltage support application. In Proceedings of the IEEE Smart Gird Comm 2013 SymposiumSmart Grid Standards, Co-Simulation, Test-Beds and Field Trails, Vancouver, BC, Canada, 21–24 October 2013; pp. 594–599. [Google Scholar]
- Lai, L.L. Power System Restructuring and Regulation Trading, Performance and Information Technology, 1st ed; John Wiley & Sons: Hoboken, NJ, USA, 2001; pp. 110–151. [Google Scholar]
- Wang, Y.; Huang, Z.; Li, Z.; Wu, X.; Lai, L.L.; Xu, F. Transactive energy trading in reconfigurable multi-carrier energy systems. J. Mod. Power Syst. Clean Energy 2020, 8, 67–76. [Google Scholar] [CrossRef]
- Li, Z.; Lai, C.S.; Xu, X.; Zhao, Z.; Lai, L.L. Electricity trading based on distribution locational marginal price. Int. J. Electr. Power Energy Syst. 2021, 124, 1–13. [Google Scholar] [CrossRef]
- Lai, C.S.; Lai, L.L.; Lai, Q.H. A Narrowband Internet of Thing-Based Temperature Prediction for Valve-Regulated Lead Acid Battery. In Smart Grids and Big Data Analytics for Smart Cities, 1st ed.; Springer: Berlin, Germany, 2020; pp. 345–363. [Google Scholar]
- Wang, H.; Liu, Y.; Wei, Y.; He, Y.; Tsang, K.F.; Lai, L.L.; Lai, C.S. LP-INDEX: Explore the best practice of LPWAN technologies in smart city. In Proceedings of the 2020 IEEE International Smart Cities Conference (ISC2), Piscataway, NJ, USA, 28 September–1 October 2020; pp. 1–5. [Google Scholar]
- Adil, M.; Ali, J.; Ta, Q.T.H.; Attique, M.; Chung, T.-S. A reliable sensor network infrastructure for electric vehicles to enable dynamic wireless charging based on machine learning technique. IEEE Access 2020, 8, 187933–187947. [Google Scholar] [CrossRef]
- Lai, C.S.; Lai, L.L.; Lai, Q.H. Blockchain Applications in Microgrid Clusters. In Smart Grids and Big Data Analytics for Smart Cities, 1st ed.; Springer: Berlin, Germany, 2020; pp. 265–305. [Google Scholar]
- Li, Z.; Kang, J.; Yu, R.; Ye, D.; Deng, Q.; Zhang, Y. Consortium blockchain for secure energy trading in industrial internet of things. IEEE Trans. Ind. Inform. 2018, 14, 3690–3700. [Google Scholar] [CrossRef] [Green Version]
- Li, Y.; Hu, B. An iterative two-Layer optimization charging and discharging trading scheme for electric vehicle using consortium blockchain. IEEE Trans. Smart. Grid. 2020, 11, 2627–2637. [Google Scholar] [CrossRef]
- Devine, M.T.; Cuffe, P. Blockchain electricity trading under demurrage. IEEE Trans. Smart. Grid. 2019, 10, 2323–2325. [Google Scholar] [CrossRef]
- Dang, C.; Zhang, J.; Kwong, C.; Li, L. Demand side load management for big industrial energy users under blockchain-based peer-to-peer electricity market. IEEE Trans. Smart. Grid. 2019, 10, 6426–6435. [Google Scholar] [CrossRef]
- Jin, R.; Zhang, X.; Wang, Z.; Sun, W.; Yang, X.; Shi, Z. Blockchain-enabled charging right trading among EV charging stations. Energies 2019, 12, 3922. [Google Scholar] [CrossRef] [Green Version]
- Wang, S.; Taha, A.F.; Wang, J.; Kvaternik, K.; Hahn, A. Energy crowdsourcing and peer-to-peer energy trading in blockchain-enabled smart grids. IEEE Trans. Syst. Man. Cybern. 2019, 49, 1612–1623. [Google Scholar] [CrossRef] [Green Version]
- Zhao, Z.; Guo, J.; Luo, X.; Xue, J.; Lai, C.S.; Xu, Z.; Lai, L.L. Energy transaction for multi-microgrids and internal microgrid based on blockchain. IEEE Access 2020, 8, 144362–144372. [Google Scholar] [CrossRef]
- Liu, H.; Zhang, Y.; Zheng, S.; Li, Y. Electric vehicle power trading mechanism based on blockchain and smart contract in V2G network. IEEE Access 2019, 7, 160546–160558. [Google Scholar] [CrossRef]
- Andoni, M.; Robu, V.; Flynn, D.; Abram, S.; Geach, D.; Jenkins, D.; McCallum, P.; Peacock, A. Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renew. Sust. Energy Rev. 2019, 100, 143–174. [Google Scholar] [CrossRef]
- Gai, K.; Wu, Y.; Zhu, L.; Qiu, M.; Shen, M. Privacy-preserving energy trading using consortium blockchain in smart grid. IEEE Trans. Ind. Inform. 2019, 15, 3548–3558. [Google Scholar] [CrossRef]
- Ping, J.; Yan, Z.; Chen, S.; Yao, L.; Qian, M. Coordinating EV charging via blockchain. J. Mod. Power Syst. Clean Energy 2020, 8, 573–581. [Google Scholar]
- Huang, Z.; Chen, D.; Lai, C.S.; Zhao, Z.; Lai, L.L.; Wang, M. A distributed transaction mechanism for electricity market with electric vehicles and blockchain. In Proceedings of the 2020 8th International Conference on Power Electronics Systems and Application, Hong Kong, China, 7–10 December 2020. [Google Scholar]
- Mei, S.; Liu, F.; Wei, W. Game-Theoretic Engineering Basis and its Application in Power System; Science Press: Beijing, China, 2020; pp. 87–89. (In Chinese) [Google Scholar]
- Wang, Y.; Saad, W.; Han, Z.; Poor, H.V.; Basar, T. A game-theoretic approach to energy trading in the smart grid. IEEE Trans. Smart. Grid. 2014, 5, 1439–1450. [Google Scholar] [CrossRef] [Green Version]
- Popescu, C.R.G.; Popescu, G.N. An exploratory study based on a questionnaire concerning green and sustainable finance, corporate social responsibility, and performance: Evidence from the Romanian business environment. J. Risk Financ. Manag. 2019, 12, 1–79. [Google Scholar]
- Lai, C.S.; Jia, Y.; Lai, L.L.; Xu, Z.; McCulloch, M.D.; Wong, K.P. A comprehensive review on large-scale photovoltaic system with applications of electrical energy storage. Renew. Sust. Energy Rev. 2017, 78, 439–451. [Google Scholar] [CrossRef]
- Jia, Y.; Gao, Y.; Xu, Z.; Wong, K.P.; Lai, L.L.; Xue, Y.; Dong, Z.; Hill, D.J. Powering China’s sustainable development with renewable energies: Current status and future trend. Electr. Power Syst. Res. 2015, 43, 1193–1204. [Google Scholar] [CrossRef]
- Muratori, M. Impact of uncoordinated plug-in electric vehicle charging on residential power demand. Nat. Energy 2018, 3, 193–201. [Google Scholar] [CrossRef]
- Fathabadi, H. Novel grid-connected solar/wind powered electric vehicle charging station with vehicle-to-grid technology. Energy 2017, 132, 1–11. [Google Scholar] [CrossRef]
- Lai, C.S.; Jia, Y.; Lai, L.L. Smart mobility under the smart city environment. In Proceedings of the 8th International Conference on Power Electronics Systems and Applications, Hong Kong, China, 7–10 December 2020. [Google Scholar]
0.25 | 0.15 | 1000 | 100 | 50 | 0.05 | 1000 |
Number of Transactions | Total Profit ($) | Total Profit Increase (%) | Mean of Profit ($) | |
---|---|---|---|---|
Buyer | 68 | 18.45 | 22.74 | 0.271 |
Seller | 51 | 13.99 | 28.75 | 0.274 |
Total | 119 | 32.44 | - | 0.273 |
Number of Transactions | Total Profit ($) | Total Profit Increase (%) | Mean of Profit ($) | |
---|---|---|---|---|
Buyer | 13 | 3.15 | 19.42 | 0.242 |
Seller | 11 | 3.33 | 34.28 | 0.303 |
Total | 24 | 6.48 | - | 0.270 |
Number of Transactions | Total Profit ($) | Total Profit Increase (%) | Mean of Profit ($) | |
---|---|---|---|---|
Buyer | 134 | 54.70 | 29.55 | 0.408 |
Seller | 90 | 56.42 | 68.12 | 0.627 |
Total | 224 | 111.12 | - | 0.496 |
Number of Transactions | Total Profit ($) | Total Profit Increase (%) | Mean of Profit ($) | |
---|---|---|---|---|
Buyer | 139 | 73.15 | 22.79 | 0.526 |
Seller | 100 | 70.42 | 53.54 | 0.704 |
Total | 239 | 143.57 | - | 0.601 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Huang, Z.; Li, Z.; Lai, C.S.; Zhao, Z.; Wu, X.; Li, X.; Tong, N.; Lai, L.L. A Novel Power Market Mechanism Based on Blockchain for Electric Vehicle Charging Stations. Electronics 2021, 10, 307. https://doi.org/10.3390/electronics10030307
Huang Z, Li Z, Lai CS, Zhao Z, Wu X, Li X, Tong N, Lai LL. A Novel Power Market Mechanism Based on Blockchain for Electric Vehicle Charging Stations. Electronics. 2021; 10(3):307. https://doi.org/10.3390/electronics10030307
Chicago/Turabian StyleHuang, Zhaoxiong, Zhenhao Li, Chun Sing Lai, Zhuoli Zhao, Xiaomei Wu, Xuecong Li, Ning Tong, and Loi Lei Lai. 2021. "A Novel Power Market Mechanism Based on Blockchain for Electric Vehicle Charging Stations" Electronics 10, no. 3: 307. https://doi.org/10.3390/electronics10030307
APA StyleHuang, Z., Li, Z., Lai, C. S., Zhao, Z., Wu, X., Li, X., Tong, N., & Lai, L. L. (2021). A Novel Power Market Mechanism Based on Blockchain for Electric Vehicle Charging Stations. Electronics, 10(3), 307. https://doi.org/10.3390/electronics10030307