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PETS: P2P Energy Trading Scheduling Scheme for Electric Vehicles in Smart Grid Systems
- Source :
- IEEE Transactions on Intelligent Transportation Systems. 23:14361-14374
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- Due to the lack of improper access control policies and decentralized access controllers, security and privacy-aware peer-to-peer (P2P) energy trading among electric vehicles (EVs) and the smart grid is challenging. Most of the solutions reported in the literature for P2P energy trading are based upon centralized controllers having various security flaws resulting in their limited applicabilities in real-world scenarios. To handle these issues, in this paper, we propose a P2P energy trading scheduling scheme called as P2P Energy Trading Scheduling (PETS) using blockchain technology. PETS is based on real-time energy consumption monitoring for balancing the energy gap between service providers (SPs), i.e., smart grids and service consumers, i.e., EVs. In PETS, the Stackelberg game theory-based 1-leader multiple-followers scheme is proposed to depict the interactions between EVs and the SP. The selection of the leader among all SPs is made using a second-price reverse auction. As per the announced energy price by the leader, EVs manage energy consumption by minimizing their energy bills. In PETS, on the leader's side, we propose the Genetic algorithm to maximize its profit. In contrast, on the followers' side, i.e., EVs, we use the Stackelberg Equilibrium to minimize their energy bills. Simulation results demonstrate that the proposed PETS scheme outperforms the existing state-of-the-art schemes using various performance evaluation metrics. Specifically, it reduces the peak-to-average ratio (PAR) by 12.5% of EVs' energy load in comparison to the existing state-of-the-art scheme.
Details
- ISSN :
- 15580016 and 15249050
- Volume :
- 23
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Intelligent Transportation Systems
- Accession number :
- edsair.doi...........fb8665a4561d73b0a7d4ddaa6241758e