Demcrp-et: decentralized multi-criteria ranked prosumers energy trading using distributed ledger technology

Due to global warming and climate change, power generation from renewable resources, such as solar, wind, fuel cells, and others, is becoming increasingly important. The smart grid’s incorporation of renewable energy resources makes Peer-to-Peer (P2P) energy trading through the local energy market possible. Due to its distributed nature, Peer-to-Peer (P2P) energy trading faces many challenges regarding security and privacy in energy trading. Most existing works do not consider data privacy among participants. A P2P energy trading model with Decentralized Multi-Criteria Ranked Prosumers called DeMCRP is introduced to address these privacy and security challenges in smart grid energy trading. The proposed DeMCRP trading model uses Corda-distributed ledgers, consensus, and smart contracts for energy trading between energy buyers and sellers, also termed prosumers. In the DeMCRP model, the mapping procedure between prosumers is done based on priority values for such prosumers determined using a multi-criteria decision-making method, namely the Preference Ranking for Organization Method for Enrichment Evaluation II (PROMETHEE II). Furthermore, an energy-trading smart contract performs energy trading between buyers and sellers. The transactions’ validation, verification, and uniqueness process is acheived through consensus of the distributed ledger by notaries. The Distributed Service Operator (DSO) selects 51% of sellers as notaries according to the rank of sellers. The rank of sellers is determined using a multi-criteria decision-making method called PROMETHEE II by considering various parameters such as energy surplus, trust factor, and the number of times the seller is selected as notary. Moreover, the performance analysis of the proposed scheme has been carried out in terms of network deployment time and memory utilization. The proposed DeMCRP technique works in polynomial time if the number of participants increase. Furthermore, security analysis has been carried out for various security features like data sharing privacy, users’ privacy, non-repudiation, integrity, and preventing double-spending. The proposed method protects the privacy of participants’ data and stops double spending, which is not taken into account by many existing methods.