Incentive model for fair and resilient peer-to-peer energy trading based on the time value of money
Peer-to-peer (P2P) energy trading among residential prosumers requires incentive mechanisms that adapt to dynamic grid conditions and emergencies. We propose a novel Time Value of Money (TVM) based incentive model that adjusts rewards and penalties over time to promote early energy transactions, ensuring fairness and resilience. Under normal grid conditions, our TVM scheme achieves an average fairness index of 0.91 (10% higher than a conventional double-auction approach) and increases total energy shared by 12%. During a 24-hour resilience event, fairness increases by 0.94 (versus 0.85) and energy shared grows by 18%, while average per-prosumer incentives rise by 25%. Sensitivity analysis across community sizes (33-200 prosumers) demonstrates that the TVM model maintains fairness above 0.87 and scales energy sharing linearly with network size, even under 20% higher demand. These results confirm that our TVM mechanism consistently outperforms traditional methods in efficiency, equity, and emergency responsiveness. • Novel TVM-based incentive mechanism adapts rewards over time for P2P trading. • An Incentive/penalty scheme ensures fair energy sharing with a 10% advantage over a Double Auction. • TVM increases the energy shared by up to 18% during emergencies relative to the baseline. • Sensitivity tests show fairness > 0.87 and a linear relationship between energy sharing and the number of users (33 to 200).
Authors
- Naveen Chilamkurti (ORCID: https://orcid.org/0000-0002-5396-8897)
- Ankush Sharma (ORCID: https://orcid.org/0000-0003-2332-6313)
- Mayank Arora (ORCID: https://orcid.org/0000-0002-0509-0186)
- Gururaj Mirle Vishwanath
Institutions
- La Trobe University (AU)
- Indian Institute of Technology Kanpur (IN)
Publication Details
- Journal
- Utilities Policy
- Published
- 2025-12-02
- DOI
- https://doi.org/10.1016/j.jup.2025.102122
- Citations
- 1
- Primary Topic
- Smart Grid Energy Management
- Type
- article
- Field-Weighted Citation Impact
- 0.73