Causality-based Cost Allocation for Peer-to-Peer Energy Trading in Distribution System
Hyun Joong Kim, Yong Hyun Song, Jip Kim
TL;DR
The paper tackles the misalignment between peer-to-peer (P2P) energy trading and distribution-grid constraints, proposing a causality-based network cost allocation that ties peer costs to their actual grid impact. It develops a grid-aware P2P trading model, derives cost allocations from power-flow derivatives via a Taylor expansion, and proves that this approach achieves the social optimum, outperforming universal (exogenous) cost allocation. A negotiation-based coordination flow with iterative cost updates demonstrates improved social welfare and grid reliability in a 33-node test system, under scenarios with loss costs only and with all network costs. The work provides a practical method for aligning P2P trades with grid conditions, potentially enabling scalable, grid-friendly P2P markets and informing DSOs about fair cost signals tied to physical impacts.
Abstract
While peer-to-peer energy trading has the potential to harness the capabilities of small-scale energy resources, a peer-matching process often overlooks power grid conditions, yielding increased losses, line congestion, and voltage problems. This imposes a great challenge on the distribution system operator (DSO), which can eventually limit peer-to-peer energy trading. To align the peer-matching process with the physical grid conditions, this paper proposes a cost causality-based network cost allocation method and the grid-aware peer-matching process. Building on the cost causality principle, the proposed model utilizes the network cost (loss, congestion, and voltage) as a signal to encourage peers to adjust their preferences ensuring that matches are more in line with grid conditions, leading to enhanced social welfare. Additionally, this paper presents mathematical proof showing the superiority of the causality-based cost allocation over existing methods.