Consensus-based approach to peer-to-peer electricity markets with product differentiation

TL;DR

This paper proposes a decentralized peer-to-peer electricity market model with product differentiation, using a Multi-Bilateral Economic Dispatch framework and a Relaxed Consensus+Innovation algorithm, demonstrating effective market outcomes aligned with consumer preferences.

Contribution

It introduces a novel decentralized market structure based on MBED with product differentiation and a RCI solution method, enabling efficient, preference-respecting peer-to-peer trading.

Findings

Decentralized market outcomes differ from centralized ones.

The RCI algorithm converges with negligible optimality gap.

Market structures can effectively incorporate consumer preferences.

Abstract

With the sustained deployment of distributed generation capacities and the more proactive role of consumers, power systems and their operation are drifting away from a conventional top-down hierarchical structure. Electricity market structures, however, have not yet embraced that evolution. Respecting the high-dimensional, distributed and dynamic nature of modern power systems would translate to designing peer-to-peer markets or, at least, to using such an underlying decentralized structure to enable a bottom-up approach to future electricity markets. A peer-to-peer market structure based on a Multi-Bilateral Economic Dispatch (MBED) formulation is introduced, allowing for multi-bilateral trading with product differentiation, for instance based on consumer preferences. A Relaxed Consensus+Innovation (RCI) approach is described to solve the MBED in fully decentralized manner. A set of realistic case studies and their analysis allow us showing that such peer-to-peer market structures can effectively yield market outcomes that are different from centralized market structures and optimal in terms of respecting consumers preferences while maximizing social welfare. Additionally, the RCI solving approach allows for a fully decentralized market clearing which converges with a negligible optimality gap, with a limited amount of information being shared.