Pricing and Energy Trading in Peer-to-peer Zero Marginal-cost Microgrids

TL;DR

This paper explores peer-to-peer energy trading in microgrids with renewable energy, analyzing optimal pricing and proposing a novel negotiation algorithm that achieves near-optimal welfare while preserving privacy.

Contribution

It introduces a centralized welfare-maximizing dispatch analysis and a new P2P trading algorithm that converges to optimal solutions with privacy considerations.

Findings

Optimal pricing alone does not induce ideal agent actions.

The proposed P2P algorithm converges to the centralized solution.

Simulations show near-optimal welfare within 0.1% after several iterations.

Abstract

Efforts to utilize 100% renewable energy in community microgrids require new approaches to energy markets and transactions to efficiently address periods of scarce energy supply. In this paper we contribute to the promising approach of peer-to-peer (P2P) energy trading in two main ways: analysis of a centralized, welfare-maximizing economic dispatch that characterizes optimal price and allocations, and a novel P2P system for negotiating energy trades that yields physically feasible and at least weakly Pareto-optimal outcomes. Our main results are 1) that optimal pricing is insufficient to induce agents with batteries to take optimal actions, 2) a novel P2P algorithm to address this while keeping private information, 3) a formal proof that this algorithm converges to the centralized solution in the case of two agents negotiating for a single period, and 4)numerical simulations of the P2P algorithm performance with up to 10 agents and 24 periods that show it converges on average to total welfare within 0.1% of the social optimum in on the order of 10s to 100s of iterations, increasing with the number of agents, time periods, and total storage capacity.