A Novel Interactive Two-stage Joint Retail Electricity Market for Multiple Microgrids

TL;DR

This paper proposes a novel two-stage joint retail electricity market for microgrids, integrating stochastic energy trading and peer-to-peer flexibility transactions to improve market efficiency amid uncertainties.

Contribution

It introduces an interactive two-stage market mechanism with a distributed algorithm and an improved ADMM method for efficient operation under uncertainty.

Findings

Market mechanism improves efficiency under uncertainties

Distributed algorithm converges to equilibrium

Enhanced ADMM solves complex dispatch problems efficiently

Abstract

To accommodate the advent of microgrids (MG) managing distributed energy resources (DER) in distribution systems, an interactive two-stage joint retail electricity market mechanism is proposed to provide an effective platform for these prosumers to proactively join in retail transactions. Day-ahead stochastic energy trading between the distribution system operator (DSO) and MGs is conducted in the first stage of a centralized retail market, where a chance-constrained uncertainty distribution locational marginal price (CC-UDLMP) containing the cost of uncertainty precautions is used to settle transactions. In the second stage, a novel intra-day peer-to-peer-based (P2P) flexibility transaction pattern is implemented between MGs in local flexibility markets under the regulation of DSO to eliminate power imbalances caused by rolling-based estimates whilst considering systematic operations. A fully distributed iterative algorithm is presented to find the equilibrium solution of this two-stage sequential game framework. Moreover, in order to enhance the versatility of this algorithm, an improved Lp-box alternating direction methods of multipliers (ADMM) algorithm is used to efficiently resolve the first-stage stochastic economic dispatch problem with a mixed-integer second-order cone structure. It is verified that the proposed market mechanism can effectively improve the overall market efficiency under uncertainties.