A Flow-based Distributed Trading Mechanism in Regional Electricity Market with Energy Hub

TL;DR

This paper introduces a flow-based distributed trading mechanism for regional electricity markets incorporating Energy Hubs, enabling decentralized optimization, market integration, and improved energy efficiency through an ADMM-based algorithm.

Contribution

It proposes a novel two-level distributed trading framework with an ADMM algorithm for integrating Energy Hubs into regional electricity markets.

Findings

Effective decomposition of power flow in case studies

Increased energy efficiency demonstrated

Distributed algorithm achieves global optimality

Abstract

The concept of Energy Hub (EH) has been emerged to accommodate renewable energy sources in a multi-energy system to deploy the synergies between electricity and other energy sources. However, the market mechanisms for the integration of the EHs into the energy markets are not sufficiently elaborated. This paper proposes a flow-based two-level distributed trading mechanism in the regional electricity market with EH. At the lower level, the regional system operator coordinates the regional grids transactions in two markets, the local energy market with EH and the wholesale market of the upstream grid. Every nodal agent as an independent stakeholder leverages price discrepancy to cross arbitrage from different markets. At the upper level, the EH is a third player intending to maximize profit from trading in the regional electricity market and gas market. The regional electricity market clearing problem is formulated as a mathematical program with equilibrium constraints, for which we develop an ADMM-based distributed algorithm to obtain the equilibrium solution. The DC power flow is decomposed into optimization problems for the regional system operator and agents at different nodes, which can be solved in a distributed manner to achieve global optimality without violating the privacy of players. Case studies based on a realistic regional grid verify the effectiveness of the proposed algorithm and show that the mechanism is effective in decomposing power flow and increasing energy efficiency.