Co-optimisation and Settlement of Power-Gas Coupled System in Day-ahead Market under Multiple Uncertainties

TL;DR

This paper presents a data-driven stochastic co-optimisation model for integrated electricity and gas systems in day-ahead markets, addressing uncertainties from renewables and loads, and introduces a novel valuation method for flexibility contributions.

Contribution

It develops a novel integrated stochastic co-optimisation model for power-gas systems and proposes a new expected locational marginal value for fair settlement under uncertainty.

Findings

The model improves efficiency and security in power-gas systems.

The solution method is efficient and scalable with parallel and decentralised computation.

Expected locational marginal value fairly credits PtGs' flexibility contributions.

Abstract

The interdependency of power systems and natural gas systems is being reinforced by the emerging power-to-gas facilities (PtGs), and the existing gas-fired generators. To jointly improve the efficiency and security under diverse uncertainties from renewable energy resources and load demands, it is essential to co-optimise these two energy systems for day-ahead market clearance. In this paper, a data-driven integrated electricity-gas system stochastic co-optimisation model is proposed. The model is accurately approximated by sequential mixed integer second-order cone programming, which can then be solved in parallel and decentralised manners by leveraging generalised Benders decomposition. Since the price formation and settlement issues have rarely been investigated for integrated electricity-gas systems in an uncertainty setting, a novel concept of expected locational marginal value is proposed to credit the flexibility of PtGs that helps hedging uncertainties. By comparing with a deterministic model and a distributionally robust model, the advantage of the proposed stochastic model and the efficiency of the proposed solution method are validated. Detailed results of pricing and settlement for PtGs are presented, showing that the expected locational marginal value can fairly credit the contribution of PtGs and reflect the system deficiency of capturing uncertainties.