  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.
