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. A data-driven integrated electricity-gas system stochastic co-optimisation model is proposed in tis work. 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.
Highlights
1.1 MotivationPower-to-gas (PtG) is quite effective in storing large quantity of excess renewable electricity compared with conventional powerto-power energy storage technologies [1]
It can be proved that when Problem (5) is solved to optimality [Since Problem (5) is a mixed integer nonlinear programming (MINLP), solving it to optimality is defined as: fixing the binary variables as their optima, and re-solving the NLP problem to optimality to obtain the optimal multipliers.]: i) the electric power consumed by power-to-gas facilities (PtGs) v is non-zero if and only if ψv,t is non-positive; ii) ψv,t is negative if and only if the capacity of PtG is inadequate
The simulation cost for the distributionally robust model is highest, regardless of the lowest wind curtailment level. Another reason for the conservativeness is that the ambiguity set of DR-integrated electricity-gas system (IEGS) fails to model the correlation of random variables, and the extremal distribution contains many fast ramping events that are unlikely to occurs in reality
Summary
Power-to-gas (PtG) is quite effective in storing large quantity of excess renewable electricity compared with conventional powerto-power energy storage technologies [1]. The development of PtGs and the growth of GfUs tightly couple the electric power system with the natural gas system [6]. The liberalization of both the electricity market and the natural gas market [5, 7, 8], together with the interactive safety and reliability requirements of IEGS [6, 9, 10], are appealing for a security-constrained co-optimisation regime and corresponding settlement methods. The challenges of co-optimizing IEGS in day-ahead markets include: i) the uncertainties from both renewable generations and electricity/gas demands, i) the non-convexity of the natural gas flow model, and iii) the requirement of decentralised decision making. Another practical challenge is the pricing issue or the settlement of these two energy sectors. The traditional price formation mechanism in day-ahead markets must be systematically reevaluated and improved, because the original pricing regime may not be equitable and incentive enough for market participants who provide flexibilities and reserves
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