A distributed calculation method for robust day-ahead scheduling of integrated electricity-gas systems. (March 2022)

• Record Type: Journal Article
• Authors: Zhang, Gang; Zhang, Feng; Meng, Ke; Dong, Zhaoyang
• Is Part Of: International journal of electrical power & energy systems. Volume 136(2022)
• Publication Date: 2022-03
• Subjects: Distributed calculation -- Day-ahead scheduling -- Multi-energy system -- Robust optimization; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2021.107636
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A distributed method for DAS of IEGS is proposed. The two-stage mixed-integer nonlinear DAS model can be effectively solved. Limited information is shared between electricity and gas system operators. A high-quality DAS solution can be obtained. The security of IEGS against uncertainties can be strictly guaranteed. Abstract: The urgency to address the uncertainty in the day-ahead scheduling (DAS) of integrated electricity and natural gas systems (IEGSs) has been highlighted, and the robust optimization has been proven to be an effective method. However, the gas system and electricity system are generally operated by different utilities in practice, i.e., gas system operator (GSO) and electricity system operator (ESO), which poses a considerable challenge to solve this two-stage mixed-integer nonlinear DAS model due to the limit on information sharing. In this paper, a novel distributed calculation method is proposed to solve the robust DAS model of IEGSs in a decentralized way. First, a mixed-integer master problem and a non-convex max–min subproblem can be obtained from the original DAS model by employing the column and constraint generation (CCG) method. Afterwards, based on the limited information exchange between ESO and GSO, an innovative decomposition method is proposed for the master problem to obtain the optimal DAS solution, and then a novel inner CCG algorithm is presented for the subproblem to guarantee the security of IEGSs. By doing so, the robust DAS of IEGS is decomposed into several mixed-integer linear programming (MILP) problems, and GSO and ESO can coordinately solve these MILPs with limited information sharing. Last, numerical tests on two IEGSs verify the superiority of the proposed method in hedging against the wind uncertainty and achieving the solution optimality.