A two-stage distributionally robust optimization model for P2G-CCHP microgrid considering uncertainty and carbon emission
Zhuoya Siqin,
DongXiao Niu,
Xuejie Wang,
Hao Zhen,
MingYu Li and
Jingbo Wang
Energy, 2022, vol. 260, issue C
Abstract:
The utilization of energy efficient combined cooling heat and power (CCHP) microgrid systems provide an opportunity for us to considering both the increase of economic benefits and environmental costs, simultaneously. The goal of this paper is to propose a P2G-CCHP microgrid system integration framework, connect power to gas (P2G) devices to CCHP microgrid, and provide a two-stage distributionally robust optimization (DRO) model to solve the problem of economic dispatch. DRO model uses the Wasserstein metric to extract the ambiguity set of the probability distribution information of the wind power and photovoltaic output uncertainty. And environmental cost is also considered in the optimal operation of the system. In addition, based on the strong duality theory, the DRO model is transformed into an easy-to-solve MILP problem. The simulation results show that: 1) The introduction of a P2G device can effectively improve the electrical-gas coupling of the P2G-CCHP microgrid system, and improve the stability and economy of the system operation. 2) Considering environmental cost, the pollutant emission of the P2G-CCHP microgrid system is significantly reduced, which ensures the low-carbon operation of system. 3) The DRO model can resist the interference of uncertain wind power and PV output with relatively low conservatism and computational complexity and has the characteristics of data-driven.
Keywords: P2G-CCHP microgrid System; Carbon emission; Distributionally robust optimization model; Wasserstein metric; Pollutant emission (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222016991
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:260:y:2022:i:c:s0360544222016991
DOI: 10.1016/j.energy.2022.124796
Access Statistics for this article
Energy is currently edited by Henrik Lund and Mark J. Kaiser
More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().