2024 : 4 : 24
Aliasghar Ghadimi

Aliasghar Ghadimi

Academic rank: Associate Professor
ORCID: https://orcid.org/0000-0001-7276-2221
Education: PhD.
ScopusId: 56678490500
Faculty: Engineering
Address: Arak University
Phone: 08632625620

Research

Title
Multi-energy microgrid optimal operation with integrated power to gas technology considering uncertainties
Type
JournalPaper
Keywords
Multi-energy microgrid Robust optimization Information gap decision theory Point estimate method Fuel-cell electric vehicle Renewable generation
Year
2022
Journal Journal of Cleaner Production
DOI
Researchers Ali Mobasseri ، Marcos Tostado Veliz ، Aliasghar Ghadimi ، Mohammad Reza Miveh ، Francisco Jurado

Abstract

In recent years, multi-energy microgrids (MEMGs) have emerged as an invaluable framework for enabling the use of clean and efficient electro-thermal resources as well as the integration of multi-energy storage facilities. Uncertainties modelling in such systems is a challenge because of the heterogeneity of the resources and consumers involved. This paper tackles this issue by proposing a hybrid robust energy management tool for MEMGs encompassing electric, heat, hydrogen and gas sub-networks. The variety of uncertainties brought by unpredictable demand and renewable generation are managed using adequate techniques. This way, renewable generation is modelled using the Hong 2m + 1 approach, the electrical and heat demands are managed using the information gap decision theory and the fuel-cell electric vehicles refueling demand is modelled via scenarios. The novel methodology is validated on a benchmark case study, in which extensive simulations are performed. The obtained results demonstrate the accurateness of the novel proposal and its effectiveness to manage a wide variety of uncertainties. The evidence for accurateness is that the difference in the objective function with the Monte Carlo and Hong 2m + 1 uncertainty modelling approaches only differs by ~0.2%. Moreover, the new proposal is computationally competitive with the Monte Carlo simulation, improving its computation time by 2–3 times.