Intelligent Cost Optimization in Hybrid Electricity-Gas Microgrids Considering Distributed Generation and Energy Storage

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Behnia Shahryar; Kharrati Saeed; Khosravi Farshad; rastgou abdollah

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Novel Researches on Smart Power Systems Year:2024 | Volume:13 | Issue:4 (پیاپی 40) Page(s): 65-83

Download Full-Text

Persian Verion

View:

Download:

Cites:

Information Journal Paper

Title

Intelligent Cost Optimization in Hybrid Electricity-Gas Microgrids Considering Distributed Generation and Energy Storage

Author(s)

Behnia Shahryar | Kharrati Saeed | Khosravi Farshad | rastgou abdollah | Issue Writer Certificate

Keywords

Hybrid microgrid

Energy hub

Robust planning

Cost optimization

Energy storage

DRCC

Renewable energy sources

Abstract

This paper presents a comprehensive framework for smart cost optimization in integrated electricity-gas microgrids considering distributed generation units, energy storage systems, and market price uncertainties. The primary objective is to minimize operational costs while enhancing the performance of microgrids under realistic and uncertain conditions. To this end, a hybrid optimization model based on Mixed-Integer Linear Programming (MILP) and Distributionally Robust Chance Constraints (DRCC) is developed and implemented in GAMS software using the CPLEX solver. The proposed model is formulated within a two-stage market structure, including the day-ahead and real-time markets, to ensure efficient planning and adaptive corrections under uncertainty. To evaluate the performance of the model, a typical hybrid microgrid comprising photovoltaic (PV), wind turbine (WT), microturbines, fuel cells, and battery energy storage systems (BESS) along with variable and interruptible loads is simulated. Numerical results indicate that the proposed framework leads to an 18% reduction in total operational cost, a 27% reduction in load curtailment, and over 95% utilization rate of renewable energy sources. Moreover, the deviation from market schedules is reduced by 55%, indicating improved robustness and resilience. The flexibility of the proposed model also enables its extension to other energy carriers such as heating and cooling, making it a promising solution for future multi-energy systems.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans