Robust multi-objective optimization for the Iranian electricity market considering green hydrogen and analyzing the performance of different demand response programs

Reza Khalili, Arian Khaledi, Mousa Marzband, Amin Foroughi Nematollahi, Behrooz Vahidi and Pierluigi Siano

Applied Energy, 2023, vol. 334, issue C, No S0306261923001010

Abstract: Using renewable energy sources (RES) and green hydrogen has increased dramatically as one of the best solutions to global environmental issues. Applying demand response programs (DRPs) in this context could enhance the system’s efficiency. Evaluating different DRPs’ performances and assessing economic impacts on different parts of the electricity market is essential. The inherent uncertainty of RES and prices is inevitable in electricity markets. As a result of the lack of information, it is crucial to mitigate the risks as much as possible, such as risks related to changes in demand, unit outages, or other traders’ bid strategies. This research introduces a robust multi-objective optimization method to reach the most confident plan for the retailer based on uncertainty in RES and price. The integration of different DRPs is assessed according to the cost to retailers and benefits for consumers using a multi-objective model to survey the impacts of different parts’ decisions on each other. The trade-off among DRPs is considered in this model, and they are traded using a new model to illustrate the daily effect of these programs in monthly operations. This paper uses hydrogen storage (HS) integrated with PV as a distributed energy resource. As the Iranian electricity market has just been established, this research proposes a framework for decision-making in new electricity markets to join future smart energy systems. The mid-term pricing evaluates the system’s performance for more accurate monthly results. Also, the operation cost of the hydrogen storage is modeled to assess its performance in non-robust and robust scheduling. Mixed-integer linear programming (MILP) has been used to model this problem in GAMS. A developed linearizing method is considered with a controllable amount of errors to reduce the volume and time of the computation. Finally, the cost of consumers in non-robust and robust market planning in the presence of DRPs is reduced by 8.77 % and 9.66 %, respectively, and HS has a compelling performance in peak-shaving and load-shifting.

Keywords: Robust optimization; Electricity market; Green hydrogen; Demand response; PV (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261923001010
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:appene:v:334:y:2023:i:c:s0306261923001010

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2023.120737

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().