Robust Multi-Objective Optimization for the Iranian Electricity Market Considering Green Hydrogen and Analyzing the Performance of Different Demand Response Programs

Khalili, Reza, Khaledi, Arian, Marzband, Mousa, Nematollahi, Amin Foroughi, Vahidi, Behrooz and Siano, Pierluigi (2023) Robust Multi-Objective Optimization for the Iranian Electricity Market Considering Green Hydrogen and Analyzing the Performance of Different Demand Response Programs. Applied Energy, 334. p. 120737. ISSN 0306-2619

MrKhalili_Paper1_2_.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0.

Download (3MB) | Preview
Official URL:


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.

Item Type: Article
Uncontrolled Keywords: Robust Optimization, Electricity Market, green hydrogen, demand response, PV
Subjects: H600 Electronic and Electrical Engineering
H800 Chemical, Process and Energy Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Rachel Branson
Date Deposited: 19 Jan 2023 15:15
Last Modified: 27 Jan 2024 03:30

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics