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

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

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.

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  • Khalili, Reza & Khaledi, Arian & Marzband, Mousa & Nematollahi, Amin Foroughi & Vahidi, Behrooz & 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, Elsevier, vol. 334(C).
    • Handle: RePEc:eee:appene:v:334:y:2023:i:c:s0306261923001010
    DOI: 10.1016/j.apenergy.2023.120737
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    Cited by:

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    2. Lerch, Philipp & Scheller, Fabian & Reichelt, David G. & Menzel, Katharina & Bruckner, Thomas, 2024. "Electricity cost and CO2 savings potential for chlor-alkali electrolysis plants: Benefits of electricity price dependent demand response," Applied Energy, Elsevier, vol. 355(C).
    3. Li, Fei & Wang, Dong & Guo, Hengdao & Zhang, Jianhua, 2024. "Distributionally Robust Optimization for integrated energy system accounting for refinement utilization of hydrogen and ladder-type carbon trading mechanism," Applied Energy, Elsevier, vol. 367(C).
    4. Shi, Jihao & Zhang, Xinqi & Zhang, Haoran & Wang, Qiliang & Yan, Jinyue & Xiao, Linda, 2024. "Automated detection and diagnosis of leak fault considering volatility by graph deep probability learning," Applied Energy, Elsevier, vol. 361(C).
    5. Khaledi, Arian & Saifoddin, Amirali, 2023. "Three-stage resilience-oriented active distribution systems operation after natural disasters," Energy, Elsevier, vol. 282(C).
    6. Yang, Senmiao & Wang, Jianda & Dong, Kangyin & Dong, Xiucheng & Wang, Kun & Fu, Xiaowen, 2024. "Is artificial intelligence technology innovation a recipe for low-carbon energy transition? A global perspective," Energy, Elsevier, vol. 300(C).
    7. Zhu, Mengshu & Ai, Xiaomeng & Fang, Jiakun & Cui, Shichang & Wu, Kejing & Zheng, Lufan & Wen, Jinyu, 2024. "Optimal scheduling of hydrogen energy hub for stable demand with uncertain photovoltaic and biomass," Applied Energy, Elsevier, vol. 360(C).
    8. Yang, Lijun & Jiang, Yaning & Chong, Zhenxiao, 2023. "Optimal scheduling of electro-thermal system considering refined demand response and source-load-storage cooperative hydrogen production," Renewable Energy, Elsevier, vol. 215(C).
    9. Pengfei Duan & Mengdan Feng & Bingxu Zhao & Qingwen Xue & Kang Li & Jinglei Chen, 2024. "Operational Optimization of Regional Integrated Energy Systems with Heat Pumps and Hydrogen Renewable Energy under Integrated Demand Response," Sustainability, MDPI, vol. 16(3), pages 1-18, January.

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