A Framework for Stochastic Risk-Averse Decision Making in Hydrogen-Powered Intelligent Electric Vehicles Parking Management with Carbon and Green Certificate Considerations
محورهای موضوعی : Electrical Engineeringصابر کشیری 1 , Jafar Siahbalaee 2 , امانگلدی کوچکی 3
1 - دانشگاه آزاد اسلامی واحد علی آباد کتول
2 - aliabadkatoul branch, Islamic Azad University
3 - دانشگاه آزاد اسلامی واحد علی آباد کتول
کلید واژه: Intelligent parking, Electric vehicles, Renewable energy sources, Honey Badger Algorithm optimization, Two-point estimation, Hydrogen storage system, ,
چکیده مقاله :
Abstract – Energy management in Intelligent Electric Parking Lots (IPL) plays a crucial role in achieving technical and environmental goals by utilizing renewable energy sources (RES) and hydrogen storage systems (HSS). This article proposes a framework for risk-averse decision-making in hydrogen-powered smart parking management, considering carbon considerations and green certifications. Given the uncertainty in input parameters such as solar radiation, temperature, wind speed, and IPL load, a probabilistic model is developed using a combination of two-point estimation method and Information Gap Decision Theory (IGDT). Furthermore, a combined optimization method, Differential Honey Badger Algorithm (DHBA), is employed to optimize operational costs, including energy procurement from the grid, electric vehicle (EV) charging costs in smart parking lots, and costs associated with green certifications and carbon emissions, as the main objectives of the optimization problem. The main idea of this article is for a typical IPL comprising a hydrogen storage system (HSS) consisting of a fuel cell, electrolyzer, and hydrogen storage tank, alongside load demand alongside RES. Additionally, alongside energy management, Demand Response (DR) management has also been optimized. Simulation results achieve all technical and economic objectives with the presence of renewable energy sources and electric vehicles, resulting in a 15.5% increase in profit. Furthermore, considering uncertainty leads to a 9.6% decrease in profit compared to the absence of these sources. Moreover, considering green certifications and carbon emissions results in a significant reduction in pollution emissions.
– Energy management in Intelligent Electric Parking Lots (IPL) plays a crucial role in achieving technical and environmental goals by utilizing renewable energy sources (RES) and hydrogen storage systems (HSS). This article proposes a framework for risk-averse decision-making in hydrogen-powered smart parking management, considering carbon considerations and green certifications. Given the uncertainty in input parameters such as solar radiation, temperature, wind speed, and IPL load, a probabilistic model is developed using a combination of two-point estimation method and Information Gap Decision Theory (IGDT). Furthermore, a combined optimization method, Differential Honey Badger Algorithm (DHBA), is employed to optimize operational costs, including energy procurement from the grid, electric vehicle (EV) charging costs in smart parking lots, and costs associated with green certifications and carbon emissions, as the main objectives of the optimization problem. The main idea of this article is for a typical IPL comprising a hydrogen storage system (HSS) consisting of a fuel cell, electrolyzer, and hydrogen storage tank, alongside load demand alongside RES. Additionally, alongside energy management, Demand Response (DR) management has also been optimized. Simulation results achieve all technical and economic objectives with the presence of renewable energy sources and electric vehicles, resulting in a 15.5% increase in profit. Furthermore, considering uncertainty leads to a 9.6% decrease in profit compared to the absence of these sources. Moreover, considering green certifications and carbon emissions results in a significant reduction in pollution emissions.
[1] I.F. Davoudkhani, A. Dejamkhooy, S.A. Nowdeh, A novel cloud-based framework for optimal design of stand-alone hybrid renewable energy system considering uncertainty and battery aging, Appl Energy 344 (2023) 121257. https://doi.org/10.1016/J.APENERGY.2023.121257.
[2] M. Alinejad, O. Rezaei, A. Kazemi, S. Bagheri, An Optimal Management for Charging and Discharging of Electric Vehicles in an Intelligent Parking Lot Considering Vehicle Owner’s Random Behaviors, J Energy Storage 35 (2021) 102245. https://doi.org/10.1016/J.EST.2021.102245.
[3] P. Zare, A. Dejamkhooy, I.F. Davoudkhani, Efficient expansion planning of modern multi-energy distribution networks with electric vehicle charging stations: A stochastic MILP model, Sustainable Energy, Grids and Networks 38 (2024) 101225. https://doi.org/10.1016/J.SEGAN.2023.101225.
[4] M. Yazdani-Damavandi, M.P. Moghaddam, M.-R. Haghifam, M. Shafie-khah, J.P.S. Catalao, Modeling Operational Behavior of Plug-in Electric Vehicles’ Parking Lot in Multienergy Systems, IEEE Trans Smart Grid 7 (2016) 124–135. https://doi.org/10.1109/TSG.2015.2404892.
[5] S. Arabi Nowdeh, A. Naderipour, I. Faraji Davoudkhani, J.M. Guerrero, Stochastic optimization – based economic design for a hybrid sustainable system of wind turbine, combined heat, and power generation, and electric and thermal storages considering uncertainty: A case study of Espoo, Finland, Renewable and Sustainable Energy Reviews 183 (2023) 113440. https://doi.org/10.1016/J.RSER.2023.113440.
[6] A.F. Marzoghi, S. Bahramara, F. Adabi, S. Nojavan, Interval multi-objective optimization of hydrogen storage based intelligent parking lot of electric vehicles under peak demand management, J Energy Storage 27 (2020) 101123. https://doi.org/10.1016/J.EST.2019.101123.
[7] A. El-Zonkoly, L. dos Santos Coelho, Optimal allocation, sizing of PHEV parking lots in distribution system, International Journal of Electrical Power & Energy Systems 67 (2015) 472–477. https://doi.org/10.1016/J.IJEPES.2014.12.026.
[8] M. Mojarad, M. Sedighizadeh, M. Dosaranian‐Moghadam, Optimal allocation of intelligent parking lots in distribution system: A robust two‐stage optimization model, IET Electrical Systems in Transportation 12 (2022) 102–127. https://doi.org/10.1049/els2.12042.
[9] M.A. Baherifard, R. Kazemzadeh, A.S. Yazdankhah, M. Marzband, Intelligent charging planning for electric vehicle commercial parking lots and its impact on distribution network’s imbalance indices, Sustainable Energy, Grids and Networks 30 (2022) 100620. https://doi.org/10.1016/J.SEGAN.2022.100620.
[10] M. Tostado-Véliz, A.R. Jordehi, S.A. Mansouri, F. Jurado, A two-stage IGDT-stochastic model for optimal scheduling of energy communities with intelligent parking lots, Energy 263 (2023) 126018. https://doi.org/10.1016/J.ENERGY.2022.126018.
[11] L. Zhang, D. Liu, G. Cai, L. Lyu, L.H. Koh, T. Wang, An optimal dispatch model for virtual power plant that incorporates carbon trading and green certificate trading, International Journal of Electrical Power & Energy Systems 144 (2023) 108558. https://doi.org/10.1016/J.IJEPES.2022.108558.
[12] W. Chang, Q. Yang, Low carbon oriented collaborative energy management framework for multi-microgrid aggregated virtual power plant considering electricity trading, Appl Energy 351 (2023) 121906. https://doi.org/10.1016/j.apenergy.2023.121906.
[13] V. Saini, S. Tiwari, G.N. Tiwari, Environ economic analysis of various types of photovoltaic technologies integrated with greenhouse solar drying system, J Clean Prod 156 (2017) 30–40. https://doi.org/10.1016/J.JCLEPRO.2017.04.044.
[14] H. Mikulčić, M. Vujanović, K. Urbaniec, N. Duić, Reducing greenhouse gasses emissions by fostering the deployment of alternative raw materials and energy sources in the cleaner cement manufacturing process, J Clean Prod 136 (2016) 119–132. https://doi.org/10.1016/J.JCLEPRO.2016.04.145.
[15] C.G. Hoehne, M. V. Chester, Optimizing plug-in electric vehicle and vehicle-to-grid charge scheduling to minimize carbon emissions, Energy 115 (2016) 646–657. https://doi.org/10.1016/J.ENERGY.2016.09.057.
[16] M.-K. Kim, J. Oh, J.-H. Park, C. Joo, Perceived value and adoption intention for electric vehicles in Korea: Moderating effects of environmental traits and government supports, Energy 159 (2018) 799–809. https://doi.org/10.1016/J.ENERGY.2018.06.064.
[17] J. Jannati, D. Nazarpour, Optimal performance of electric vehicles parking lot considering environmental issue, J Clean Prod 206 (2019) 1073–1088. https://doi.org/10.1016/J.JCLEPRO.2018.09.222.
[18] S. Khan, A. Ahmad, F. Ahmad, M. Shafaati Shemami, M. Saad Alam, S. Khateeb, A Comprehensive Review on Solar Powered Electric Vehicle Charging System, Smart Science 6 (2018) 54–79. https://doi.org/10.1080/23080477.2017.1419054.
[19] E. Yoo, M. Kim, H.H. Song, Well-to-wheel analysis of hydrogen fuel-cell electric vehicle in Korea, Int J Hydrogen Energy 43 (2018) 19267–19278. https://doi.org/10.1016/J.IJHYDENE.2018.08.088.
[20] A. Mazzucco, M. Dornheim, M. Sloth, T.R. Jensen, J.O. Jensen, M. Rokni, Bed geometries, fueling strategies and optimization of heat exchanger designs in metal hydride storage systems for automotive applications: A review, Int J Hydrogen Energy 39 (2014) 17054–17074. https://doi.org/10.1016/J.IJHYDENE.2014.08.047.
[21] M. Ghiyasiyan-Arani, M. Salavati-Niasari, Effect of Li 2 CoMn 3 O 8 Nanostructures Synthesized by a Combustion Method on Montmorillonite K10 as a Potential Hydrogen Storage Material, The Journal of Physical Chemistry C 122 (2018) 16498–16509. https://doi.org/10.1021/acs.jpcc.8b02617.
[22] A. Salehabadi, M. Salavati-Niasari, T. Gholami, Green and facial combustion synthesis of Sr3Al2O6 nanostructures; a potential electrochemical hydrogen storage material, J Clean Prod 171 (2018) 1–9. https://doi.org/10.1016/J.JCLEPRO.2017.09.250.
[23] J. Guo, Y. Lv, H. Zhang, S. Nojavan, K. Jermsittiparsert, Robust optimization strategy for intelligent parking lot of electric vehicles, Energy 200 (2020) 117555. https://doi.org/10.1016/J.ENERGY.2020.117555.
[24] M.H. Amini, M.P. Moghaddam, O. Karabasoglu, Simultaneous allocation of electric vehicles’ parking lots and distributed renewable resources in smart power distribution networks, Sustain Cities Soc 28 (2017) 332–342. https://doi.org/10.1016/J.SCS.2016.10.006.
[25] K. Seddig, P. Jochem, W. Fichtner, Integrating renewable energy sources by electric vehicle fleets under uncertainty, Energy 141 (2017) 2145–2153. https://doi.org/10.1016/J.ENERGY.2017.11.140.
[26] M. Honarmand, A. Zakariazadeh, S. Jadid, Self-scheduling of electric vehicles in an intelligent parking lot using stochastic optimization, J Franklin Inst 352 (2015) 449–467. https://doi.org/10.1016/J.JFRANKLIN.2014.01.019.
[27] R. Razipour, S.-M. Moghaddas-Tafreshi, P. Farhadi, Optimal management of electric vehicles in an intelligent parking lot in the presence of hydrogen storage system, J Energy Storage 22 (2019) 144–152. https://doi.org/10.1016/J.EST.2019.02.001.
[28] M. Honarmand, A. Zakariazadeh, S. Jadid, Integrated scheduling of renewable generation and electric vehicles parking lot in a smart microgrid, Energy Convers Manag 86 (2014) 745–755. https://doi.org/10.1016/J.ENCONMAN.2014.06.044.
[29] M.S. Kuran, A. Carneiro Viana, L. Iannone, D. Kofman, G. Mermoud, J.P. Vasseur, A Smart Parking Lot Management System for Scheduling the Recharging of Electric Vehicles, IEEE Trans Smart Grid 6 (2015) 2942–2953. https://doi.org/10.1109/TSG.2015.2403287.
[30] M. Honarmand, A. Zakariazadeh, S. Jadid, Optimal scheduling of electric vehicles in an intelligent parking lot considering vehicle-to-grid concept and battery condition, Energy 65 (2014) 572–579. https://doi.org/10.1016/J.ENERGY.2013.11.045.
[31] L. Zhang, Y. Li, A Game-Theoretic Approach to Optimal Scheduling of Parking-Lot Electric Vehicle Charging, IEEE Trans Veh Technol 65 (2016) 4068–4078. https://doi.org/10.1109/TVT.2015.2487515.
[32] S. Kashiri, J. Siahbalaee, A. Koochaki, Stochastic management of electric vehicles in an intelligent parking lot in the presence of hydrogen storage system and renewable resources, Int J Hydrogen Energy 50 (2024) 1581–1597. https://doi.org/10.1016/J.IJHYDENE.2023.10.146.
[33] S. Arabi Nowdeh, I.F. Davoudkhani, M.J. Hadidian Moghaddam, E.S. Najmi, A.Y. Abdelaziz, A. Ahmadi, S.E. Razavi, F.H. Gandoman, Fuzzy multi-objective placement of renewable energy sources in distribution system with objective of loss reduction and reliability improvement using a novel hybrid method, Appl Soft Comput 77 (2019) 761–779. https://doi.org/10.1016/J.ASOC.2019.02.003.
[34] S. Zhang, W. Hu, X. Cao, J. Du, C. Bai, W. Liu, M. Tang, W. Zhan, Z. Chen, Low-carbon economic dispatch strategy for interconnected multi-energy microgrids considering carbon emission accounting and profit allocation, Sustain Cities Soc 99 (2023) 104987. https://doi.org/10.1016/J.SCS.2023.104987.
[35] F.A. Hashim, E.H. Houssein, K. Hussain, M.S. Mabrouk, W. Al-Atabany, Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems, Math Comput Simul 192 (2022) 84–110. https://doi.org/10.1016/J.MATCOM.2021.08.013.
[36] F. Huang, L. Wang, Q. He, An effective co-evolutionary differential evolution for constrained optimization, Appl Math Comput 186 (2007) 340–356. https://doi.org/10.1016/J.AMC.2006.07.105.
[37] Juan.M. Morales, Juan. Perez-Ruiz, Point Estimate Schemes to Solve the Probabilistic Power Flow, IEEE Transactions on Power Systems 22 (2007) 1594–1601. https://doi.org/10.1109/TPWRS.2007.907515.
[38] C.-L. Su, Probabilistic Load-Flow Computation Using Point Estimate Method, IEEE Transactions on Power Systems 20 (2005) 1843–1851. https://doi.org/10.1109/TPWRS.2005.857921.
[39] T. Sriyakul, K. Jermsittiparsert, Economic scheduling of a smart microgrid utilizing the benefits of plug-in electric vehicles contracts with a comprehensive model of information-gap decision theory, J Energy Storage 32 (2020). https://doi.org/10.1016/j.est.2020.102010.
[40] X. Cao, J. Wang, J. Wang, B. Zeng, A Risk-Averse Conic Model for Networked Microgrids Planning with Reconfiguration and Reorganizations, IEEE Trans Smart Grid 11 (2020) 696–709. https://doi.org/10.1109/TSG.2019.2927833.
[41] M. Tostado-Véliz, H.M. Hasanien, A.R. Jordehi, R.A. Turky, F. Jurado, Risk-averse optimal participation of a DR-intensive microgrid in competitive clusters considering response fatigue, Appl Energy 339 (2023). https://doi.org/10.1016/j.apenergy.2023.120960.
[42] S. Nojavan, M. Majidi, K. Zare, Risk-based optimal performance of a PV/fuel cell/battery/grid hybrid energy system using information gap decision theory in the presence of demand response program, Int J Hydrogen Energy 42 (2017) 11857–11867. https://doi.org/10.1016/J.IJHYDENE.2017.02.147.
[43] M.A. Jirdehi, S. Ahmadi, The optimal energy management in multiple grids: Impact of interconnections between microgrid–nanogrid on the proposed planning by considering the uncertainty of clean energies, ISA Trans 131 (2022) 323–338. https://doi.org/10.1016/j.isatra.2022.04.039.