پیوندهای مفید
آمار
| تعداد نشریات | 26 |
| تعداد شماره‌ها | 326 |
| تعداد مقالات | 2,803 |
| تعداد مشاهده مقاله | 4,227,623 |
| تعداد دریافت فایل اصل مقاله | 2,881,153 |
Risk Averse Optimal Operation of Coastal Energy Hub Considering Seawater Desalination and Energy Storage Systems | ||
| Journal of Operation and Automation in Power Engineering | ||
| دوره 10، شماره 2، آبان 2022، صفحه 90-104 اصل مقاله (1.28 M) | ||
| نوع مقاله: Research paper | ||
| شناسه دیجیتال (DOI): 10.22098/joape.2022.8777.1614 | ||
| نویسندگان | ||
| A. Benyaghoob sani1؛ M. Sedighizadeh* 2؛ D. Sedighizadeh3؛ R. Abbasi1 | ||
| 1Department of Electrical, Biomedical, and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran | ||
| 2Faculty of Electrical Engineering, Shahid Beheshti University, Evin, Tehran, Iran | ||
| 3Department of Industrial Engineering, College of Technical and Engineering, Saveh Branch, Islamic Azad University , Saveh, Iran | ||
| چکیده | ||
| An optimal day-ahead operation of a microgrid based on coastal energy hub is presented in this paper. The proposed CEH included wind turbine, photovoltaic unit, combined cooling, heat and power, and seawater desalination. The purpose of the optimization is minimization of the operational and environmental costs considering several technical limitations. The CEH includes an ice storage conditioner together with an energy storage system, i.e. thermal energy storage system. Particularly, the impacts of an innovative rechargeable and emerging ESS that is solar-powered compressed air energy storage is scrutinized, on the efficiency and operational and pollution costs of the CEH. It is clear that there is an intrinsic deviation between predicted and actual uncertainty variables in MG. This paper presents a bi-level stochastic optimal operation model based on risk averse strategy of information gap decision theory to overcome this information gap and to help Microgrid operator. To reduce the complexity of the proposed model, Karush-Kuhn-Tucker method is used for converting the bi-level problem into a single level. The Augmented Epsilon Constraint method is used to deals with multi objective optimization problem to harvest the maximum horizon of the uncertainties of the parameters. The proposed model implemented the Time of Use program as a price-based demand response program. Finally, the efficacy of the SPCAES for minimizing the operational cost and pollutions in the day-ahead operation is depicted by implementation of the presented model on the typical CEH. | ||
| کلیدواژه‌ها | ||
| Augmented Epsilon Constraint (AUGMECON) method؛ Compressed Air Energy Storage (CAES)؛ Combined Cooling, Heat and Power (CCHP) | ||
| مراجع | ||
|
[1] M. Mohammadi et al., “Energy hub: from a model to a concept–a review”, Renew. Sustain. Energy Rev., vol. 80, pp. 1512-27, 2017. [2] W. Ho et al., “Optimal scheduling of energy storage for renewable energy distributed energy generation system”, Renew. Sustain. Energy Rev., vol. 58, pp. 1100-7, 2016. [3] H. Shayeghi and Y. Hashemi, “Potentiometric of the renewable hybrid system for electrification of gorgor station”, J. Oper. Autom. Power Eng., vol. 8, pp. 1-14, 2020. [4] B. Zhou et al., “Multi-objective optimal operation of coastal hydro-electrical energy system with seawater reverse osmosis desalination based on constrained NSGA-III”, Energy Conv. Manage., vol. 207, p. 112533, 2020. [5] A. Soroudi, A. Rabiee, and A. Keane, “Information gap decision theory approach to deal with wind power uncertainty in unit commitment”, Electr. Power Syst. Res., vol. 145, pp. 137-48, 2017. [6] V. Amir and M. Azimian, “Dynamic multi-carrier microgrid deployment under uncertainty”, Appl. Energy, vol. 260, p. 114293, 2020. [7] M. Sedighizadeh, M. Esmaili, and N. Mohammadkhani, “Stochastic multi-objective energy management in residential microgrids with combined cooling, heating, and power units considering battery energy storage systems and plug-in hybrid electric vehicles”, J. Cleaner Prod., vol. 195, pp. 301-17, 2018. [8] Z. Zhang et al., “A two-layer model for microgrid real-time dispatch based on energy storage system charging/discharging hidden costs”, IEEE Trans. Sustain. Energy, vol. 8, pp. 33-42, 2016. [9] D. Zhu, R. Yang, and G. Hug-Glanzmann, “Managing microgrids with intermittent resources: A two-layer multi-step optimal control approach”, North American Power Symp., 2010. [10] J. Wasilewski, “Integrated modeling of microgrid for steady-state analysis using modified concept of multi-carrier energy hub”, Int. J. Electr. Power Energy Syst., vol. 73, pp. 891-8, 2015. [11] A. Shahmohammadi et al., “Optimal design of multicarrier energy systems considering reliability constraints”, IEEE Trans. Power Del., vol. 30, pp. 878-86, 2014. [12] L. Ramírez-Elizondo and G. Paap, “Scheduling and control framework for distribution-level systems containing multiple energy carrier systems: Theoretical approach and illustrative example”, Int. J. Electr. Power Energy Syst., vol. 66, pp. 194-215, 2015. [13] S. Pazouki and M. Haghifam, “Optimal planning and scheduling of energy hub in presence of wind, storage and demand response under uncertainty”, Int. J. Electr. Power Energy Syst., vol. 80, pp. 219-39, 2016. [14] T. Ma, J. Wu, and L. Hao, “Energy flow modeling and optimal operation analysis of the micro energy grid based on energy hub”, Energy Conv. Manage., vol. 133, pp. 292-306, 2017. [15] X. Zhang et al., “Optimal expansion planning of energy hub with multiple energy infrastructures”, IEEE Trans. Smart Grid, vol. 6, pp. 2302-11, 2015. [16] F. Brahman, M. Honarmand, and S. Jadid, “Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system”, Energy Build., vol. 90, pp. 65-75, 2015. [17] M. Rastegar, M. Fotuhi-Firuzabad, and M. Lehtonen, “Home load management in a residential energy hub”, Electr. Power Syst. Res., vol. 119, pp. 322-8, 2015. [18] A. Parisio, C. Del Vecchio, and A. Vaccaro, “A robust optimization approach to energy hub management”, Int. J. Electr. Power Energy Syst., vol. 42, pp. 98-104, 2012. [19] Y. Wang et al., “Mixed-integer linear programming-based optimal configuration planning for energy hub: Starting from scratch”, Appl. Energy, vol. 210, pp. 1141-50, 2018. [20] M. Moeini-Aghtaie et al., “Multiagent genetic algorithm: an online probabilistic view on economic dispatch of energy hubs constrained by wind availability”, IEEE Trans. Sustain. Energy, vol. 5, pp. 699-708, 2013. [21] A. Heidari, S. Mortazavi, and R. Bansal, “Stochastic effects of ice storage on improvement of an energy hub optimal operation including demand response and renewable energies”, Appl. Energy, vol. 261, p. 114393, 2020. [22] F. Jamalzadeh et al., “Optimal operation of energy hub system using hybrid stochastic-interval optimization approach”, Sustain. Cities Soc., vol. 54, p. 101998, 2020. [23] S. Bozorgavari et al., “Two-stage hybrid stochastic/robust optimal coordination of distributed battery storage planning and flexible energy management in smart distribution network”, J. Energy Storage, vol. 26, p. 100970, 2019. [24] Y. Yao et al., “Coupled model and optimal operation analysis of power hub for multi-heterogeneous energy generation power system”, J. Cleaner Prod., vol. 249, p. 119432, 2020. [25] M. Rahmatian, A. Shamim, and S. Bahramara, “Optimal operation of the energy hubs in the islanded multi-carrier energy system using Cournot model”, Appl. Thermal Eng., vol. 191, p. 116837, 2021. [26] H. Chamandoust, G. Derakhshan, and S. Bahramara, “Multi-objective performance of smart hybrid energy system with multi-optimal participation of customers in day-ahead energy market”, Energy Build., vol. 216, p. 109964, 2020. [27] H. Chamandoust et al., Tri-objective optimal scheduling of smart energy hub system with schedulable loads”, J. Cleaner Prod., vol. 236, p. 117584, 2019. [28] M. Taghizadeh et al., “Optimal operation of storage-based hybrid energy system considering market price uncertainty and peak demand management”, J. Energy Storage, vol. 30, p. 101519, 2020. [29] A. Eladl et al., “Optimal operation of energy hubs integrated with renewable energy sources and storage devices considering CO2 emissions”, Int. J. Electr. Power Energy Syst., vol. 117, p. 105719, 2020. [30] C. Chen et al., “Two-stage robust planning-operation co-optimization of energy hub considering precise energy storage economic model”, Appl. Energy, vol. 252, p. 113372, 2019. [31] C. Laijun et al., “Review and prospect of compressed air energy storage system”, J. Modern Power Syst. Clean Energy, vol. 4, pp. 529-41, 2016. [32] X. Luo and J. Wang, “Overview of current development on compressed air energy storage”, School of Engineering, University of Warwick, Coventry, UK, 2013. [33] E. Akbari et al., “Stochastic programming-based optimal bidding of compressed air energy storage with wind and thermal generation units in energy and reserve markets”, Energy, vol. 171, pp. 535-46, 2019. [34] W. Cai et al., “Optimal bidding and offering strategies of compressed air energy storage: A hybrid robust-stochastic approach”, Renew. Energy, vol. 143, pp. 1-8, 2019. [35] S. Sadeghi and I. Askari, “Prefeasibility techno-economic assessment of a hybrid power plant with photovoltaic, fuel cell and Compressed Air Energy Storage (CAES)”, Energy, vol. 168, pp. 409-24, 2019. [36] M. Simpson et al., “Integrating solar thermal capture with compressed Air energy storage”, London, UK, 2016. [37] F. Jabari et al., “Day-ahead economic dispatch of coupled desalinated water and power grids with participation of compressed air energy storages”, J. Oper. Autom. Power Eng., vol. 7, pp. 40-8, 2019. [38] J. Yu et al., “Planning and design of a micro energy network for seawater desalination and regional energy interconnection”, Glob. Energy Interconnection, vol. 2, pp. 224-34, 2019. [39] Y. Babaei, J. Salehi, and N. Taghizadegan, “Bi-level unit commitment considering virtual power plants and demand response programs using information gap decision theory”, J. Oper. Autom. Power Eng., vol. 9, pp. 88-102, 2021. [40] M. Sedighizadeh, M. Esmaili, and N. Mohammadkhani, “Stochastic multi-objective energy management in residential microgrids with combined cooling, heating, and power units considering battery energy storage systems and plug-in hybrid electric vehicles”, J. Cleaner Prod., vol. 195, pp. 301-17, 2018. [41] N. Mohammadkhani, M. Sedighizadeh, and M. Esmaili, “Energy and emission management of CCHPs with electric and thermal energy storage and electric vehicle”, Thermal Sci. Eng. Prog., vol. 8, pp. 494-508, 2018. [42] M. Esmaeili, M. Sedighizadeh, and M. Esmaili, “Multi-objective optimal reconfiguration and DG (distributed generation) power allocation in distribution networks using big bang-big crunch algorithm considering load uncertainty”, Energy, vol. 103, pp. 86-99, 2016. [43] Y. Ben-Haim, “Info-gap decision theory: decisions under severe uncertainty”, Elsevier, 2006. [44] A. Conejo et al., “Decomposition techniques in mathematical programming: engineering and science applications”, Springer Science & Business Media, 2006. [45] M. Mazidi, H. Monsef, and P. Siano, “Design of a risk-averse decision making tool for smart distribution network operators under severe uncertainties: An IGDT-inspired augment ε-constraint based multi-objective approach”, Energy, vol. 116, pp. 214-35, 2016. [46] Z. Li and M. Ierapetritou, “A new methodology for the general multiparametric mixed-integer linear programming (MILP) problems”, Ind. Eng. Chem. Res., vol. 46, pp. 5141-51, 2007. [47] Y. Collette and P. Siarry, “Multiobjective optimization: principles and case studies”, Springer Science & Business Media, 2013. [48] M. Karimi, M. Kheradmandi, and A. Pirayesh, “Risk-constrained transmission investing of generation companies”, IEEE Trans. Power Syst., vol. 34, pp. 1043-53, 2018. [49] G. Mavrotas, “Effective implementation of the ε-constraint method in multi-objective mathematical programming problems”, Appl. Math. Comput., vol. 213, pp. 455-65, 2009. [50] M. Sedighizadeh, S. Alavi, and A. Mohammadpour, “Stochastic optimal scheduling of microgrids considering demand response and commercial parking lot by AUGMECON method”, Iran. J. Electr. Electron. Eng., vol. 16, pp. 393-411, 2020. [51] A. Zerrahn and W. Schill, “Long-run power storage requirements for high shares of renewables: review and a new model”, Renew. Sustain. Energy Rev., vol. 79, pp. 1518-34, 2017. [52] Y. Zheng et al., “Assessment for hierarchical medical policy proposals using hesitant fuzzy linguistic analytic network process”, Knowledge-Based Syst., vol. 161, pp. 254-67, 2018. [53] D. Singh and P. Kaushik, “Intrusion response prioritization based on fuzzy ELECTRE multiple criteria decision making technique”, J. Inf. Security Appl., vol. 48, p. 102359, 2019. [54] G. Yalcin and N. Erginel, “Determining weights in multi-objective linear programming under fuzziness”, Proc. World Cong. Eng., 2011. [55] M. Sedighizadeh et al., “Optimal distribution feeder reconfiguration and generation scheduling for microgrid day-ahead operation in the presence of electric vehicles considering uncertainties”, J. Energy Storage, vol. 21, pp. 58-71, 2019. [56] H. Chen et al., “Compressed air energy storage”, Energy Storage Tech. Appl., vol. 4, pp. 101-12, 2013. [57] I. Dincer and M. Rosen, “Thermal energy storage: systems and applications”, John Wiley & Sons, 2002. [58] Z. Kang et al., “Research status of ice-storage air-conditioning system”, Procedia Eng., vol. 205, pp. 1741-7, 2017. [59] M. Sedighizadeh, M. Esmaili, and M. Esmaeili, “Application of the hybrid Big Bang-Big Crunch algorithm to optimal reconfiguration and distributed generation power allocation in distribution system”, Energy, vol. 76, pp. 920-930, 2014. | ||
|
آمار تعداد مشاهده مقاله: 1,204 تعداد دریافت فایل اصل مقاله: 1,038 |
||