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Multi-objectives Optimal Scheduling in Smart Energy Hub System with Electrical and Thermal Responsive Loads Cover

Multi-objectives Optimal Scheduling in Smart Energy Hub System with Electrical and Thermal Responsive Loads

Environmental and Climate Technologies
Volume 24 (2020): Issue 1 (January 2020)
By: Heydar ChamandoustORCID,  Ghasem DerakhshanORCID,  Seyed Mehdi HakimiORCID and  Salah BahramaraORCID  
Open Access
|Apr 2020

References

  1. [1] Chamandoust H., Hashemi A., Derakshan G., Abdi B. Optimal hybrid system design based on renewable energy resources. Presented at IEEE Smart Grid Conference (SGC), 2017. https://doi.org/10.1109/SGC.2017.830887810.1109/SGC.2017.8308878
    Search in Google ScholarBack to article
  2. [2] Chamandoust H., Hashemi A., Derakshan G., Hakimi M. Scheduling of Smart Micro Grid Considering Reserve and Demand Side Management. Presented at IEEE Smart Grid Conference (SGC), 2018. https://doi.org/10.1109/SGC.2018.877792610.1109/SGC.2018.8777926
    Search in Google ScholarBack to article
  3. [3] Gelazanskas L., Gamage K. A. Demand side management in smart grid: A review and proposals for future direction. Sustainable Cities and Society 2014:11:22–30. https://doi.org/10.1016/j.scs.2013.11.00110.1016/j.scs.2013.11.001
    Search in Google ScholarBack to article
  4. [4] Blumberga A., Timma L., Blumberga D. System Dynamic Model for the Accumulation of Renewable Electricity using Power-to-Gas and Power-to-Liquid Concepts. Environmental and Climate Technologies 2016:16:54–68. https://doi.org/10.1515/rtuect-2015-001210.1515/rtuect-2015-0012
    Search in Google ScholarBack to article
  5. [5] Rold Blay C., Escrivá-Escrivá G., Roldán-Porta C., Álvarez-Belet C. An optimisation algorithm for distributed energy resources management in micro-scale energy hubs. Energy 2017:132:126–135. https://doi.org/10.1016/j.energy.2017.05.03810.1016/j.energy.2017.05.038
    Search in Google ScholarBack to article
  6. [6] Liu T., Zhang D., Wang S., Wu T. Standardized modelling and economic optimization of multi-carrier energy systems considering energy storage and demand response. Energy Conversion and Management 2019:182:126–142. https://doi.org/10.1016/j.enconman.2018.12.07310.1016/j.enconman.2018.12.073
    Search in Google ScholarBack to article
  7. [7] Rakipour D., Barati H. Probabilistic optimization in operation of energy hub with participation of renewable energy resources and demand response. Energy 2019:173:384–399. https://doi.org/10.1016/j.energy.2019.02.02110.1016/j.energy.2019.02.021
    Search in Google ScholarBack to article
  8. [8] Jadidbonab M., Babaei E., Mohammadi-Ivatloo B. CVaR-constrained Scheduling Strategy for Smart Multi Carrier Energy Hub Considering Demand Response and Compressed Air Energy Storage. Energy 2019:174:1238–1250. https://doi.org/10.1016/j.energy.2019.02.04810.1016/j.energy.2019.02.048
    Search in Google ScholarBack to article
  9. [9] Ghorab M. Energy hubs optimization for smart energy network system to minimize economic and environmental impact at Canadian community. Applied Thermal Engineering 2019:151:214–230. https://doi.org/10.1016/j.applthermaleng.2019.01.10710.1016/j.applthermaleng.2019.01.107
    Search in Google ScholarBack to article
  10. [10] Gholizadeh N., Vahid-Pakdel M. J., Mohammadi-ivatloo B. Enhancement of demand supply’s security using power to gas technology in networked energy hubs. Electrical Power and Energy Systems 2019:109:83–94. https://doi.org/10.1016/j.ijepes.2019.01.04710.1016/j.ijepes.2019.01.047
    Search in Google ScholarBack to article
  11. [11] Zhang N., Cheng J., Wang Y. Probabilistic Optimal Energy Flow of District Multi-energy Systems: An MPLP-based Online Dictionary-Learning Approach. IEEE Transactions on Industrial Informatics. Accepted for publishing.
    Search in Google ScholarBack to article
  12. [12] Aghamohamadi M., Samad M., Rahmat I. Energy Generation Cost in Multi-energy Systems; an Application to a Non-merchant Energy Hub in Supplying Price Responsive Loads. Energy 2018:161:878–891. https://doi.org/10.1016/j.energy.2018.07.14410.1016/j.energy.2018.07.144
    Search in Google ScholarBack to article
  13. [13] Ayele G. T., Haurant P., Laumert B., Lacarrière B. An extended energy hub approach for load flow analysis of highly coupled district energy networks: Illustration with electricity and heating. Applied Energy 2018:212:850–867. https://doi.org/10.1016/j.apenergy.2017.12.09010.1016/j.apenergy.2017.12.090
    Search in Google ScholarBack to article
  14. [14] Huo D., Gu C., Ma K., Wei W., Xiang Y., Le Blond S. Chance Constrained Optimization for Multi Energy Hub Systems in a Smart City. IEEE Transactions on Industrial Electronics 2019:66:1402–1412. https://doi.org/10.1109/TIE.2018.286319710.1109/TIE.2018.2863197
    Search in Google ScholarBack to article
  15. [15] Chen Y., Wei W., Liu F., Wu Q., Mei S. Analyzing and validating the economic efficiency of managing a cluster of energy hubs in multi-carrier energy systems. Applied Energy 2018:230:403–416. https://doi.org/10.1016/j.apenergy.2018.08.11210.1016/j.apenergy.2018.08.112
    Search in Google ScholarBack to article
  16. [16] Davatgaran V., Saniei M., Mortazavi S. S. Optimal bidding strategy for an energy hub in energy market. Energy 2018:148:482–493. https://doi.org/10.1016/j.energy.2018.01.17410.1016/j.energy.2018.01.174
    Search in Google ScholarBack to article
  17. [17] Dolatabadi A., Mohammadi-Ivatloo B. Stochastic Risk-constrained Scheduling of Smart Energy Hub in the Presence of Wind Power and Demand Response. Applied Thermal Engineering 2017:123:40–49. https://doi.org/10.1016/j.applthermaleng.2017.05.06910.1016/j.applthermaleng.2017.05.069
    Search in Google ScholarBack to article
  18. [18] Dolatabadi A., Jadidbonab M., Mahammadi-ivatloo B. Short-term Scheduling Strategy for Wind-based Energy Hub: A Hybrid Stochastic/IGDT Approach. IEEE Transactions on Sustainable Energy 2019:10:438–448. https://doi.org/10.1109/TSTE.2017.278808610.1109/TSTE.2017.2788086
    Search in Google ScholarBack to article
  19. [19] Hemmati S., Ghaderi S. F., Ghazizadeh M. S. Sustainable Energy Hub Design under Uncertainty Using Benders Decomposition Method. Energy 2018:143:1029–1047. https://doi.org/10.1016/j.energy.2017.11.05210.1016/j.energy.2017.11.052
    Search in Google ScholarBack to article
  20. [20] Huo D., Le Blond S., Gu C., Wei W., Yu D. Optimal operation of interconnected energy hubs by using decomposed hybrid particle swarm and interior-point approach. Electrical Power and Energy Systems 2018:95:36–46. https://doi.org/10.1016/j.ijepes.2017.08.00410.1016/j.ijepes.2017.08.004
    Search in Google ScholarBack to article
  21. [21] Chen C., Sun H., Shen X., Guo Y., Guo Q., Xia T. Two-stage robust planning-operation co-optimization of energy hub considering precise energy storage economic model. Applied Energy 2019:252. In press. https://doi.org/10.1016/j.apenergy.2019.11337210.1016/j.apenergy.2019.113372
    Search in Google ScholarBack to article
  22. [22] Ma T., Wu J., Hao L. Energy flow modeling and optimal operation analysis of the micro energy grid based on energy hub. Energy Conversion and Management 2017:133:292–306. https://doi.org/10.1016/j.enconman.2016.12.01110.1016/j.enconman.2016.12.011
    Search in Google ScholarBack to article
  23. [23] Li Ma et al. Real-time Rolling Horizon Energy Management or the Energy-Hub-Coordinated Prosumer Community from a Cooperative Perspective. IEEE Transactions on Power Systems 2019:34:1227–1242. https://doi.org/10.1109/TPWRS.2018.287723610.1109/TPWRS.2018.2877236
    Search in Google ScholarBack to article
  24. [24] Moghaddas-Tafreshia S. M., Jafari M., Mohseni S., Kelly S. Optimal operation of an energy hub considering the uncertainty associated with the power consumption of plug-in hybrid electric vehicles using information gap decision theory. Electrical Power and Energy Systems 2019:112:92–108. https://doi.org/10.1016/j.ijepes.2019.04.04010.1016/j.ijepes.2019.04.040
    Search in Google ScholarBack to article
  25. [25] Salehimaleh M., Akbarimajd A., Valipour K., Dejamkhooy A. Generalized modeling and Optimal Management of Energy Hub based Electricity, Heat and Cooling Demands. Energy 2018:159:669–685. https://doi.org/10.1016/j.energy.2018.06.12210.1016/j.energy.2018.06.122
    Search in Google ScholarBack to article
  26. [26] Pazouki S., Haghifam M. A. Uncertainty modeling in optimal operation of energy hub in presence of wind, storage and demand response. Electrical Power and Energy Systems 2014:61:335–345. https://doi.org/10.1016/j.ijepes.2014.03.03810.1016/j.ijepes.2014.03.038
    Search in Google ScholarBack to article
  27. [27] Vahid-Pakdel M. J., Nojavan S., Mohammadi-ivatloo B., Zare K. Stochastic optimization of energy hub operation with consideration of thermal energy market and demand response. Energy Conversion and Management 2017:145:117–128. https://doi.org/10.1016/j.enconman.2017.04.07410.1016/j.enconman.2017.04.074
    Search in Google ScholarBack to article
  28. [28] Zhong W., Yang C., Xie K., Xie S., Zhang Y. ADMM-Based Distributed Auction Mechanism for Energy Hub Scheduling in Smart Buildings. IEEE Access 2018:6:45635–45645. https://doi.org/10.1109/ACCESS.2018.286562510.1109/ACCESS.2018.2865625
    Search in Google ScholarBack to article
  29. [29] Shu K., Ai X., Fang J., Yao W., Chen Z., He H., Wen J. Real-time subsidy based robust scheduling of the integrated power and gas system. Applied Energy 2019:236:1158–1167. https://doi.org/10.1016/j.apenergy.2018.12.05410.1016/j.apenergy.2018.12.054
    Search in Google ScholarBack to article
  30. [30] Sheykhloei B., Abedinzadeh T., Mohammadian L., Mohammadi-Ivatloo B. Optimal co-scheduling of distributed generation resources and natural gas network considering uncertainties. Journal of Energy Storage 2019:21:383–392. https://doi.org/10.1016/j.est.2018.11.01810.1016/j.est.2018.11.018
    Search in Google ScholarBack to article
  31. [31] Nojavan S., Majidi M., Zare K. Optimal scheduling of heating and power hubs under economic and environment issues in the presence of peak load management. Energy Conversion and Management 2018:156:34–44. https://doi.org/10.1016/j.enconman.2017.11.00710.1016/j.enconman.2017.11.007
    Search in Google ScholarBack to article
  32. [32] Khodemani-Yazdi M., Tavakkoli-Moghaddam R., Bashiri M., Rahimi Y. Solving a new bi-objective hierarchical hub location problem with an M/M/C queuing framework. Engineering Applications of Artificial Intelligence 2019:78:53–70. https://doi.org/10.1016/j.engappai.2018.10.00410.1016/j.engappai.2018.10.004
    Search in Google ScholarBack to article
  33. [33] Amiri S., Honarvar M., Sadegheih A. Providing an Integrated Model for Planning and Scheduling Energy Hubs and Preventive Maintenance. Energy 2018:163:1093–1114. https://doi.org/10.1016/j.energy.2018.08.04610.1016/j.energy.2018.08.046
    Search in Google ScholarBack to article
  34. [34] Sani M. M., Noorpoor A., Shafie-Pour M. M. Optimal model development of energy hub to supply water, heating and electrical demands of a cement factory. Energy 2019:177:574–592. https://doi.org/10.1016/j.energy.2019.03.04310.1016/j.energy.2019.03.043
    Search in Google ScholarBack to article
  35. [35] Maroufmashat A. et al. Modeling and optimization of a network of energy hubs to improve economic and emission considerations. Energy 2015:93:2546–2558. https://doi.org/10.1016/j.energy.2015.10.07910.1016/j.energy.2015.10.079
    Search in Google ScholarBack to article
  36. [36] Majidi M., Nojavan S., Zare K. A cost-emission framework for hub energy system under demand response program. Energy 2017:134:157–166. https://doi.org/10.1016/j.energy.2015.10.07910.1016/j.energy.2015.10.079
    Search in Google ScholarBack to article
  37. [37] Soudmand B. M., Esfetanaj N. N., Mehdipour S., Rezaeipour R. Heating hub and power hub models for optimal performance of an industrial consumer. Energy Conversion and Management 2017:150:425–432. https://doi.org/10.1016/j.enconman.2017.08.03710.1016/j.enconman.2017.08.037
    Search in Google ScholarBack to article
  38. [38] Wei P., He F., Li L., Shi X., Simoes R. Multi-objective problem based operation and emission cots for heat and power hub model through peak load management in large scale users. Energy Conversion and Management 2018:171:411–426. https://doi.org/10.1016/j.enconman.2018.05.02510.1016/j.enconman.2018.05.025
    Search in Google ScholarBack to article
  39. [39] Shabanpour-Haghighi A., Seifi A. R. Multi-objective operation management of a multi-carrier energy system. Energy 2015:88:430–442. https://doi.org/10.1016/j.energy.2015.05.06310.1016/j.energy.2015.05.063
    Search in Google ScholarBack to article
  40. [40] Kampouropoulos K., Andrade F., Sala E., Espinosa A., Romeral L. Multiobjective Optimization of Multi-Carrier Energy System using a combination of ANFIS and Genetic Algorithms. IEEE Transactions on Smart Grid 2018:2276–2283. https://doi.org/10.1109/TSG.2016.260974010.1109/TSG.2016.2609740
    Search in Google ScholarBack to article
  41. [41] Zhao F., Zhang C., Sun B. Initiative Optimization Operation Strategy and Multi-objective Energy Management Method for Combined Cooling Heating and Power. IEEE/CAA Journal of Automatica Sinica 2016:(3):385–393. https://doi.org/10.1109/JAS.2016.751007910.1109/JAS.2016.7510079
    Search in Google ScholarBack to article
  42. [42] Eriksson E. L. V., Gray E. MacA. Optimization of renewable hybrid energy systems – A multi-objective approach. Renewable Energy 2019:133:971–999. https://doi.org/10.1016/j.renene.2018.10.05310.1016/j.renene.2018.10.053
    Search in Google ScholarBack to article
  43. [43] Jing R. et al. Comparative study of posteriori decision-making methods when designing building integrated energy systems with multi-objectives. Energy & Buildings 2019:194:123–139. https://doi.org/10.1016/j.enbuild.2019.04.02310.1016/j.enbuild.2019.04.023
    Search in Google ScholarBack to article
  44. [44] Chamandoust H. et al. Tri-objective optimal scheduling of smart energy hub system with schedulable loads. Journal of Cleaner Production 2019:236:117584. https://doi.org/10.1016/j.jclepro.2019.07.05910.1016/j.jclepro.2019.07.059
    Search in Google ScholarBack to article
  45. [45] Bariss U., Bazbauers G., Blumberga A., Blumberga D. System Dynamics Modeling of Households' Electricity Consumption and Cost-Income Ratio: A Case Study of Latvia. Environmental and Climate Technologies 2017:20:36–50. https://doi.org/10.1515/rtuect-2017-000910.1515/rtuect-2017-0009
    Search in Google ScholarBack to article
  46. [46] Chamandoust H. Economic Scheduling of Micro Grid Based on Energy Management and Demand Response. Electrical, Control and Communication Engineering 2018:14:100–107. https://doi.org/10.2478/ecce-2018-001210.2478/ecce-2018-0012
    Search in Google ScholarBack to article
  47. [47] Kittipongvises S., Chavalparit O., Sutthirat C. Greenhouse Gases and Energy Intensity of Granite Rock Mining Operations in Thailand: A Case of Industrial Rock-Construction. Environmental and Climate Technologies 2016:18:64–75. https://doi.org/10.1515/rtuect-2016-001410.1515/rtuect-2016-0014
    Search in Google ScholarBack to article
  48. [48] Chamandoust H. et al. Multi-objective performance of smart hybrid energy system with Multi-optimal participation of customers in day-ahead energy market. Energy and Buildings 2020:216:109964. https://doi.org/10.1016/j.enbuild.2020.10996410.1016/j.enbuild.2020.109964
    Search in Google ScholarBack to article
  49. [49] Mavrotas G. Effective implementation of the ∈-constraint method in Multi-Objective Mathematical Programming problems. Applied Mathematics and Computation 2009:213:455–465. https://doi.org/10.1016/j.amc.2009.03.03710.1016/j.amc.2009.03.037
    Search in Google ScholarBack to article
  50. [50] Chamandoust H. et al. Tri-objective scheduling of residential smart electrical distribution grids with optimal joint of responsive loads with renewable energy sources. Journal of Energy Storage 2020:27:101–112. https://doi.org/10.1016/j.est.2019.10111210.1016/j.est.2019.101112
    Search in Google ScholarBack to article
  51. [51] Saberi K. et al. Optimal performance of CCHP based microgrid considering environmental issue in the presence of real time demand response. Sustainable Cities and Society 2019:45:596–606. https://doi.org/10.1016/j.scs.2018.12.02310.1016/j.scs.2018.12.023
    Search in Google ScholarBack to article
DOI: https://doi.org/10.2478/rtuect-2020-0013 | Journal eISSN: 2255-8837 | Journal ISSN: 1691-5208
Journal RSS Feed
Language: English
Page range: 209 - 232
Published on: Apr 13, 2020
Published by: Riga Technical University
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year
Keywords:
Decision-making method,
Demand Side Management (DSM),
Multi-objectives optimal scheduling,
Smart energy Hub system (SEHS),
ε-constraint method
Related subjects:
Life sciences,
Life sciences, other

© 2020 Heydar Chamandoust, Ghasem Derakhshan, Seyed Mehdi Hakimi, Salah Bahramara, published by Riga Technical University
This work is licensed under the Creative Commons Attribution 4.0 License.

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