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:: Volume 8, Issue 3 (1-2020) ::
ieijqp 2020, 8(3): 68-77 Back to browse issues page
Optimization of Operation of Microgrid by Thermal Demand Response Considering Enhancement of Consumers’ Thermal Comfort
S.Mahdi Kazemi-Razi1 , Hossein Askarian-Abyaneh * 1, Hamed Nafisi1 , Mousa Marzband2 , Masoud Samadian-Zakaria3
1- Amirkabir University of Technology
2- Northumbria University- Newcastle- United Kingdom
3- Tehran Province Gas Company
Abstract:   (4083 Views)
In recent years, multi-energy microgrids including electricity, gas and thermal are more grown; that presents demand response (DR) models considering multi-energy storages and renewable resources. Appropriate DR management with storages may lead to optimal flexibility. In this paper, a probabilistic linear model is introduced to assess the effect of flexibility and DR. In the proposed model, electrical and thermal DR, multi-energy storages, and participation in reserve market are considered as the main contribution. The proposed model guarantees thermal comfort as well as increasing flexibility and reserve commitment. By applying the proposed method on a distribution network in UK, it is illustrated that by utilization of the proposed DR program the flexibility of microgrid increases and the cost of operation decreases.
Keywords: Flexibility, multi-energy microgrid, multi-energy storage, electrical and thermal demand response, thermal comfort
Full-Text [PDF 1445 kb]   (896 Downloads)    
Type of Study: Research | Subject: Special
Received: 2019/08/28 | Accepted: 2020/01/7 | Published: 2020/02/1
References
1. [1] M. Yazdani-Damavandi, N. Neyestani, G. Chicco, M. Shafie-Khah, and J. P. S. Catalao, "Aggregation of Distributed Energy Resources Under the Concept of Multienergy Players in Local Energy Systems," IEEE Trans. Sustain. Energy, vol. 8, no. 4, pp. 1679-1693, 2017. [DOI:10.1109/TSTE.2017.2701836]
2. [2] N. Good and P. Mancarella, "Flexibility in Multi-Energy Communities With Electrical and Thermal Storage: A Stochastic, Robust Approach for Multi-Service Demand Response," IEEE Trans. Smart Grid, vol. 10, no. 1, pp. 503-513, 2019. [DOI:10.1109/TSG.2017.2745559]
3. [3] N. Good, E. Karangelos, A. Navarro-Espinosa, and P. Mancarella, "Optimization under uncertainty of thermal storage-based flexible demand response with quantification of residential users' discomfort," IEEE Trans. Smart Grid, vol. 6, no. 5, pp. 2333-2342, 2015. [DOI:10.1109/TSG.2015.2399974]
4. [4] E. A. Martínez Ceseña, N. Good, A. L. A. Syrri, and P. Mancarella, "Techno-economic and business case assessment of multi-energy microgrids with co-optimization of energy, reserve and reliability services," Appl. Energy, vol. 210, pp. 896-913, 2018. [DOI:10.1016/j.apenergy.2017.08.131]
5. [5] J. Le Dréau and P. Heiselberg, "Energy flexibility of residential buildings using short term heat storage in the thermal mass," Energy, vol. 111, pp. 991-1002, 2016. [DOI:10.1016/j.energy.2016.05.076]
6. [6] B. Alimohammadisagvand, J. Jokisalo, S. Kilpeläinen, M. Ali, and K. Sirén, "Cost-optimal thermal energy storage system for a residential building with heat pump heating and demand response control," Appl. Energy, vol. 174, pp. 275-287, 2016. [DOI:10.1016/j.apenergy.2016.04.013]
7. [7] Y. Chen, P. Xu, J. Gu, F. Schmidt, and W. Li, "Measures to improve energy demand flexibility in buildings for demand response (DR): A review," Energy and Buildings, vol. 177. pp. 125-139, 2018. [DOI:10.1016/j.enbuild.2018.08.003]
8. [8] N. Good, E. A. Martínez Ceseña, C. Heltorp, and P. Mancarella, "A transactive energy modelling and assessment framework for demand response business cases in smart distributed multi-energy systems," Energy, vol. 184, pp. 165-179, 2019. [DOI:10.1016/j.energy.2018.02.089]
9. [9] X. Jin, K. Baker, S. Isley, and D. Christensen, "User-preference-driven model predictive control of residential building loads and battery storage for demand response," in Proceedings of the American Control Conference, 2017. [DOI:10.23919/ACC.2017.7963592]
10. [10] A. Taşcıkaraoğlu, N. G. Paterakis, O. Erdinç, and J. P. S. Catalão, "Combining the Flexibility From Shared Energy Storage Systems and DLC-Based Demand Response of HVAC Units for Distribution System Operation Enhancement," IEEE Trans. Sustain. Energy, vol. 10, no. 1, pp. 137-148, 2019. [DOI:10.1109/TSTE.2018.2828337]
11. [11] S. M. Kazemi-Razi, M. Mirsalim, H. Askarian-Abyaneh, H. Nafisi, and M. Marzband, "Maximization of Wind Energy Utilization and Flicker Propagation Mitigation Using SC and STATCOM," in 2018 Smart Grid Conference (SGC), 2018, pp. 1-6. [DOI:10.1109/SGC.2018.8777744]
12. [12] P. Mancarella and G. Chicco, "Real-Time Demand Response From Energy Shifting in Distributed Multi-Generation," IEEE Trans. Smart Grid, vol. 4, no. 4, pp. 1928-1938, 2013. [DOI:10.1109/TSG.2013.2258413]
13. [13] E. A. Martínez Ceseña, N. Good, and P. Mancarella, "Electrical network capacity support from demand side response: Techno-economic assessment of potential business cases for small commercial and residential end-users," Energy Policy, vol. 82, pp. 222-232, Jul. 2015. [DOI:10.1016/j.enpol.2015.03.012]
14. [14] L. Zhang, N. Good, and P. Mancarella, "Building-to-grid flexibility: Modelling and assessment metrics for residential demand response from heat pump aggregations," Appl. Energy, vol. 233-234, pp. 709-723, Jan. 2019. [DOI:10.1016/j.apenergy.2018.10.058]
15. [15] N. P. Good, "Techno-Economic Assessment of Flexible Demand," The University of Manchester (United Kingdom), 2015.
16. [16] O. Mégel, J. L. Mathieu, and G. Andersson, "Scheduling distributed energy storage units to provide multiple services under forecast error," Int. J. Electr. Power Energy Syst., vol. 72, pp. 48-57, 2015. [DOI:10.1016/j.ijepes.2015.02.010]
17. [17] S. Pfenninger and L. Staffell, "Renewables.ninja." [Online]. Available: https://www.renewables.ninja/.
18. [18] APX-ENDEX., "APX Power U.K.," (Nov. 10, 2014). [Online]. Available: https://www.apxgroup.com/market-results/apx-power-uk/ukpx-rpdhistorical-data/.
19. [19] Elexon., "SSP/SBP/NIV.," Nov. 10, 2014. [Online]. Available: http://www.elexonportal.co.uk/sspsbpniv.
20. [20] ICE-ENDEX, "ICE ENDEX OCM market data 2014," accessed December 3, 2014. [Online]. Available: http://www.iceendex.com/market-data/spot-markets/ocm/.
21. [21] H. Heitsch and W. Römisch, "Scenario reduction algorithms in stochastic programming," Comput. Optim. Appl., vol. 24, no. 2-3, pp. 187-206, 2003.
22. [22] E. S. Trust, "Measurement of domestic hot water consumption in dwellings," Energy Sav Trust, 2008.



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Kazemi-Razi S, Askarian-Abyaneh H, Nafisi H, Marzband M, Samadian-Zakaria M. Optimization of Operation of Microgrid by Thermal Demand Response Considering Enhancement of Consumers’ Thermal Comfort. ieijqp 2020; 8 (3) :68-77
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Volume 8, Issue 3 (1-2020) Back to browse issues page
نشریه علمی- پژوهشی کیفیت و بهره وری صنعت برق ایران Iranian Electric Industry Journal of Quality and Productivity
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