ارزیابی اثرات اقتصادی و زیست‌محیطی سیستم‌های مدیریت هوشمند سرمایش و گرمایش در ساختمان: مطالعه‌ی موردی ساختمان اداری در شهر تهران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 هیات علمی دانشگاه تهران

2 دانشکده مهندسی عمران، پردیس دانشکده های دانشکده فنی، دانشگاه تهران

3 مدیر نظارت، شرکت پیشران انرژی، تهران، ایران

چکیده

با توجه به روند افزایشی مصرف انرژی در سطح جهان و آثار مخرب زیست‌محیطی ناشی از آن و با توجه به ‌به‌کارگیری منابع تجدیدناپذیر انرژی نظیر سوخت‌های فسیلی، توجه به بحث مدیریت و بهینه‌سازی مصرف انرژی اهمیت بیشتری یافته است.با توجه به سهم ۴۰ درصدی صنعت ساختمان از کل مصرف انرژی و همچنین سهم 80 درصدی انرژی مصرفی دوره بهره‌برداری از کل انرژی مصرفی در طول چرخه حیات ساختمان، توجه به حوزه‌های مدیریت و بهینه‌سازی مصرف انرژی در طول دوره بهره برداری ساختمان‌ها می تواند تاثیرات عمده‌ای بر بهبود مصرف انرژی داشته باشد. در پژوهش حاضر ضمن شناسایی ابزارهای مدیریت هوشمند انرژی سیستم­های سرمایش و گرمایش ساختمان و مطالعه پژوهش‌های پیشین و ارزیابی تاثیرات ‌به‌کارگیری ابزارهای مدیریت مصرف انرژی ساختمان، تاثیرات اقتصادی و زیست‌محیطی ‌به‌کارگیری این ابزارها بر روی میزان مصرف انرژی سالیانه در یک ساختمان اداری در شهر تهران به عنوان مطالعه موردی بررسی ‌شده‌است. نتایج ‌به‌دست آمده نشان دهنده کاهش 32 درصدی میزان انرژی مصرفی و همچنین کاهش قابل توجه انتشار آلاینده‌های زیست‌محیطی در حالت هوشمند در مقایسه با حالت پایه است. علاوه بر آن با در نظر گرفتن هزینه­های اجتماعی ناشی از انتشار آلاینده­های زیست­محیطی و همچنین دوره بازگشت سرمایه سعی ‌شده‌است تا عوامل موثر در توجیه­پذیری اقتصادی ‌به‌کارگیری سامانه­های مدیریت هوشمند سیستم­های سرمایش و گرمایش مشخص شود. ، ‌به‌کارگیری فناوری‌های مدیریت هوشمند انرژی ساختمان می‌تواند به عنوان گام موثری در زمینه بهینه‌سازی و مدیریت مصرف انرژی در بخش ساختمان شناخته شود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Evaluating the Economic and Environmental Impacts of Smart Management Systems for Cooling and Heating Systems in Building: Case study of Office Building in Tehran

نویسندگان [English]

  • Gholamreza Heravi 1
  • Milad Rostami 2
  • Maryam Shekari 3
1 University of Tehran
2 School of Civil Engineering, College of Engineering, University of Tehran
3 Monitoring Manager, Pishrun energy Company, Tehran, Iran
چکیده [English]

Considering the increasing rate of energy consumption and its environmental detrimental effects, as well as considering the use of non-renewable energy sources such as fossil fuels, energy management issues have become more important. Given the 40% share of the building industry's total energy consumption, as well as the 80% share of energy consumed during the operation period, attention to the areas of energy management and optimization during the operation period of the buildings can have a major impact on buildings’ energy performance. In this research, through identifying building energy management tools and studying previous studies, and assessing the effects of building energy management systems, the economic and environmental impacts of using building energy management systems on the annual energy consumption in an office building in Tehran as a case study has been investigated. The results indicate a 32 percent reduction in energy consumption and a significant reduction in the release of the environmental pollutants in smart mode compared to the base mode. Moreover, considering the social costs associated with the emitted pollutants as well as the return period, it has been attempted to identify the factors contributing to the economic justification of using smart heating and cooling systems. According to the results, the use of smart energy management systems can be considered an effective step in optimizing and managing energy consumption in the construction sector.

کلیدواژه‌ها [English]

  • Building management system
  • Smart building
  • Energy consumption management
  • Demand response management
  • Energy consumption optimization
 [1] L. Pérez-Lombard, J. Ortiz, C. Pout, A review on buildings energy consumption information, Energy and buildings, 40(3) (2008) 394-398.
[2] F. Barbir, T.N. Veziroǧlu, H.J. Plass Jr, Environmental damage due to fossil fuels use, International journal of hydrogen energy, 15(10) (1990) 739-749.
[3] P.H. Shaikh, N.B.M. Nor, P. Nallagownden, I. Elamvazuthi, T. Ibrahim, A review on optimized control systems for building energy and comfort management of smart sustainable buildings, Renewable and Sustainable Energy Reviews, 34 (2014) 409-429.
[4] T. Ramesh, R. Prakash, K. Shukla, Life cycle energy analysis of buildings: An overview, Energy and buildings, 42(10) (2010) 1592-1600.
[5] M.H. Amjadi, M. Nezamabadi-Pour, M.M. Farsangi, Estimation of electricity demand of Iran using two heuristic algorithms,Energy Conversion and Management, 51(3) (2010) 493-497.
[6] رستمی، مهدی، خائم­وطنی، عسگر، امیدعلی، مصطفی، پیشبینی تقاضای برق در ایران: کاربرد مدل ترکیبی تعدیل جزئی پویا و میانگین متحرک خود همبسته یکپارچه (ARIMA)، 7(25) (1397)، 177-199.
[7] H. Koukkari, L. Brangança, Review on the European strategies for energy-efficient buildings, International Journal of Sustainable Building Technology and Urban Development, 2(1) (2011) 87-99.
[8] L. Stankeviciute, P. Criqui, Energy and climate policies to 2020: the impacts of the European “20/20/20” approach, International Journal of Energy Sector Management, 2(2) (2008) 252-273.
[9] H. Rashidi Aghdam, L. Yarmohammadi, H. Malakooti, Studying Variety of Intelligent Control System Techniques in Hospitals for Optimization of Energy Consumption, 11(21) (2017) 57-63.
[10] P. Palensky, D. Dietrich, Demand side management: Demand response, intelligent energy systems, and smart loads, IEEE transactions on industrial informatics, 7(3) (2011) 381-388.
[11] B. Chai, J. Chen, Z. Yang, Y. Zhang, Demand response management with multiple utility companies: A two-level game approach, IEEE Transactions on Smart Grid, 5(2) (2014) 722-731.
[12] F.K. Aldrich, Smart homes: past, present and future, in:  Inside the smart home, Springer, 2003, pp. 17-39.
[13] B. Asare-Bediako, P.F. Ribeiro, W.L. Kling, Integrated energy optimization with smart home energy management systems, in:  Innovative Smart Grid Technologies (ISGT Europe), 2012 3rd IEEE PES International Conference and Exhibition on, IEEE, 2012, pp. 1-8.
[14] P. Rocha, A. Siddiqui, M. Stadler, Improving energy efficiency via smart building energy management systems: A comparison with policy measures, Energy and Buildings, 88 (2015) 203-213.
[15] L. Wang, Z. Wang, R. Yang, Intelligent multiagent control system for energy and comfort management in smart and sustainable buildings, IEEE transactions on smart grid, 3(2) (2012) 605-617.
[16] J.A. Barbosa, C. Araújo, R. Mateus, L. Bragança, Smart interior design of buildings and its relationship to land use, Architectural Engineering and Design Management, 12(2) (2016) 97-106.
[17] M. Morales-Beltran, P. Teuffel, Towards smart building structures: adaptive structures in earthquake and wind loading control response–a review, Intelligent Buildings International, 5(2) (2013) 83-100.
[18] D. Minoli, K. Sohraby, B. Occhiogrosso, IoT considerations, requirements, and architectures for smart buildings—Energy optimization and next-generation building management systems, IEEE Internet of Things Journal, 4(1) (2017) 269-283.
[19] Y. Agarwal, B. Balaji, R. Gupta, J. Lyles, M. Wei, T. Weng, Occupancy-driven energy management for smart building automation, in:  Proceedings of the 2nd ACM workshop on embedded sensing systems for energy-efficiency in building, ACM, 2010, pp. 1-6.
[20] D.E. King, M.G. Morgan, Customer-focused assessment of electric power microgrids, Journal of Energy Engineering, 133(3) (2007) 150-164.
[21] M. Zhou, Y. Gao, G. Li, Study on improvement of available transfer capability by demand side management, in:  Electric Utility Deregulation and Restructuring and Power Technologies, 2008. DRPT 2008. Third International Conference on, IEEE, 2008, pp. 545-550.
[22] A. Bagherian, S.M. Tafreshi, A developed energy management system for a microgrid in the competitive electricity market, in:  PowerTech, 2009 IEEE Bucharest, IEEE, 2009, pp. 1-6.
[23] F.A. Mohamed, H.N. Koivo, System modelling and online optimal management of microgrid using mesh adaptive direct search, International Journal of Electrical Power & Energy Systems, 32(5) (2010) 398-407.
[24] M. Stadler, A. Siddiqui, C. Marnay, H. Aki, J. Lai, Control of greenhouse gas emissions by optimal DER technology investment and energy management in zero‐net‐energy buildings, European Transactions on Electrical Power, 21(2) (2011) 1291-1309.
[25] F. Brahman, M. Honarmand, S. Jadid, Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system, Energy and Buildings, 90 (2015) 65-75.
[26] K. Ma, T. Yao, J. Yang, X. Guan, Residential power scheduling for demand response in smart grid, International Journal of Electrical Power & Energy Systems, 78 (2016) 320-325.
[27] S. Moon, J.-W. Lee, Multi-residential demand response scheduling with multi-class appliances in smart grid, IEEE transactions on smart grid, 9(4) (2018) 2518-2528.
[28] K. Amasyali, N.M. El-Gohary, A review of data-driven building energy consumption prediction studies, Renewable and Sustainable Energy Reviews, 81 (2018) 1192-1205.
[29] D.T. Delaney, G.M. O'Hare, A.G. Ruzzelli, Evaluation of energy-efficiency in lighting systems using sensor networks, in:  Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, ACM, 2009, pp. 61-66.
[30] J. Lu, T. Sookoor, V. Srinivasan, G. Gao, B. Holben, J. Stankovic, E. Field, K. Whitehouse, The smart thermostat: using occupancy sensors to save energy in homes, in:  Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, ACM, 2010, pp. 211-224.
[31] Z. Wang, L. Wang, A.I. Dounis, R. Yang, Multi-agent control system with information fusion based comfort model for smart buildings, Applied Energy, 99 (2012) 247-254.
[32] Z. Wang, L. Wang, Occupancy pattern based intelligent control for improving energy efficiency in buildings, in:  Automation Science and Engineering (CASE), 2012 IEEE International Conference on, IEEE, 2012, pp. 804-809.
[33] P.H. Shaikh, N.B.M. Nor, P. Nallagownden, I. Elamvazuthi, T. Ibrahim, Intelligent multi-objective control and management for smart energy efficient buildings, International Journal of Electrical Power & Energy Systems, 74 (2016) 403-409.
[34] S. Aslam, Z. Iqbal, N. Javaid, Z.A. Khan, K. Aurangzeb, S.I. Haider, Towards efficient energy management of smart buildings exploiting heuristic optimization with real time and critical peak pricing schemes, Energies, 10(12) (2017) 2065.
[35] R. Yang, L. Wang, Multi-objective optimization for decision-making of energy and comfort management in building automation and control, Sustainable Cities and Society, 2(1) (2012) 1-7.
[36] V.L. Erickson, A.E. Cerpa, Occupancy based demand response HVAC control strategy, in:  Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, ACM, 2010, pp. 7-12.
 [37] I. Georgievski, V. Degeler, G.A. Pagani, T.A. Nguyen, A. Lazovik, M. Aiello, Optimizing energy costs for offices connected to the smart grid, IEEE Transactions on Smart Grid, 3(4) (2012) 2273-2285.
[38] C. Bharathi, D. Rekha, V. Vijayakumar, Genetic algorithm based demand side management for smart grid, Wireless Personal Communications, 93(2) (2017) 481-502.
[39] S. Bahrami, V.W. Wong, An autonomous demand response program in smart grid with foresighted users, in:  Smart Grid Communications (SmartGridComm), 2015 IEEE International Conference on, IEEE, 2015, pp. 205-210.
[40] S. Bahrami, V.W. Wong, J. Huang, An online learning algorithm for demand response in smart grid, IEEE Transactions on Smart Grid, 9(5) (2018) 4712-4725.
[41] A. Barbato, G. Carpentieri, Model and algorithms for the real time management of residential electricity demand, in:  Energy Conference and Exhibition (ENERGYCON), 2012, pp. 701-706.
[42] M. Kummert, M.-A. Leduc, A. Moreau, Using MPC to reduce the peak demand associated with electric heating, in:  Model predictive control in buildings workshop, 2011.
[43] K.-h. Lee, J.E. Braun, Model-based demand-limiting control of building thermal mass, Building and Environment, 43(10) (2008) 1633-1646.
[44] S.D. Ramchurn, P. Vytelingum, A. Rogers, N. Jennings, Agent-based control for decentralised demand side management in the smart grid, in:  The 10th International Conference on Autonomous Agents and Multiagent Systems-Volume 1, International Foundation for Autonomous Agents and Multiagent Systems, 2011, pp. 5-12.
[45] F. Umbach, Global energy security and the implications for the EU, Energy policy, 38(3) (2010) 1229-1240.
[46]  خانی، محمد سعید، فلاحی، اسماعیل، بانشی، مهدی، ارائه مدل مدیریت تامین انرژی در ایران بر اساس معیارهای فنی، اقتصادی، و زیست­محیطی، 5(18) (1395) 29-60
[47] C. Kühnel, T. Westermann, F. Hemmert, S. Kratz, A. Müller, S. Möller, I'm home: Defining and evaluating a gesture set for smart-home control, International Journal of Human-Computer Studies, 69(11) (2011) 693-704.
[48] شهری، رضا، زمانی، حیدر، حاملی، منوچهر، بررسی هوشهندسازی در ساختمان (BMS)، دومین کنفرانس ملی معماری و منظر شهری پایدار، اردیبهشت 1394، ایران.
[49] کماسی، مهدی، درویشی، حمید، محب زندی، سپیده، بررسی نقش سیستم مدیریت هوشمند BMS در کاهش مصرف انرژی و هزینه­های ساختمان، نخستین کنفرانس بین­المللی انسان، معماری، مهندسی عمران و شهر، خرداد 1394، تبریز، ایران.
[50] E. Baneshi, M.H. Mehraban, Investigate the performance of smart buildings and building management system, in:  International Conference on Research in Science and Technology, Kualalampur, Malaysia, 2015.
[51] M. Emamgholizadeh, M. Salari, Optimization of Energy Consumption in an Administrative Building by Calculating the Impact of External Components and Automating the Powehouse, Geography, Civil, and Urban Management Studies, 3(1) (2017) 102-111.
[52] M. , Y. Baffalio, A. Duplan, B. Ferrand, Smart Home: Hope or hype, Greenwich Consulting,  (2013).
[53] E.M. Smith, D.R. Sewell, P.T. Golden, System and method for energy management, in, Google Patents, 2004.
[54] Smart Home Statistics: Home Automation is the Future, in 2020, January 5, https://innotechtoday.com/smart-home-statistics/.
 ]55[ ترازنامه انرژی ایران، 1394
 ]56[ آمار صنعت برق، 1391،  https://amar.tavanir.org.ir/pages/report/stat91/
 ]57[ خداداد کاشی، فرهاد، اکابری تفتی، مهدی، موسوی جهرمی، یگانه، خسروی نزاد، علی اکبر، محاسبه هزینه اجتماعی انتشار دی­اکسید کربن به تفکیک است‌آن‌های مختلف در ایران، فصلنامه پژوهش­های سیاستگذاری و برنامه­ریزی انرژی، 2(2) (1395) 77-110.
 ]58[ مرکز آمار ایران، دی 1398، https://www.amar.org.ir/
]59[ علی مشایخی، تدوین چارچوب خودکار انتخاب گزینه مناسب بنای ساختم‌آن‌های مسکونی هوشمند بر مبنای موازنه هزینه ساخت و مصرف انرژی طول عمر با استفاده از مدلسازی اطلاعات ساختمان، پایان­نامه کارشناسی ارشد، شهریور 1396.