کاهش شدت انرژی در بیمارستان پس از پیاده‌سازی سیستم مدیریت انرژی با در نظر گرفتن ترجیحات فازی مصرف‌کننده

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشیار، گروه مهندسی سیستم‌‌های اقتصادی و اجتماعی، دانشکده مهندسی صنایع و سیستم‌‌ها، دانشگاه تهران، تهران، ایران

2 دانشیار، گروه مدیریت صنعتی، دانشکده مدیریت، دانشگاه تهران، تهران، ایران

چکیده

مراکزی که خدمات عمومی ارائه می‌‌کنند، مانند بیمارستان‌‌های دولتی، ازجمله مصرف‌‌کنندگان بزرگ انرژی الکتریکی هستند که لازم است مصرف انرژی آن‌ها مدیریت شود. در این پژوهش با در نظر گرفتن ترجیحات مصرف‌‌کننده و تعرفه قیمت انرژی الکتریکی، یک مدل برنامه‌‌ریزی ریاضی برای بهینه‌‌سازی مصرف‌ انرژی برق بیمارستان‌‌ها ارائه شده است. در یک مطالعه موردی، فهرستی از دستگاه‌‌های مورداستفاده توسط یک مصرف‌‌کننده نمونه دریافت شده و با تعیین مشخصات دستگاه‌‌ها و ترجیحات کاربران آن‌ها، مدل برنامه‌‌ریزی و زمان‌‌بندی برای آن‌ها اجرا شده است. سه سناریوی کمینه‌‌سازی اوج انرژی مصرفی (مدیریت اضافه‌بار)، کمینه‌‌سازی هزینه انرژی مصرفی و تلفیق سناریوهای اول و دوم، به‌عنوان اهداف پیاده‌‌سازی سیستم مدیریت انرژی، موردمطالعه و بررسی قرار گرفته‌‌اند. نهایتاً شاخص شدت انرژی، قبل و پس از بهینه‌‌سازی، مورد ارزیابی قرار گرفته است. نتایج نشان داد میزان کاهش شدت انرژی در سناریوی تلفیقی که هر دو هدف را به‌طور هم‌زمان در نظر می‌‌گیرد، بیشتر است.

کلیدواژه‌ها


عنوان مقاله [English]

Reduction of Energy Intensity in a Hospital after Implementation of an Energy Management System Considering Consumer Fuzzy Preferences

نویسندگان [English]

  • Hamed Shakouri Gangavi 1
  • Aliyeh Kazemi 2
1 Associate Professor, School of Industrial and Systems Engineering, College of Engineering, University of Tehran, Tehran, Iran
2 Associate Professor, Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran
چکیده [English]

Public service providers such as state hospitals are one of the major consumers of electrical energy and it is necessary to manage their consumption. In this study, considering consumer preferences and electricity price tariffs, a mathematical programming model has been presented to optimize electrical energy consumption of hospitals. Data for a set of devices utilized by a sample consumer have been gathered. After determining device specifications and consumer preferences, a scheduling model has been implemented. As the objectives of energy management system, three scenarios consist of peak energy minimization (load management), minimization of energy costs and combining the first and the second scenarios have been studied and analyzed. Finally, the energy intensity index has been assessed before and after optimization. The results showed that in the combined scenario that both objectives are considered simultaneously, greater reduction of energy intensity is observed.

کلیدواژه‌ها [English]

  • Energy consumption management
  • Hospitals
  • Smart buildings
  • Optimization
  • Fuzzy Goal Programming

[1]      Mirfakhraddiny S.H., Babaei M. H., Morovati S. A., Predicting Energy Consumption of Iran via a Hybrid Model of Artificial Neural Networks and Genetic Algorithms and Comparing It with Traditional Models. Management Researches in Iran; 2013: 17(2), 50-53.

[2]      Alavi K. Energy consumption optimization in medical centers, rethinking in architecture of a hospital. MED & LA Engineering Magazine; 2012 : 138, 50-53.

[3]      Albadi MH, El-Saadany EF.  Demand response in electricity markets:  an overview. In: IEEE Power Eng. Soc. Gen. Meeting; 2007.

[4]      Lui TJ, Stirling W, Marcy HO. Get smart. IEEE Power Energy Mag 2010; 8: 66–78.

[5]      Pyrko J. Load demand pricing - case studies in residential buildings. In: International Energy Efficiency in Domestic Appliances and Lighting Conference 2006.

[6]      Wu TY, Shieh SS, Jang SS, Liu CCL. Optimal energy management integration for a petrochemical plant under considerations of uncertain power supplies. IEEE Trans Power Systems 2005; 20:1431–9.

[7]      Nehrir MH, LaMeres B.J, Gerez V, A customer-interactive  electric  water heater  demand-side  management  strategy  using  fuzzy  logic,  IEEE Power Engineering Society 1999 Winter Meeting 1.1999: 433 – 436.

[8]      Wacks K.  Utility load management using home automation.  IEEE Trans Consumer Electron 1991; 37:168–74.

[9]      Tompros S, Mouratidis N, Draaijer M,  Foglar A, Hrasnica H. Enabling applicability of energy  saving  applications  on the appliances of the  home environment. IEEE Network 2009; 23: 8–16.

[10]   Zhu Z, Tang J, Lambotharan S, Chin WH, Fan Z. An Integer Linear Programming Based Optimization for Home Demand-side Management in Smart Grids. In: IEEE PES Innovative Smart Grid Technologies 2012.

[11]   Adika, C.O., & Wang, L. (2014). Smart charging and appliance scheduling approaches to demand side management. Electrical Power and Energy Systems, 57, 232–240.

[12]   Chavali, P., Yang, P., & Nehorai, A. (2014). A distributed algorithm of appliance scheduling for home energy management system. Proceedings of the IEEE transactions on smart grid, 282–290.

[13]   Galvan-Lopez, E., Harris, C., Trujillo, L., Rodriguez-Vazquez, K., Clarke, S., & Cahill, V.(2014). Autonomous demand-side management system based on Monte Carlo tree search. Proceedings of the 2014 IEEE International Energy Conference(ENERGYCON), 1263–1270.

[14]   Missaoui, R., Joumaa, H., Ploix, S., & Bacha, S. (2014). Managing energy Smart Homes according to energy prices: Analysis of a Building Energy Management System. Energy and Buildings, 71, 155–167.

[15]   Lu, Y., Wang, S., Sun, Y., & Yan, C. (2015). Optimal scheduling of buildings withenergy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming. Applied Energy, 147,49–58.

[16]   Shirazi, E., Zakariazadeh, A., & Jadid, S. (2015). Optimal joint scheduling ofelectrical and thermal appliances in a smart home environment. Energy Conversion and Management, 106, 181–193.

[17]   Zhang, D., Evangelisti, S., Lettieri, P., & Papageorgiou, L. G. (2015). Energy consumption scheduling of smart homes with micro grid under multi-objective optimization. Computer Aided Chemical Engineering, 37, 2441–2446.

[18]   Steen, D., Tuan, L. A., & Carlson, O. (2016). Effects of network tariffs on residential distribution systems and price-responsive customers under hourly electricity pricing. IEEE Transactions on Smart Grid, 7(2), 617–626.

[19]   Ma, K., Yao, T., Yang, J., & Guan, X. (2016). Residential power scheduling for demand response in smart grid. Electrical Power and Energy Systems, 78,320–325.

[20]   Ozkan, H. A. (2016). Appliance based control for Home Power Management Systems. Energy, 114, 693–707.

[21]   Ogunjuyigbe, A. S. O., Ayodele, T. R., & Akinola, O. A. (2017). User satisfaction-induced demand side load management in residential buildings with user budget constraint. Applied Energy, 187, 352–366.

[22]   Vardakas, J. S., Zorba, N., & Verikoukis, C. V. (2014). Performance evaluation of power demand scheduling scenarios in a smart grid environment. Energy and Buildings, 75, 133–148.

[23]   Caprino, D., Vedova, M. L. D., & Facchinetti, T. (2015). Peak shaving through real-time scheduling of household appliances. Applied Energy, 142,164–178.

www.nyiso.com