IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i3p1220-d744045.html
   My bibliography  Save this article

A Demand Response Implementation with Building Energy Management System

Author

Listed:
  • Prasertsak Charoen

    (National Electronics and Computer Technology Center, National Science and Technology Development Agency, 112 Phahonyothin Road, Khlong Nueng, Khlong Luang District, Pathum Thani 12120, Thailand)

  • Nathavuth Kitbutrawat

    (National Electronics and Computer Technology Center, National Science and Technology Development Agency, 112 Phahonyothin Road, Khlong Nueng, Khlong Luang District, Pathum Thani 12120, Thailand)

  • Jasada Kudtongngam

    (National Electronics and Computer Technology Center, National Science and Technology Development Agency, 112 Phahonyothin Road, Khlong Nueng, Khlong Luang District, Pathum Thani 12120, Thailand)

Abstract

The demand response (DR) program is one of the most promising components in the development of the Smart Grid. However, there are many challenges in practical operation to improve the existing and outdated system to comply with the DR programs. In Thailand, the major pain point of the office building owner in the DR program is the additional equipment, modification and operation cost of the existing equipment. Moreover, the sophisticated solution and control are other obstacles that need more measurements and data, and they make the operation difficult to work with. In this paper, we implemented a simple yet cost-effective hardware and software solution targeting an outdated air-conditioning system without voiding the warranty of the outdated equipment and without installing any additional measurements. In addition, the proposed operation is designed to be easy to operate under the equipment limitation and unskilled labor. More importantly, indoor temperature setpoint schedules during the DR event are forecasted with some public datasets to determine the capacity of the energy management system that can reduce the power consumption in the office building without an effect on the occupants’ comfort. To confirm the practicality of the proposed solution, the actual operation of the proposed solution can achieve the maximum power reduction at 19.80 kW (43.79% of the maximum power consumption) while keeping only 1 °C of difference from the typical room temperature (26–28 °C).

Suggested Citation

  • Prasertsak Charoen & Nathavuth Kitbutrawat & Jasada Kudtongngam, 2022. "A Demand Response Implementation with Building Energy Management System," Energies, MDPI, vol. 15(3), pages 1-21, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1220-:d:744045
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/3/1220/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/3/1220/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christina Turley & Margarite Jacoby & Gregory Pavlak & Gregor Henze, 2020. "Development and Evaluation of Occupancy-Aware HVAC Control for Residential Building Energy Efficiency and Occupant Comfort," Energies, MDPI, vol. 13(20), pages 1-30, October.
    2. Joowook Kim & Doosam Song & Suyeon Kim & Sohyun Park & Youngjin Choi & Hyunwoo Lim, 2020. "Energy-Saving Potential of Extending Temperature Set-Points in a VRF Air-Conditioned Building," Energies, MDPI, vol. 13(9), pages 1-17, May.
    3. Xue, Xue & Wang, Shengwei & Yan, Chengchu & Cui, Borui, 2015. "A fast chiller power demand response control strategy for buildings connected to smart grid," Applied Energy, Elsevier, vol. 137(C), pages 77-87.
    4. Rachael Sherman & Hariharan Naganathan & Kristen Parrish, 2021. "Energy Savings Results from Small Commercial Building Retrofits in the US," Energies, MDPI, vol. 14(19), pages 1-16, September.
    5. Pedro Macieira & Luis Gomes & Zita Vale, 2021. "Energy Management Model for HVAC Control Supported by Reinforcement Learning," Energies, MDPI, vol. 14(24), pages 1-14, December.
    6. Sehar, Fakeha & Pipattanasomporn, Manisa & Rahman, Saifur, 2016. "An energy management model to study energy and peak power savings from PV and storage in demand responsive buildings," Applied Energy, Elsevier, vol. 173(C), pages 406-417.
    7. Wang, Yi & Gan, Dahua & Sun, Mingyang & Zhang, Ning & Lu, Zongxiang & Kang, Chongqing, 2019. "Probabilistic individual load forecasting using pinball loss guided LSTM," Applied Energy, Elsevier, vol. 235(C), pages 10-20.
    8. Prasertsak Charoen & Marios Sioutis & Saher Javaid & Chalie Charoenlarpnopparut & Yuto Lim & Yasuo Tan, 2019. "User-Centric Consumption Scheduling and Fair Billing Mechanism in Demand-Side Management," Energies, MDPI, vol. 12(1), pages 1-20, January.
    9. Ghulam Hafeez & Nadeem Javaid & Sohail Iqbal & Farman Ali Khan, 2018. "Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units," Energies, MDPI, vol. 11(3), pages 1-27, March.
    10. Hafeez, Ghulam & Alimgeer, Khurram Saleem & Khan, Imran, 2020. "Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid," Applied Energy, Elsevier, vol. 269(C).
    11. Turner, W.J.N. & Walker, I.S. & Roux, J., 2015. "Peak load reductions: Electric load shifting with mechanical pre-cooling of residential buildings with low thermal mass," Energy, Elsevier, vol. 82(C), pages 1057-1067.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jonas Sievers & Thomas Blank, 2023. "A Systematic Literature Review on Data-Driven Residential and Industrial Energy Management Systems," Energies, MDPI, vol. 16(4), pages 1-21, February.
    2. Eder Andrade da Silva & Carlos Alejandro Urzagasti & Joylan Nunes Maciel & Jorge Javier Gimenez Ledesma & Marco Roberto Cavallari & Oswaldo Hideo Ando Junior, 2022. "Development of a Self-Calibrated Embedded System for Energy Management in Low Voltage," Energies, MDPI, vol. 15(22), pages 1-21, November.
    3. Fernando Cassola & Leonel Morgado & António Coelho & Hugo Paredes & António Barbosa & Helga Tavares & Filipe Soares, 2022. "Using Virtual Choreographies to Identify Office Users’ Behaviors to Target Behavior Change Based on Their Potential to Impact Energy Consumption," Energies, MDPI, vol. 15(12), pages 1-21, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Yongbao & Chen, Zhe & Xu, Peng & Li, Weilin & Sha, Huajing & Yang, Zhiwei & Li, Guowen & Hu, Chonghe, 2019. "Quantification of electricity flexibility in demand response: Office building case study," Energy, Elsevier, vol. 188(C).
    2. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    3. Zhang, Xiangyu & Pipattanasomporn, Manisa & Rahman, Saifur, 2017. "A self-learning algorithm for coordinated control of rooftop units in small- and medium-sized commercial buildings," Applied Energy, Elsevier, vol. 205(C), pages 1034-1049.
    4. Hany Habbak & Mohamed Mahmoud & Khaled Metwally & Mostafa M. Fouda & Mohamed I. Ibrahem, 2023. "Load Forecasting Techniques and Their Applications in Smart Grids," Energies, MDPI, vol. 16(3), pages 1-33, February.
    5. Ghulam Hafeez & Khurram Saleem Alimgeer & Zahid Wadud & Zeeshan Shafiq & Mohammad Usman Ali Khan & Imran Khan & Farrukh Aslam Khan & Abdelouahid Derhab, 2020. "A Novel Accurate and Fast Converging Deep Learning-Based Model for Electrical Energy Consumption Forecasting in a Smart Grid," Energies, MDPI, vol. 13(9), pages 1-25, May.
    6. Li, Yiyan & Zhang, Si & Hu, Rongxing & Lu, Ning, 2021. "A meta-learning based distribution system load forecasting model selection framework," Applied Energy, Elsevier, vol. 294(C).
    7. Chu, Wenfeng & Zhang, Yu & Wang, Donglin & He, Wei & Zhang, Sheng & Hu, Zhongting & Zhou, Jinzhi, 2023. "Capacity determination of renewable energy systems, electricity storage, and heat storage in grid-interactive buildings," Energy, Elsevier, vol. 285(C).
    8. Tang, Hong & Wang, Shengwei & Li, Hangxin, 2021. "Flexibility categorization, sources, capabilities and technologies for energy-flexible and grid-responsive buildings: State-of-the-art and future perspective," Energy, Elsevier, vol. 219(C).
    9. Wang, Pengcheng & Liu, Zhongbing & Liu, Ruimiao & Zhang, Feng & Zhang, Ling, 2023. "Energy flexibility of PCM-integrated building: Combination parameters design and operation control in multi-objective optimization considering different stakeholders," Energy, Elsevier, vol. 268(C).
    10. Fanidhar Dewangan & Almoataz Y. Abdelaziz & Monalisa Biswal, 2023. "Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review," Energies, MDPI, vol. 16(3), pages 1-55, January.
    11. Luo, Na & Langevin, Jared & Chandra-Putra, Handi & Lee, Sang Hoon, 2022. "Quantifying the effect of multiple load flexibility strategies on commercial building electricity demand and services via surrogate modeling," Applied Energy, Elsevier, vol. 309(C).
    12. Cui, Borui & Gao, Dian-ce & Xiao, Fu & Wang, Shengwei, 2017. "Model-based optimal design of active cool thermal energy storage for maximal life-cycle cost saving from demand management in commercial buildings," Applied Energy, Elsevier, vol. 201(C), pages 382-396.
    13. Chen, Yongbao & Xu, Peng & Chen, Zhe & Wang, Hongxin & Sha, Huajing & Ji, Ying & Zhang, Yongming & Dou, Qiang & Wang, Sheng, 2020. "Experimental investigation of demand response potential of buildings: Combined passive thermal mass and active storage," Applied Energy, Elsevier, vol. 280(C).
    14. Sun, Fangyuan & Kong, Xiangyu & Wu, Jianzhong & Gao, Bixuan & Chen, Ke & Lu, Ning, 2022. "DSM pricing method based on A3C and LSTM under cloud-edge environment," Applied Energy, Elsevier, vol. 315(C).
    15. Ren, Haoshan & Sun, Yongjun & Albdoor, Ahmed K. & Tyagi, V.V. & Pandey, A.K. & Ma, Zhenjun, 2021. "Improving energy flexibility of a net-zero energy house using a solar-assisted air conditioning system with thermal energy storage and demand-side management," Applied Energy, Elsevier, vol. 285(C).
    16. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    17. Kim, Minsoo & Park, Taeseop & Jeong, Jaeik & Kim, Hongseok, 2023. "Stochastic optimization of home energy management system using clustered quantile scenario reduction," Applied Energy, Elsevier, vol. 349(C).
    18. Fu, Yangyang & Han, Xu & Baker, Kyri & Zuo, Wangda, 2020. "Assessments of data centers for provision of frequency regulation," Applied Energy, Elsevier, vol. 277(C).
    19. Tang, Rui & Li, Hangxin & Wang, Shengwei, 2019. "A game theory-based decentralized control strategy for power demand management of building cluster using thermal mass and energy storage," Applied Energy, Elsevier, vol. 242(C), pages 809-820.
    20. Taimoor Ahmad Khan & Amjad Ullah & Ghulam Hafeez & Imran Khan & Sadia Murawwat & Faheem Ali & Sajjad Ali & Sheraz Khan & Khalid Rehman, 2022. "A Fractional Order Super Twisting Sliding Mode Controller for Energy Management in Smart Microgrid Using Dynamic Pricing Approach," Energies, MDPI, vol. 15(23), pages 1-14, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1220-:d:744045. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.