IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v50y2013icp110-119.html
   My bibliography  Save this article

A physically-based model for simulating inverter type air conditioners/heat pumps

Author

Listed:
  • Gomes, A.
  • Antunes, C. Henggeler
  • Martinho, J.

Abstract

The engagement in demand response activities is increasingly becoming more attractive for several entities in the power systems sector. In general terms, the main goal of such activities is to change the demand level and patterns by implementing management actions over groups of loads to minimize peak demand or electricity bill, maximize profits or increase the systems reliability, among other objectives. However, in order to avoid potential undesirable impacts it is necessary to anticipate and adequately assess the changes in demand originated by such actions. This assessment requires adequate simulation tools and models with the ability to simulate demand management actions. This work presents a physically-based model that allows reproducing the behavior of an inverter type heat pump. This model can be used to simulate the demand of an individual device or several devices. Besides, it allows simulating and assessing the impacts of implementing demand management actions over this type of end-use loads. The results show that the model can effectively reproduce the demand of this type of equipment, becoming a useful tool for the prior assessment and even the design and selection of demand response actions to be applied over these loads.

Suggested Citation

  • Gomes, A. & Antunes, C. Henggeler & Martinho, J., 2013. "A physically-based model for simulating inverter type air conditioners/heat pumps," Energy, Elsevier, vol. 50(C), pages 110-119.
  • Handle: RePEc:eee:energy:v:50:y:2013:i:c:p:110-119
    DOI: 10.1016/j.energy.2012.11.047
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544212009152
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2012.11.047?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fouda, A. & Melikyan, Z., 2010. "Assessment of a modified method for determining the cooling load of residential buildings," Energy, Elsevier, vol. 35(12), pages 4726-4730.
    2. van Ruijven, Bas & de Vries, Bert & van Vuuren, Detlef P. & van der Sluijs, Jeroen P., 2010. "A global model for residential energy use: Uncertainty in calibration to regional data," Energy, Elsevier, vol. 35(1), pages 269-282.
    3. Centolella, Paul, 2010. "The integration of Price Responsive Demand into Regional Transmission Organization (RTO) wholesale power markets and system operations," Energy, Elsevier, vol. 35(4), pages 1568-1574.
    4. Pina, André & Silva, Carlos & Ferrão, Paulo, 2012. "The impact of demand side management strategies in the penetration of renewable electricity," Energy, Elsevier, vol. 41(1), pages 128-137.
    5. Kriett, Phillip Oliver & Salani, Matteo, 2012. "Optimal control of a residential microgrid," Energy, Elsevier, vol. 42(1), pages 321-330.
    6. Faria, P. & Vale, Z., 2011. "Demand response in electrical energy supply: An optimal real time pricing approach," Energy, Elsevier, vol. 36(8), pages 5374-5384.
    7. Finn, P. & Fitzpatrick, C. & Connolly, D., 2012. "Demand side management of electric car charging: Benefits for consumer and grid," Energy, Elsevier, vol. 42(1), pages 358-363.
    8. Zarnikau, Jay W., 2010. "Demand participation in the restructured Electric Reliability Council of Texas market," Energy, Elsevier, vol. 35(4), pages 1536-1543.
    9. Moura, Pedro S. & de Almeida, Aníbal T., 2010. "The role of demand-side management in the grid integration of wind power," Applied Energy, Elsevier, vol. 87(8), pages 2581-2588, August.
    10. Shahnawaz Ahmed, S. & Shah Majid, Md. & Novia, Hendri & Abd Rahman, Hasimah, 2007. "Fuzzy logic based energy saving technique for a central air conditioning system," Energy, Elsevier, vol. 32(7), pages 1222-1234.
    11. Greening, Lorna A., 2010. "Demand response resources: Who is responsible for implementation in a deregulated market?," Energy, Elsevier, vol. 35(4), pages 1518-1525.
    12. Pantoš, Miloš, 2011. "Stochastic optimal charging of electric-drive vehicles with renewable energy," Energy, Elsevier, vol. 36(11), pages 6567-6576.
    13. Zhang, H.-F. & Ge, X.-S. & Ye, H., 2007. "Modeling of a space heating and cooling system with seasonal energy storage," Energy, Elsevier, vol. 32(1), pages 51-58.
    14. Tashtoush, Bourhan & Molhim, M. & Al-Rousan, M., 2005. "Dynamic model of an HVAC system for control analysis," Energy, Elsevier, vol. 30(10), pages 1729-1745.
    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. Ahn, Jae Hwan & Lee, Joo Seong & Baek, Changhyun & Kim, Yongchan, 2016. "Performance improvement of a dehumidifying heat pump using an additional waste heat source in electric vehicles with low occupancy," Energy, Elsevier, vol. 115(P1), pages 67-75.
    2. Reis, Inês F.G. & Gonçalves, Ivo & Lopes, Marta A.R. & Antunes, Carlos Henggeler, 2022. "Towards inclusive community-based energy markets: A multiagent framework," Applied Energy, Elsevier, vol. 307(C).
    3. Xiong, Yongkang & Zeng, Zhenfeng & Xin, Jianbo & Song, Guanhong & Xia, Yonghong & Xu, Zaide, 2023. "Renewable energy time series regulation strategy considering grid flexible load and N-1 faults," Energy, Elsevier, vol. 284(C).
    4. Gonçalves, Ivo & Gomes, Álvaro & Henggeler Antunes, Carlos, 2019. "Optimizing the management of smart home energy resources under different power cost scenarios," Applied Energy, Elsevier, vol. 242(C), pages 351-363.
    5. Xie, Jiantong & Pan, Yiqun & Jia, Wenqi & Xu, Lei & Huang, Zhizhong, 2019. "Energy-consumption simulation of a distributed air-conditioning system integrated with occupant behavior," Applied Energy, Elsevier, vol. 256(C).
    6. Reis, Inês F.G. & Gonçalves, Ivo & Lopes, Marta A.R. & Antunes, Carlos Henggeler, 2020. "A multi-agent system approach to exploit demand-side flexibility in an energy community," Utilities Policy, Elsevier, vol. 67(C).
    7. Ana García-Garre & Antonio Gabaldón & Carlos Álvarez-Bel & María Del Carmen Ruiz-Abellón & Antonio Guillamón, 2018. "Integration of Demand Response and Photovoltaic Resources in Residential Segments," Sustainability, MDPI, vol. 10(9), pages 1-31, August.
    8. Carsten Palkowski & Andreas Zottl & Ivan Malenkovic & Anne Simo, 2019. "Fixing Efficiency Values by Unfixing Compressor Speed: Dynamic Test Method for Heat Pumps," Energies, MDPI, vol. 12(6), pages 1-16, March.
    9. Inês F. G. Reis & Ivo Gonçalves & Marta A. R. Lopes & Carlos Henggeler Antunes, 2021. "Assessing the Influence of Different Goals in Energy Communities’ Self-Sufficiency—An Optimized Multiagent Approach," Energies, MDPI, vol. 14(4), pages 1-32, February.
    10. Soares, Ana & Gomes, Álvaro & Antunes, Carlos Henggeler, 2014. "Categorization of residential electricity consumption as a basis for the assessment of the impacts of demand response actions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 490-503.
    11. Antonio Gabaldón & Carlos Álvarez & María Del Carmen Ruiz-Abellón & Antonio Guillamón & Sergio Valero-Verdú & Roque Molina & Ana García-Garre, 2018. "Integration of Methodologies for the Evaluation of Offer Curves in Energy and Capacity Markets through Energy Efficiency and Demand Response," Sustainability, MDPI, vol. 10(2), pages 1-27, February.

    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. Woo, C.K. & Sreedharan, P. & Hargreaves, J. & Kahrl, F. & Wang, J. & Horowitz, I., 2014. "A review of electricity product differentiation," Applied Energy, Elsevier, vol. 114(C), pages 262-272.
    2. Olmos, Luis & Ruester, Sophia & Liong, Siok-Jen & Glachant, Jean-Michel, 2011. "Energy efficiency actions related to the rollout of smart meters for small consumers, application to the Austrian system," Energy, Elsevier, vol. 36(7), pages 4396-4409.
    3. Soares, Ana & Antunes, Carlos Henggeler & Oliveira, Carlos & Gomes, Álvaro, 2014. "A multi-objective genetic approach to domestic load scheduling in an energy management system," Energy, Elsevier, vol. 77(C), pages 144-152.
    4. Boßmann, Tobias & Eser, Eike Johannes, 2016. "Model-based assessment of demand-response measures—A comprehensive literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1637-1656.
    5. Wang, Yong & Li, Lin, 2013. "Time-of-use based electricity demand response for sustainable manufacturing systems," Energy, Elsevier, vol. 63(C), pages 233-244.
    6. Zhang, Qi & Mclellan, Benjamin C. & Tezuka, Tetsuo & Ishihara, Keiichi N., 2013. "A methodology for economic and environmental analysis of electric vehicles with different operational conditions," Energy, Elsevier, vol. 61(C), pages 118-127.
    7. Zheng, Yingying & Jenkins, Bryan M. & Kornbluth, Kurt & Kendall, Alissa & Træholt, Chresten, 2018. "Optimization of a biomass-integrated renewable energy microgrid with demand side management under uncertainty," Applied Energy, Elsevier, vol. 230(C), pages 836-844.
    8. Alagoz, B. Baykant & Kaygusuz, Asim & Akcin, Murat & Alagoz, Serkan, 2013. "A closed-loop energy price controlling method for real-time energy balancing in a smart grid energy market," Energy, Elsevier, vol. 59(C), pages 95-104.
    9. Boukettaya, Ghada & Krichen, Lotfi, 2014. "A dynamic power management strategy of a grid connected hybrid generation system using wind, photovoltaic and Flywheel Energy Storage System in residential applications," Energy, Elsevier, vol. 71(C), pages 148-159.
    10. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    11. Fernando Lezama & Ricardo Faia & Pedro Faria & Zita Vale, 2020. "Demand Response of Residential Houses Equipped with PV-Battery Systems: An Application Study Using Evolutionary Algorithms," Energies, MDPI, vol. 13(10), pages 1-18, May.
    12. Chiara Magni & Alessia Arteconi & Konstantinos Kavvadias & Sylvain Quoilin, 2020. "Modelling the Integration of Residential Heat Demand and Demand Response in Power Systems with High Shares of Renewables," Energies, MDPI, vol. 13(24), pages 1-19, December.
    13. Zhang, Yunchao & Islam, Md Monirul & Sun, Zeyi & Yang, Sijia & Dagli, Cihan & Xiong, Haoyi, 2018. "Optimal sizing and planning of onsite generation system for manufacturing in Critical Peaking Pricing demand response program," International Journal of Production Economics, Elsevier, vol. 206(C), pages 261-267.
    14. Woo, C.K. & Li, R. & Shiu, A. & Horowitz, I., 2013. "Residential winter kWh responsiveness under optional time-varying pricing in British Columbia," Applied Energy, Elsevier, vol. 108(C), pages 288-297.
    15. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems)," Energy, Elsevier, vol. 55(C), pages 1044-1054.
    16. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2022. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes," Energies, MDPI, vol. 15(8), pages 1-34, April.
    17. Doostizadeh, Meysam & Ghasemi, Hassan, 2012. "A day-ahead electricity pricing model based on smart metering and demand-side management," Energy, Elsevier, vol. 46(1), pages 221-230.
    18. Škugor, Branimir & Deur, Joško, 2015. "Dynamic programming-based optimisation of charging an electric vehicle fleet system represented by an aggregate battery model," Energy, Elsevier, vol. 92(P3), pages 456-465.
    19. Summerbell, Daniel L. & Khripko, Diana & Barlow, Claire & Hesselbach, Jens, 2017. "Cost and carbon reductions from industrial demand-side management: Study of potential savings at a cement plant," Applied Energy, Elsevier, vol. 197(C), pages 100-113.
    20. Hussein Jumma Jabir & Jiashen Teh & Dahaman Ishak & Hamza Abunima, 2018. "Impacts of Demand-Side Management on Electrical Power Systems: A Review," Energies, MDPI, vol. 11(5), pages 1-19, April.

    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:eee:energy:v:50:y:2013:i:c:p:110-119. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.