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The application of household appliances' flexibility by set of sequential uninterruptible energy phases model in the day-ahead planning of a residential microgrid

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  • Mohseni, Amin
  • Mortazavi, Seyed Saeidollah
  • Ghasemi, Ahmad
  • Nahavandi, Ali
  • Talaei abdi, Masoud

Abstract

In this work, an accurate energy consumption model of household appliances based on Set of Sequential Uninterruptible Energy Phases (SSUEP) is applied to day-ahead energy management framework of a residential microgrid in order to effectively activate time-based demand response programs. The homes in the microgrid include the essential and/or shiftable household appliances accurately modeled by the SSUEP. These homes are also equipped with the photovoltaic systems, battery energy storages and electric vehicles. The residential microgrid is assumed to be connected to a smart grid such that bi-directional exchange of electric power would be possible. Being aware of the amount of power demand for the appliances and the day-ahead prices of the energy, the consumer provides the required energy from the photovoltaic systems, battery energy storages and electric vehicles (by Vehicle-to-Home and Vehicle-to-Grid capabilities). Moreover, using the flexibility of the shiftable loads, the consumer can be involved in the demand response strategies to reduce the costs. This flexibility is a result of delaying or anticipating the start time and the inter-phase delay modeled by the SSUEP. Lastly, the effects of the accurate SSUEP model on the day-ahead planning of the residential microgrid will be investigated by various scenarios.

Suggested Citation

  • Mohseni, Amin & Mortazavi, Seyed Saeidollah & Ghasemi, Ahmad & Nahavandi, Ali & Talaei abdi, Masoud, 2017. "The application of household appliances' flexibility by set of sequential uninterruptible energy phases model in the day-ahead planning of a residential microgrid," Energy, Elsevier, vol. 139(C), pages 315-328.
    • Handle: RePEc:eee:energy:v:139:y:2017:i:c:p:315-328
    DOI: 10.1016/j.energy.2017.07.149
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    12. Fei Wang & Lidong Zhou & Hui Ren & Xiaoli Liu, 2017. "Search Improvement Process-Chaotic Optimization-Particle Swarm Optimization-Elite Retention Strategy and Improved Combined Cooling-Heating-Power Strategy Based Two-Time Scale Multi-Objective Optimizat," Energies, MDPI, vol. 10(12), pages 1-23, November.
    13. Marlene Ofelia Sanchez-Escobar & Julieta Noguez & Jose Martin Molina-Espinosa & David Escobar-Castillejos & Sergio Ruiz-Loza, 2023. "Policy Design for Electricity Efficiency: A Case Study of Bottom-Up Energy Modeling in the Residential Sector and Buildings," Energies, MDPI, vol. 16(19), pages 1-39, September.
    14. Vaclovas Miskinis & Arvydas Galinis & Inga Konstantinaviciute & Vidas Lekavicius & Eimantas Neniskis, 2019. "Comparative Analysis of the Energy Sector Development Trends and Forecast of Final Energy Demand in the Baltic States," Sustainability, MDPI, vol. 11(2), pages 1-27, January.
    15. Subramanian, Vignesh & Das, Tapas K., 2019. "A two-layer model for dynamic pricing of electricity and optimal charging of electric vehicles under price spikes," Energy, Elsevier, vol. 167(C), pages 1266-1277.
    16. Yahia, Z. & Pradhan, A., 2018. "Optimal load scheduling of household appliances considering consumer preferences: An experimental analysis," Energy, Elsevier, vol. 163(C), pages 15-26.
    17. Bishwajit Dey & Fausto Pedro García Márquez & Sourav Kr. Basak, 2020. "Smart Energy Management of Residential Microgrid System by a Novel Hybrid MGWOSCACSA Algorithm," Energies, MDPI, vol. 13(13), pages 1-23, July.
    18. Sun, Mei & Ji, Jian & Ampimah, Benjamin Chris, 2018. "How to implement real-time pricing in China? A solution based on power credit mechanism," Applied Energy, Elsevier, vol. 231(C), pages 1007-1018.
    19. Chu, Wenfeng & Zhang, Yu & He, Wei & Zhang, Sheng & Hu, Zhongting & Ru, Bingqian & Ying, Shangxuan, 2023. "Research on flexible allocation strategy of power grid interactive buildings based on multiple optimization objectives," Energy, Elsevier, vol. 278(PB).
    20. Henggeler Antunes, Carlos & Alves, Maria João & Soares, Inês, 2022. "A comprehensive and modular set of appliance operation MILP models for demand response optimization," Applied Energy, Elsevier, vol. 320(C).
    21. Yamaguchi, Yohei & Chen, Chien-fei & Shimoda, Yoshiyuki & Yagita, Yoshie & Iwafune, Yumiko & Ishii, Hideo & Hayashi, Yasuhiro, 2020. "An integrated approach of estimating demand response flexibility of domestic laundry appliances based on household heterogeneity and activities," Energy Policy, Elsevier, vol. 142(C).

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