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Model predictive control for building loads connected with a residential distribution grid

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  • Mirakhorli, Amin
  • Dong, Bing

Abstract

Aggregated control of electrical loads in a large cluster of buildings has been a challenge due to the complexity of the system involving generators, grid constraints, load serving entities complex load models, and people behavior. This paper introduces a novel load aggregation method in an electricity distribution system with Model Predictive Controlled (MPC) loads. This method closes the control loop from power generation to people behavior, resulting in a more stable and efficient integrated buildings-to-grid system. A behavior-driven price-based MPC is introduced for a residential building energy management system, which controls the air conditioner (AC), electric vehicle (EV), water heater, and battery energy storage system. A nodal pricing method is introduced representing power generation and distribution costs, which is mathematically proven to stabilize the system with MPC controlled loads. The method is tested in a 342-node residential building distribution network with 15,000 buildings which is inverse sampled from hundreds of actual smart meter data. The results show a 21% reduction in generation cost, 17% reduction in peak load, and reduced nodal voltage drop from the coordinated control system.

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  • Mirakhorli, Amin & Dong, Bing, 2018. "Model predictive control for building loads connected with a residential distribution grid," Applied Energy, Elsevier, vol. 230(C), pages 627-642.
    • Handle: RePEc:eee:appene:v:230:y:2018:i:c:p:627-642
    DOI: 10.1016/j.apenergy.2018.08.051
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    1. 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.
    2. Patteeuw, Dieter & Henze, Gregor P. & Helsen, Lieve, 2016. "Comparison of load shifting incentives for low-energy buildings with heat pumps to attain grid flexibility benefits," Applied Energy, Elsevier, vol. 167(C), pages 80-92.
    3. García-Villalobos, J. & Zamora, I. & Knezović, K. & Marinelli, M., 2016. "Multi-objective optimization control of plug-in electric vehicles in low voltage distribution networks," Applied Energy, Elsevier, vol. 180(C), pages 155-168.
    4. Anvari-Moghaddam, Amjad & Rahimi-Kian, Ashkan & Mirian, Maryam S. & Guerrero, Josep M., 2017. "A multi-agent based energy management solution for integrated buildings and microgrid system," Applied Energy, Elsevier, vol. 203(C), pages 41-56.
    5. Baldi, Simone & Michailidis, Iakovos & Ravanis, Christos & Kosmatopoulos, Elias B., 2015. "Model-based and model-free “plug-and-play” building energy efficient control," Applied Energy, Elsevier, vol. 154(C), pages 829-841.
    6. Honarmand, Masoud & Zakariazadeh, Alireza & Jadid, Shahram, 2014. "Optimal scheduling of electric vehicles in an intelligent parking lot considering vehicle-to-grid concept and battery condition," Energy, Elsevier, vol. 65(C), pages 572-579.
    7. Hu, Junjie & Morais, Hugo & Sousa, Tiago & Lind, Morten, 2016. "Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1207-1226.
    8. Dong, Bing & Li, Zhaoxuan & Taha, Ahmad & Gatsis, Nikolaos, 2018. "Occupancy-based buildings-to-grid integration framework for smart and connected communities," Applied Energy, Elsevier, vol. 219(C), pages 123-137.
    9. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
    10. Baeten, Brecht & Rogiers, Frederik & Helsen, Lieve, 2017. "Reduction of heat pump induced peak electricity use and required generation capacity through thermal energy storage and demand response," Applied Energy, Elsevier, vol. 195(C), pages 184-195.
    11. Li, Xiwang & Malkawi, Ali, 2016. "Multi-objective optimization for thermal mass model predictive control in small and medium size commercial buildings under summer weather conditions," Energy, Elsevier, vol. 112(C), pages 1194-1206.
    12. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2016. "Occupancy-based demand response and thermal comfort optimization in microgrids with renewable energy sources and energy storage," Applied Energy, Elsevier, vol. 163(C), pages 93-104.
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