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A Modelica library for the agent-based control of building energy systems

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  • Bünning, Felix
  • Sangi, Roozbeh
  • Müller, Dirk

Abstract

Building energy systems account for one third of the primary energy demand in the OECD member countries. Due to global warming and growing energy scarcity, a higher energy efficiency of these systems is required. As a result, building energy systems become more complex and often contain multiple heat and cold suppliers. The selection of the ideal supplier based on the current system state requires new control strategies. Multi agent systems depict a promising technology for the problem. There are agent-based frameworks available for many programming languages, but not for the modelling language Modelica, which is commonly used for building simulation. In the course of this work, a Modelica library for the agent-based control of building energy systems based on market mechanisms is introduced. The structure of the library, different types of agents, the concepts of agent communication and the trading of capacity adjustments are discussed. The library offers a variety of cost functions in order to realize different optimization goals. The functionality of the concept is proven with a simulation example of a building energy system controlled with agents from the introduced library. The system depicts a plug&play solution to optimize the performance of complex building energy systems with interchangeable optimization goals. Due to communication via Ethernet, the control system can be coupled to real energy systems.

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  • Bünning, Felix & Sangi, Roozbeh & Müller, Dirk, 2017. "A Modelica library for the agent-based control of building energy systems," Applied Energy, Elsevier, vol. 193(C), pages 52-59.
    • Handle: RePEc:eee:appene:v:193:y:2017:i:c:p:52-59
    DOI: 10.1016/j.apenergy.2017.01.053
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    Cited by:

    1. Zhang, Xiang & Rasmussen, Christoffer & Saelens, Dirk & Roels, Staf, 2022. "Time-dependent solar aperture estimation of a building: Comparing grey-box and white-box approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    2. Zhang, Ying & Deng, Shuai & Zhao, Li & Lin, Shan & Ni, Jiaxin & Ma, Minglu & Xu, Weicong, 2018. "Optimization and multi-time scale modeling of pilot solar driven polygeneration system based on organic Rankine cycle," Applied Energy, Elsevier, vol. 222(C), pages 396-409.
    3. Bünning, Felix & Wetter, Michael & Fuchs, Marcus & Müller, Dirk, 2018. "Bidirectional low temperature district energy systems with agent-based control: Performance comparison and operation optimization," Applied Energy, Elsevier, vol. 209(C), pages 502-515.
    4. Su, Bing & Wang, Shengwei, 2020. "An agent-based distributed real-time optimal control strategy for building HVAC systems for applications in the context of future IoT-based smart sensor networks," Applied Energy, Elsevier, vol. 274(C).
    5. Hu, Maomao & Xiao, Fu & Wang, Shengwei, 2021. "Neighborhood-level coordination and negotiation techniques for managing demand-side flexibility in residential microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    6. Enghok Leang & Pierre Tittelein & Laurent Zalewski & Stéphane Lassue, 2020. "Impact of a Composite Trombe Wall Incorporating Phase Change Materials on the Thermal Behavior of an Individual House with Low Energy Consumption," Energies, MDPI, vol. 13(18), pages 1-32, September.
    7. Lin, Haiyang & Liu, Yiling & Sun, Qie & Xiong, Rui & Li, Hailong & Wennersten, Ronald, 2018. "The impact of electric vehicle penetration and charging patterns on the management of energy hub – A multi-agent system simulation," Applied Energy, Elsevier, vol. 230(C), pages 189-206.
    8. Li, Wenzhuo & Wang, Shengwei, 2020. "A multi-agent based distributed approach for optimal control of multi-zone ventilation systems considering indoor air quality and energy use," Applied Energy, Elsevier, vol. 275(C).

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