Multi-agent Control of Thermal Systems in Buildings


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In buildings, the thermal functions of heating, ventilation, air conditioning and domestic hot water production are often interdependent. Additionally, it is more and more complex to control them, given the increasing use of alternative energy sources, such as solar thermal collectors or heatpumps. In this work, we propose an approach allowing to design and optimize the control of thermal systems in the buildings, while improving flexibility and reusability. Consumer, producer, distributor and environmental agents are used to represent the building and its appliances. These agents' internal models allow them to compute the energy needs, energy resources and associated costs, and take into account the specificities of the thermal systems. Following this modeling step, a distributed mechanism automatically controls the system, by combining a multi-criteria selection, a local optimization and a distributed allocation of the available resources. This approach was used to control a compact unit providing heating, ventilation and domestic hot water production in a low-energy building. The system was evaluated using a thermal simulator, and managed to improve the thermal comfort by 35% compared to the initial control system, for only a 2.5% increase in costs.

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Multi-agent Control of Thermal Systems in Buildings

  1. 1. Multi-agent Control of Thermal Systems in Buildings Benoit Lacroix, CEA-LIST Cédric Paulus, CEA-LITEN / INES David Mercier, CEA-LIST Agent Technologies in Energy Systems 2012 (ATES@AAMAS’12)
  2. 2. 2 Context and motivations• CEA-LIST and French National Institute on Solar Energy• Objective  Control heating, cooling and domestic hot water production in buildings• Issues  Optimize the system using different criteria  Ease the design of control systems Solar Combisystem by Atlantic & CEA-LITEN / INES
  3. 3. 3 Outline1. Objectives and constraints2. Description of the approach3. Implementation and results  Demonstration4. Conclusion and future works
  4. 4. 4 Objectives & constraints• Objectives  Specificities of new energy sources  Specificities of energy transfers as heat  Prove the concept on a real system » Compact unit providing heating, cooling and hot water production• Main constraint  Provide at least similar comfort as existing solutions• Proposed solution 1. Agent-based description of the physical system 2. Automated mechanism for the control and optimization
  5. 5. 5 Example Inside Outside Ventilators Heat recovery ventilation Irreversible Heat Pump Reversible Heat Pump << Electrical resistance < Water heater Thermal solar collectorSolar Combisystem by Atlantic & CEA-LITEN / INES
  6. 6. 6 The agents• Four types of agents  Producer agents » Produce thermal energy  Consumer agents » Perform a comfort function  Distributor agents » Represent a sub-part of the distribution network  Environmental agents » Represent external information
  7. 7. 7 Agents (1/3)• Producer agents  Produce thermal energy  Internal model » Forecast of energy resources » Associated energy consumption  Set of devices (sensors or actuators) » Value, internal model, forecast and history• Example: an heat pump  Internal model » ep = (a.Tevap + b.Tevap² + c.Tcond + d) . Δt Tevap Tcond » ec = Pmax . Δt ON / OFF  On/off command
  8. 8. 8 Agents (2/3)• Consumer agents  Perform a comfort function  Internal model » Forecast of energy needs  Objective and utility functions  Set of devices (no actuators) Tcons• Example of the thermal comfort  Internal model of the building Tint » eb = c . (Tcons + Tint) + ua . (3.Tint/2- Tcons/2 - Text) . Δt  Temperature set point » 19°C evening and week-ends, 16°C day-time  Temperature inside the building Tint
  9. 9. 9 Agents (3/3)• Distributor agents  Represent sub-parts of the distribution network » Transfer of resources from a set of suppliers to a set of clients  Internal model » Cost of the energy distribution  Set of devices (sensors or actuators)• Example of the ventilation rev HP irr HP  Two suppliers , the heat pumps  One client, the thermal comfort Ventilation  Ventilators energy consumption Cventil » eb = Pmax . γ . Δt  Ventilators command Thermal comfort
  10. 10. 10 Example rev HP irr HP Elec Res Solar Ccrev, cvp cirr cr Switch sol pump cvb csol Ventil Water H cvThermal C DHW C Weather Elec cost
  11. 11. 11 Automated control system• Based on the multi-agent description
  12. 12. 12Focus on the distributors
  13. 13. 13Automated control system (2/2)
  14. 14. 14 Application• Implementation TRNSYS  Thermal simulation software (TRNSYS) » Dynamic thermal simulator » Used to develop the existing control system  Multi-Agent System (Repast) » For rapid prototyping and results visualization  Co-simulation between the two tools Sensors Actuators » TRNSYS computes the thermal simulation values values » Repast computes the actuators values, based on Repast the sensors values from TRNSYS
  15. 15. 15Demonstration
  16. 16. 16 Experimental protocol• Comparison of the results of 3 control systems  A basic control system » Designed by the thermal engineers » Based on reactive rules using temperature setpoints  An optimized control system » Designed by the thermal engineers » Adaptive rules, anticipation of the heating needs, linear control of the actuators  The multi-agent control system• One-year simulation in a low-energy house  120 m², central-european weather conditions (Strasbourg, France)  Comparison of the obtained results
  17. 17. 17 Results Comparison of the basic, optimized, and MAS control systems » Thermal comfort: +35% (-14h/year of discomfort) » Operating cost: +2.5% (+5.2 €/year)
  18. 18. 18 Conclusion• Approach to design control systems  Combination of two steps » Agent-based description of the physical system » Automated mechanism for the control and optimization  Applied to control a real system » Improvement of the thermal comfort, small increase in costs » Enhanced reusability and flexibility• Future works  Evaluation on a physical test bench (next week!)  Introduction of more complex comfort functions  Self-adaptation (on-site calibration of the internal models)
  19. 19. 19Thank you for your attention