Multi-agent Control of Thermal Systems in Buildings
Benoit Lacroix [email protected], CEA-LIST
Cédric Paulus [email protected], CEA-LITEN / INES
David Mercier [email protected], CEA-LIST
Agent Technologies in Energy Systems 2012
(ATES@AAMAS’12)
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
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Solar Combisystem by
Atlantic & CEA-LITEN / INES
Outline
1. Objectives and constraints
2. Description of the approach
3. Implementation and results
Demonstration
4. Conclusion and future works
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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
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Solar Combisystem by Atlantic & CEA-LITEN / INES
Example
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Inside
Water heater
Thermal solar
collector
Electrical resistance <<
<
Reversible Heat Pump
Irreversible Heat Pump
Heat recovery
ventilation
Ventilators
Outside
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
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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
» ec = Pmax . Δt
On/off command
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ON / OFF
Tevap Tcond
Agents (2/3)
• Consumer agents
Perform a comfort function
Internal model » Forecast of energy needs
Objective and utility functions
Set of devices (no actuators)
• Example of the thermal comfort
Internal model of the building » 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
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Tint
Tcons
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
Two suppliers , the heat pumps
One client, the thermal comfort
Ventilators energy consumption » eb = Pmax . γ . Δt
Ventilators command
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Cventil
Ventilation
rev HP irr HP
Thermal
comfort
Example
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irr HP
cirr
rev HP
crev, cvp
Elec Res
cr
Solar C
DHW C Thermal C
Switch
cvb
sol pump
csol
Ventil
cv
Water H
Elec cost
Weather
Automated control system
• Based on the multi-agent description
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Focus on the distributors
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Automated control system (2/2)
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Application
• Implementation
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 » TRNSYS computes the thermal simulation
» Repast computes the actuators values, based on the sensors values from TRNSYS
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Repast
Sensors
values
TRNSYS
Actuators
values
Demonstration
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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
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Results
Comparison of the basic, optimized, and MAS control systems » Thermal comfort: +35% (-14h/year of discomfort)
» Operating cost: +2.5% (+5.2 €/year)
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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)
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Thank you for your attention
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