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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
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
2
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
3
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
4
Solar Combisystem by Atlantic & CEA-LITEN / INES
Example
5
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)
18
Thank you for your attention
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