Dymola-JADE Co-Simulation for Agent-Based Control in Office Spaces

Constantin, Ana; Löwen, Artur; Ponci, Ferdinanda; Huchtemann, Kristian; Müller, Dirk

doi:10.3384/ecp17132345

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…A third approach is based in the application of co-simulation, [58][59][60][61] where multiple tools with different functionalities are combined to represent the final system. [62][63][64][65] These approaches present the following respective limitations: offering limited support for describing continuoustime equations by only using explicit ODE; requiring manual anipulations of the model equations to discretize them to apply any numerical integration approach for the simulation; and requiring to use a combination of different tools to describe the whole system, that usually involves learning how to use different tools, different modeling approaches, additional programming skills, and so on. The Modelica language represents the state-of-the art in equation-based modeling of continuous-time systems.…”

Section: Combination Of Abms With Continuous-time Equationsmentioning

confidence: 99%

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Combining PDEVS and Modelica for describing agent-based models

Sanz

Urquía

2022

SIMULATION

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Modelica is a general-purpose modeling language mainly designed to facilitate the development, reusability and exchange of models. It represents the state-of-the-art in equation-based modeling of continuous-time systems. Modelica libraries facilitate the description of multi-formalism and multi-domain models. However, the description of agent-based models (ABMs) in Modelica is not currently supported, mainly due to the characteristics of the language and its simulation algorithm. The combination of ABMs with continuous-time equations provides a powerful tool for describing and analyzing complex systems. An approach for describing ABMs using the Modelica language is presented in this manuscript, with the objective of facilitating the combination of ABMs with the rest of Modelica functionality. Agent behavior is described using a process-oriented modeling approach. Agents are described as individual entities that move across a flowchart diagram, that represents the processes that agents undergo. Processes are formally described using the Parallel DEVS formalism, extended to describe the interface with other Modelica models. The environment where agents interact is described as a cellular automaton. This approach has been implemented in a free Modelica library, named ABMLib. Three case studies are discussed to illustrate the modeling functionality of the library and its combination with other models: a basic traffic model, a sheep–wolves predator–prey model and a consumer market model.

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Section: Combination Of Abms With Continuous-time Equationsmentioning

confidence: 99%

“…A third approach is based in the application of co-simulation, 58–61 where multiple tools with different functionalities are combined to represent the final system. 62–65…”

Section: Related Workmentioning

confidence: 99%

Combining PDEVS and Modelica for describing agent-based models

Sanz

Urquía

2022

SIMULATION

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“…The following models were used and/or developed: More information on the modeling principles, the communication between JADE and Dymola and the particularities of adjusting the dynamic of the agents systems to the simulation are to be found in [26].…”

Section: Simulation Models and Set-upsmentioning

confidence: 99%

Design, Implementation and Demonstration of Embedded Agents for Energy Management in Non-Residential Buildings

et al. 2017

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With the building sector being responsible for 30% of the total final energy consumption, great interest lies in implementing adequate policies and deploying efficient technologies that would decrease this number. However, building comfort and energy management systems (BCEM) are challenging to manage on account of their increasing complexity with regard to the integration of renewable energy sources or the connection of electrical, thermal and gas grids. Multi-agent systems (MAS) deal well with such complex issues. This paper presents an MAS for non-residential buildings from the design, implementation and demonstration, both simulation based and in a field test. Starting from an ontology and an attached data model for BCEM application, we elaborated use cases for developing and testing the MAS framework. The building and technical equipment are modeled using the modeling language Modelica under Dymola. The agents are programmed in JADE and communicate with Dymola via TCP/IP and with the real devices via BACnet. Operatively, the agents can take on different control strategies: normal operation with no optimization, optimization of energy costs, where energy is delivered through the room through the devices that have the lowest operating costs, and relaxation of the comfort constraint, where the costs of the productivity loss under sub-optimal comfort conditions is taken into account during optimization. Comfort is expressed as a function of indoor air temperature. Simulation, including a comparison with a benchmark system, and field test results are presented to demonstrate the features of the proposed BCEM.

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Dymola-JADE Co-Simulation for Agent-Based Control in Office Spaces

Cited by 2 publications

References 4 publications

Combining PDEVS and Modelica for describing agent-based models

Combining PDEVS and Modelica for describing agent-based models

Design, Implementation and Demonstration of Embedded Agents for Energy Management in Non-Residential Buildings

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