Towards a DESTEST: a District Energy Simulation Test Developed in IBPSA Project 1

Sælens, Dirk; Jaeger, Ina De; Bünning, Felix; Mans, Michael; Vandermeulen, Annelies; Heijde, Bram van der; Garreau, Enora; Maccarini, Alessandro; Rønneseth, Øystein; Sartori, Igor; Helsen, Lieve

doi:10.26868/25222708.2019.210806

Cited by 11 publications

(13 citation statements)

References 19 publications

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“…The first use case is based on the 16 buildings introduced as the IBPSA Project 1 DESTEST [45]. The purpose of the network is to create a reusable and comparable network layout for DHC modeling approaches.…”

Section: Use Case 1: Application To An Open-source Benchmarking Syste...mentioning

confidence: 99%

Development and Application of an Open-Source Framework for Automated Thermal Network Generation and Simulations in Modelica

et al. 2022

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District heating and cooling (DHC) networks, and in particular, the fifth generation of DHC networks, offer great potential in increasing the overall system efficiency and reducing CO2 emissions in the heating and cooling of urban districts. Due to the growing complexity of these energy systems, the use of new planning methods, such as the use of dynamic simulation models based on Modelica, becomes more important. However, especially with large, complex thermal networks, there is a high effort for manual model construction and parameterization. For this reason, we present a framework for automated model generation of DHC networks based on simulation models in Modelica written in Python. The core function of the Python framework is to transform a graph representation of a district heating network into a dynamic simulation model. The authors briefly describe the workflow and demonstrate its applicability with three different use cases. We investigate the impact of different design decisions, e.g., comparing the difference between central and decentral pumps as well as a combination of both in one network. In addition, we present the results of evaluating the impact of different network temperature levels or pipe insulation compared to the overall energy supplied to the network, leading to the conclusion that the presented framework is capable of reducing the manual effort for performing DHC network simulations with Modelicaand allows to easily perform parameter studies in an early planning phases in the future.

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Section: Use Case 1: Application To An Open-source Benchmarking Syste...mentioning

confidence: 99%

Development and Application of an Open-Source Framework for Automated Thermal Network Generation and Simulations in Modelica

et al. 2022

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“…This building heating load features a single-family residential building in the heating dominated climate of Belgium. More information regarding development and testing of the building model can be found in Saelens et al [38].…”

Section: System Descriptionmentioning

confidence: 99%

A New Steam Medium Model for Fast and Accurate Simulation of District Heating Systems

et al. 2021

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“…The third case study is a variation of the benchmark problem from (Saelens et al, 2020), integrating low-temperature waste heat into a district heating network via heat pumps. The explicit consideration of thermal losses and temperatures at different points of the network results in a nonconvex mixedinteger quadratically-constrained quadratic programming (MIQCQP) problem.…”

Section: Case Studiesmentioning

confidence: 99%

“…In this case study, we extend components of previous work (Hering et al, 2020) to describe a district heating network and apply them to a design optimization of the network described by Saelens et al (2020). The system comprises a source of waste heat, a distribution network, and 16 consumers.…”

Section: Case Study 3: Design Of a Low-temperature District Heating N...mentioning

confidence: 99%

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COMANDO: A Next-Generation Open-Source Framework for Energy Systems Optimization

Langiu,

Shu,

Baader

et al. 2021

Preprint

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Existing open-source modeling frameworks dedicated to energy systems optimization typically utilize (mixed-integer) linear programming ((MI)LP) formulations, which lack modeling freedom for technical system design and operation. We present COMANDO, an open-source Python package for component-oriented modeling and optimization for nonlinear design and operation of integrated energy systems. COMANDO allows to assemble system models from component models including nonlinear, dynamic and discrete characteristics. Based on a single system model, different deterministic and stochastic problem formulations can be obtained by varying objective function and underlying data, and by applying automatic or manual reformulations. The flexible open-source implementation allows for the integration of customized routines required to solve challenging problems, e.g., initialization, problem decomposition, or sequential solution strategies. We demonstrate features of COMANDO via case studies, including automated linearization, dynamic optimization, stochastic programming, and the use of nonlinear artificial neural networks as surrogate models in a reduced-space formulation for deterministic global optimization.

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Towards a DESTEST: a District Energy Simulation Test Developed in IBPSA Project 1

Cited by 11 publications

References 19 publications

Development and Application of an Open-Source Framework for Automated Thermal Network Generation and Simulations in Modelica

Development and Application of an Open-Source Framework for Automated Thermal Network Generation and Simulations in Modelica

A New Steam Medium Model for Fast and Accurate Simulation of District Heating Systems

COMANDO: A Next-Generation Open-Source Framework for Energy Systems Optimization

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