Search

Spawn is DOE's next-generation tool chain for whole building energy control simulation. Spawn couples traditional imperative load-based envelope modelling with new equation-based modelling of HVAC and controls. Spawn uses EnergyPlus for the former and the Modelica Buildings Library for the latter. Because it leverages the Modelica Buildings Library, Spawn can evaluate advanced energy systems at the building and district scale, including new architectures and controls for heat pump systems with storage, and the coupling of such systems to electrical distribution networks. Spawn's Modelica integration likewise enables it to simulate realistic control sequences and therefore to bridge energy simulation and control implementation workflows. From EnergyPlus, Spawn inherits efficient envelope simulation and the ability to use existing envelope model authoring tools. This paper describes the architecture and implementation of Spawn, which automatically couples Modelica and EnergyPlus for run-time data exchange. This paper closes with examples that illustrate Spawn's modelling and simulation processes.

Nuclear microreactors are a potential technology to provide heat and electricity for remote microgrids. There is potential for the microgrid on the island of El Hierro to use a microreactor, within an integrated energy system (IES), to generate electricity and provide desalinated water. This work proposes a workflow for optimizing and analyzing IESs for microgrids. In this study, an IES incorporating a microreactor, thermal energy storage (TES) system, combined heat and power plant, and a thermal desalination plant was designed, optimized, and analyzed using Idaho National Laboratory's Framework for Optimization of Resources and Economics (FORCE) toolset. The optimization tool, Holistic Energy Resource Optimization Network (HERON), was used to determine the optimal capacity sizes and dispatch for the reactor and thermal energy storage systems to meet demand. The optimized reactor and TES sizes were found to be 11.61 MWth and 58.47 MWhth, respectively, when optimizing the IES to replace 95% of the island's existing diesel generation needs. A dynamic model of the system was created in the Modelica language, using models from the HYBRID repository, to analyze and verify the dispatch from the optimizer. The dynamic model was able to meet the ramp rates while maintaining reactor power with minimal control adjustments. [ABSTRACT FROM AUTHOR]

Simulative comparison of the heat demand calculation of a single‐family house – Differences between AixLib and EnergyPlus for a high‐order model In the IBPSA Project 1, the core library Modelica‐IBPSA as well as the AixLib library were developed for open source modeling of building energy systems using Modelica. Compared to the open source tool EnergyPlus which was developed specifically for building performance simulation, the Modelica libraries allow for a more physically realistic modeling of the control of modern HVAC systems. However, the heat transfer through the building envelope is modeled with a higher level of detail in EnergyPlus. It is unclear to what extent the simplifications made in the AixLib influence the results of a building performance simulation. In this study, the heat load and the heat demand of a single‐family house are computed and compared using annual simulations of the high‐order building model of the single‐family house contained in the AixLib and an EnergyPlus building model of the single‐family house. The results of the AixLib building model show an upward shift in the heat load compared to the results of the EnergyPlus building model. In the cold period of the year, the heat demand is calculated to be about 1 % higher, in the warm period about 31 % higher and in the whole year about 2 % higher. A major influence on the high percentage deviation in the warm period of the year is the additional discretization of the individual wall layers implemented in EnergyPlus leading to higher surface temperatures of the exterior walls in summer. [ABSTRACT FROM AUTHOR]

The PSDF (photovoltaic, storage, direct current, and flexibility) energy system represents an innovative approach aimed at achieving carbon neutrality. This study focused on rural buildings and utilized Modelica to develop a dynamic simulation model of the PSDF system. The research introduced a framework for direct current distribution microgrid systems with flexible regulatory mechanisms, employing a virtual inertia control strategy to provide stable adjustments for flexible operations and support integration with local grids. Case simulation results indicated that the system equipped with a water tank saved 3.15 kWh compared to the system without a water tank, resulting in an energy savings rate of 22.14%. Compared to traditional photovoltaic systems, the PSDF system significantly enhanced energy management flexibility and system reliability through the integration of thermal storage and battery management. This research made significant contributions to the fields of renewable energy and building energy systems by offering a scalable and practical solution suitable for rural contexts. [ABSTRACT FROM AUTHOR]

This article presents a matching algorithm for bipartite graphs containing repetitive structures and represented by intension as Set-Based Graphs. Under certain conditions on the structure of the graphs, the computational cost of this novel algorithm is not affected by the cardinality of the sets of vertices and edges. The main application of the algorithm is that of matching large Equation-Based Models where provided that most equations are defined using for loop statements that iterate over vectors of unknown variables, the computational cost becomes independent of the growth of the vectors involved. Besides introducing the algorithm, the article describes its implementation in a Modelica compiler and studies its performance over different test models. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) and photovoltaics (PVs) are expected to be broadly adopted in future power systems. However, the temporal variability of EV load and PV production presents challenges for integrating them into the power grid. This study evaluates and assesses the synergies between EVs and PV systems to maximize solar energy utilization for EV load coverage. The configurations studied include EV charging via the national grid as a reference case (Case 1) and two solar energy harvesting options: EVs powered directly by vehicle-mounted PVs (Case 2) and EV chargers connected to residential PV installations (Case 3). These cases are evaluated across different urban environments with large EV fleets and dissimilar weather conditions: Berlin and Los Angeles. A customized operation profile based on the worldwide harmonized light-duty test cycle (WLTC) and a charge-right-away (CRA) strategy is used. Energy performance analysis is conducted through dynamic simulations using the Modelica language, with environmental and economic indices derived. Key findings highlight the superior performance of residential PV systems in both cities compared to current solar EV technologies, with both solutions offering significant benefits over the reference case. Cases 2 and 3 result in a 44% and 59% reduction in annual energy consumption, greenhouse gas emissions, and charging costs in Berlin, while in Los Angeles, the reductions are 67% and 98%. The average daily solar driving range reaches 20.3% in Berlin and 30.4% in Los Angeles. [ABSTRACT FROM AUTHOR]

Interest in automated building analytics, including fault detection and diagnostics has been increasing; however, developers of these solutions have lacked access to ground-truth-validated data across a wide range of weather conditions for algorithm development and performance assessment. This study presents the development, and validation of faulted and fault-free models for air handling units (AHUs) – a common HVAC system design. Detailed models for the single-duct AHU (Modelica) and dual-duct AHU (HVACSIM+) were used to conduct annual simulations, for common sensor, mechanical, and control sequence faults. We report lessons learned during the efforts, including challenges and insights regarding how these simulation models, typically used for design applications, can be purposed to accurately reflect real-world system operational behaviours. Finally, we highlight considerations for researchers and FDD developers who may wish to leverage this dataset to assess the performance of their algorithms, and evolving performance of FDD solutions over time. [ABSTRACT FROM AUTHOR]

In vertical vehicle dynamics control, semi-active dampers are used to enhance ride comfort and road-holding with only minor additional energy expenses. However, a complex control problem arises from the combined effects of (1) the constrained semi-active damper characteristic, (2) the opposing control objectives of improving ride comfort and road-holding, and (3) the additionally coupled vertical dynamic system. This work presents the application of Reinforcement Learning to the vertical dynamics control problem of a real street vehicle to address these issues. We discuss the entire Reinforcement Learning-based controller design process, which started with deriving a sufficiently accurate training model representing the vehicle behavior. The obtained model was then used to train a Reinforcement Learning agent, which offered improved vehicle ride qualities. After that, we verified the trained agent in a full-vehicle simulation setup before the agent was deployed in the real vehicle. Quantitative and qualitative real-world tests highlight the increased performance of the trained agent in comparison to a benchmark controller. Tests on a real-world four-post test rig showed that the trained RL-based controller was able to outperform an offline-optimized benchmark controller on road-like excitations, improving the comfort criterion by about 2.5% and the road-holding criterion by about 2.0% on average. [ABSTRACT FROM AUTHOR]

A linguagem Modelica surge em 1996 e é uma POO (Programação Orientada a Objetos), que permite a reutilização de elementos programados individualmente. Devido a essa característica, tornou-se uma linguagem ideal para a modelagem de vários sistemas físicos e matemáticos, incluindo áreas da Engenharia como a hidráulica, eletrônica e química. O software OMedit utiliza a linguagem Modelica, mas ainda carece de uma biblioteca pneumática. Este trabalho, portanto, propõe a criação de uma biblioteca computacional de elementos pneumáticos, assim como a validação de diversos modelos matemáticos utilizados. Foi realizada uma revisão bibliográfica com o objetivo de identificar as principais equações que regem os componentes pneumáticos. Foram discutidos o comportamento da vazão em massa através de válvulas solenoides, como também a dinâmica das pressões nas câmaras dos atuadores pneumáticos ao longo do tempo. Depois de modelados computacionalmente cada um dos elementos no software, experimentos no laboratório foram realizados para a coleta de dados empíricos para obter não só o comportamento das partes como também os parâmetros que serão utilizados nos modelos computacionais. Uma placa de aquisição de dados foi utilizada junto com transdutores de pressão e posição para obter o comportamento dos componentes ao longo do tempo. Houve a necessidade de um tratamento de dados como o descarte de leituras de ruídos, devido aos instrumentos de medição utilizados, e um levantamento de aproximações polinomiais para a aplicação de métodos matemáticos de minimização de erros. Por fim, foram simulados e obtidos com êxito curvas características da pressurização de um reservatório e avanço e retorno de um atuador pneumático computacionalmente, com as curvas experimentais e teóricas comparadas para expor o grau de exatidão obtido. Com isso, obtém-se uma ferramenta para a simulação computacional de um atuador pneumático utilizando-se linguagem Modelica, bem como a proposta de uma biblioteca pneumática para o OMedit.

This paper investigates the energy performance and sizing criteria of 5th generation district heating and cooling (5GDHC) networks as a function of the heat distribution temperature in the building systems connected to the district network. An energy simulation model of a 5GDHC network was developed in Modelica for a case study located in Denmark. Calculations were carried out for four different building heating systems. Simulation results show that reducing the heat distribution temperatures from 70 °C to 23 °C leads to around 40% annual electric energy savings (from 10.4 kWh/m2 to 6.2 kWh/m2) for the operation of the heat pumps. Heat distribution temperatures of 23 °C cause higher water mass flow rates through the network, leading to annual electric energy consumption for the circulation pumps that are almost doubled (from 0.16 kWh/m2 to 0.3 kWh/m2) compared to the reference case at 70 °C. Furthermore, the paper discusses how the results obtained from the Danish case study can be generalized and applied to other cases using a simplified mathematical approach. It is found that about 1.5% of electric energy savings can be achieved for each temperature degree reduction in the heat distribution system.

Abstract Space heating applications account for a high share of global greenhouse gas emissions. To increase the renewable share of heat generation, seasonal thermal energy storage (STES) can be used to make thermal energy from fluctuating renewable sources available in times of high demand. A popular STES technology is pit thermal energy storage (PTES), where heat is stored underground, using water as a storage medium. To evaluate the use of PTES in an energy system, easily adaptable, publicly accessible and tool independent models are needed. In this paper, we improve an existing PTES model developed in the Modelica modeling language. The model is cross-compared with a more detailed and previously validated COMSOL model, considering different amounts of insulation, showing a deviation of 2–13% in the observed annual charged and discharged amount of heat. The results indicate that the presented model is well suited for early design stage and an exemplary case study is performed to demonstrate its applicability in a system context. Dimensions of system components are optimized for the levelized cost of heat (LCOH), both with and without subsidies, highlighting the importance of subsidies for the transition towards climate friendly heating solutions, as the gas boiler use is reduced from 47.6% to 2.7%.

Modelica is a powerful modelling language for modelling and simulation of Multiphysics Engineering Systems behaviour. This work explores a model to be implemented in the control design of a 6-axis industrial manipulator whose structure approaches the robotic arm ABB IRB 140, through inverse kinematics calculation. The significance of an inverse kinematics model is to bring accuracy to the reference inputs into a closed-loop control system involved in an industrial setting. Inverse kinematics can complement the path planner or trajectory generator function in object-oriented Modelica language, optimizing the study of an industrial robot physical process such a Pick&Place system. Finally, obtained results are extrapolated for similar cases and applications. [ABSTRACT FROM AUTHOR]

The transition from fetal to newborn condition involves complex physiological adaptations for extrauterine life. A crucial event in this process is the clamping of the umbilical cord , which can be categorized as immediate or delayed. The type of clamping significantly influences the hemodynamics of the newborn. In this study, we developed a simulator based on existing cardiovascular models to better understand this practice. The simulator covers the period from late gestation to 24 h after birth and faithfully reproduces flow patterns observed in real-life situations (as evaluated by clinical specialists), considering factors such as the timing of cord clamping and the altitude of the birth location. It also reproduces blood pressure values reported in clinical data. Under similar conditions, the simulation results indicate that delayed cord clamping leads to increased oxygen concentration and improved blood volume compared to immediate cord clamping. Delayed cord clamping also had a positive impact on sustained placental respiration. Furthermore, this study provides further evidence that umbilical cord clamping should be based on physiological criteria rather than predefined time intervals. [Display omitted] • The model simulates pulmonary oxygen uptake and delivery across the alveolar-capillary membrane. • The model simulates placental oxygen uptake by incorporating oxygen consumption across the placental barrier. • The model simulates gas transport and consumption using new mathematical equations. • The model uses math to simulate the cessation of placental blood transfer following cord clamping. • It models neonatal transition at different altitudes, studying oxygen saturation impact. [ABSTRACT FROM AUTHOR]

Both the SysML and Modelica standards are used in the field of Systems Engineering (SE) to model systems from different perspectives, on different abstraction levels. SysML is strong when modeling systems on a functional level because it provides different views. With the capabilities of other simulation specifications, the engineers can simulate the system architecture. On the other hand, an open standard, such as Modelica is a key enabler for representing multi‐physical systems described by differential, algebraic, and discrete equations. With the symbolic manipulation, the dynamics of the systems are represented in state space form, and solved by the numerical integration methods fixed or variable step. However, it is clear that the connection between systems engineering and system simulation, with their respective domain knowledge of the actual equipment in their system, is missing. By seeing these complementary values, the authors demonstrate language interaction to integrate SysML and Modelica to achieve complimentary values through bi‐directional transformation and simulation. [ABSTRACT FROM AUTHOR]

Space heating applications account for a high share of global greenhouse gas emissions. To increase the renewable share of heat generation, seasonal thermal energy storage (STES) can be used to make thermal energy from fluctuating renewable sources available in times of high demand. A popular STES technology is pit thermal energy storage (PTES), where heat is stored underground, using water as a storage medium. To evaluate the use of PTES in an energy system, easily adaptable, publicly accessible and tool independent models are needed. In this paper, we improve an existing PTES model developed in the Modelica modeling language. The model is cross-compared with a more detailed and previously validated COMSOL model, considering different amounts of insulation, showing a deviation of 2–13% in the observed annual charged and discharged amount of heat. The results indicate that the presented model is well suited for early design stage and an exemplary case study is performed to demonstrate its applicability in a system context. Dimensions of system components are optimized for the levelized cost of heat (LCOH), both with and without subsidies, highlighting the importance of subsidies for the transition towards climate friendly heating solutions, as the gas boiler use is reduced from 47.6% to 2.7%. [ABSTRACT FROM AUTHOR]

The increasing installment of solar and wind renewable energy systems create a volatile energy demand to be met by electricity providers. A nuclear hybrid energy system is a nuclear reactor with energy storage that integrates into the grid with renewable energy sources. The Natrium design by TerraPower and GE Hitachi is a sodium fast reactor with molten salt energy storage. The Natrium design operates at steady state of 345 MWe and can boost up to 500 MWe for 5.5 hours. This study uses Dymola and the Modelica language to model the Natrium‐based nuclear‐renewable hybrid energy system. The dynamic system model is tested using hourly historical data from the state of Texas 2021 to show how renewables affect the electricity demand and how energy storage affects the Natrium system response to the demand. According to the results, while the available storage will allow the Natrium design to boost electricity production when the demand and electricity price is high making it more economically viable, the current molten salt storage is slightly undersized for the ERCOT market. [ABSTRACT FROM AUTHOR]

The ongoing research on model predictive control (MPC) for building air conditioning systems predominantly centers on improving the predictive capabilities of system models. In this paper, the impacts of three additional pivotal factors on MPC performance are assessed by examining a generic MPC design for a typical variable air volume (VAV) system that serves large commercial buildings. The three factors encompass the nuanced reformulation of optimization, the judicious relaxation of constraints, and the meticulous tuning of parameters. Detailed case studies with an integrated Modelica and EnergyPlus model of the US Department of Energy's Commercial Reference Building are conducted. The results confirm that the optimization formulation, along with relaxation methods, significantly affects MPC performance in terms of energy savings, zonal thermal comfort level, and computational demand. They also reveal that the impact of the MPC control parameters on the energy savings and thermal comfort may vary by season and can be non-monotonic. [ABSTRACT FROM AUTHOR]

The development of accurate dynamic battery pack models for electric vehicles (EVs) is critical for the ongoing electrification of the global automotive vehicle fleet, as the battery is a key element in the energy performance of an EV powertrain system. The equivalent circuit model (ECM) technique at the cell level is commonly employed for this purpose, offering a balance of accuracy and efficiency in representing battery operation within the broader powertrain system. In this study, a second-order ECM model of a battery cell has been developed to ensure high accuracy and performance. Modelica, an acausal and object-oriented equation-based modeling language, has been used for its advantageous features, including the development of extendable, modifiable, modular, and reusable models. Parameter lookup tables at multiple levels of state of charge (SoC), extracted from lithium-ion (Li-ion) battery cells with four different commonly used cathode materials, have been utilized. This approach allows for the representation of the battery systems that are used in a wide range of commercial EV applications. To verify the model, an integrated EV model is developed, and the simulation results of the US Environmental Protection Agency Federal Test Procedure (FTP-75) driving cycle have been compared with an equivalent application in MATLAB Simulink. The findings demonstrate a close match between the results obtained from both models across different system points. Specifically, the maximum vehicle velocity deviation during the cycle reaches 1.22 km/h, 8.2% lower than the corresponding value of the reference application. The maximum deviation of SoC is limited to 0.06%, and the maximum value of relative voltage deviation is 1.49%. The verified model enables the exploration of multiple potential architecture configurations for EV powertrains using Modelica. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative method for characterizing, implementing, and validating both three-phase and single-phase induction motor models, accompanied by a variable speed drive model. The primary goal is to investigate interactions between the electrical power grid and other dynamic domains (e.g., thermofluidic) that impact motor/load drive behavior. Our approach involves establishing a mechanical interface based on a physically meaningful equation linking motor torque/speed to the electrical model in the phasor domain. This allows seamless integration of diverse domain subsystems into a unified multi-domain model using Modelica v4.0.0 and the OpenIPSL library v3.0.1, overcoming co-simulation limitations. The proposed model, which requires only one Modelica-compliant tool for simulation, introduces additional dynamics through the mechanical interface, enabling explicit simulation of load disturbances based on constitutive physics. This deepens our understanding of dynamic interactions between the electrical power domain and other subsystems connected through the motor. We detail the modeled components using mathematical equations and textual descriptions, emphasizing the Modelica modeling approach. Simulation examples validate the implementation, demonstrating the multi-domain modeling capabilities of the newly developed components. [ABSTRACT FROM AUTHOR]

Reusable rockets must rely on well-designed Guidance, Navigation and Control (GNC) algorithms. Because they are tested and verified in closed-loop, high-fidelity simulators, emphasizing the strategy to achieve such advanced models is of paramount importance. A wide spectrum of complex dynamic behaviors and their cross-couplings must be captured to achieve sufficiently representative simulations, hence a better assessment of the GNC performance and robustness. This paper focuses on of the main aspects related to the physical (acausal) modeling of reusable rockets, and the integration of these models into a suitable simulation framework oriented towards GNC Validation and Verification (V&V). Firstly, the modeling challenges and the need for physical multibody models are explained. Then, the Vertical Landing Vehicles Library (VLVLib), a Modelica-based library for the physical modeling and simulation of reusable rocket dynamics, is introduced. The VLVLib is built on specific principles that enable quick adaptations to vehicle changes and the introduction of new features during the design process, thereby enhancing project efficiency and reducing costs. Throughout the paper, we explain how these features allow for the rapid development of complex vehicle simulation models by adjusting the selected dynamic effects or changing their fidelity levels. Since the GNC algorithms are normally tested in Simulink®, we show how simulation models with a desired fidelity level can be developed, embedded and simulated within the Simulink® environment. Secondly, this work details the modeling aspects of four relevant vehicle dynamics: propellant sloshing, Thrust Vector Control (TVC), landing legs deployment and touchdown. The CALLISTO reusable rocket is taken as study case: representative simulation results are shown and analyzed to highlight the impact of the higher-fidelity models in comparison with a rigid-body model assumption. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) is promising for optimizing building's operation but high hardware, software and know-how requirements impede its commercialization. Therefore, rule-based controllers (RBC) are state-of-the-art. Approximate MPC (AMPC) can help bridge this gap by replacing the optimization with an explicit functional relation called approximator. Literature lacks reproducible use cases and benchmarks and a comparison of sophisticated and traditional approximators. This study aims to close this gap by applying AMPC to BOPTEST's two-zone heat pump testcase. The BOPTEST testcase includes predefined RBCs and KPIs promoting repeatability. Then, a comparison was made between artificial neural networks (ANNs), random forest (RF), linear, and logistic regression. The results show that feature selection significantly affects the performance. After adapting the features, the ANNs and RF outperform the RBC with cost savings of up to 33% and discomfort reductions of 70%, while requiring 15% of the MPC's computation time. The traditional approximators fail to outperform the RBC. [ABSTRACT FROM AUTHOR]

In current building design practices, the operation and performance of heating, ventilation and air conditioning (HVAC) systems are rarely documented in detail. This is mainly caused by the manual burden of generating detailed HVAC simulation models. To lower the barrier for detailed HVAC simulations, this paper presents an automated toolchain that generates and simulates models in Modelica language utilizing building information structured in a web-based common data environment. This approach differs from previous approaches by its focus on HVAC systems and integration with a common data environment over file-based BIM, which increases interoperability and allows users to run simulation studies in the cloud. The tool successfully generated and simulated a model of the HVAC systems in a small building under ideal and faulty operation of the heating system and thus demonstrated a fully interoperable data exchange between a common data environment and a simulation environment while showcasing the potential of analyzing HVAC systems with Modelica. [ABSTRACT FROM AUTHOR]

This paper describes the dynamic process model for a solar fuels synthesis plant currently being built in Germany. The open-source, equation-based modelling language Modelica is used as the foundation. In this plant, biogas and steam are reformed, producing synthesis gas and subsequently turned into synthetic crude oil. This work contains the necessary model setups for the thermal energy storage, solar-absorbing gas receiver and reforming reactor to consider the thermohydraulic, thermochemical and radiative interactions occurring in the process. The remaining infrastructure is modeled with the Modelica Standard Library. A way to fit these models with experimental data for validation is also outlined.

Decarbonization and resilience to heat waves have recently become high priorities for building and district energy systems. Geothermal coupled district heating and cooling systems that operate a water loop near ground temperature gain increasing adoption to support decarbonization. In these systems, vapor-compression machines, distributed in the energy transfer stations, lift the temperature up or down to the needs of the particular building. In principle, these systems can provide low-power, free cooling from the geothermal bore field during heat waves when electricity is often scarce. However, the performance of such a resilience operation mode and its implication on the energy system configuration and the sizing of the bore field and HVAC equipment is not yet understood. Consequently, we are assessing their resilience, power use and design implications under a scenario of a heat wave on five working days during which chillers are switched off to reduce electrical consumption. Our analysis is based on high-fidelity, coupled dynamic models of district energy, building-side HVAC and actual control logic, with whole building energy simulation used to assess thermal conditions in a 2004 vintage multi-zone office building in Chicago, IL. The results show that relying only on waterside economizer cooling, the indoor thermal conditions can be maintained in a tolerable range for the majority of the building zones with half the electrical energy compared to standard chiller operation. Thermal comfort in the hottest zones can be further improved by oversizing the cooling coil. However, the waterside economizer has significant implications on the system configuration and sizing: The geothermal bore field needs to be sized about 30% larger than the upper limit of the range observed for conventional geothermal systems. Nevertheless, if a central chiller plant is added, the bore field can be downsized to the typical design range. The latter configuration still allows compressor-less cooling during the heat wave with peak power reduced by 60% compared to the standard design and chiller operation.

Nuclear microreactors are a potential technology to provide heat and electricity for remote microgrids. There is potential for the microgrid on the island of El Hierro to use a microreactor, within an integrated energy system (IES), to generate electricity and provide desalinated water. This work proposes a workflow for optimizing and analyzing IESs for microgrids. In this study, an IES incorporating a microreactor, thermal energy storage (TES) system, combined heat and power plant, and a thermal desalination plant was designed, optimized, and analyzed using Idaho National Laboratory’s Framework for Optimization of Resources and Economics (FORCE) toolset. The optimization tool, Holistic Energy Resource Optimization Network (HERON), was used to determine the optimal capacity sizes and dispatch for the reactor and thermal energy storage systems to meet demand. The optimized reactor and TES sizes were found to be 11.61 MWth and 58.47 MWhth, respectively, when optimizing the IES to replace 95% of the island’s existing diesel generation needs. A dynamic model of the system was created in the Modelica language, using models from the HYBRID repository, to analyze and verify the dispatch from the optimizer. The dynamic model was able to meet the ramp rates while maintaining reactor power with minimal control adjustments.

In U.S. district heating (DH) systems, steam is the most common heat transport medium. Industry demand for new advanced modeling capabilities of complete steam DH systems is increasing; however, the existing models for water/steam thermodynamics are too slow for large system simulations because of computationally expensive algebraic loops that require the solution to nonlinear systems of equations. For practical applications, this work presents a novel split-medium approach that implements numerically efficient liquid water models alongside various water/steam models, breaking costly algebraic loops by decoupling mass and energy balance equations. New component models for steam DH systems are also presented. We implemented the models in the equation based Modelica language and evaluated accuracy and computing speed across multiple scales: from fundamental thermodynamic properties to complete districts featuring 10 to 200 buildings. Compared to district models with the IF97 water/steam model and equipment models from the Modelica Standard Library, the new implementation improves the scaling rate for large districts from cubic to quadratic with negligible compromise to accuracy. For an annual simulation with 180 buildings, this translates to a computing time reduction from 33 to 1–1.5 h. These results are critically important for industry practitioners to simulate steam DH systems at large scales.

Dynamic thermo-hydraulic simulations of district heating networks (DHN) are essential to investigate novel concepts for their sustainable design and operation. To develop solutions for a particular case study, numerous long-term simulations are required. Therefore, computational effort for simulation is critical. Heat consumers (HC) are numerous and determine the dynamics of mass flows and return temperatures in the DHN. Thus, the way in which HCs are modeled has significant impact on the computational effort and the results of the simulation. This article presents a novel Modelica-based model for HCs that builds on an existing simplified modeling approach (open-loop design). The calculation of mass flow and return temperature is improved in terms of robustness, plausible behavior and low computational effort. In particular, the model reacts to limited differential pressure and supply temperatures to ensure plausible behavior across all operating conditions, including undersupply situations. The model is successfully tested using an exemplary DHN. The analysis proves that the HC model itself requires little time to simulate. Nevertheless, it significantly influences the simulation time for the entire DHN, which varies by a factor of five for the investigated system depending on the HC model. Fast dynamics, including a bypass in the model and correction of deviations between set point and actual heat load increase the simulation time, so users should sensibly choose how to use these options. HC models triggering many state events result in high computational effort. Compared to other simple HC models, the proposed model produces more plausible results while maintaining at least equal simulation performance (for models without bypass) or even improving it (for models with bypass, CPU time is reduced by at least 35%). [ABSTRACT FROM AUTHOR]

We consider both primal and dual formulations of singular autonomous systems of three different types of fractional‐order differential equations. We present a comprehensive study which proves that by using the spectrum of a linear pencil, a polynomial matrix of first order, and not the fractional‐order pencil of the prime system, we will receive information for all properties for both the prime and its dual system. In addition, by using this spectrum, the solutions for all systems can be obtained by using formulas without additional computational cost. Finally, we provide examples including a computational analysis in Modelica. [ABSTRACT FROM AUTHOR]

Open-source modelling libraries facilitate the standardization and harmonization of model development. In the context of building energy systems, Modelica is a suitable modelling language as it is equation-based and object-oriented. As an outcome of the IBPSA project 1 cooperation, four open-source modelling libraries have been successfully deployed which all share the core library Modelica IBPSA. One of them is the AixLib modelling library. AixLib supports different modelling depths ranging from component to district level and covers all relevant domains in the context of building energy systems. To ensure high-quality simulations, continuous integration has been successfully added to automatically compare simulation results with existing validation data. This paper presents AixLib's key features, scope, and associated tools. We present three use cases that highlight the broad application range of AixLib models. Furthermore, an overview of relevant research and industry projects is provided. Finally, we give an outlook on future development goals. [ABSTRACT FROM AUTHOR]

Efficient control strategies for managing the mass flow rate (MFR) of heat transfer fluids (HTF) during cloud transients in Solar Tower receivers play a pivotal role in optimizing plant profitability and receiver durability. This study focuses on the performance and durability of Solar Tower receivers during cloud transients. It evaluates adaptive feedback and feedforward control methods, which adjust the flow rate of heat transfer fluids based on real-time measurements of direct normal irradiance (DNI), receiver outlet and receiver panel outlet temperatures. The effectiveness of an aggressive all-sky and conservative clear-sky control strategy is explored against a conventional PI controller, emphasizing energy efficiency and receiver longevity. Simulations using a thermal Modelica model resembling a 100 MWel Crescent Dunes-like solar tower plant reveal that both advanced controllers provide precise setpoint tracking, while the PI controller struggles. The conservative controller which has a cloud standby mode prevents overheating during cloud transients by using a clear sky mass flow rate, while the aggressive controller uses the receiver panel outlet temperatures to correct for upstream tube temperature variations allowing for fast tracking correction and disturbance rejection, albeit with slight overshoots. Furthermore, the controllers significantly decrease the creep-fatigue damage accumulated in the receiver panels during cloudy days, due to limiting the increase in wall temperature spikes when cloud events end. Overall, this study underscores the pivotal role of HTF mass flow rate control systems in influencing receiver system failure modes and longevity and offers a new tool in controller design and operation assessment.

Model Predictive Control (MPC) is a promising technique to address growing needs for heating, ventilation, and air-conditioning (HVAC) systems to operate more efficiently and with greater flexibility. However, due to a number of factors, including the required implementation expertise, lack of high quality data, and a risk-adverse industry, MPC has yet to gain widespread adoption. While many previous studies have shown the advantages of MPC, few analyzed the implementation effort and associated practical challenges. In addition, previous work has developed an open-source, Modelica-based tool-chain that automatically generates optimal control, parameter estimation, and state estimation problems aimed at facilitating MPC implementation. Therefore, this study demonstrates usage of this tool-chain to implement MPC in a real office building, discusses practical challenges of implementing MPC, and estimates the implementation effort associated with various tasks in order to inform the development of future workflows and serve as an initial benchmark for their impact on reducing implementation effort. This study finds that the implemented MPC saves approximately 40% of HVAC energy over the existing control during a two-month trial period and that tasks related to data collection and controller deployment activities can each require as much effort as model generation.

Classical EMT-type simulators are mostly programmed in procedural languages, e.g. Fortran or C. In these languages, the focus is mainly on the solution methods. Modern languages, such as Modelica, are declarative and primarily focused on modeling and simulation. Modelica offers a much higher abstraction level, which makes the codes more concise and understandable. This paper contributes to the electromagnetic transient modeling and simulation of asynchronous machines in Modelica. In this paper, the modeling of a three-phase squirrel cage (single and double cage) and wound-rotor induction machine in three different reference frames is described and implemented. The accuracy and performance of Modelica models are compared and validated with the classical modeling approach used in the reference software EMTP. It is demonstrated that Modelica-based models with variable-step solvers offer fast and accurate results for time-domain simulations of motor sequential startup cases.

While equation-based object-oriented modeling language Modelica can evaluate practical energy improvements for district cooling systems, few have adopted Modelica for this type of large-scale thermo-fluid system. Further, to our best knowledge, district cooling modeling studies have yet to include hydraulics in piping networks alongside plant models featuring realistic mechanical systems and controls. These are critical details to include when looking to make energy and control improvements in many physical system installations. To fill these gaps, this study released new open-source district cooling models at the Modelica Buildings Library and leveraged these models for a real-world case study at the University of Colorado Boulder. The site includes six buildings connected to a central chiller plant featuring a waterside economizer. Several energy saving strategies are pursued based on the validated model, including control setpoint optimization, equipment modification, and pump setpoint adjustments. Results indicate that a combination of the studied measures can save the campus annually 84.6 MWh of energy, 8.9% of electricity costs, 58.0 metric tons of carbon dioxide emissions, while the waterside economizer cuts down chillers’ run times by 201 days/year, reducing maintenance costs and extending chiller life.

ASHRAE Guideline 36 (G36) publishes high-performance control sequences for Variable Air Volume (VAV) system operation. Retrofitting existing VAV control sequences to G36 promises to have a large potential for energy savings. However, it is difficult to estimate the savings accurately and the process of doing so can be costly and time-consuming. This paper evaluates the energy use of a multi-zone VAV system with terminal reheat using the G36 sequences and compares it to a group of baseline control sequences that represent existing practices. Spawn of EnergyPlus is used for the whole building simulation, where the envelope is modelled in EnergyPlus and the HVAC equipment and its pressure-flow network and the control sequences are modelled in Modelica. The comparison of the control sequences performance is further conducted in parametric studies. For a medium-sized commercial building, the G36 sequences provide a wide range of HVAC energy savings with an average of 31%.

Electric vehicles (EVs) and photovoltaics (PVs) are expected to be broadly adopted in future power systems. However, the temporal variability of EV load and PV production presents challenges for integrating them into the power grid. This study evaluates and assesses the synergies between EVs and PV systems to maximize solar energy utilization for EV load coverage. The configurations studied include EV charging via the national grid as a reference case (Case 1) and two solar energy harvesting options: EVs powered directly by vehicle-mounted PVs (Case 2) and EV chargers connected to residential PV installations (Case 3). These cases are evaluated across different urban environments with large EV fleets and dissimilar weather conditions: Berlin and Los Angeles. A customized operation profile based on the worldwide harmonized light-duty test cycle (WLTC) and a charge-right-away (CRA) strategy is used. Energy performance analysis is conducted through dynamic simulations using the Modelica language, with environmental and economic indices derived. Key findings highlight the superior performance of residential PV systems in both cities compared to current solar EV technologies, with both solutions offering significant benefits over the reference case. Cases 2 and 3 result in a 44% and 59% reduction in annual energy consumption, greenhouse gas emissions, and charging costs in Berlin, while in Los Angeles, the reductions are 67% and 98%. The average daily solar driving range reaches 20.3% in Berlin and 30.4% in Los Angeles.

In vertical vehicle dynamics control, semi-active dampers are used to enhance ride comfort and road-holding with only minor additional energy expenses. However, a complex control problem arises from the combined effects of (1) the constrained semi-active damper characteristic, (2) the opposing control objectives of improving ride comfort and road-holding, and (3) the additionally coupled vertical dynamic system. This work presents the application of Reinforcement Learning to the vertical dynamics control problem of a real street vehicle to address these issues. We discuss the entire Reinforcement Learning-based controller design process, which started with deriving a sufficiently accurate training model representing the vehicle behavior. The obtained model was then used to train a Reinforcement Learning agent, which offered improved vehicle ride qualities. After that, we verified the trained agent in a full-vehicle simulation setup before the agent was deployed in the real vehicle. Quantitative and qualitative real-world tests highlight the increased performance of the trained agent in comparison to a benchmark controller. Tests on a real-world four-post test rig showed that the trained RL-based controller was able to outperform an offline-optimized benchmark controller on road-like excitations, improving the comfort criterion by about 2.5% and the road-holding criterion by about 2.0% on average.

To minimize the indoor transmission of contaminants, such as the virus that can lead to COVID-19, buildings must provide the best indoor air quality possible. Improving indoor air quality can be achieved through the building's HVAC system to decrease any concentration of indoor contaminants by dilution and/or by source removal. However, doing so has practical downsides on the HVAC operation that are not always quantified in the literature. This paper develops a temporal simulation capability that is used to investigate the indoor virus concentration and operational cost of an HVAC system for two mitigation strategies: (1) supplying 100% outdoor air into the building and (2) using different HVAC filters, including MERV 10, MERV 13, and HEPA filters. These strategies are applied to a hypothetical medium office building consisting of five occupied zones and located in a cold and dry climate. We modeled the building using the Modelica Buildings library and developed new models for HVAC filtration and virus transmission to evaluate COVID-19 scenarios. We show that the ASHRAE-recommended MERV 13 filtration reduces the average virus concentration by about 10% when compared to MERV 10 filtration, with an increase in site energy consumption of about 3%. In contrast, the use of 100% outdoor air reduces the average indoor concentration by about an additional 1% compared to MERV 13 filtration, but significantly increases heating energy consumption. Use of HEPA filtration increases the average indoor concentration and energy consumption compared to MERV 13 filtration due to the high resistance of the HEPA filter.

The Air-packed bed system is a cost-competitive concentrating solar thermal system that employs air as the heat transfer fluid and a solid packed bed as the thermal energy storage medium. The thermocline behavior of the packed bed plays an important role in the performance and operation of the system, so a dynamic model of the TES is needed to evaluate the design and performance of the system. A high-fidelity first-principle TES model is often too computationally expensive to incorporate into a larger system model for simulation and optimization at an annual time scale. Here, we develop in Modelica a system model for a CST concept plant with a reduced order TES model to ensure the necessary thermocline fidelity while maintaining suitable computational performance. The validity of the reduced order model is quantified both against simulation results from a first-principle finite-difference model and against real-world test data taken at the Heliogen Test Facility in Lancaster, CA. The thermocline fidelity and computational performance of the system model are then demonstrated with an annual simulation run of the concept air-packed bed plant.

This study quantifies and compares operational strategies as applied on a solar-assisted ground-source trans-critical CO2 heat pump (CO2 SAGSHP) system for simultaneous space and water heating, optimized on an hourly basis over the course of a week. Four (4) boundary conditions, representing spring, summer, autumn, and winter, were considered. To enable the use of a dynamic model for optimization simulations, the use of a previously developed surrogate Artificial Neural Network (ANN) model of a Modelica CO2 heat pump model was evaluated for use in this study. The simulation results from the system model that applies the surrogate ANN model demonstrated strong alignment with those generated by the full Modelica model, yielding low root mean square errors and mean absolute percentage errors. Furthermore, it accelerated the speed of results generation for a single forward run by 6–15 times, depending on the boundary conditions. It can be inferred from the results of the optimization runs that when warm climate conditions dominate, implementation of the on/off operation with thermal energy storage (TES) should be prioritized, given that it could potentially yield greater operational cost benefits (12% to 58 % cost reduction) compared to hourly discharge pressure optimization (0.5 to 1.5 % cost reduction). Conversely, when colder seasons are longer, optimizing the CO2 heat pump's discharge pressure becomes more practical (6.5 % to 6.9 % cost reduction) than on/off operation, particularly when only a small TES is installed (1.2 % to 2.6 % cost reduction). The optimization of the mass flow rate of the supply side working fluid of a CO2 SAGSHP system exhibited minimal benefit, except notably in the summer where it generated an even better cost reduction than controlling the discharge pressure. This shows the practicality of this strategy for conditions where relatively high solar irradiation and relatively low heat demand are concurrently expected. In general, weekly reductions in operational costs were more substantial when both the discharge pressure and the on/off operation of the CO2 heat pump were concurrently optimized, especially when using a larger TES tank. Hence, both should be applied if there are no practical or cost limitations that restrict their implementation. While previous studies on CO2 heat pumps primarily emphasized efficiency optimization via discharge pressure control, this study shows that comparable or greater operational cost reductions can also be achieved from other strategies, depending on the boundary conditions.

Optimal operation of petroleum production is important in a transition from energy systems based on fossil fuel to sustainable systems. One sub-process in petroleum production deals with transport from the (subsea) well-bore to a topside separator. Good control design for such operation requires a dynamic model of the petroleum flow from the well-bore to the separator. Here, such a dynamic model is considered for liquid production (oil/water) using an electric submersible pump (ESP) to aid in counteracting gravity and friction forces. Based on an existing model used for industrial control design, one goal is to report a complete dynamic model in a single paper. Emphasis is put on dimensionless equipment models for the simple change of units, and the model is developed from physical laws for easy replacement of sub-models, if needed. All the necessary information (equations, parameters) for model implementation is provided, and two candidate equation-based modeling languages are selected and compared: Modelica and ModelingToolkit [MTK] for Julia. The simulation results are virtually identical for the two languages and make sense from physics; however, there is a minor discrepancy in one plot—likely caused by slight differences in accuracy in handling initialization in the implicit algebraic equations. The implementation structures of the model in Modelica and MTK are similar. Modelica is a mature and excellent modeling tool, handles large-scale models, and has tools for producing C code and integration with other tools. MTK is still in rapid development, supports more model types than Modelica, and is integrated in an eco-system with excellent support for control design, optimization, model fitting, and more. To illustrate the suitability of using the developed model for control design, a simple PI controller is designed within the eco-system of MTK/Julia. [ABSTRACT FROM AUTHOR]

Mathematical optimization is a widespread method in order to improve, for instance, the efficiency of energy systems. A simulation approach based on partial differential equations can typically not be formulated as an optimization problem, thus requiring interfacing to an external optimization environment. This is, among others, also true for the programming language Modelica. Because of high computation time, such coupled approaches are often limited to small-scale optimization problems. Since simulation models tend to get more complex, simulation time and, in turn, associated optimization time rise significantly. To enable proper sampling of the search space, individual optimization runs need to be solved in acceptable times. This paper addresses the search for a proper optimization approach and tool to couple with Modelica/Dymola. The optimization is carried out on an exemplary power plant model from the ClaRa-Library using an evolutionary algorithm (SPEA2-based) with Ansys optiSlang. To verify and evaluate the results, a comparison with the standard Dymola optimization library is performed. Both parallelization and indirect optimization with surrogate models achieved a significant runtime reduction by a factor of up to 5.4. The use of meta models is particularly advantageous for repetitive optimization runs of the same optimization problem but may lead to deviations due to the calculated approximations. [ABSTRACT FROM AUTHOR]