Search

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.

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.

Energy Management and Information Systems are a family of analytics technologies that include energy information systems, fault detection and diagnostics (FDD), and automated system optimization tools. Such systems have the potential to enable buildings to meet energy management goals of reducing total energy consumption and cost. Most current market offerings use data-driven and rule-based analytics. However, the use of physics-based models in the analytics offers potential improvements by providing an accurate estimation of outputs based on representation of the physical principles governing the building system behaviors. This also permits the use of design stage models to inform commissioning and operation. This paper describes the development and testing of a hybrid data-driven and physics model-based operational tool for energy efficiency in central cooling plants. The tool offers FDD functionality, setpoint optimization, and visualization of key performance parameters. It was demonstrated at a university campus in the mixed-humid ASHRAE Climate Zone 4A. Key performance metrics that were analyzed include plant electricity use reduction, plant model calibration, and system economics. Annual simulations indicate the tool can provide electricity savings of greater than 10% for approximately six months of the year, mainly during the winter season when wet bulb temperatures are low, though only 1.38% savings for the entire year. Additionally, over a 4-day period in April, recommended optimal setpoints were implemented, resulting in 17% savings versus metered baseline consumption. With respect to model calibration, the difference between model-predicted and measured parameters was less than 10% for 90% of data points acquired for three of six chillers, and for each ten cooling towers. Finally, the tool users reported that satisfaction with the capabilities was equal to or better than that with the preexisting BAS system.

This paper describes Open-Instance Power System Library (OpenIPSL) version 2.0.0 and its most recent enhancements. This new version brings upgrades that include more robust models that are better documented, and example systems that illustrate certain functionalities to users. Repository and library documentations have been enhanced and expanded, and the library is now released under a new license. Changes are meant to foster user and developer communities, while providing more attractive frameworks for collaborative work to be carried out with the library.

This paper describes tool that is created for massive data generation employing phasor time-domain Modelica simulations and using the Open-Instance Power System Library (OpenIPSL). provides a pipeline for generating large amounts of data, considering a wide range of operating conditions and potential contingencies experienced by a power system. The need for large-scale power system dynamic data arises with the development of Machine Learning (ML) solutions in the context of the modernization of the existing power grid. implements algorithms to process different types of input data, perform steady-state computations, run dynamic simulations and linear analysis routines, and label the resulting data sets. The tool has been developed entirely in Python 3 and is compatible with the Modelica IDEs - Dymola and OpenModelica.

Building systems and their heating, ventilation and air conditioning flow networks, are becoming increasingly complex. Some building energy simulation tools simulate these flow networks using pressure drop equations. These flow network models typically generate coupled algebraic nonlinear systems of equations, which become increasingly more difficult to solve as their sizes increase. This leads to longer computation times and can cause the solver to fail. These problems also arise when using the equation-based modelling language Modelica and Annex 60-based libraries. This may limit the applicability of the library to relatively small problems unless problems are restructured. This paper discusses two algebraic loop types and presents an approach that decouples algebraic loops into smaller parts, or removes them completely. The approach is applied to a case study model where an algebraic loop of 86 iteration variables is decoupled into smaller parts with a maximum of five iteration variables.

We evaluate an ambient, bidirectional thermal network, which uses a single circuit for both district heating and cooling. When in net more cooling is needed than heating, the system circulates from a central plant in one direction. When more heating is needed, the system circulates in the opposite direction. A large benefit of this design is that buildings can recover waste heat from each other directly. We analyze the thermodynamic performance of the bidirectional system. Because the bidirectional system represents the state-of-the-art in design for district systems, its peak energy efficiency represents an upper bound on the thermal performance of any district heating and cooling system. However, because any network has mechanical and thermal distribution losses, we develop a diversity criterion to understand when the bidirectional system may be a more energy-efficient alternative to modern individual-building systems. We show that a simple model of a low-density, high-distribution loss network is more efficient than aggregated individual buildings if there is at least 1 unit of cooling energy per 5.7 units of simultaneous heating energy (or vice versa). We apply this criterion to reference building profiles in three cities to look for promising clusters.

We evaluate an ambient, bidirectional thermal network, which uses a single circuit for both district heating and cooling. When in net more cooling is needed than heating, the system circulates from a central plant in one direction. When more heating is needed, the system circulates in the opposite direction. A large benefit of this design is that buildings can recover waste heat from each other directly. We analyze the thermodynamic performance of the bidirectional system. Because the bidirectional system represents the state-of-the-art in design for district systems, its peak energy efficiency represents an upper bound on the thermal performance of any district heating and cooling system. However, because any network has mechanical and thermal distribution losses, we develop a diversity criterion to understand when the bidirectional system may be a more energy-efficient alternative to modern individual-building systems. We show that a simple model of a low-density, high-distribution loss network is more efficient than aggregated individual buildings if there is at least 1 unit of cooling energy per 5.7 units of simultaneous heating energy (or vice versa). We apply this criterion to reference building profiles in three cities to look for promising clusters.

Copyright of Rolling Stock (1002-7602) is the property of Rolling Stock Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Model-based optimization is an important means by which to analyze the energy-saving potential of chiller plants. To obtain reliable energy-saving results, model calibration is essential, which strongly depends on operating data. However, sufficient data cannot always be satisfied in reality. To improve the prediction accuracy of the model with limited data, a model calibration method based on error reverse correction was investigated. A traditional optimization-based calibration method was first used for preliminary model calibration to obtain simulation data and simulation errors. Then, the sources of the simulation errors were analyzed to determine the distribution characteristics of the corresponding operating conditions of the model. Finally, the performance of the model was reversely corrected by adding a correction term to the original model. The proposed calibration method was tested on a chiller plant in Xiamen, China. The results showed that the proposed calibration method improved prediction accuracy by 2.61% (the coefficient of variation of the root mean square error (CV (RMSE)) was reduced from 3.96% to 1.35%) compared to the traditional method. The maximum mean bias error (MBE) for monthly chiller energy consumption was 2.66% with the proposed calibration method, while it was 10.42% with the traditional method. Overall, in scenarios with limited data, the proposed calibration method can effectively improve the accuracy of simulation results. [ABSTRACT FROM AUTHOR]

In designing the heat exchanger model, the existing epsilon-number of transfer units (NTU) method cannot reflect factors inside the heat exchanger, such as the local flow rate changes. Furthermore, it is difficult to apply a three-dimensional model to a one-dimensional analysis since high computational power is required. In contrast, a pseudo-three-dimensional heat exchanger model which has higher accuracy than the existing epsilon-NTU method and has faster speed for application in one-dimensional analysis. However, pseudo-three-dimensional model has still improvements in the simulation time as the mesh counts increase. In this study, the improved pseudo-three-dimensional heat exchanger model was developed and integrated within a one-dimensional analysis loop capable of simulating vehicle driving and the cooling system. The integrated model calculated the change in the energy consumption of the entire cooling system with the change in the aspect ratio of the heat exchanger. Also, the optimal operation strategy of the cooling system, which incorporated a proportional integral derivative controller of the three-way valve, was determined to reduce the parasitic losses. As a result, parasitic loss of the cooling system decreased by 14.1 % as aspect ratio increased by 46 % under FTP-72 (UDDS) driving schedule and simulation time was reduced 90.1 % while accuracy degrades only 2.7 %. [ABSTRACT FROM AUTHOR]

Direct evaporative coolers (DECs) are a low-energy cooling alternative to conventional air conditioning in hot-dry climates. The key component of DEC is the cooling pad, which evaporatively cools the air passing through it. While detailed numerical models of heat and mass transfer have been proposed for the cooling pad, these require many input parameters that are not readily accessible. Alternatively, simplified models lack accuracy and are confined to common types of cooling pad. To address these limitations, we developed and validated a physics-based model, that only needs the nominal data to compute the heat and mass transfer with considerable accuracy. The proposed model is implemented in Modelica, an equation-based object-oriented modeling language. For comparison, a basic lumped model from EnergyPlus based on the efficiency curve of the cooling pad is also implemented. The physics-based model exhibits <2% error from the experimental data and the lumped model exhibits a 12.3% error. [ABSTRACT FROM AUTHOR]

Transient effects in the buildings energy balance are very important and could be dominant in some cases, especially for long setback schedule in the heating system operation. Thermal behaviour of insulated and intermittently heated buildings requires advanced calculation tools for proper estimation of the room/building heat balance. This is especially important for buildings where the heating system is on for up to 10 hours per day. In these buildings the transient effects are dominant and inner walls, due to their significant thermal capacity, strongly influence the variation of the room air temperature in time. In this paper inner walls are divided into side and top/bottom walls and are modelled with transient heat conduction equation each, instead of lumped-thermal-capacity method. Model for simulation is made by using Modelica Buildings Library in Dymola environment. Temperature measurements are recorded in one representative room and measured values are compared with results of simulation. Two cases are analyzed, one with variation of the air room temperature when the thermal inertia of the inner walls is taken into account, and the other where this effect is neglected. The results showed significant variation of the room temperature in the case where heat exchange between the inner walls and room air is neglected compared to the case where thermal inertia of the inner walls is taken into consideration. In case with no influence of the inner walls, due to small thermal inertia of the room air, the analyzed room overheats up to 27 °C during the heating period, while it drops to 9 °C when the heating system is off. Also, internal wall heat fluxes from and towards the room are quantified and analyzed for situations when the heating system is on and is off. It is shown that, for massive and thermally insulated buildings, these heat fluxes can be dominant in the room heat exchange.

Virtual labs are valuable educational resources in control education, and are widely used in the process industry as tools for operator training and decision aid. In these application domains, virtual labs typically rely on the interactive simulation of large-scale hybrid-DAE models with components of different engineering domains, whose description can be greatly simplified by the use of the Modelica language. Existing free and commercial Modelica libraries of different domains can be used to describe these models. The Interactive Modelica library facilitates developing virtual labs based on Modelica models, using only Modelica. A new major release of the Interactive Modelica library is presented in this paper, whose most relevant feature is to facilitate the implementation of collaborative virtual labs written using only the Modelica language. This library can be used with the environment OpenModelica, facilitating the implementation of cooperative virtual labs using only open software. This type of virtual lab, which allows several students to interact cooperatively with the same model simulation run, is an effective tool in the context of collaborative learning methods. The efficient communication among the graphical user interfaces and the simulation model is a key issue. We developed a new communication protocol, a synchronization algorithm, and redesigned the Modelica classes of the library to make the communication completely transparent to virtual lab developers. The implementation of a collaborative virtual lab for process control education, based on a simplified version of the Tennessee Eastman process, is discussed. The Interactive Modelica library is freely distributed under Modelica License 2 and can be downloaded from http://www.euclides.dia.uned.es/Interactive

Printed circuit heat exchangers (PCHEs) are widely used as recuperators in the supercritical carbon dioxide (S-CO2) Brayton cycle design. The variation of heat sources will have a great impact on the heat transfer effect of the recuperator. It is of interest to study the fast calculation of flow and heat transfer performance of PCHEs under different operating conditions to obtain the optimal comprehensive performance and provide guidance for the operation control strategy analysis. Herein, a fast calculation method is established through a one-dimensional model of a PCHE based on Modelica. The effects of working medium mass flow rate and inlet temperature on the flow and heat transfer process are analyzed from the three aspects of heat transfer rate, flow pressure drop, and comprehensive performance, and the mass flow rate matching optimization is realized. The results show that increased mass flow rate increases heat transfer rate and flow pressure drop. The efficiency evaluation coefficient (EEC) has a maximum value at which the mass flow rate values of the cold and hot channels are best matched, and the comprehensive performance is optimal. When the mass flow rate of the heat channel is 4.8 g/s, the maximum EEC is 1.42, corresponding to the mass flow rate of the cold channel, 4.2 g/s. Compared with the design condition, the heat transfer rate increases by 62.1%, and the total pump power increases by 14.2%. When the cold channel inlet temperature increases, EEC decreases rapidly, whereas EEC increases when the hot channel inlet temperature increases. The conclusions can provide theoretical support for the design and operation of PCHEs.

DC electrical distribution systems offer many potential advantages over their AC counterparts. They can facilitate easier integration with distributed energy resources, improve system energy efficiency by eliminating AC/DC converters at end-use devices (e.g., laptop chargers), and reduce installation material, time, and cost. However, DC electrical distribution systems present additional design considerations, largely resulting from potentially greater magnitude and variation in cable losses. Modeling and simulation are rarely used to design such systems. However, the greater dependency of DC system energy efficiency on design choices such as distribution voltages, architecture, and integration of PV and BESS suggests that modeling and simulation may be required. Such system performance analysis is currently not a standard practice, in part due to limited availability and validation of capable software tools. This paper characterizes the accuracy of a Modelica-based Building Electrical Efficiency Analysis Model (BEEAM) toolkit, as a precursor for validating its use to perform system performance analysis and inform design decisions. The study builds upon previous verification research by characterizing complete systems comprised of commercially available equipment, and providing a more detailed analysis of simulation results. Five lighting systems with varying electrical distribution architectures were designed using market-available equipment, installed in a laboratory environment, modeled using BEEAM, and simulated using three Modelica integrated development environments (IDEs). Simulated and measured results were compared to characterize toolkit accuracy. Initial results revealed that simulated performance was mostly within ±5% of measured system-level and device-level performance. While simulation results were not found to be dependent on the IDE, some Modelica compiler interoperability issues were identified. Although the BEEAM toolkit showed promise for the targeted use case, further work is needed to determine whether the demonstrated 5% accuracy is sufficient for making real-world design decisions, and for BEEAM to advance from an interesting research tool to one that can impact real-world building projects.

The exploitation of solar irradiation is a critical weapon for facing the energy crisis and critical environmental problems. One of the most emerging solar technologies is the use of solar towers (or central receiver systems) coupled with high-performance thermodynamic cycles. In this direction, the present investigation examines a solar tower coupled to a closed-loop Brayton cycle which operates with supercritical CO2 (sCO2) as the working medium. The system also includes a storage system with two molten salt tanks for enabling proper thermal storage. The sCO2 is an efficient fluid that presents significant advancements, mainly reduced compression work when it is compressed close to the critical point region. The novelty of the present work is based on the detailed dynamic investigation of the studied configuration for the year period using adjustable time step and its sizing for achieving a continuous operation, something that makes possible the establishment of this renewable technology as a reliable one. The analysis is conducted with a developed model in the Modelica programming language by also using the Dymola solver. According to the simulation results, the yearly solar thermal efficiency is 50.7%, the yearly thermodynamic cycle efficiency is 42.9% and the yearly total system efficiency is 18.0%.

Since its 3.3 release, Modelica offers the possibility to specify models of dynamical systems with multiple modes having different DAE-based dynamics. However, the handling of such models by the current Modelica tools is not satisfactory, with mathematically sound models yielding exceptions at runtime. In this article, we propose several contributions to this multifaceted issue, namely: an efficient and scalable multimode extension of the structural analysis of Modelica models; a systematic way of rewriting a multimode Modelica model, based on this analysis, so that the rewritten model is guaranteed to be correctly compiled by state-of-the-art Modelica tools; a proposal for the handling of the consistent initialization of multimode models; multimode structural analysis algorithms that handle both multiple modes and mode change events in a unified framework, coupled with a compile-time algorithm for identifying and quantifying impulsive behaviors at mode changes. Our approach is illustrated on relevant example models, and the performance of our implementations is assessed on a variable dimension large-scale model. [ABSTRACT FROM AUTHOR]

Electromagnetic Transient (EMT) simulation tools are typically developed using conventional procedural programming languages. On the other hand, modern high-level and equation-based programming languages, such as Modelica, are currently available. Modelica allows formulating models that are easy to develop, maintain and understand by expressing what needs to be computed without stating how it should be computed. This paper presents a Modelica-based simulator for electromagnetic transients. It is demonstrated that this approach offers significant advantages for developing sophisticated models. Computational performance and accuracy are compared to a conventional EMT-type simulation tool. [ABSTRACT FROM AUTHOR]

Direct current (DC) power distribution has recently gained traction in buildings research due to the proliferation of on-site electricity generation and battery storage, and an increasing prevalence of internal DC loads. The research discussed in this paper uses Modelica-based simulation to compare the efficiency of DC building power distribution with an equivalent alternating current (AC) distribution. The buildings are all modeled with solar generation, battery storage, and loads that are representative of the most efficient building technology. A variety of parametric simulations determine how and when DC distribution proves advantageous. These simulations also validate previous studies that use simpler approaches and arithmetic efficiency models. This work shows that using DC distribution can be considerably more efficient: a medium sized office building using DC distribution has an expected baseline of 12% savings, but may also save up to 18%. In these results, the baseline simulation parameters are for a zero net energy (ZNE) building that can island as a microgrid. DC is most advantageous in buildings with large solar capacity, large battery capacity, and high voltage distribution.

Bidirectional low temperature networks are a novel concept that promises more efficient heating and cooling of buildings. Early research shows theoretical benefits in terms of exergy efficiency over other technologies. Pilot projects indicate that the concept delivers good performance if heating and cooling demands are diverse. However, the operation of these networks is not yet optimized and there is no quantification of the benefits over other technologies in various scenarios. Moreover, there is a lack of understanding of how to integrate and control multiple distributed heat and cold sources in such networks. Therefore, this paper develops a control concept based on a temperature set point optimization and agent-based control which allows the modular integration of an arbitrary number of sources and consumers. Afterwards, the concept is applied to two scenarios representing neighborhoods in San Francisco and Cologne with different heating and cooling demands and boundary conditions. The performance of the system is then compared to other state-of-the-art heating and cooling solutions using dynamic simulations with Modelica. The results show that bidirectional low temperature networks without optimization produce 26% less emissions in the San Francisco scenario and 63% in the Cologne scenario in comparison to the other heating and cooling solutions. Savings of energy costs are 46% and 27%, and reductions of primary energy consumption 52% and 72%, respectively. The presented operation optimization leads to electricity use reductions of 13% and 41% when compared to networks with free-floating temperature control and the results indicate further potential for improvement. The study demonstrates the advantage of low temperature networks in different situations and introduces a control concept that is extendable for real implementation.

Bidirectional low temperature networks are a novel concept that promises more efficient heating and cooling of buildings. Early research shows theoretical benefits in terms of exergy efficiency over other technologies. Pilot projects indicate that the concept delivers good performance if heating and cooling demands are diverse. However, the operation of these networks is not yet optimized and there is no quantification of the benefits over other technologies in various scenarios. Moreover, there is a lack of understanding of how to integrate and control multiple distributed heat and cold sources in such networks. Therefore, this paper develops a control concept based on a temperature set point optimization and agent-based control which allows the modular integration of an arbitrary number of sources and consumers. Afterwards, the concept is applied to two scenarios representing neighborhoods in San Francisco and Cologne with different heating and cooling demands and boundary conditions. The performance of the system is then compared to other state-of-the-art heating and cooling solutions using dynamic simulations with Modelica. The results show that bidirectional low temperature networks without optimization produce 26% less emissions in the San Francisco scenario and 63% in the Cologne scenario in comparison to the other heating and cooling solutions. Savings of energy costs are 46% and 27%, and reductions of primary energy consumption 52% and 72%, respectively. The presented operation optimization leads to electricity use reductions of 13% and 41% when compared to networks with free-floating temperature control and the results indicate further potential for improvement. The study demonstrates the advantage of low temperature networks in different situations and introduces a control concept that is extendable for real implementation.

District heating and cooling are considered effective solutions to decarbonise the energy use in the building sector. The latest generation of district heating and cooling also increases the potential of integrating heat pumps and chillers in each building substation. The benefits of such integration are the reduction of network temperature and distribution losses; the recovery of waste heat through a bidirectional network; and the decentralised production of heating and cooling. Sizing the network depends mainly on the heat flows between connected buildings. The substation performance and technical installations determine these heat flows. We present in this paper Modelica-based simulations of two design cases for substations. The first design case involves installed heat pump, chiller, and circulation pumps. Alternatively, the second design enables the heat pump to provide direct cooling through a heat exchanger. The models for these installations were developed using the Modelica language to perform continuous-time simulations. The performance in each design case was evaluated in terms of seasonal coefficient of performance and total electric energy use. An analysis on a cluster of 11 buildings suggests that the addition of the direct cooling heat exchanger can save up to 10% of the total annual electric energy use. Additional savings can be achieved by optimising the building supply temperatures and the district network temperature.

District heating and cooling systems play an important role in future energy infrastructure by accessing local energy potentials, reducing temperature levels and exploiting synergies between energy systems. Therefore, a transition to next generation heating and cooling networks is needed, which can be realized by multi-level design, bidirectional usage of production and consumption or combined heating and cooling systems. This will increase the complexity of network design which may be hard to analyze and assess without the support of a system simulation. The following paper presents the dhcSim Modelica library which can be used to model complex district heating and cooling networks within a multi-level network structure. The library was developed as extension to the Modelica Buildings library and Modelica Standard library and provides a clear and easy to use model structure. This structure distinguishes between two-, four and multi-port-models which allows the modeling of highly complex multi-level DHC systems. The library provides models for both district heating and cooling and can be easily adapted to individual user cases. To illustrate the implementation of the library and how it can be used, a case study of a three-level district heating network is presented. Using the network simulation of the introduced example, it could be shown that the supply temperature requirements of some consumers could not be achieved by the proposed network and thus further action is needed.

The development of high-tech manufacturing in recent years has promoted the rapid growth of industrial cleanrooms. The strict requirements for indoor environment control in cleanrooms lead to huge cooling energy requirements, which has given rise to research on energy modeling of cooling systems relevant to the industrial sector. Efficient and accurate modeling of demand-side Heating, Ventilation and Air-conditioning (HVAC) systems can significantly enhance water-side and air-side control strategies, thereby improving overall energy efficiency. However, existing grey-box methods usually only consider independent air-side or room models in industrial scenarios, and the inefficient handling of missing facility data makes it difficult to achieve optimal model performance. Meanwhile, there is also a lack of systematic investigation on the performance of black-box and grey-box methods in industrial demand-side HVAC modeling scenarios. In this study, based on a real industrial park, a demand-side HVAC system model was established using both the Modelica-based method and pure data-driven method to predict the cooling load under summer conditions. By using a machine learning approach, the true value of the missing internal heat gain is inferred instead of using a fixed value. The model results after Genetic Algorithm (GA) calibration show that in the case of small sample size, the prediction error of the machine learning-enhanced Modelica-based model is reduced by about 34.5 % compared with the original modelica-based model. Compared with the data-driven model with hyperparameter tuning, the reduction is about 38.8 %/21.3 % in the case of small/large sample size, respectively. Then the reasons for the differences in the performance of these modeling methods are compared and discussed in detail. The framework of machine learning-enhanced modeling method proposed in this study can also be applied to other industrial demand-side HVAC system modeling scenarios with missing on-site facility data, ultimately achieving more efficient industrial production through optimal control strategies for water-side systems. • Modelica-based demand-side HVAC system modeling method with machine-learning for missing model input data inference. • Validation of the modeling methods using measurement data from a real industrial park. • The machine learning-enhanced model achieves over 30 % lower error than both original Modelica model and data-driven model. • Compare and analyze the practicability of Modelica-based methods and pure data-driven methods in industrial HVAC modeling. [ABSTRACT FROM AUTHOR]

Simulation and optimisation of district heating and cooling networks requires efficient and realistic models of the individual network elements in order to correctly represent heat losses or gains, temperature propagation and pressure drops. Due to more recent thermal networks incorporating meshing decentralised heat and cold sources, the system often has to deal with variable temperatures and mass flow rates, with flow reversal occurring more frequently. This paper presents the mathematical derivation and software implementation in Modelica of a thermo-hydraulic model for thermal networks that meets the above requirements and compares it to both experimental data and a commonly used model. Good correspondence between experimental data from a controlled test set-up and simulations using the presented model was found. Compared to measurement data from a real district heating network, the simulation results led to a larger error than in the controlled test set-up, but the general trend is still approximated closely and the model yields results similar to a pipe model from the Modelica Standard Library. However, the presented model simulates 1.7 (for low number of volumes) to 68 (for highly discretized pipes) times faster than a conventional model for a realistic test case. A working implementation of the presented model is made openly available within the IBPSA Modelica Library. The model is robust in the sense that grid size and time step do not need to be adapted to the flow rate, as is the case in finite volume models.

Direct current (DC) power distribution has recently gained traction in buildings research due to the proliferation of on-site electricity generation and battery storage and an increasing prevalence of end uses operating internally on DC. The research discussed in this paper uses Modelica-based simulation to compare the efficiency of DC building power distribution with an equivalent alternating current (AC) distribution. A variety of parametric simulations determine how and when DC distribution proves advantageous. This work shows that using DC distribution can be considerably more efficient than AC: a medium office building using DC distribution has an expected baseline of 11% savings, but may save up to 17%. In these results, the baseline simulation parameters are for a zero net energy (ZNE) building with enough battery storage to act as an islanding microgrid. DC is generally most advantageous in buildings with large solar capacity, large battery capacity, and high voltage DC distribution. In addition, based on the efficiency modeling results, a comparison of the economic performance of DC vs. AC distribution systems in commercial buildings is conducted. The results for the baseline scenario show that DC distribution systems in buildings can be cost effective when PV generation and battery storage are included in the building.

The integration of photovoltaic panels and heat pumps in domestic environments is a topic that has been studied extensively. Due to their electrical nature and the presence of elements that add thermal inertia to the system (water tanks and the building itself), the functioning of compression heat pumps can be manipulated to try to fulfill a certain objective. In this paper, following a rule-based control concept that has been identified in commercial solutions and whose objective is to improve the self-consumption of the system by actively modulating the heat pump compressor, a parametric analysis is presented. By making use of a lab-tested model, the performance of the implemented control algorithm is analyzed. The main objective of this analysis is to identify and quantify the effects of the main parameters in the performance of the system, namely the climate (conditioning both heating and cooling demands), the photovoltaic installation size, the thermal insulation of the building and the control activation criteria. A total of 168 yearly simulations have been carried out. The results show that the average improvement in self-consumption is around 13%, while the cost is reduced by 2.5%. On the other hand, the heat from the heat pump and the power consumed increase by 3.7% and 5.2%, respectively. Finally, a linear equation to estimate the performance of the controller is proposed.

Energy Management Systems (EMS) for buildings are pivotal in leveraging flexibility from sector coupling in future power systems. Currently, most EMS are designed and evaluated using non-standardized and incompatible simulation models built within the specific EMS development cycle. Such evaluation techniques make it difficult to compare different EMS solutions according to well-defined and universal performance indicators. Therefore, open-access benchmark models of realistic building energy systems would be beneficial for wider research community. This article introduces the ProHMo benchmarking framework which provides experimentally validated commercial heating and cooling equipment models in various building energy system configurations. The building energy system models are available as openly accessible Functional Mock-Up Units (FMU) to allow for toolchain-independent benchmarking of EMS. The framework includes a Python code template that enables easy integration with different EMS interfaces. In a case study, we show the potential of the benchmarking framework by comparing a rule-based, optimization-based, and reinforcement learning-based EMS. The results show that the optimization-based EMS with perfect foresight achieves the lowest costs. Although the reinforcement learning-based EMS performs slightly poorer, it operates independently of forecasts, which makes it attractive for practical applications. The ProHMo benchmarking framework is designed to equip researchers with a robust framework for developing, evaluating, and comparing different EMS, particularly those focused on optimization and data-driven control methods. • Modelica library with experimentally validated building energy system models. • Benchmarking framework for comparing Energy Management System (EMS) control methods. • Open-access Functional Mock-Up Units (FMU) allowing software-independent use. • Python code template facilitating communication between EMS and FMU. • Case study comparing the potential of different EMS approaches. [ABSTRACT FROM AUTHOR]

Particle-based thermal energy storage technologies could facilitate increased penetration of variable renewable energy resources by decreasing the costs of providing dispatchable, carbon-free generation resources. However, the technology is not yet commercially available, and its performance has not been thoroughly characterized. This work creates a system modeling platform for particle-based thermal energy storage systems that can characterize performance and is transferable to emerging forms and applications of particle-based thermal energy storage. A library of key component models developed for particle-based thermal energy storage is described and benchmarked against high-fidelity models or with experimental results. A notional 135 MW e power plant employing particle thermal energy storage for grid-scale, electricity storage applications is conceived and simulated. The results show these systems can achieve greater than 50% annual round-trip efficiencies in a variety of durations, locations, dispatch schedules, and key design parameters. A 1.5% decrease in round-trip efficiency was observed when increasing storage duration from 10 to 100 h. Dispatch schedules can impact round-trip efficiency by greater than 0.5%. In regards to component design and performance, the insulation thickness of the silos and the heat exchanger approach temperature impacted performance the most. Particle-based thermal energy storage has the potential to assist in the decarbonization of not only electricity but industry and building sectors as well. This work enables the analysis of the performance of systems in these sectors and serves as a detailed technical model for design optimization in the future. [Display omitted] • New component models developed for particle-based thermal energy storage systems. • Annual simulations completed of a novel electrical thermal energy storage system. • Round-trip efficiencies near 51% are predicted for the system across sensitivities. • Duration, dispatch, approach temperature and insulation impact efficiency by > 0.5%. [ABSTRACT FROM AUTHOR]

Shallow geothermal offers great potential to supply emission-free heating and cooling and has therefore a pivotal role in the transition of heating and cooling networks. This paper presents a novel heating and cooling network with possible geothermal energy sharing between decentralized borefields located at each building level. The system consolidates the benefits of both standalone and centralized energy systems to attain high-efficiency gains and ensure a secure heating and cooling supply. To study the expected system performance, a simulation model is demonstrated and applied to a real-world case study consisting of three new buildings located in Malmö, Sweden. Simulation results revealed that the established synergy through geothermal energy sharing can reduce the purchased electric energy by 23 % and increase the annual performance factors for heating and cooling by 31 and 35 %, respectively compared to the standalone system. [ABSTRACT FROM AUTHOR]

An efficient smart and connected community (SCC) depends on the interconnectivity of essential infrastructure systems. However, current modeling tools are unable to determine which interconnections are most important to include, particularly as system dynamics become more complex with high-order effects. To bridge this gap, we propose a comprehensive framework that incorporates multi-layers, multi-blocks, and multi-agents to model interdependent infrastructure systems. Interconnections span cyber, physical, and logical aspects, including human interactions. With the equation-based object-oriented language Modelica, we model energy, transportation, communication, and water systems for a hypothetical SCC and assess higher-order interdependency effects during normal operation. Additionally, we develop a quasi-Monte Carlo sensitivity analysis framework and use variance-based sensitivity metrics to assess the impact of interdependencies on energy system operation. Compared to the decoupled baseline system, the energy consumption of logical interdependency cases varied by 23.3%, the cyber interdependency by 2.0%, and the nested global interdependency by 21.5%. The sensitivity analysis further revealed that interrelationships are not linear nor quadratic, but involve higher-order interactions between parameters. Specifically, occupancy and cyber delays had significant first-order effects. Road delays had a significant higher-order effect, which corresponded to a stronger influence on other model parameters. By modeling higher-order cascading dependencies, our proposed framework has the potential to improve the planning, operation, and co-design of SCCs by quantifying the importance of complex system interactions. • First to model the interaction of energy, communication, transportation, and water systems in one platform. • Includes physical, cyber, and logical interdependencies for comprehensive analysis. • Quantifies the sensitivity of complex system interactions through Quasi-Monte Carlo approach. • An efficient tool to improve the planning, operation, and co-design of Smart Connected Communities (SCC). [ABSTRACT FROM AUTHOR]

Integrated energy systems can improve flexibility on future energy grids with one option being Nuclear-Solar hybrid systems. Integrating solar generated heat from parabolic troughs into the feedwater line allows the plant to alleviate turbine bled steam and transiently power boost 15% above nominal power for a nominal small modular PWR cycle. This article presents a parametric study of the design of such a system and provides a full system dynamic model written in the Modelica language to analyse the dispatch in transient load following operation. The control of the system is presented, and the transient analysis is shown to help inform sizing of the concrete storage and parabolic trough arrays. The trade off in design between improved degrees of power boosting and system nominal efficiency is investigated with the work suggesting that higher steam generator entry temperatures offer improved opportunities of up to 42% for power boosting flexibility. • An open-source model of the full proposed energy system is written in Modelica. • The model allows a high time fidelity simulation of all key system variables. • Solar feedwater heating allows power boosting without changing reactor conditions. • Transient analysis informs sizing the concrete thermal storage and solar array. [ABSTRACT FROM AUTHOR]

The new generation of low-earth-orbit (LEO) satellite constellation systems has the characteristics of low delay, strong signal and global coverage, and it is an important direction for the development of next-generation communication technology. A major disadvantage is that the constellation system is huge, often composed of hundreds or thousands of satellites, which puts forward high requirements for the design and health management of the constellation system, and the existing telemetry data monitoring system cannot meet the actual needs. CPS is a multidimensional complex system that integrates computation, communication and control (3C). Through the deep integration and cooperation of 3C, the real-time monitoring and dynamic control of large-scale engineering systems are realized, which is completely suitable for the operation and maintenance requirements of the satellite constellation system. This paper firstly establishes the entire satellite constellation system model, which is integrated from the satellite multidomain system, the constellation orbit environment system and the communication link system. Then, according to the technical concept of cyber-physical systems (CPS), an implementation framework of a prognostics and health (PHM) system driven by a model–data hybrid for satellite constellation systems is proposed. The framework is based on model simulation data and telemetry data and combines virtual and real data fusion, fault diagnosis, simulation prediction and other technologies to generate enhanced data to drive the effective operation of the PHM system. Finally, a verification case is designed to prove that the satellite constellation health management system implemented under this framework has a positive effect on the reliable operation and maintenance of the satellite constellation system. [ABSTRACT FROM AUTHOR]

The trajectory planning method with dynamics is the key to improving the motion performance of manipulators. The optimal control method (OCM) is a key technology to solve optimal problems with dynamics. There are direct and indirect methods in OCM; indirect methods are difficult to apply to engineering applications, and so direct methods are widely applied instead. The direct collocation method (DCM) is a technology in OCM to transform an optimal control problem (OCP) to a nonlinear problem (NLP), so that plenty of solvers can be used directly. However, the general DCM, for which it has been found that the explicit form of the right-hand-side (RHS) functions of state equations of the complex system in the OCP is hard to derive, is limited to solving the OCP of three-axis manipulators. This paper proposes an improved DCM to solve the OCP of six-axis manipulators, which can find the solution of the time-optimal trajectory for the motion of six-axis manipulators based on the improved DCM. The proposed method derives the RHS equations implicitly by introducing a Functional Mock-up Unit (FMU), which simplifies the representation of the RHS equations as a black-box model, so that the DCM can be applied to the OCP of six-axis manipulators. A simulation case of a three-axis manipulator accomplished in a related study works as a reference compared with our improved method to verify the solution consistence between the DCM using the explicit RHS equations or using the implicit RHS equations, and the loss of computational efficiency is acceptable. In the meantime, a simulation solution and an experiment of six-axis manipulators, which is a novel advancement, are presented to validate the proposed method. [ABSTRACT FROM AUTHOR]

The swash plate axial piston pump is a typical complex system of electro-hydraulic mixing. Its faults are hardly identified, and a multi-domain simulation modeling method based on actual mechanical structure is on the demand. In this regard, a Modelica-based modeling and simulation approach for the swash plate piston pump is carried out. First, based on the actual mechanical structure parameters of a certain type of piston pumps, component division and assembly modeling are carried out, and a local hydraulic system for the swash plate piston pump is built. Second, common failures of the piston pumps, including leakage, pressure loss, blockage and cavitation, are simulated by the model, and quantitative simulation analysis is carried out. The impact of different faults on the hydraulic system is identified. This demonstrates that the proposed model can work well for the analysis on common faults of piston pumps. This model improves the accuracy of multi-domain unified modeling of swash plate axial piston pumps and provides reference for fault diagnosis and health monitoring. [ABSTRACT FROM AUTHOR]

Home energy retrofit has recurred in public policy throughout recent decades. However, the savings in energy usage attributable to home retrofit have remained difficult to accurately predict. Occupants cause prediction inaccuracies by varying different factors, especially heating setpoints temperatures and heating patterns. Acting together, such occupant factors result in distributions — not single values — of heat-energy usage, even among similar homes. Datasets of heat-energy distributions can be found by building performance simulation using modern grey-box models. This study presents a methodology to simulate grey-box models of home heating through ranges of heating setpoints and patterns. An entire process to calibrate, validate and simulate at a large scale is described, and then demonstrated using case studies. Grey-box models, written in Modelica language, can conveniently simulate through large ranges of occupant factors. The case studies exploited this advantage of grey-box models to simulate empirical data on occupant factors. (For instance, empirical data found that home heating setpoints shifted before and after home energy retrofit.) In doing so, the datasets of simulation results enabled the exploration of home heat-energy usage with the normal and Weibull statistical distributions. Additionally, the heat-energy distributions of case-study homes were statistically tested, first for retrofit savings, second for equality to each other and third for equality to an official heat-energy estimate. Results demonstrate that home heat-energy usage, at a large scale, is best expressed as a Weibull distribution not normality. After home energy retrofit, heat-energy usage displays less variation (in general), less skewness, and thus becomes closer to normality. Occupant factors were found to vary home heat-energy usage into distinct distributions, even within similar homes. Therefore, in most case-study homes, heat-energy usage did not equal an official estimate. Finally, shallow retrofit of a modern home in Ireland fails to save heat-energy usage by most occupants. [ABSTRACT FROM AUTHOR]

Nowadays, industrial products are getting increasingly complex, and time-to-market is significantly shorter. Modeling and simulation tools for cyber-physical systems need to keep up with the increased complexity. This paper presents OpenModelica.jl, a modular and extensible Modelica compiler framework in Julia targeting ModelingToolkit.jl and supporting Variable Structured Systems. We extended the Modelica language with three new operators to support continuous-time mode-switching and reconfiguration via recompilation at runtime. Therefore, our compiler supports the Modelica language and variable structure systems via the aforementioned extensions. To our knowledge, there are no other Modelica tools available that support both standard Modelica and variable structure systems. We evaluated our framework using a standardized benchmark suite, in terms of simulation, compilation and recompilation performance. The results concerning compilation and simulation time performance were compared with the results of running the existing OpenModelica compiler with the same set of models. A custom benchmark was devised to estimate the cost in terms of recompilation when simulating variable structure systems. The performance experiments showed that OpenModelica.jl is currently about four times slower in terms of compilation time when compiling a transmission line model with tens of thousands of equations and variables. The difference in simulation performance between the two compilers was negligable. Furthermore, the impact of recompilation during the simulation was usually small compared with the simulation time for long simulations. The results are promising for a prototype, and we outline approaches to further improve both compilation and simulation performance as future research. [ABSTRACT FROM AUTHOR]

Research activity in the field of combined cooling heating power (or trigeneration) systems with high renewable energy source (RES) contributions has increased rapidly over the last few years, in line with the European Union legislation about energy communities. However, technical challenges arise regarding the synergetic, sustainable and optimal integration of RES in local energy systems. In the present study, the operation of a trigeneration system located in the student residences of Democritus University of Thrace in Greece is examined. The system involves a combination of highly promising renewable and storage technologies, including solar thermal energy and biomass for heat generation, hot water tanks for thermal energy storage, absorption refrigeration for cooling, along with Organic Rankine Cycle and photovoltaic systems for electricity generation. System modeling and simulation have been implemented in Dymola environment with the use of Modelica equation-based modeling language. The accuracy of the model response has been validated against available measurements. Specific design and control measures have been proposed, simulated in a transient fashion and evaluated in terms of (i) RES generation, (ii) solar fraction and (iii) temporal flexibility. The measures examined, including the placement of a Li-ion battery, resulted in an increase of 24.6% in the heating demand solar contribution and of 7.9% in the renewable energy generated for the electricity demand, over the examined periods. [ABSTRACT FROM AUTHOR]

Modelica is an open-source, object-oriented equation-based modeling language. It is suitable for describing sophisticated dynamic systems (symmetry/asymmetry) as it uses mathematical acausal equations to express physical characteristics. The hydraulic mechanical units (HMU) of gas turbine engine control systems couple the contents of mechanical, hydraulic, symmetry, and other multidisciplinary fields. This paper focuses on the Modelica description method of those HMU models. The content of this work is threefold: firstly, the division form of basic elements in HMU is defined, and the method for describing these element models with Modelica is proposed; secondly, the organization of the element models is defined by using the inheritance characteristics of Modelica, and a lightweight (small code scale) component model is designed; and finally, the causal/acausal connections are designed according to bond graph theory, and the elements and components are integrated into a prototype modeling library. In this paper, the modeling library is verified by comparing simulation results of five typical HMU subsystem models with commercial modeling and simulation software. [ABSTRACT FROM AUTHOR]

Integration of borehole heat exchangers (BHE) into district heating (DH) should be supported by numerical simulations to ensure efficient operation. Co-simulation allows for the use of dedicated software for above and below ground sub-models, facilitating the use of detailed 3D geological models. This paper presents a methodology for coupling DH models in Modelica to 3D FEM subsurface models. An interface which implements BHE models in Modelica and one with BHE models in the FEM model are compared to a benchmark model. Furthermore, an adaptive control of the communication steps reduces communication error and computational times simultaneously. A fictional solar DH system with underground thermal energy storage is co-simulated to demonstrate potential advantages of the proposed method. Overall, co-simulation of DH systems and BHE arrays facilitates accurate performance assessment of systems for which this would not be possible otherwise, but should be applied carefully, due to the increased computational effort. [ABSTRACT FROM AUTHOR]