Co-simulation Framework Research Articles

Time Horizon-Based Model Predictive Volt/VAR Optimization for Smart Grid Enabled CVR in the Presence of Electric Vehicle Charging Loads

This paper investigates the need of coordinated operation of conservation voltage reduction (CVR) in the presence of electric vehicle (EV) penetration in the active distribution network. In order to analyze the impact of both the technologies (CVR and EV), a time horizon-based model predictive Volt/VAR optimization (VVO) methodology has been introduced in smart grid framework. The proposed VVO methodology operates in centralized as well as local controls under different time scale of operation, including cloud transient effects on solar photovoltaic (PV) power output. Moreover, the control algorithms also consider the uncertainties in load demand and PV power generation. The VVO methodology has been validated with and without presence of EV loads in the distribution network. The VVO includes the impact of different EV charging loads having the ability of participation in reactive power support at selected charging points. This is also referred to as vehicle-to-grid operation in terms of reactive power dispatch only. Besides, the voltage and VAR regulation through smart inverters of PVs and EV charging station has been fruitfully utilized in global as well as local domain. A real-time Volt/VAR droop based controller has been introduced to control the smart inverters reactive power dispatch. To validate the developed methodology, a real-time cosimulation framework, using real-time digital simulator and Python interface, has been built. The proposed model predictive VVO algorithm has been tested and validated on a modified IEEE 34 bus test system. The simulated results reveal that significant CVR energy savings and losses reduction has been achieved without violating the system constraints. The voltage control algorithm works well in both slow and fast time scales.

Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

Nowadays, reliability of sensors is one of the most important challenges for widespread application of Internet-of-things data in key emerging fields such as the automotive and manufacturing sectors. This paper presents a brief review of the main research and innovation actions at the European level, as well as some on-going research related to sensor reliability in cyber-physical systems (CPS). The research reported in this paper is also focused on the design of a procedure for evaluating the reliability of Internet-of-Things sensors in a cyber-physical system. The results of a case study of sensor reliability assessment in an autonomous driving scenario for the automotive sector are also shown. A co-simulation framework is designed in order to enable real-time interaction between virtual and real sensors. The case study consists of an IoT LiDAR-based collaborative map in order to assess the CPS-based co-simulation framework. Specifically, the sensor chosen is the Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. The modeling library for predicting error with machine learning methods is implemented at a local level, and a self-learning-procedure for decision-making based on Q-learning runs at a global level. The study supporting the experimental evaluation of the co-simulation framework is presented using simulated and real data. The results demonstrate the effectiveness of the proposed method for increasing sensor reliability in cyber-physical systems using Internet-of-Things data.

A framework for FMI-based co-simulation of human–machine interfaces

A framework for co-simulation of human–machine interfaces in Cyber-Physical Systems (CPS) is presented. The framework builds on formal (i.e. mathematical) methods. It aims to support the work of formal methods experts in charge of modelling and analysing safety-critical aspects of user interfaces in CPS. To carry out these modelling and analysis activities, formal methods experts usually need to engage with domain experts that may not fully understand the mathematical details of formal analysis results. The framework presented in this work mitigates this communication barrier by allowing formal methods experts to create interactive prototypes driven by formal models. The prototypes closely resemble the visual appearance of the system being developed. They can be used to discuss details of the formal analysis effort without showing any mathematical detail. An existing prototyping toolkit based on formal methods is used as baseline technology. Novel functionalities are developed for automatic generation of interactive prototypes supporting the Functional Mockup Interface (FMI), a de-facto standard technology for simulation of complex systems. Using the FMI interface, the prototypes can be integrated with simulations of other system components. The architecture of the framework is presented, along with a verification of core aspects of its functionalities. A case study based on a medical system is used to demonstrate the capabilities of the framework.

Synchronization conditions and Real-time constraints in co-simulation and Hardware-in-the-Loop techniques for Cyber–Physical Energy System assessment

Abstract Co-simulation and Hardware-in-the-Loop, as well as the integration of Hardware-in-the-Loop to a co-simulation framework are innovative approaches that can get a step closer to a complete and realistic assessment and validation of cyber–Physical energy system (i.e. smart grid). While the coupling techniques and the master algorithms ensure good functionality of the experiment, the whole framework has to follow a rigorous time semantic so that the results would be accurate and meaningful. In this paper, we present a formalization of the synchronization aspect in co-simulation and Hardware-in-the-Loop. Using this formulation, we propose synchronization and real-time conditions under which a co-simulation/Hardware-in-the-Loop framework is well-posed and correctly synchronized. Moreover, the classical real-time constraint in Hardware-in-the-Loop is relaxed for more flexible application. The proposed formulation is illustrated via an cross-infrastructure implementation of secondary control in micro-grid, with integration of remote controllers to real-time co-simulation framework.

Simulation-Based Valuation of Transactive Energy Systems

Transactive energy (TE) has been recognized as a promising technique for integrating responsive loads and distributed energy resources as well as advancing grid modernization. To help the industry better understand the value of TE and compare different TE schemes in a systematic and transparent manner, a comprehensive simulation-based TE valuation method is developed. The method has the following salient features: first, it formally defines the valuation scenarios, use cases, baseline, and valuation metrics; second, an open-source simulation platform for transactive energy systems has been developed by integrating transmission, distribution and building simulators, and plugin TE and non-TE agents through the framework for network cosimulation (FNCS); third, transparency and flexibility of the valuation is enhanced through separation of simulation and valuation, base valuation metrics, and final valuation metrics. A valuation example based on the smart grid interoperability panel use Case 1 is provided to demonstrate the developed TE simulation program and the valuation method.

Framework for large-scale implementation of wholesale-retail transactive control mechanism

Transactive energy is a control technique that uses market mechanisms to achieve desired control objectives. Several simulation studies and field demonstrations were carried out in recent years, but all focused on small scale systems and purpose-built simplified models which are not always capable of pointing out all the advantages and shortcomings of transactive energy methods. This work describes a co-simulation framework built on a hierarchical control architecture that allows for conducting studies on the impacts of a very large-scale deployment of transactive energy. The hierarchical transactive control architecture adopted in this work helps with alleviating computation and communication burden to facilitate a more effective large scale real-time market operation among device level resources and the system level operators. The co-simulation framework is evaluated using off-the-shelf simulators on an integrated power system model of unprecedented scale composed of the Western Electricity Coordination Council transmission system with tens of thousands of distribution systems deployed with flexible device-level distributed energy resources.

CFD study of pulverized coal-fired boiler evaporator and radiant superheaters at varying loads

This paper describes a CFD modelling methodology used to evaluate the thermal performance of the water wall evaporator, platen and final stage superheaters of a subcritical pulverized coal-fired boiler at full and reduced boiler loads. The heat exchanger elements are modelled as explicit walls with an effective thermal resistance boundary condition rather than fixed wall temperatures. This enables the direct radiation component to be properly accounted for together with variations in the wall temperatures. The modelling approach was applied to the boiler of a 620 MWe power plant. The results for 99%, 81% and 60% boiler loads compare well with experimental data. The proposed methodology forms the bases for a co-simulation framework which combines the CFD with a systems-level process modelling tool that can capture the internal steam maldistribution at low loads and the resultant effect on the metal temperatures.

A flexible co-simulation framework for penetration studies of power electronics based renewable sources: A new algorithm for phasor extraction

This work proposes a co-simulation environment that interfaces the OpenDSS, a frequency-domain program, with a time-domain-based electromagnetic transients software, aiming at performing studies on renewable resources penetration in distribution feeders. Each program handles part of the grid in a parallel manner, exchanging information at only predefined instants during the simulation. For the data exchange between time-domain and frequency-based simulations, the second order generalized integrator structure is used for extracting the phasors. Comparisons with the traditional curve fitting method show improved computational efficiency while keeping reasonable accuracy. Results, presented for the IEEE 13-bus distribution system benchmark operating with three photovoltaic plants, show accurate results obtained 12 times faster than the full time-domain simulation.

Price and carbon-based energy flexibility of residential heating and cooling loads using model predictive control

Model predictive controllers (MPC) have shown great potential for activating the energy flexibility of thermal loads, especially in buildings equipped with heat pump systems. In this work, an MPC controller is developed and tested within a co-simulation framework which couples an optimization software with a dynamic building simulation tool. The development phase is described in detail, in particular the methods to obtain simplified models to be used by the controller. The building envelope and the heat pump performance (based on experimental data) were thus modelled, both in heating and cooling seasons. Three different objective functions of the MPC are tested on a study case consisting of a Spanish residential building: promising results are obtained when the controller aims at minimizing operational costs (savings of 13–29%) or CO2 marginal emissions (savings of 19–29%). The development efforts, the required tuning and sensitivity of the MPC algorithm parameters, the adaptations needed between the cooling and heating operations are also discussed and put into perspective with the obtained benefits in terms of savings, comfort and load-shifting.

Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials

This paper presents a co-simulation framework for the simultaneous optimization of building geometries and multi-energy systems using the energy hub approach. The rationale for such coupling is that building geometry has an impact on energy demands and solar potentials on roofs and façades, thus also altering the corresponding optimal energy system. As a demonstration of this approach, we formulate bi-objective optimization problems for minimizing operational cost and carbon emissions, with decision variables for building geometry and for selection and sizing of energy system technologies. The methodology is applied to a case study in the city of Zurich, Switzerland, involving four office buildings. Different carbon emissions targets are studied to show the impact on cost, densities, sizing of the multi-energy system, and architectural design implications. Results for this case study show a clear relation between emissions targets and densities due to available solar potentials for renewable energy generation, with an optimal density in the strictest emissions target reaching only 10% of the optimal density without emissions target. This indicates that differentiated environmental targets should be defined depending on the location of a building, where rural sites could have stricter targets than dense urban sites to reflect the respective marginal costs for achieving the targets. Our study shows that coupling multiple simulators into a common optimization and design workflow brings together architectural aspects, such as geometry, with engineering aspects, such as the energy system design, and microclimate conditions, such as local solar potentials. Thus, essential interdependencies between the energy supply and demand side can be captured in the design of energy efficient cities.

A modeling framework for optimization-based control of a residential building thermostat for time-of-use pricing

Heating, ventilation and air conditioning for residential and commercial buildings requires a substantial share of electric energy, and ultimately drives summer peak demand in the United States. Variable electric rates are becoming more common in the residential market, as utilities try to encourage users to shift their energy demand. Model predictive controls, one method of reducing energy usage, employ an optimization model to minimize peak demand, energy usage, or electricity costs. This paper details the development of a co-simulation framework to rapidly model and simulate building energy use and optimize cooling setpoint controls. The framework integrates commercially available software to: (i) simulate all energy interactions between the building, internal gains, outdoor environment, and heating and cooling systems via a building energy simulation program (EnergyPlus), (ii) algebraically formulate an optimization problem (with AMPL) using a black-box, reduced-order model for rapid calculations, (iii) employ Simulink as the environment that links calls to EnergyPlus and AMPL, and (iv) solve the optimization model (with CPLEX) to minimize electricity costs and user discomfort. Variable electric time-of-use rates are analyzed in the context of total cooling electricity costs, thermal comfort of users, and peak demand shedding. The framework uses a model predictive control formulation capable of reducing cooling electricity costs by up to 30%; however, cost savings and peak demand shedding are highly dependent on the time-of-use electricity rate schedule.

CPES Testing with mosaik: Co-Simulation Planning, Execution and Analysis

The complex nature of cyber-physical energy systems (CPES) makes systematic testing of new technologies for these setups challenging. Co-simulation has been identified as an efficient and flexible test approach that allows consideration of interdisciplinary dynamic interactions. However, basic coupling of simulation models alone fails to account for many of the challenges of simulation-based multi-domain testing such as expert collaboration in test planning. This paper illustrates an extended CPES test environment based on the co-simulation framework mosaik. The environment contains capabilities for simulation planning, uncertainty quantification and the development of multi-agent systems. An application case involving virtual power plant control is used to demonstrate the platform’s features. Future extensibility of the highly modular test environment is outlined.

Construction of simulation framework for dynamic analysis of a superconducting magnetic levitation train with flexible car bodies

Magnetic levitation (maglev) trains are eco-friendly and help to transport passengers efficiently and safely. Among two main types of maglev train, the EDS type is more appropriate for ultra-high-speed maglev trains. However, it has disadvantages with regard to vertical and lateral stability induced by external and internal vibrations. To analyze and evaluate the dynamics of ultra-high-speed maglev, we proposed a co-simulation framework for dynamic analysis of an EDS-type maglev with ultra-high-speed and all-flexible car bodies using couple-field analysis. First, electromagnetic forces were calculated with the superconducting coils and magnets using 2D analytic model. And a lookup table for electromagnetic forces was constructed with a large range of levitation from −100 mm to 100 mm and guidance from −50 mm to 50 mm. A rigid car body model was then constructed using a multi-body dynamics program. The flexible car body were modeled using the ANSYS program and converted using rigid body element (RBE2 element). The total simulation framework was constructed using MATLAB Simulink, and the dynamic models, electromagnetic model and lookup table were connected using a cosimulation process. The constructed co-simulation framework was used to investigate the dynamics of a high-speed train under conditions of random irregularity. As results, we found that the first bending and middle layer modes of the flexible car body mutually affected the vibration characteristics of the flexible car body compared to the rigid car body. In addition, flexible car bodies showed greater vibration than rigid car bodies because of elastic modal effects and additional vibration induced by the interactions of the car bodies.

A Distributed IoT Infrastructure to Test and Deploy Real-Time Demand Response in Smart Grids

In this paper, we present a novel distributed framework for real-time management and co-simulation of demand response (DR) in smart grids. Our solution provides a (near-) real-time co-simulation platform to validate new DR-policies exploiting Internet-of-Things approach performing software-in-the-loop. Hence, the behavior of real-world power systems can be emulated in a very realistic way and different DR-policies can be easily deployed and/or replaced in a plug-and-play fashion, without affecting the rest of the framework. In addition, our solution integrates real Internet-connected smart devices deployed at customer premises and along the smart grid to retrieve energy information and send actuation commands. Thus, the framework is also ready to manage DR in a real-world smart grid. This is demonstrated on a realistic smart grid with a test case DR-policy.

Maestro: The INTO-CPS co-simulation framework

Cyber-Physical Systems (CPSs) often operate in a critical context where it is crucial that they behave as intended. However, the heterogeneous nature of CPSs makes them inherently challenging to develop. To assist in the development process, one can perform co-simulation, where models of constituents of a CPS are coupled to jointly simulate the full system. The challenge herein is to combine heterogeneous formalisms in a sound fashion and address practical needs such as stability, performance, platform compatibility and so forth. To address this, Maestro is a tool for co-simulation using models adhering to the Functional Mock-up Interface standard for co-simulation. Its development was driven by needs from different industry domains such as railways, agriculture, building automation and automotive. It supports both a fixed and variable constraint-based iteration scheme along with platform distribution capabilities. The tool is open-source as an attempt to increase adoption of co-simulation and encourage researchers to collaborate. Maestro has been validated by industry through application in the aforementioned domains. It is a step in the direction of the two-folded long-term goals: ensure trustworthy co-simulation results and make co-simulation a technology taken for granted.

An integrated framework for high-performance, high-fidelity simulation of ground vehicle-tyre-terrain interaction

Assessing the mobility of off-road vehicles is a complex task that most often falls back on semi-empirical approaches to quantify the tire-terrain interaction. We introduce a high-fidelity ground vehicle mobility simulation framework that uses physics-based models of the vehicle, tyres, and terrain to factor in both tyre flexibility and soil deformation. The tyres are modelled using a nonlinear finite element approach that involves layers of orthotropic shell elements. The soil is represented as a large collection of rigid elements that interact through contact, friction, and cohesive forces. The high-fidelity vehicle models incorporate suspension, steering, driveline, and powertrain models. To alleviate the prohibitive computational costs associated with a coupled simulation of the overall problem, we propose a decoupled approach implemented as an explicit, force-displacement co-simulation framework which is demonstrated on several full-vehicle on soft soil simulations. UNCLASSIFIED: Distribution Statement A. Approved for public release; distribution is unlimited. #28937.

A Language and Platform Independent Co-Simulation Framework Based on the Functional Mock-Up Interface

The main goal of the Functional Mock-up Interface (FMI) standard is to allow the sharing of simulation models across tools. To accomplish this, FMI relies on a combination of XML-files and compiled C-code packaged in a zip archive. This archive is called a Functional Mock-up Unit (FMU). In theory, an FMU can support multiple platforms, but not necessarily in practice. Furthermore, software libraries for interacting with FMUs may not be available in a particular language or platform. Another issue is related to the protection of intellectual property (IP). While an FMU is free to only provide the C-code in its binary form, other resources within the FMU may be unprotected. Distributing models in binary form also opens up the possibility that they may contain malicious code. In order to meet these challenges, this paper presents an open-source co-simulation framework based on FMI, which is language and platform independent thanks to the use of well-established remote procedure call (RPC) technologies. One or more FMUs are wrapped inside a server program supporting one or more language independent RPC systems over various network protocols. Together, they allow cross-platform invocation of FMUs from multiple, including previously unsupported, languages. The client-server architecture allows the effective protection of IP while also providing a means of protecting users from malicious code.

An integrated testbed for locally monitoring SCADA systems in smart grids

A testbed for evaluating if and how process-aware monitoring may increase the security of decentralized SCADA networks in power grids is presented. The testbed builds on the co-simulation framework Mosaik, and co-simulates in an integrated way, the power distribution network on different voltage levels, as well as the control network (Modbus/TCP). The existing simulators were extended to allow topology changes, and a controller (RTU) simulator connected to a SCADA server enabling remote control was implemented. Using the developed testbed, a recently proposed local monitoring approach was investigated. The results show that for so-called interlocks the proposed monitoring approach prevents the execution of 33.3% of the commands, that would result in an unsafe state of the power distribution grid. Furthermore, it is shown that unsafe transformer tap positions can also be avoided. To illustrate the relevance and importance of the proposed testbed, a detailed comparison of related work on process-aware intrusion detection approaches and testbeds combining (parts of) the control network and the power grid is provided.

A testing framework for predictive driving features with an electronic Horizon

This paper proposes a novel approach for automated functional testing of map-based fusion algorithms in complex vehicle networks. The hybrid data representation of detailed digital maps and physical automotive sensors provides an extended view of the ego-vehicle environment and thereby facilitates improved inferences and more competent decision-making. It has therefore been instrumental in the ongoing development of predictive driving features, e.g. fuel-efficient driving, traffic sign recognition and highway-pilot. The presented approach utilises a closed-loop Hardware-in-the-Loop (HiL) co-simulation framework to evaluate the performance of the decision level fusion algorithms. The method contains both the structural design and resource-efficient integration into the HiL test bench in the example of traffic sign recognition. In reality, discrepancy between visual and map data is omnipresent due to map errors, old map data or optical detection failure. Through fault injection, defined inconsistencies can be produced within the HiL simulation environment. Amongst others, the fault injection covers the placement and value of traffic signs. These failures can be used for robustness testing of the fusion algorithms. Subsequently, a method for correlation analysis between field observations and synthetic simulation is realised to extend the requirements-based test coverage adaptively and systematically by modular and parameterised scenario specifications. In summary, the results show that the extended HiL environment is capable of generating electronic Horizon data which can easily be adapted or extended.

Novel method to simulate large-scale thermal city models

This study presents a method used to simulate large-scale thermal models of cities that achieves two improvements compared to the state-of-the-art techniques: 1) Current state-of-the-art methods cannot simulate the dynamic interaction between subcomponents of a smart energy system at urban scale. This method proposes detailed dynamic simulation approaches for large-scale thermal models. 2) Currently applied co-simulation frameworks are not applicable to large-scale models. In the present study, the dynamic building simulation tool IDA Indoor Climate and Energy, which uses parallelization methods for large-scale models, is coupled with a co-simulation platform. The methods are applied to a semi-virtual case study, which consists of 1561 buildings and a new development area. The building stock is analyzed using an automated method based on publicly available data. In contrast, the virtual urban development area is investigated using a co-simulation framework with three dynamic simulation tools: IDA Indoor Climate and Energy for buildings (256 thermal zones and 29 heating systems), TRNSYS for the energy supply unit and Dymola/Modelica for the district heating network. The influence of co-simulation on the accuracy and on the computation time are investigated. The major finding of this study is that the computation time can be significantly reduced by decoupling methods.