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The Scenario in this framework consists of a JSON configuration file in which all the necessary links and parameters for the instances are made explicit. Firstly, the Python Scenario API allows creating a Python script Scenario in which instantiates and establishes input/output relationships between Model Instances and Simulators. Co-simulation of building energy and control systems with the Building 2013; Garau etal. SQU researchers develop system to ensure reliable electricity Engineering Associate (Energy Management Systems) - Salary.com Energy Informatics HELICS architecture is distributed so each Federate can communicate with others through a publish/subscribe approach(Eugster etal. Each Main Container spawns N child Containers that handle M/N Agents. The four Simulators (or Containers in the AIOMAS perspective) are: (i) the Meteo Simulator, (ii) the PV Simulator, (iii) the Building Simulator, and (iv) the Power Grid Simulator. IEEE Transactions on Industry Applications. As already mentioned, a comparison with Mosaik is only possible below 10k Model Instances. Finally, the above features and proposed results allow for a comparison of the overall scalability performance. 2018). An advanced adaptive multi-rate co-simulation platform was developed and validated in this study for accurate and accelerated simulation of power system transients. ACM Comput Surveys (CSUR) 35(2):114131, Garau M, Ghiani E, Celli G, Pilo F, Corti S (2018) Co-simulation of smart distribution network fault management and reconfiguration with lte communication. AIOMAS). The aim of this paper is the scalability analysis of Mosaik and HELICS, two of the most widely adopted co-simulation frameworks in literature. (c) AIOMAS can be used to implement a co-simulation framework, but, being a Generic Python library for MAS, the implementation is up to the user. Springer. Such a transition can not be left to chance and the development of novel Information and Communication Technology (ICT) tools, platforms, and frameworks for driving this transition are attracting a strong research effort from the scientific community. The simulation program interface that the BCVTB provides is explained. Simul Model Pract Theory 95:148163, Sergi B, Pambour K (2022) An evaluation of co-simulation for modeling coupled natural gas and electricity networks. By looking at the timing performance when scaling up it does not bring any benefits with respect to the Mosaik or AIOMAS classic solutions, and it suffers from the same model instance limit of Mosaik. Total execution time represents the time duration of the entire co-simulation process from Initialization to the end of all the tasks. In a very large scenario, it performs slightly better than HELICS with Classic configuration but it cannot be compared with the performances of multi-processing HELICS. (2021) and takes advantage of the Python electrical system analysis tool pandapower(Thurner etal. 2016; Barbierato etal. IEEE. Finally, each Simulator iteratively runs M Model Instances in its process; (b) The Multi-process Co-simulation implementation (see Fig. Instead, with longer simulations, this effect could be negligible. 2017) and, in particular, Cyber-Physical Energy Systems (CPES) (Zhang et al. The main components required to build a co-simulation framework are depicted in Fig. In a nutshell, these operations are related to the Initialization task of a general co-simulation framework. They are language agnostic, thus allowing the integration of different programming languages (i.e. Computer Simulation of Solvent Swelling of Coal Molecules: Effect of Different Solvents. The complexity of the models involved was heterogeneous, but the interactions between them were kept simple. Each Simulator node manages iteratively its M Model Instances in a single process. Model Instances of Building and PV Simulators were scaled up. Combining such disparate disciplines into a single tool for modeling and analyzing such a complex environment as a Multi-Energy System requires tremendous effort. Springer Nature. AIOMAS allows implementing co-simulation for very large scenarios, but it does not scale perfectly because of its exponential increase in overhead. However, HELICS outperforms AIOMAS in terms of simulation time if the simulators are run in multiprocess. The proposed configurations are: (i) two configurations of the Classic Co-simulation (Fig. Each Simulator is presented in the following sections. The benchmark design will consider the general Scenario depicted above. The output of this simulator is the generating power for the given time step, which is forwarded to the Power Grid Simulator. No documentation for this configuration is present, making its implementation require more effort. It is not an actual physical component of the co-simulation framework. Figure9 reports this KPI for all the benchmarked co-simulation frameworks. In these literature definitions, co-simulation allows integrating together heterogeneous domain-specific Simulators creating a shared simulation environment. ITM . This means that the end user must take into account this task in a programmatic way encapsulating time management into Agents or creating an external agent as an orchestrator. The information exchange among Simulators and the Orchestrator instead is identical to the previous configuration. Two cluster nodes implement a simple Container structure that handles respectively the Meteo Agent and the Power Grid Agent. Thus, it is suitable for small/medium scenarios. Large latency can compromise the overall co-simulation environment when dealing with strict time constraints of a particular Simulator that could internally trigger a time step overflow. To conclude and easily interpret the analysis of experimental results, in the next section, a summary of all the tested configurations is presented in Table1. p. 98107, Camus B, Paris T, Vaubourg J, Presse Y, Bourjot C, Ciarletta L et al (2016) MECSYCO: a Multi-agent DEVS Wrapping Platform for the Co-simulation of Complex Systems. Energy Inf 1(1):5571, Authors U (2021) Glasgow Climate Pact. 2021). 4c) is the typical configuration of the AIOMAS framework in which agents are represented as simulators. Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Turin, Italy, Department of Control and Computer Engineeing, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Turin, Italy, Pietro Rando Mazzarino,Marco Montarolo,Alberto Macii&Edoardo Patti, You can also search for this author in Energy in Cities - GitLab Mosaik is a Python co-simulation framework developed to couple existing Simulators in the Smart Grid field. 2020), of which the most prominent . The proposed Scenario only uses the heat pumps real-time power demand as output for the Power Grid Simulator. IEEE. On the other hand, HELICS demonstrated the ability to scale up to 1M model instances. Each Time Step is a complex routine in which each Simulator retrieves the input dependencies for its Model Instance collection, iteratively executes each Model Instance calculation updating its state, and, finally, collects Model Instance collection outputs to forward them to other Simulators. In addition, a reasonable carbon liability allocation and carbon cost calculation on the user side are carried out. In: 2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe); p. 16, Mihal P, Schvarcbacher M, Rossi B, Pitner T (2022) Smart grids co-simulations: survey & research directions. In this simulation, it is actually used as a discrete event simulator that is triggered by the reception of both building and PV data. 8, the setup execution time performances of the different configurations are presented. IEEE Ind Electron Mag 11(1):3450, Palensky P, Cvetkovic M, Gusain D, Joseph A (2021) Digital twins and their use in future power systems. Optimal Co-Design of Integrated Thermal-Electrical Networks and Control To conclude, the former model has four main functionalities that calculate: (i) the real-time heat pump power demand, (ii) the heat pump power demand forecasting, (iii) the heat pump energy flexibility, and (iv) the actuation commands of the heat pump control strategy. It estimates the hourly energy production of PV solar panels for each simulation time step. Cite this article. (2017). For instance, the Main PV Container spawns child Containers \(A_1, , A_N\) each one managing M/N equally distributed Agents; (d) The Classic Co-simulation implementation with encapsulated Multi-Process Multi-Agent Systems (see Fig. 2017; Bhattarai etal. Accessed 26 Jun 2022. In: 2011 IEEE First International Workshop on Smart Grid Modeling and Simulation (SGMS). 7, the Multi-model energetic Scenario has been used as a baseline for the seven proposed configurations in Benchmark configuration section. The SimManager starts the Simulators that govern their Model Instance collection and, subsequently, handles their data exchange. AIOMAS instead is more complex. In a Container, Agents implement RPC clients that manage remote communication, using the Container as a gateway to reach Agents that belong to other Containers. 2022) and unlike the other solutions they are also thought to be extended to MES or general purpose applications [e.g. Apply for the Job in Engineering Associate (Energy Management Systems) at San Antonio, TX. To ease the coupling of simulators, researchers have started defining standards for co-simulation, such as Functional Mock-up Interface (FMI)(Blochwitz etal. However, the setup of the multi-process execution may take longer and could be a drawback that reduces the configuration performances. Lu, Dayou Affiliate Research Collaborator dlu315@gatech.edu For the Mosaik-AIOMAS framework, results are similar to the Mosaik multi-process framework with the main difference that the major time increment occurs when going from 10s for 100 instances to 61s for 10k instances. Specifically, both the PV and the Building Simulators take weather data from the Meteo Simulator as inputs to perform their calculations. Co-simulation is an emerging enabling technique, where global simulation of a coupled system can be achieved by composing the simulations of its parts. Research Associates - Power Systems Control and Automation Laboratory Co-simulation frameworks, such as Mosaik and HELICS, have been developed to ease such integration. The above two directions of scalability are not mutually exclusive and, instead, are typically used in a jointed configuration to improve the scalability of a co-simulation framework. 2016; Abgottspon etal. It has been chosen to present parallelism between MAS and co-simulation frameworks. The 6th International Conference on Power and Energy Systems Engineering (CPESE 2019), September 20-23, 2019, Okinawa, Japan. 2017). In conclusion, up to 100 Model Instances for each Simulator, Mosaik with a Multi-processing configuration, HELICS with a Classic configuration, and HELICS with encapsulated AIOMAS configuration are the best performing solutions. Co-Simulation of Dynamic Energy System Simulation and - ResearchGate Your privacy choices/Manage cookies we use in the preference centre. p. 253267, Pipattanasomporn M, Feroze H, Rahman S (2009) Multi-agent systems in a distributed smart grid: design and implementation. 516521, Schtte S, Scherfke S, Trschel M (2011) Mosaik: a framework for modular simulation of active components in smart grids. Omani Research Team designs smart grid monitoring system Then, the trend flattens out because the same number of parallel processes have been launched. It also estimates the cell temperature using the so-called NOCT method(Brihmat and Mekhtoub 2014) when wind speed is not available as input and, on the other hand, it uses the Mattei method(Mattei etal. AIOMAS). The main objective of this paper is to understand which of the proposed benchmark setups performs better in terms of simulation time when it comes to increase the size of the simulated environment. Total execution time of the proposed Scenario for the different co-simulation frameworks and their configurations. Cores are components embedded in Simulators that allow their Federates to join Federations and enable communication with the HELICS architecture. Your US state privacy rights, Each of the child Container manages M/N Agents like in the Classic Multi-Agent System implementation of Main PV/Building Containers; Finally, the analysis was conducted on seven different co-simulation configurations. Experimental results show that a Multi-processing configuration of HELICS reaches the best performance in terms of KPIs defined to assess the scalability among the co-simulation frameworks. PubMedGoogle Scholar. So, the time-based KPIs are still related to the kind of MES scenario and simulators involved. 7 and are: Different implementations of the co-simulation framework benchmark configurations: (a) Classic Co-simulation, (b) Multi-process co-simulation, (c) MAS as co-simulation, and (d) Classic Co-simulation with encapsulated MAS. The Initialization process finally sets up the co-simulation environment by establishing all the relationships and connections among Model Instances of all Simulators involved in the co-simulation environment. In our case, this threshold is above 100 Model instances. (a) The Classic Co-simulation implementation (see Fig. The software architecture is a modular design based on Ptolemy II, a software environment for design and analysis of heterogeneous systems. 2018; Pan etal. 2019), which are: (i) Discrete Event (DE) or event-based regulation, and (ii) Continuous Time (CT) or time-stepped regulation. (f) Mosaik with encapsulated AIOMAS configuration integrates the MAS simulator inside the Mosaik framework. A high-level representation of the multi-model energetic scenario is presented in Fig. The traditional methodology for conducting technical assessments of multi-energy systems involved using domain-specific modeling tools to focus on the energy sector of interest, while making simplifying assumptions about any coupled energy sector. The limitations of both classic and the AIOMAS frameworks are clearly visible when dealing with more than 10k Model Instances. 2021; Nunna and Doolla 2012; Jung etal. 2003), complex interactions among simulators are easier to implement. This work helps to understand which of the three frameworks and four configurations to select depending on the scenario to analyse. The implementation of this configuration is not straightforward but some examples in Mosaik documentation are present. To achieve such a reduction, a transition from classic fossil fuels to Renewable Energy Sources (RES) as well as the adoption of integrated energy system components, such as micro co-generators, are required. Considering M Model Instances, each Simulator process manages M/N Model Instances. The remaining nodes manage respectively the PV Simulator and the Building Simulator. The co-simulation IA adopted here is based on one of the most commonly employed IA for PHIL interfaces, which is the ideal transformer model (ITM) . Moreover, the development of co-simulation scenarios in cluster computing systems by integrating simulators and co-simulation frameworks in containers, such as Dockers managed by a Kubernetes orchestrator, is part of our future work to study the scalability of large co-simulation setups. IEEE. This article has been published as part of Energy Informatics Volume 5 Supplement 4, 2022: Proceedings of the Energy Informatics. Besides the different co-simulation frameworks such as Mosaik and HELICS, which have different functions and implementations, a shared general architecture can be highlighted. HELICS and Mosaik) and a particular implementation of a well-known multi-agent systems library (i.e. Depending on the Simulators, their Model Instances, and their relationships defined by the Scenario, vertical scaling could be applied with different methods and strategies. The authors declare that they have no competing interests. Figure10 presents trend similarities with the average time step duration KPI in Fig. In: Confrence Internationale des Energies Renouvelables CIER13/International Journal of Scientific Research & Engineering Technology. This was acceptable since the interactions between energy domains were minimal. Frontiers in Energy Research It is suitable for highly customized scenarios such as event-based Simulators that behave in a sequential workflow. (2019) with a co-simulation between COMSOL Multiphysics 3 and Dymola showed, besides the very low computational performance, also a loss of information was observed .

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