research associate co simulation of energy systems

p. 16, Mattei M, Notton G, Cristofari C, Muselli M, Poggi P (2006) Calculation of the polycrystalline PV module temperature using a simple method of energy balance. 7c) that uses the four cluster nodes without the master node. IEEE. The Orchestrator is the main component of a co-simulation framework and manages the exchange of data from the Simulators and their time regulation and synchronization. This was acceptable since the interactions between energy domains were minimal. Omani Research Team designs smart grid monitoring system Then, the scalability problem of co-simulation frameworks is introduced presenting four benchmark configurations to test their ability to scale in terms of a number of running instances. This is due to the threshold used to decide when AIOMAS starts the creation of the multi-process containers. AIOMAS demonstrated the same ability to scale as HELICS with respect to the number of instances. Cookies policy. The tasks required to establish the co-simulated environment are similar to the aforementioned frameworks and are: (i) the Container creation, (ii) the Agent collection generation, (iii) and the distributed orchestration infrastructure start-up (i.e. p. 253267, Pipattanasomporn M, Feroze H, Rahman S (2009) Multi-agent systems in a distributed smart grid: design and implementation. solvers) to perform their domain-specific computations. Some co-simulation frameworks are able to handle both paradigms, resulting in a hybrid regulation paradigm. 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. (2021)]. It is found that the indirect carbon emissions caused by different users are . Affiliation: Politecnico di Torino. 516521, Schtte S, Scherfke S, Trschel M (2011) Mosaik: a framework for modular simulation of active components in smart grids. volume5, Articlenumber:53 (2022) For example, if the goal is to provide heat and electricity to a group of buildings while reducing natural gas use, integrated energy . The model calculates the hourly heating demand of the building considering also the heat contributions generated from human occupancy and appliances through archetypes patterns. Colorado Energy Research Collaboratory | LinkedIn 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. Its general architecture does not preclude other domain applications. AIOMAS instead is more complex. The choice to include AIOMAS in this study follows the recent trends of coupling co-simulation and MAS concepts (Jung etal. They contain a physical model that could belong to different mathematical types, ranging from pure algebraic equations to differential equations, as well as finite element methods or behavioural models(Palensky etal. We will refer to this case as MAS as co-simulation. The complexity of the models involved was heterogeneous, but the interactions between them were kept simple. Mosaik is a Python co-simulation framework developed to couple existing Simulators in the Smart Grid field. 2019), which are: (i) Discrete Event (DE) or event-based regulation, and (ii) Continuous Time (CT) or time-stepped regulation. We will refer to this concept as MAS as a simulator. The transition to a low-carbon society will completely change the structure of energy systems from a standalone hierarchical centralised vision to cooperative and distributed Multi-Energy Systems. The former Co-simulation Process could be decomposed into its main time interval units, the Time Steps. A comparison study of co-simulation frameworks for multi-energy systems: the scalability problem, $$\begin{aligned} \mu _{T} = \max _{i\in I}\mu _{T}^{i} \,\,\,\,\,where\,\,\,\,\, \mu _{T}^{i} = \frac{\sum _{n=1}^{S} T_{n}^{i}}{S},\,\,\,I = \{Sim_{1}, , Sim_{M}\} \end{aligned}$$, https://doi.org/10.1186/s42162-022-00231-6, Proceedings of the Energy Informatics.Academy Conference 2022 (EI.A 2022), Enabling technologies for co-simulation environments, https://unhabitat.org/urban-themes/energy/, https://energyinformatics.springeropen.com/articles/supplements/volume-5-supplement-4, http://creativecommons.org/licenses/by/4.0/. Each of the identified sub-systems deals with a well-defined problem while interacting with each other. This case requires a complex time regulation algorithm where the synchronization task becomes even more critical. 2020). (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 . Co-simulation frameworks, such as Mosaik and HELICS, have been developed to ease such integration. AIOMAS allows implementing co-simulation for very large scenarios, but it does not scale perfectly because of its exponential increase in overhead. Researchers have overcome this effort by using co-simulation techniques that give the possibility of integrating existing domain-specific simulators in a single environment. Accessed 26 Jun 2022, Coelho VN, Cohen MW, Coelho IM, Liu N, Guimares FG (2017) Multi-agent systems applied for energy systems integration: state-of-the-art applications and trends in microgrids. vol. 1. International Publisher &C. O. p. 00, Bruinenberg J, Colton L, Darmois E, Dorn J, Doyle J, Elloumi O et al (2012) CEN CENELEC ETSI Smart Grid Coordination Group on Smart Grid Reference Architecture. However, since these modifications are not user-friendly and are not included in Mosaiks API implementation, this framework was used as is, with this scaling limitation. Co-simulation is often related to Cyber-Physical Systems (CPS)(Palensky etal. This configuration exploits the capabilities of spawning Containers of the Multi-Agent System as Co-simulation configuration without incurring typical drawbacks of the Multi-Process Co-simulation configuration, raising the performance of this configuration. Summary: Professor Anderson's research interests . Simulators operate in parallel as depicted in Fig. Geographical scalability, on the other hand, is the representation of the complexity of managing an increasing number of geographically distributed computational nodes (e.g., different laboratories) to implement a co-simulation Scenario. In fact, the Time Synchronization and Regulation is distributed among the Main Container via the Clock/RPC. Then, the trend flattens out because the same number of parallel processes have been launched. SQU researchers develop system to ensure reliable electricity (Suppl 4), 53 (2022). The implementations of the Scenario for each configuration and its cluster deployment are described in Fig. Therefore, this paradigm allows decomposing a complex system in a System-of-System (SoS) structure by applying system engineering. A blockchain based transactive energy market solution proposed in [21], performs power flow simulation using OpenDSS on a Distribution System Simulation platform to understand the impacts of the market. This particular hardware acts as a vertical scaling component of the jointed scaling vision to enable fast real-time simulation of the power grid model. This work helps to understand which of the three frameworks and four configurations to select depending on the scenario to analyse. 2011), and co-simulation frameworks, such as Mosaik(Schtte etal. It is worth noting that this scaling direction commonly results in limited scaling of the size of the complex system. Simulation and visualization of energy-related occupant behavior in In: 2018 International Conference on High Performance Computing & Simulation (HPCS). In particular, Mosaik and HELICS are gaining much attention from the energy research community and were used by several research projects focused on MES. The remaining nodes manage respectively the PV Simulator and the Building Simulator. 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. 3. Co-Simulation: A Survey | Request PDF - ResearchGate Research Associates - Power Systems Control and Automation Laboratory However, the cluster nodes, assigned to PV and Building Simulators, replicate on N processes each individual Simulator, equally distributing in each process the M Model Instances. J Build Perform Simul. In: 2015 International symposium on smart electric distribution systems and technologies (EDST). The proposed co-simulation benchmark configurations: (a) Classic Co-simulation, (b) Multi-process Co-simulation, (c) Multi-Agent System as Co-simulation framework, and (d) Classic Co-simulation configuration with encapsulated multi-process Multi-Agent System, Decomposition of the Total Execution Time in its main contribution: (i) the Scenario Setup, (ii) the Co-simulation, and (iii) the Termination Processes. 7a) for HELICS and Mosaik; (ii) two configurations of the Multi-process Co-simulation (Figure7b) for HELICS multi-process and Mosaik multi-process; (iii) one configuration of MAS as a Co-simulation framework (Fig. 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. However, they differ in the possible implementation of vertical scaling. With the growing trend of emerging new technologies in distribution networks, such as wind turbines, solar panels, electric vehicles, and distributed generations, the passive distribution systems may become 'active' which requires more study in the area of integrated transmission and distribution systems (ITDSs) and corresponding bilateral inter. The heat load is then converted to the heat pump power request. The other two cluster nodes manage respectively the Main PV Container and Main Building Container, each one handling M Agents. This is due to the higher setup execution time of these configurations and greater variability of the average time step execution. Thus it is suitable for very large scenarios, or very complex systems populated by lots of modelled components to be. The aim of this paper is the scalability analysis of Mosaik and HELICS, two of the most widely adopted co-simulation frameworks in literature. In fact, our study fulfills this gap focusing in particular on the scalability aspect of the three above-mentioned frameworks. These processes are depicted in Fig. The implementation of this configuration is not straightforward but some examples in Mosaik documentation are present. 2021; Nunna and Doolla 2012; Jung etal. The Research & Analysis Associate is responsible for supporting the Policy & Research team on tasks related to Active Efficiency, the EE Impact Report and general energy efficiency research and data analysis. 5560, Schweiger G, Gomes C, Engel G, Hafner I, Schoeggl J, Posch A et al (2019) An empirical survey on co-simulation: promising standards, challenges and research needs. In a nutshell, these operations are related to the Initialization task of a general co-simulation framework. By analyzing this graph, it can be seen that Mosaik and Mosaik-AIOMAS diverge about 80s while Mosaik multi-process by about 50s from the best performing solutions. From the point of view of Simulators interactions have the same qualities of classic HELICS. (a) The Classic Co-simulation configuration (see Fig. This peculiarity addresses one of the main challenges of co-simulation, which is the complex time regulation when it comes to hybrid simulation; with AIOMAS, it is possible to distribute the time regulation and customize it at the expense of more implementation effort. To draw some conclusions, it is possible to say that Mosaik is a very useful framework when dealing with small/medium size environments, offering good quality results and ease of implementation. According to the United Nations Habitat, cities consume about 78% of global energy demand and generate more than 60% of greenhouse gas emissions primarily through the consumption of fossil fuels for energy supply and transportation(United Nations 2022). In current building performance simulation programs, occupant presence and interactions with building systems are over-simplified and less indicative of real world scenarios, contributing to the discrepancies between simulated and actual energy use in buildings. Author guidelines Ready to publish? 6 Conclusion. 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. The most complicated and debated issue in co-simulation applications is scalability which is defined as the property of a co-simulation framework to handle an increasing amount of heterogeneous Simulators and their model instances, considering the composite relationships that interconnect them together to run a large-scale complex system, such as a Multi-Energy System (MES). The simulation program interface that the BCVTB provides is explained. Procedia CIRP 72:874879, Massano M, Macii E, Patti E, Acquaviva A, Bottaccioli L (2019) A grey-box model based on unscented Kalman filter to estimate thermal dynamics in buildings. CiteScore 14,453 Citations Submit your research Start your submission and get more impact for your research by publishing with us. They are language agnostic, thus allowing the integration of different programming languages (i.e. The Low-level API instead offers the possibility to establish a plain network socket for exchanging serialized JSON data to extend Mosaik Simulators integration capabilities. The last consideration is that the reasoning on interactions above mentioned for Mosaik-AIOMAS still holds for this configuration, making HELICS-AIOMAS the best choice among the solutions with encapsulated AIOMAS. 7a) that uses the master node as Orchestrator and the four cluster nodes, one for each of the above-mentioned Simulators. 2a. Simulator A), replicating it in N Simulator processes (e.g. IEEE 2016:98319836, Paris T, Ciarletta L, Chevrier V (2017) Designing co-simulation with multi-agent tools: a case study with NetLogo. The chosen simulators and related physical models are heterogeneous ranging from very simple to more complex. In: 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC); p. 14051410, Barbierato L, Pons E, Mazza A, Bompard E, Subramaniam Rajkumar V, Palensky P et al (2022) Stability and accuracy analysis of a distributed digital real-time co-simulation infrastructure. However, this process could suffer from errors in deploying the correct connection among Model Instances. A high-level representation of the multi-model energetic scenario is presented in Fig. The proposed Scenario only uses the heat pumps real-time power demand as output for the Power Grid Simulator. Finally, administrative scalability represents the difficulties in managing a co-simulation framework when dealing with increasing both previous scalability dimensions, thus the engineering effort required to avoid the complex setup of the co-simulation framework, the orchestrator, and the distribution of Simulators and their Model Instances among network nodes, and their interconnections. The information exchange among Simulators and the Orchestrator instead is identical to the previous configuration. In this paper, we propose a comparison of the scalability performance of two major co-simulation frameworks (i.e. The power grid simulator is a stateless Simulator that emulates a power grid model with different connected loads. In MES context, the simulation of different energy systems will require a broader vision and, consequently, a larger number of domains from different systems involved. However, the setup of the multi-process execution may take longer and could be a drawback that reduces the configuration performances. In fact, it represents the configuration offered by the co-simulation framework that manages the startup of the Orchestrator, the initialization of the Simulators, and states the relationships that occur between Model Instances. The benchmark design will consider the general Scenario depicted above. Only the simplest interactions and data exchanges have been included (same time loop, feedback exchanges, or control actions have been excluded) in order to keep the simulation workflow as simple and linear as possible. Dr Amer bin Saif al Hinai, Associate Professor at the Department of Electrical and Computer Engineering, Vice Chancellor of Sultan Qaboos University for Postgraduate Studies and Research, said that the research team embarked on a simulation model for the smart grid. 2019) and a fine-grained model of the heat pump with all subsystems (e.g., emission, distribution, and generation) is provided. What We Do - Ara 2020) in Fig. Larger computational gains are expected from co-simulation of larger systems, in which the EMT subsystems are a small proportion of the entire system. All of these simulators have really short wall-clock times for executing a single step thus the simulation time-step could range from seconds to hours. IEEE Std 15162-2010 (Revision of IEEE Std 15162-2000). This parallel execution impacts the accuracy of the co-simulated solution with a finite time step latency related to Simulator input/output dependencies that causes negligible inaccuracies of the solution with respect to a standalone simulation(Steinbrink etal. At an higher level of abstraction, AIOMAS provides four main classes: (i) the Container, (ii) the Agent , (iii) the Remote Procedure Call (RPC) along with the Clock and (iv) the Object Oriented Programming (OOP) Scenario. Provided by the Springer Nature SharedIt content-sharing initiative. (d) The Classic Co-simulation configuration with encapsulated multi-process Multi-Agent Systems (see Fig. Appl Energy 187:820832, Estebsari A, Mazzarino PR, Bottaccioli L, Patti E (2021) IoT-enabled real-time management of smart grids with demand response aggregators. 2006) when wind speed is available. p. 235241, Nunna HK, Doolla S (2012) Multiagent-based distributed-energy-resource management for intelligent microgrids. 7d) for HELICS-AIOMAS and Mosaik-AIOMAS. To test the scalability of the different implementations presented in Fig. Co-Simulation: A Survey: ACM Computing Surveys: Vol 51, No 3 Zoom on the first 10,000 instances for the Total execution time. It forwards these outputs to both PV and Building Simulators. 2017). 10 consolidate the performance considerations made in Average time step duration section, showing the HELICS multi-process as the best performing framework. 2a: (i) the Scenario, (ii) the Orchestrator, (iii) the Simulator, and (iv) the Model Instance. IEEE Trans Ind Appl 58(1):102112, Eugster PT, Felber PA, Guerraoui R, Kermarrec AM (2003) The many faces of publish/subscribe. (c) The Multi-Agent System as Co-simulation configuration (see Fig. Integrated energy system simulation is an approach in which researchers consider a multi-system energy challenge holistically rather than looking at each of the systems in isolation. 2021). Barbierato, L., Rando Mazzarino, P., Montarolo, M. et al. To do that, we must be able to better predict and model building energy use in a range of applications. This individual will report to the Manager of Research & Analysis and will be expected to work collaboratively across the organization and with a myriad of external stakeholders. ACM Comput Surveys (CSUR) 51(3):133, IEEE Standard for Modeling and Simulation (M amp;S) High Level Architecture (HLA)Object Model Template (OMT) Specification. This configuration considerably raises the performance of each simulator time step execution. 2018; Mazzarino etal. The power grid is based on the model presented in Estebsari etal. Accessed 26 Jun 2022. However, HELICS outperforms AIOMAS in terms of simulation time if the simulators are run in multiprocess. 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. ICT, energy, economic and social)(Schloegl etal. the intelligence (e.g., control algorithm, management system) and the physical model respectively. A Broker could also communicate with other Brokers, and consequently with other Federations, enabling the possibility of deploying a hierarchical architecture. Two cluster nodes implement a simple Container structure that handles respectively the Meteo Agent and the Power Grid Agent. The results show that Mosaik has a limitation in the number of connected entities that stopped at 10k model instances of PV and buildings. Privacy Conversely, the CT paradigm determines the evolution of the time step with a constant time interval in which the Simulators evolve their internal states by exchanging inputs and forwarding outputs at the end of each time step. Simulators instead contain a specific Model Instance class and have different functionalities (e.g. Lu, Dayou Affiliate Research Collaborator dlu315@gatech.edu Thus, Model Instances communicate via Simulators with each other using events that might change their internal state or trigger other events. Total execution time of the proposed Scenario for the different co-simulation frameworks and their configurations. Integrated Energy Systems | Grid Modernization | NREL 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. In this view, different Simulators are distributed over different network nodes that manage their Model Instances. 7c) for AIOMAS; (iv) two configurations of the Classic Co-simulation with encapsulated MAS (Fig. AIOMAS). Agents incorporate specific models of the physical entities they describe and execute their behaviour. To conclude, the three frameworks implement both event-based and time-based simulation paradigms. Stefanos G. Baratsas, ExxonMobil Research & Engineering, USA Stelios Bekiros, University of Malta, Malta Mette Helene Bjrndal, Norwegian School of Economics and Business Administration, Norway Sergiy Butenko, Texas A&M University, USA Krystel Castillo, The University of Texas at San Antonio, USA Jun Chen, Oakland University, USA The KPI trends of the total execution time in Fig. However, the PV and Building Simulators processes on the two cluster nodes encapsulate the Main Container class that manages the spawning of N child Containers in different sub-processes. The analysis is performed by applying a comprehensive benchmark of the possible configurations that each framework could implement. Energy Inform 5 Mosaik, HELICS, and AIOMAS were tested on a simple multi-model energy scenario in order to understand their ability to increase the number of co-simulation instances.
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