A Distributed Platform for Multi-modelling Co-simulations of Smart Building Energy Behaviour

Nowadays, buildings are responsible of a large consumption of energy in our cities. Moreover, buildings can be seen as the smallest entity of urban energy systems. On these premises, in this paper we present a flexible and distributed co-simulation platform that exploits a multi-modelling approach to simulate and evaluate energy performance in smart build- ings. The developed platform exploits the Mosaik co-simulation framework and implements the Functional Mock-up Interface (FMI) standard in order to couple and synchronise heterogeneous simulators and models. The platform integrates: i) the thermal performance of the building simulated with EnergyPlus, ii) the space heating and hot water system modelled as an heat pump with PID control strategy in Modelica, and iii) different Python models used to simulate household occupancy, electrical loads, roof-top photovoltaic production and smart meters. The platform guaranties a plug-and-play integration of models and simulators, hence, one or more models can be easily replaced without affecting the whole simulation engine. Finally, we present a demonstration example to test the functionalities and capabilities of the developed platform, and discuss future developments of our framework

Cyber-physical energy systems modeling, test specification, and co-simulation based testing

The gradual deployment of intelligent and coordinated devices in the electrical power system needs careful investigation of the interactions between the various domains involved. Especially due to the coupling between ICT and power systems a holistic approach for testing and validating is required. Taking existing (quasi-) standardised smart grid system and test specification methods as a starting point, we are developing a holistic testing and validation approach that allows a very flexible way of assessing the system level aspects by various types of experiments (including virtual, real, and mixed lab settings). This paper describes the formal holistic test case specification method and applies it to a particular co-simulation experimental setup. The various building blocks of such a simulation (i.e., FMI, mosaik, domain-specific simulation federates) are covered in more detail. The presented method addresses most modeling and specification challenges in cyber-physical energy systems and is extensible for future additions such as uncertainty quantification

Future Perspectives of Co-Simulation in the Smart Grid Domain

The recent attention towards research and development in cyber-physical energy systems has introduced the necessity of emerging multi-domain co-simulation tools. Different educational, research and industrial efforts have been set to tackle the co-simulation topic from several perspectives. The majority of previous works has addressed the standardization of models and interfaces for data exchange, automation of simulation, as well as improving performance and accuracy of co-simulation setups. Furthermore, the domains of interest so far have involved communication, control, markets and the environment in addition to physical energy systems. However, the current characteristics and state of co-simulation testbeds need to be re-evaluated for future research demands. These demands vary from new domains of interest, such as human and social behavior models, to new applications of co-simulation, such as holistic prognosis and system planning. This paper aims to formulate these research demands that can then be used as a road map and guideline for future development of co-simulation in cyber-physical energy systems

PyFMI: A Python Package for Simulation of Coupled Dynamic Models with the Functional Mock-up Interface

With the advent of the Functional Mock-up Interface (FMI) standard, exchanging dynamic models between modeling and simulation tools has been greatly simplified. At the core of it, FMI is a standardized and unified model execution interface for dynamic models. FMI has gained widespread adoption among users and numerous commercial and open source tools implement support for the standard. In this article, the Python package PyFMI is introduced. PyFMI supports loading and execution of models compliant with the FMI standard, called Functional Mock-up Units (FMUs). It includes a master algorithm for simulation of coupled FMUs together with connections to both Assimulo, for simulation of single FMUs, and to SciPy, for performing parameter estimation. Accessing models compliant with FMI in Python, which is an open and accessible scripting language, is intended to further spread the standard and also promote and facilitate future development of the standard. This is due to Python being a convenient language for experimentation and prototyping of numerical algorithms. PyFMI is also demonstrated on a number of problems that highlights its viability for solving industrial grade simulation problems with FMUs