The energy system of the future is expected to be composed of a large variety of technologies and applications. However, the diverse nature of these components, their interlinked topology, and the sheer size of the system lead to an unprecedented level of complexity. Industry is confronted with severe problems in designing interoperable grid components, analyzing system stability, and improving efficiency. This paper describes the main challenges of continuous time-based and discrete event-based models of such cyber-physical energy systems. Using a characteristic test model, the scalability of the two approaches is analyzed. The results show the strengths and weaknesses of these two fundamentally different modeling principles that need to be considered when working with large scale cyber-physical energy systems.
There exists no universal tool to analyze the increasing complexity in smart grids. Domain specific simulation and engineering tools partly address the challenges of complex system behavior. Different component technologies, customer behavior and controls in the power networks are interacting in a highly dynamic manner. Results of isolated simulations may be not accurate enough on the system level. Free and open available tools like GridLAB-D, PSAT, OpenModelica and 4DIAC are well known and widely used because of their excellent domain specific expertise. With co-simulation approaches the individual strengths of each tool can be exploited to model and simulate the various aspects of complex smart grids. The achieved level of detail and realism potentially surpasses the results that the individual analyses would gain. This paper demonstrates a local smart charging control strategy implemented with the IEC 61499-based standard for distributed control systems. It is simulated with different electric vehicle driving patterns, modeled with the multi-agent environment GridLAB-D. Battery models are defined in OpenModelica and embedded as individual dynamic loads. The power system is simulated using PSAT. This work shows that boundaries and restriction in terms of modeling cross-domain specific problems can be overcome by coupling these open source applications.
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