# Interpreted Petri Nets Applied to Autonomous Components within Electric Power Systems
## 1 Introduction  
Petri nets [1] are a general modeling formalism introduced for discrete event systems [2], including automatic control, with simple structures and easy creation rules [3]. Their basic elements include places and transitions connected alternately with each other via arcs and tokens that indicate the current state of the system. They are widely supported by existing analysis and verification methods [4], as well as editing tools [5], and are currently commonly used in many areas, such as manufacturing systems [6], freight logistics, and transportation systems [7]. There are also some approaches for their use in other domains such as energy, industrial electronics, or power systems. A survey paper [8] summarized the application of Petri nets in the area of power systems up to the year 2006, listing some papers focusing on fault diagnosis, power system restoration, distribution network reconfiguration, unit commitment, power network topology analysis, reliability analysis, protective relay modeling, and hybrid power systems. Since then, some other approaches have appeared. Let us point out some of the most interesting ones. Load sharing control in distributed generation applications was supported by Petri nets in [9]. Hybrid Petri nets were used to analyze contingencies in power systems in [10]. In [11], a Petri net model with inhibitor arcs was applied to describe a repair process for the analysis of the impact of each fault in the process in order to solve a multi-fault rush-repair problem (MRRP) in power distribution networks. A direct matrix converter with space vector modulation (SVM) and transistor commutation was specified as a Petri net in [12], which allowed the reachability of particular states to be checked. The dynamic behavior of power systems protections was evaluated with Petri nets in [13]. Colored Petri nets were considered as a modeling formalism for a protective device from a single-phase-to-ground short circuit with an automatic change of current setting in electricalnetworks of 6-10 kV in a recently published conference paper [14]. A timed Petri net model expressing generically the networked behavior of photovoltaic systems was proposed in [15]. Resource-oriented Petri nets [16] were in turn used for scheduling, e.g., for cluster tools in semiconductor manufacturing [17] or for crude oil operations in refineries (hybrid, colored-timed Petri nets being an extension of resource-oriented Petri nets) [18]. Other promising approaches to the control of power and energy systems used machine learning (ML) [19], deep learning (DL) [20], reinforcement learning (RL), [21] or fuzzy logic [22]. The interpreted Petri nets that we use follow the notions formally introduced in 2019 for the specification of cyber-physical systems [23] (although there are also some other definitions of \"interpreted Petri nets\" in the literature, e.g., [24; 25]). They have proved so-far to be suitable in that domain. In contrast to traditional Petri nets, the interpreted ones additionally contain a description of the binary input and output signals of the system to communicate with the environment, with other systems, or their components. Unlike some other types of Petri nets that might also be useful, interpreted Petri nets do not involve inhibitor arcs, enabling arcs, or colored tokens, which enhances their simplicity of use. Input signals are assigned as guards to transitions, while output signals are assigned to places. What distinguishes interpreted Petri nets from ordinary Petri nets is that they are safe, which means that a place may contain only one token. An active place (that includes a token) indicates then directly the activity of the output signal assigned to it. Hence, it is also easy to generate some implementation code from them, e.g., targeted at AVR microcontrollers or FPGA devices [26]. An important issue then becomes determinism, a property of a model (not of a physical realization) meaning that it is not possible for the model to react in two or more ways to the same conditions [27].