# Interpreted Petri Nets Applied to Autonomous Components within Electric Power Systems
## 5 Experimental Verification  
Experimental research was carried out on a system with an energy storage unit with batteries made from LTO (lithium-titanium-oxide) technology (Figure 8) [31]. The LTO-type energy storage system design is based on the rechargeable battery SCiB 23 Ah cell manufactured by Toshiba. According to the catalog note, the estimated cell life is 20,000 cycles with a 70% DoD discharge rate. The power of the tested electricity storage was 100 kW and its capacity was 35 kWh. The continuous charge/discharge current at 25 \({}^{\circ}\)C is 200 A. The electronic power bidirectional DC/AC converter connecting the energy storage system with the power grid is made of two 50 kW inverters working in parallel [32]. The inverters are built using SiC technology, with galvanic isolation and high-frequency transformers (25 kHz). Figure 8: A prototype of a 100 kW energy storage system using LTO technology. Figure 7: A revised strongly deterministic interpreted Petri net describing the algorithm of power stabilization of RES and loads. The prototype of the energy storage system was installed at a transformer station and connected via fuses and main switch directly to the terminals of the MV/LV transformer with a rated power of 630 kVA. The short-circuit power \(S_{\text{{{kQ}}}}\) at the point of connection of the energy storage system is much greater than the power that can be generated by the storage, so in the tested system the criterion described by Equation (3) is always satisfied and there is no limitation of storage generation to \(P_{Lim}\). The study of the load power stabilization algorithm was carried out in two tests using the Fluke 437-II Network Parameter Analyzer. In the first test, one large receiver with a power of 18 kW was switched on, which changed its power from zero to nominal value within a minute. Additionally, other loads with variable power change characteristics were accidentally switched on, due mainly to the start-up of asynchronous motors or their operation with variable load. The general profile of the network load changes also includes the loads that normally operate in the network. In the second test, one large receiver with a power of 18 kW was turned on, which changed its power from zero to nominal value within a minute. Moreover, the loads that normally operated in the network were also included in the overall profile of the network load changes. The test results for the first test are shown in the Figure 9a. The compensation algorithm was activated at the time \(t_{0}\). Before starting the algorithm, the energy storage system was set to the appropriate state of charge 0.6 SoCMAX \(\leq\) SoC \(\leq\) 0.65 SoCMAX. As a result of the algorithm of the load power compensation using an energy storage system unit, the power consumed from the grid did not exceed 40 kW, with low-frequency fluctuations not exceeding 15 kW. While the load power was lower than the average reference power \(P_{\textit{{ref}}\,(n-1)}\), the energy storage system was charged from the grid (\(P_{ES}\) with the \"+\" sign). In this way, the energy storage system kept the power within the calculated average power \(P_{\textit{{ref}}\,(n-1)}\). In time intervals when the load power was higher than the \(P_{\textit{{ref}}\,(n-1)}\), the energy storage system returned energy to the grid (\(P_{ES}\) with the \" -\" sign), reducing the power from the grid to the determined average value \(P_{\textit{{ref}}\,(n-1)}\). The presented research results show a lack of compensation for fast-changing fluctuations in the load power. This is due to the measuring system used in the prototype cooperating with the control system. The measurement data acquisition time of 250 ms is too long to effectively stabilize the fast-changing load in the power grid. The test results for the second test, with a slowly changing load, are presented in Figure 9b. It is also noteworthy that there was no full compensation when the load changed rapidly. This is due to the relatively long acquisition time of data measurement of 250 ms.