Frequently Asked Question

Can Typhoon HIL perform co-simulation with OPAL-RT?
Last Updated 5 months ago

This article discusses a real-time co-simulation framework designed to experimentally validate the dynamic performance of control systems on a network level, based on [1]. This test environment involves the coordinated emulation of transmission and distribution networks. To achieve this, an OPAL-RT simulator is utilized to emulate the transmission grid, while the Typhoon HIL real-time simulator is employed to emulate the distribution network and the power electronics-based sources with high fidelity.

What is the motivation for including Typhoon HIL in a co-simulation setup for power systems with distributed generation?

Typhoon HIL simulators introduce significant value to testing endeavours focused on power electrical systems with distributed generation. By offering real-time, high-fidelity simulations of complex power electronics systems, Typhoon HIL empower users to precisely model and analyse the behavior of distributed generation components. This capability enables the thorough examination of how these components interact within the larger power grid, optimizing integration strategies and refining control algorithms. Consequently, the integration of Typhoon HIL solutions results in a reduced risk of errors during implementation, as potential issues and challenges are identified and addressed prior to real-world deployment. Through scenario replication, hardware validation, and rigorous testing under various conditions, Typhoon HIL technology enhances the reliability, stability, and efficiency of distributed generation systems.

How can considering the dynamics of the lower-level controllers and a more accurate modelling of power electronics-based DERs help improve assessment of overall system performance?

Incorporating lower-level controllers and high-fidelity models for converter-based Distributed Energy Resources (DERs), such as Battery Energy Storage Systems (BESS) and Photovoltaic (PV) systems, significantly enhances the fidelity of the results since it provides a more comprehensive understanding of how these systems operate and interact within the broader power infrastructure. By integrating detailed control algorithms at the lower level, we can emulate the intricate interactions between these and the higher-level controllers, replicating the complex dynamics and responses that might not be fully observable in simplified simulations. Similarly, employing precise models for converters and DER components ensures that the simulations capture the intricacies of their behavior, including voltage and current dynamics, switching losses, and response to varying conditions. Therefore, the higher fidelity in modelling combined with the real-time and interfacing capabilities enable to uncover nuanced insights into system performance, stability, and transient behaviors that might not be apparent in simplified simulations.

Example of co-simulation between Typhoon HIL and OPAL-RT [1]

Figure 1 presents the diagram of a real-time co-simulation testbed implemented to test the dynamic performance of control systems at the network level. The setup consists of a transmission network connected with a distribution feeder and several distributed energy resources (DERs).


Figure 1 - Overall diagram of the real-time co-simulation testbed [1].

The transmission network is a modified Kundur system simulated on OPAL-RT, with four interconnected areas that include synchronous generators and multiple BESS. The BESS B2 to B7 are simulated on OPAL-RT with a time step of 100 us, using average models that neglect the switching dynamics of the power converter. On the other hand, aiming to increase the fidelity of the simulation and to analyse the effects of the switching behavior in the close-loop system, BESS 1 is emulated on a Typhoon HIL402 real-time simulator with a time step of 10 us, including a high-fidelity model of the battery and the power converter operating with 10 kHz switching frequency. BESS 1 is interconnected to the transmission system by means of the transformer T1. Additional details about the configuration for the BESS 1 is shown in Figure 2.


Figure 2 - BESSs’ configuration. (a) Circuit topology. (b) Linear PI controllers [1].

The transmission level is also integrated with a distribution grid through a step-down transformer T2. This distribution grid is completely emulated on a second Typhoon HIL402 real-time simulator, which is interfaced with a commercial solar inverter and a PV emulator by means of a power amplifier.

Aiming to emulate the power transfer from BESS 1 towards the transmission system and from the distribution feeder toward the transmission network, the communication between Typhoon HIL devices and OPAL-RT was performed using analog signals following the scheme depicted in Figure 3.


Figure 3 - Voltage-type ideal transformer model interfacing algorithm. (a) Among two power system emulators using analog signals. (b) Between Typhoon HIL and a power amplifier. Detailed information in [1].

Figure 4 depicts the physical connections of all devices. The experimental setup involves the use of an OPAL-RT simulator to emulate the transmission network, BESS 2-7, and an optimal controller to regulate the voltage and frequency of the transmission system. The distribution feeder and BESS 1 are emulated using two HIL402 platforms from Typhoon HIL. Additionally, a real solar-based DER is implemented using a real inverter of 3.2 kW and a PV emulator. This photovoltaic system is interfaced with the Typhoon 2 emulator through a full 4-quadrant 3-phase AC power amplifier.


Figure 4 - Physical connections of the hardware used in the test bench [1].

This setup allows for the study of power systems with various DERs and taking into account complex dynamics. The performance and effectiveness of the co-simulation testbed presented here were validated through various scenarios. These scenarios showcase the precision and timely response of the controllers, as well as the stability and robustness of the communication links between the emulation platforms.

In one of these scenarios, a load increase of 130% is applied at node 11, which occurs at 30 seconds by closing breaker S1 in Figure 1. The control system activates nearby BESSs to address voltage and frequency deviations. As shown in Figures 5(a) and 5(b), when the contingency is detected in area 3, the corresponding controller calculates the optimal active and reactive power required to mitigate the disturbance. Concurrently, the controllers compute a setpoint reduction in a ramp form, allowing sufficient time for the synchronous generators (SGs) to compensate for the load change. Considering the limited injection capacity of B4, the contingency spreads to area 1, activating B1 and B2, as depicted in Figures 5(c) and 5(d).


Figure 5 - Transmission network response to a sudden increase of L3 located at bus 11 when the optimal controller is working in closed loop (Scenario 1). (a) Frequency response. (b) Voltage response. (c) Active power. (d) Reactive power.

Figure 6 shows the response of B1 to the load change at bus 11. Initially, the optimal control of the power system and aggregator calculate and inject active and reactive powers of 188 MW and -121 MVAr, respectively, at bus 9, as shown in Figure 6(a). The power injection is then gradually reduced to zero. BESS 1 starts with a State of Charge (SOC) of 50% and continues injecting active power for approximately 2 minutes. During this period, the energy stored in the batteries decreases to 49.73%, as depicted in Figure 6(b). The voltage at the Point of Common Coupling (PCC) between the BESS and the network also decreases to 0.98pu, remaining within the safe limits.


Figure 6 - Real-time simulation results of BESS 1 to a sudden increase in L3 located at bus 11. (a) Active and reactive powers injected at bus 9. (b) Battery State- of-charge (SOC). (c) Voltage variation at the PCC [1].


Gabriel E. Mejia-Ruiza, Mario R. Arrieta Paterninab, M. Ramirez-Gonzalezc, Felix Rafael Segundo Sevillac, Petr Korbac,and Charalambos Konstantinoua.

aKing Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
bNational Autonomous University of Mexico (UNAM), Mexico City, Mexico
cZurich University of Applied Sciences, Winterthur, Switzerland


[1] Gabriel E. Mejia-Ruiz, M. R. Arrieta Paternina, M. Ramirez-Gonzalez, F. R. S. Sevilla, and P. Korba, “Real-time co-simulation of transmission and distribution networks integrated with distributed energy resources for frequency and voltage support,” Applied Energy, vol. 347, p. 121046, 2023.

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