Microgrid Structure for testing a Real-Time Energy Management System Model

The energy management system (EMS) of a microgrid often presents a complex structure and a large number of control functions, which must be validated to ensure a reliable and optimal operation of the microgrid. Control system validation is typically performed by using hardware-in-the-loop (HIL) architectures in which the microgrid is simulated in real time and interfaced with the actual system under test. The simulation must ensure both an accurate representation of the microgrid and a reliable replica of the field communication of the EMS with all the control devices. In this paper, an overview of the various HIL architectures proposed in the literature is firstly outlined. Then, an HIL validation facility is presented and used to validate the EMS of an industrial microgrid. Finally, some results of the validation tests are reported to give evidence of the effectiveness of the proposed facility. In the proposed architecture, the soft real-time digital simulator of the microgrid is interfaced with the actual EMS using the same communication system and protocol as on the field. The main advantages of the proposed testing facility are: (i) the use of commercial PCs and the absence of dedicated interface modules, resulting in inexpensive hardware components; (ii) the capability to validate both control and communication functions of the EMS; (iii) the applicability to microgrids of different types (industrial, commercial, residential), as well as of various dimensions, including large microgrids; (iv) the easiness in changing the microgrid and the EMS under validation by only software modifications of the simulator tasks and of the exchange interface. As drawbacks, the proposed testing facility presents the need to adapt the software interface between EMS and the field to the EMS under test and the possibility of testing only the EMS functions and not fast-acting local controllers of the microgrid such as the protection systems.

The evolution of software testing technologies has significantly reduced software testing execution times, but the test design and generation are still often implemented with slow manual-oriented methods. Model-based testing (MBT) offers automation to test design and generation, and different MBT solutions are familiar from research, but more effort needs to be done to adopt MBT for industrial use. Hardware in the loop (HIL) is a simulation and testing technique used in the development and testing of embedded systems. HIL is a challenging application field for MBT due to complex and non-deterministic nature of some embedded systems. To tackle this problem, the authors present a novel prototype platform in which online and offline MBT is applied to HIL environment. MBT in general has been introduced for HIL in scientific literature before, but the application of online MBT in HIL is a novel approach. The whole novel MBT in HIL prototype platform along with the used MBT tool, platform architecture and MBT process are presented accompanied by experimental results and analysis of two case studies with an example embedded system under test.

Testing battery pack configurations for their performance under different dynamic load profiles for grid and electric vehicle applications is expensive. Lowering the cost to understand the response of packs and their control strategies is critical. Power hardware in the loop (PHIL) simulation is widely used in power engineering for system and component testing. A simulator computes the model output in real-time, and then controls a power source to generate a dynamic power output with the expected current and voltage response of the emulated battery pack. This emulated battery pack power output can be coupled to a physical load to perform testing.1 Small time steps are useful to observe the transient response of the emulated battery when there is a sudden change in the load. PHIL simulations can also be useful to study the transient response of controllers and inverters present in the battery management system by connecting these devices to the simulator. PHIL simulations can be used to test the model’s accuracy2, to conduct repeatability experiments and to test battery performance under extreme conditions without compromising on safety.3 It provides an efficient approach to designing and testing new configurations of batteries from data of a single cell without building their real prototypes. The computational speed required to solve the battery models chosen for PHIL simulations must be fast in order to achieve real-time implementation. Therefore, models chosen for these simulations tend to have less complexity. However, to provide a better understanding of a battery’s response under a specific load profile, it is necessary to use a model that that accurately represents the behavior of a real battery. Real-time simulations for physics-based battery models have been explored in the past.4 , 5 , 6 It is also essential to comprehend how different battery architectures and how aging affects the overall performance. In literature, equivalent circuit models that describe charge transfer and diffusion dynamics have been used as the main way to capture battery dynamics.7 , 8 We first show results generated using SIMULINK equivalent circuit models that are compiled to run fast on our Opal-RT real-time simulator. The Opal-RT simulator drives an AMETEK 40 kW power supply with feedback to the model, enabling an emulated battery pack response capable of 10ms dynamic control. We evaluate pack configurations with physical parameters that correspond to either LCO or NMC 18650 cells. Overall, this represents a 400V, 10kW pack. A programmable load is used to modulate the emulated battery pack and monitor its dynamic response. We further explore implementing single particle models with our PHIL simulations to help understand the internal dynamics of the cell captured by these models. Physics-based models have rarely been used for PHIL. Our results show that the PHIL emulation of the battery pack meets the expectations for the battery dynamics. However, we observe high frequency noise in the output signal of the emulated battery pack. Because this level of noise is not a trait present in real battery packs, we are exploring signal processing methods to filter and remove it, while retaining the essential dynamics of the emulated battery pack. References A. Viehweider, G. Lauss, and L. Felix, Simul. Model. Pract. Theory, 19, 1699–1708 (2011).Y. He, W. Liu, and B. J. Koch, J. Power Sources, 195, 2969–2974 (2010).H. Dai, X. Zhang, X. Wei, Z. Sun, J. Wang, and F. Hu, Electr. Power Energy Syst., 52, 174–184 (2013).P. W. C. Northrop, V. Ramadesigan, S. De, and V. R. Subramanian, J. Electrochem. Soc., 158, A1461 (2011).V. R. Subramanian, V. Boovaragavan, V. Ramadesigan, and M. Arabandi, J. Electrochem. Soc., 156, A260 (2009).P. W. C. Northrop, V. Suthar, Bharatkumar Ramadesigan, S. Santhanagopalan, R. D. Braatz, and V. R. Subramanian, J. Electrochem. Soc., 161, E3149 (2014).J. V. Barreras, C. Fleischer, A. E. Christensen, M. Swierczynski, E. Schaltz, S. J. Andreasen, and D. U. Sauer, IEEE Trans. Ind. Appl., 52, 5086–5099 (2016).H. Song, T. Kim, J. Jeong, B. Kim, D. Shin, B. Lee, and H. Heo, IET Power Electron., 6, 417–424 (2013).

Real-time simulation and its use in Hardware in the Loop and Power Hardware in the Loop testing may not be realized with one single hardware platform. Instead, multiple systems are needed to realistically address the analysis and testing needs of the smart grid. This is particularly the case when the target system is the future electrical distribution grid, with its multi-physic, hybrid nature, its pervasive power electronics and communication. The approach adopted at the authors' laboratory is presented to exemplify the interface and integration challenges when such platform is extended to realize relevant scenarios.

Power Hardware in the Loop (PHIL) systems are used to test a power system with the help of combined software and hardware. Generally, to construct a PHIL system, a switched-mode power amplifier that has a stable performance is used, because of their large, linear signal control-to-output characteristics. However, the fundamental limitations of a switch-mode power amplifier (PA) are the dynamic performance and output bandwidth. In this paper, a compound controller has been used for the rectifier part of a PA, which can ensure the stability of a PA under transient or fault operating conditions. Moreover, a compound controller, which involves a feed-forward controller, a proportional controller and a repetitive controller, is proposed in the inverter part of a PA, and it can be used for PHIL applications. Experimental results are obtained under various operating conditions, such as transient responses under load step change, and output voltage bandwidth testing for a PHIL system, it is concluded that a proposed switched-mode power amplifier is useful for the PHIL system.

Autonomous underwater vehicles (AUVs) have many, scientific, military, and commercial applications because of their potential capabilities and significant cost-performance improvements. Typical AUV development requires critical system design and many costly at-sea trials during which systems specifications can be validated. Modeling and simulation provides a cost-effective method for carrying out preliminary component or system (hardware and software) testing and verification. Due to the complexity of most AUV development and their tight schedules, the simulated systems often differ considerably from the actual embedded architecture, thereby reducing the fidelity of the entire simulation. We adopted a hardware-in-the-loop (HIL) simulation approach that includes some level of the system's hardware and software. As expected, an HIL simulation requires all components be run under hard real-time constraint. The paper presents our implementation and considerations on HIL simulation for single and multiple vehicles. A brief mention of sensor and environment modeling is also provided.

A fully distributed hierarchal controller is proposed to minimize the total generation cost (TGC) for multiple interconnected DC microgrids (MGs) cluster. It consists of global and local control levels while a two-layer sparse communication network is modeled for information sharing with a reduced complexity. The global control level, in a fully distributed manner, optimizes the power flow among MGs and defines the economic operating point of each MG by determining the optimum incremental cost (IC) value of the entire cluster. The distributed local control level not only matches the ICs of all DGs in the MG with the global assigned optimum IC to accomplish the lowest TGC of the cluster, but also guarantees generation-demand equilibrium in the MG and sequentially within the entire cluster. Feasibility of the proposed controller in case of load variations, plug-n-play capability, and communication failures are verified using hardware in the loop (HIL) prototype.

This paper presents the results obtained of the simulation and implementation in hardware in the loop of fuses and overcurrent protections. First, the steps developed for construct the different blocks in MATLAB/SIMULINK considering the details for the curve approximation in the fuses are presented. Consequently, their implementation using the Dspace 1104 and TI 280F337S boards are presented, describing the characteristics of the signals measurement and the construction of the tripping module and interconnection of all the components of the test grid. Finally, the results obtained in simulation and those obtained whit the hardware in the loop system are compared.

Europe’s electric power network is entering a period of extreme flux as we move to an era where it is dominated by non-linear, asynchronous generation such as wind power plants. The rapid rise of new technologies in this space has led to increasingly complex interactions within the power network that are difficult to cover through traditional test regimes. Existing facilities are not fully equipped to address these new system orientated challenges and while investing in new sites and facilities is one way forward, this is likely to be prohibitively expensive and runs the risk of only being applicable to niche applications. This work presents the first stage in an alternative approach which seeks to address these challenges through the pooling of existing resources. In this concept, geographically distributed test facilities are combined virtually to create new capabilities and perform complex system level validations. This can be done through a Geographically Distributed – Power Hardware in the Loop (GD-PHIL) setup. To achieve this, each site requires a PHIL setup allowing the Device Under Test (DUT) to interact in real time with a simulation. This work will present the outcome of the first step towards the development of such a network, developing and demonstrating the stable communications interfaces necessary to link geographically distributed real time simulators from two different manufacturers.

The present work focuses on the development of computational algorithms to determine on-line energy-based driving guidance for an Electric Vehicle (EV) endowed with regenerative breaking system capabilities. A predictive decision support system is designed to optimally distribute the energy flow between the instantaneous power demand requested by the driver for the powertrain engine and the different auxiliaries relating to comfort performance, such as the heating system. The proposed methodology uses an on-line iterative optimization process algorithm to search for global optimum relatively to specific objective functions, which take into account the battery autonomy, driving comfort indexes and the travel time. Our methodology has been validated for a heavy motorized quadricycle vehicle using Hardware In the Loop (HIL) simulations, for which the Energy Management System has been implemented in a DSP board communicating through a CAN protocol.

ABSTRACTThis paper gives a detailed study for the design and implementation of an energy management system (EMS) for a hybrid renewable microgrid system using real‐time software. Microgrids, with their ability to integrate renewable energy sources, face challenges in maintaining stability and reliability. The implemented EMS aimed to maximize the renewable energy sources utilization, including PV and wind power, in conjunction with a battery energy storage system. The objectives of this research included the implementation of an EMS that ensures a reliable and stable operation between the microgrid system and the main grid including the control of charge and discharge of the battery using Typhoon Hardware‐in‐the‐Loop (HIL) software. The simulation results and case studies demonstrated the effectiveness and performance of the developed EMS in managing a hybrid renewable microgrid system. The results also demonstrated that the time of charging was maximized by utilizing a higher power. By doing so, the battery was fully charged in a shorter timeframe. The battery state of charge (SOC) was maintained between the fixed values (20% and 100%) as stated by the algorithm.

The electrification of the mobility sector comes with multiple challenges such as the lack of information on when, where, how long and how fast charging processes of electric vehicles will take place. In order to keep up with increasing power demand of charging processes, besides better predictions also the active control of charging processes will be necessary to minimize infrastructure costs. This work deals with a real-time mechanism for supporting the Power Quality (PQ) in electric distribution grids in terms of congestion and voltage management. In the paper, we propose a distributed smart charging approach that considers real-time conditions of the distribution grid provided by an event-driven architecture that collects data from different points in the grid. Our approach adopts the traffic light model, which allows smooth changing of the charging power to avoid drastic changes of the grid state. In order to be ready for real-world application, the algorithm is validated by a series of experiments on two setups: Pure software (co-)simulation and Power Hardware In the Loop (PHIL) where physical charging stations and electric cars are controlled in a laboratory setup.

The aim of this paper is to propose a protection strategy for the voltage generator based inverter against the large load currents. This strategy proposes an algorithm for generators output current limiting. It depends on decreasing the amplitude of the voltage reference in case of over current on generator output current. We aim to give the source a behavior similar to that of a voltage source in series with a virtual resistance. This resistance is activated only above a certain current threshold. Simulation results for the application of this strategy on resistive and resistive-inductive load will be demonstrated, in this paper. The effectiveness of the proposed current limiting method was tested on high load current resulting from the asynchronous motor startup. The last part will discuss the implementation of this current limiting strategy on real time simulation environment RT-LAB. An experimentation using Power Hardware in the Loop and realtime simulation was realized to verify the theoretical analysis.

A comprehensive state-space model of two-stage grid-connected PV systems in transient network analysis

The inverter impedance estimation is an integral part of the practical application of the impedance-based stability assessment. Now the question arises about the validity of the estimated impedances, due to disturbances in the measurement and the estimation error of the system identification algorithm. In this paper, a practical approach to determine the validity of the estimated impedance by determining the stability limits of a power hardware in the loop (PHIL) setup is proposed. For this purpose, the dynamic behaviour of all components in the PHIL-setup is determined. To confirm the functionality of the proposed method, it is used to validate the estimated impedance of a Cinergia EL15-AC&DC dynamic load in the laboratory.