Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
February 14th, 2025
This article presents a DC microgrid with hierarchical control implemented in a simulator, OPAL RT-Lab. It details the circuit modeling, primary and secondary control strategies, and experimental validation. The results demonstrate effective control performance, highlighting the importance of a robust experimental platform for microgrid research and development.
Our research primarily focuses on developing hierarchical control strategies to real world microgrid platform to experimental validation. We aim to address practical challenges in the tests of the effectiveness of control method in dynamic microgrid environments. Recent developments in microgrid research show a great number of demonstration projects, yet most theoretical studies are still validated through simulations or through experiments.
Systematic experimental platform that integrates real world micro grid control strategies remain scars limiting the practical verification and the optimization of control methods. Our protocol offers the advantage of enabling real world hardware based instrumentation of hierarchical controller strategies in microgrids, addressing the gap between simulation and practical implementation. It provides a comprehensive hands-on approach for deploying control systems on actual platforms, ensuring better system validation.
Future research in our laboratory will focus on exploring advanced control strategies for microgrids, aiming to enhance system robustness. We seek to improve the ability of microgrid operations under real world scenarios such as sudden no changes and network workforce to ensure reliable, efficient performance in practical scenarios. To construct individual distributed energy resources, or DER, connect the positive pole of the direct current, or DC source, through a wire to the input positive pole of the buck circuit, while simultaneously connecting the corresponding negative poles.
Build a mathematical model for the buck converter to facilitate the design of control parameters for simulations and experimental setups. Use the state space averaging method to construct the state space equations for a typical buck converter. Next, transform the state space equation into the transfer function form for easier proportional integral controller design.
After constructing individual DERs, connect the corresponding positive and negative output terminals of each buck circuit. To simulate line impedance, insert small resistors in series between the positive poles of each DER. For load integration, use resistors to simulate common loads in DC microgrids.
Directly connect the resistors terminals to the confluence points of the positive and negative poles of all distributed energy resources for global loads. When line impedance is present, connect resistors at the output of each buck circuit to simulate local loads. Next, press the power button on the power supply.
Adjust the voltage to the specified value using the knob. Ensure the power supply operates within the range of zero to 300 volts and a maximum power of 600 watts. Route the input and output signals of the DCDC buck converter to a signal conversion board.
Connect the signal conversion board to the simulator hardware controller using signal cables. Finally, verify the bus and load connections. Inspect all connections for accuracy and security.
To configure the droop control module, drag and drop components such as gains and difference blocks into the control module. Double click the gain module and set the droop coefficient as required. Then, for a dual loop proportional integral control setup, drag and drop the components in the stimulator.
When selecting proportional integral control gains, use the transfer function model of the buck converter from the transfer function equation. Follow the sequence of designing the inner current control loop first, and then the outer voltage control loop. Provide different input signals to the controllers of each DER to implement distributed control within the centralized simulator controller.
For example, drag signals from DER two and DER four into the control module of DER one. Next, construct the secondary control block diagram in the simulator, based on the consensus based secondary control. Adjust the response of the secondary control by modifying the control gains within the simulator.
For real-time simulator experimental setup, click the edit button to modify the program running on the simulator. Subsequently, activate the set button to complete the development property settings. After completing the model editing, click the build button to compile the model into executable code.
Monitor the software compilation window until the message compilation successful appears. Upon successful compilation, configure the program code settings, including simulation mode, real-time communication link type, and other relevant parameters. Download the compiled executable program into the controller hardware.
Then, start the program to initiate the experiment. Connect the voltage probes of the oscilloscope to the positive and negative terminals of each DER output and clamp the current probes at the output ports.
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Overview
This article presents a DC microgrid with hierarchical control implemented in a simulator, OPAL RT-Lab. It details the circuit modeling, primary and secondary control strategies, and experimental validation.
Key Study Components
Area of Science
- Microgrid control strategies
- Experimental validation
- Circuit modeling
Background
- Focus on hierarchical control strategies for microgrids.
- Challenges in validating control methods in dynamic environments.
- Integration of real-world platforms for better validation.
- Importance of systematic experimental platforms.
Purpose of Study
- To develop and validate hierarchical control strategies.
- To bridge the gap between simulation and practical implementation.
- To provide a hands-on approach for deploying control systems.
Methods Used
- Simulation using OPAL RT-Lab.
- Circuit modeling techniques.
- Implementation of primary and secondary control strategies.
- Experimental validation of control performance.
Main Results
- Effective control performance demonstrated.
- Importance of robust experimental platforms highlighted.
- Validation of control methods in real-world scenarios.
- Addressing practical challenges in microgrid research.
Conclusions
- Hierarchical control strategies can be effectively validated.
- Real-world platforms enhance the reliability of results.
- Future research can build on this systematic approach.
Frequently Asked Questions
Executive Industry Relevance
Reliable control of distributed energy resources in DC microgrids is critical for the integration of renewables and storage in biopharma R&D facilities, where power quality and system resilience directly impact sensitive operations. Hierarchical control strategies validated on real-time simulators bridge the gap between theoretical models and practical deployment, supporting predictive confidence in energy management. This experimental platform enables risk-adjusted decisions for infrastructure investments and operational continuity in advanced research environments.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables rigorous hypothesis testing of control algorithms for distributed energy systems.
- Supports mechanistic de-risking by validating control responses under dynamic load and network conditions.
- Facilitates functional validation of power-sharing and voltage regulation strategies.
Screening & Assay Development
- Prepares validated microgrid models for downstream integration with facility automation and process control systems.
- Standardizes control parameterization and reproducibility across experimental runs.
- Generates quantitative outputs for benchmarking control performance and system stability.
Translational & Preclinical Research
- Aligns experimental microgrid platforms with real-world operational scenarios relevant to biopharma infrastructure.
- Supports continuity from simulation to hardware implementation, reducing translational risk.
- Enables risk-adjusted advancement of energy management solutions for critical research environments.
Pipeline & Workflow Integration
This experimental platform positions hierarchical control validation at the intersection of discovery, screening, and translational research for energy systems in biopharma settings.
- Discovery Biology: Provides a testbed for hypothesis-driven evaluation of distributed control strategies.
- Screening: Delivers reproducible, quantitative measurements of voltage and current regulation under varying conditions.
- Analytics: Enables oscilloscope-based readouts and statistical comparison of control responses with and without delay.
- Translational Research: Bridges simulation and hardware, supporting deployment in operational environments.
- Enterprise Reuse: Establishes a reusable experimental framework for ongoing control strategy development and validation.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence in microgrid control performance and system reliability.
- Operational Value: Standardizes experimental validation, supporting reproducibility and scalability across sites.
- Strategic Value: Informs go/no-go decisions for energy infrastructure upgrades and risk mitigation.
- Portfolio Impact: Enables risk-adjusted prioritization of energy management solutions for research and manufacturing facilities.
Implementation Considerations
- Requires expertise in power electronics, control systems, and real-time simulation platforms.
- Demands access to hardware-in-the-loop simulators and instrumentation such as oscilloscopes.
- Necessitates cross-team standardization of control parameters and experimental protocols.
- Adaptation may be needed for different DER types or facility-specific load profiles.
- Practical limitations include simulator hardware constraints and the need for secure, accurate wiring and signal routing.
