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Author: search.filters.author.Marungsri, BoonruangHas files: No

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    An Intelligent Voltage Control With Power Loss Model Integration in Active Distribution Network
    (2025-01-01)
    Pinthurat, Watcharakorn
    ;
    Deanseekeaw, Anurak
    ;
    Surinkaew, Tossaporn
    ;
    Boonraksa, Terapong
    ;
    Boonraksa, Promphak
    The increasing integration of renewable energy sources (RESs), particularly distributed PV systems, poses significant challenges to voltage stability in modern distribution systems. Existing studies use reactive power control to address voltage deviations but incur high losses, with no systematic solution achieving both voltage regulation and loss minimization. This paper proposes a novel voltage control strategy based on multi-agent deep reinforcement learning (MADRL), leveraging decentralized agent coordination to maintain voltage levels while minimizing PV inverter and system losses. Also, a new framework is formulated based on a Markov game, wherein each PV inverter operates as an autonomous agent that adjusts its reactive power output via a centralized training process. The agents, defined as PV inverters, employ the multi-agent twin-delayed deep deterministic policy gradient algorithm to collaboratively minimize voltage deviations. Through the use of local observations and shared global information during training, agents learn robust control policies that generalize to varying conditions and enable decentralized execution without ongoing coordination. Performance of the proposed control strategy is validated on a modified IEEE 33-node distribution system under high variability in PV generation and load demand. Results show that the proposed control strategy significantly improves voltage regulation and reduces power losses compared to state-of-the-art MADRL techniques.
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    Two-Stage Small-Signal Stability-Assisted Framework Using Controllable Loads in Reconfigurable Microgrids
    (2025-01-01)
    Surinkaew, Tossaporn
    ;
    Pinthurat, Watcharakorn
    ;
    Marungsri, Boonruang
    ;
    Hredzak, Branislav
    Reconfiguration in low-inertia microgrids (MGs) can often result in a critical small-signal stability margin. In this condition, the ability of inverter-based resources (IBRs) to provide voltage and frequency support may be insufficient. To maintain stable operation without interruptions, this paper presents a control strategy that first evaluates the effect of MG reconfiguration on system stability and then employs controllable loads as an enhancement mechanism to improve small-signal stability in scenarios involving reconfigurable MGs, particularly during islanded operation or high-demand situations such as sudden load changes or fault recovery. Mathematical models of system reconfiguration are presented. Then, we demonstrate how reconfiguration in MGs can result in marginal small-signal stability. The proposed framework operates in two stages: (i) assessing optimal breaker/switch configurations to ensure a baseline stability margin, and (ii) using controllable loads to fine-tune and improve damping performance. It is shown that the proposed framework can shift stability from critical or unstable levels to an acceptable range, making the initial conditions of reconfigured MGs feasible. Simulation results in a reconfigurable MG with different portions of IBRs and controllable loads demonstrate the effectiveness of the proposed framework in using controllable loads to successfully enhance small-signal stability. The proposed strategy ensures that the reconfigured MGs remain stable after reconfigurations.
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    An Adaptive Data-Driven-Based Control for Voltage Control Loop of Grid-Forming Converters in Variable Inertia MGs
    (2024-01-01)
    Pinthurat, Watcharakorn
    ;
    Kongsuk, Prayad
    ;
    Surinkaew, Tossaporn
    ;
    Marungsri, Boonruang
    In the transition towards sustainable energy systems, microgrid (MG) plays a pivotal role, especially in the context of variable-inertia MGs that integrate renewable energy sources (RESs) and distributed energy resources (DERs). Maintaining stable voltage control within such grids is imperative for reliable operation. This paper presents an adaptive data-driven control technique for the voltage control loop of grid-forming converters in variable-inertia MGs. The primary objective is to enhance control performance while accommodating the unpredictable nature of renewable energy sources. The approach utilizes advanced data-driven algorithms to continuously monitor and adjust control parameters based on real-time grid conditions. This adaptability allows for effective management of varying inertia and load demand, ensuring optimal grid performance. The data-driven nature of the approach enables self-adaptability, making it suitable for the dynamic MG environment. This new approach is a noteworthy advancement in controlling RESs and DERs to maintain stable voltage in MGs, without needing precise knowledge of the MG's parameters. Simulation tests and real-world examples confirm that the adaptive data-driven control method effectively optimizes voltage control in MGs with variable inertia, especially those with a high presence of RESs and DERs.
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    Small-Signal Stability Analysis in an Uncertain Microgrid with Distributed Energy Resources: A Data-Driven Monitoring
    (2024-01-01)
    Pinthurat, Watcharakorn
    ;
    Surinkaew, Tossaporn
    ;
    Kerdphol, Thongchart
    ;
    Marungsri, Boonruang
    In low-inertia microgrids (MGs) with intermittent distributed energy resources (DERs), the requirement to pre-determine MG parameters results in a challenge as these parameters evolve over time. Consequently, conducting small-signal stability analysis becomes impractical due to the dynamic nature of the MG's parameter variations. In this paper, we propose an adaptive data-driven approach designed for grid-forming converters of DERs. The goal is to improve small-signal stability within a dynamically shifting window range. Additionally, we propose a data-driven approach to identify the MG model within a moving window. Subsequently, small-signal stability can be assessed. Comprehensive simulation results are systematically generated for an MG with DERs under various crucial conditions, including (i) intermittent power outputs of DERs, (ii) generator outages, (iii) diverse levels of total MG inertia, and (iv) different MG operation modes.