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Embedded Intelligent Reactive Power Control for Distributed Controllable Loads to Support Grid Voltage Considering Islanding Conditions
(2025-01-01)
Surinkaew, Tossaporn
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Pinthurat, Watcharakorn
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Yang, Jun
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Ngamroo, Issarachai
The increasing integration of distributed energy resources (DERs) along with distributed controllable loads (DCLs), presents significant challenges for maintaining grid stability and effectively incorporating these resources. Traditionally, DERs have supported microgrid (MG) stability primarily through active power control loops, which has limited their ability to manage power output effectively. This paper introduces a novel embedded intelligent reactive power control framework for DCLs, designed to bolster grid voltage stability during islanding conditions. This framework seamlessly integrates reactive power control loops as ancillary services within DCLs, which can also support other grid-forming (GFM) resources. The DCL is modeled with grid-following (GFL) control loops, which incorporate an embedded intelligent reactive power control loop into the voltage control system. The proposed intelligent framework applies a modified long-short term memory neural network to imitate reactive power behavior. The network is trained with the special loss function to minimize voltage variation. This enables the intelligent GFL control to accurately follow voltage and frequency references from the GFM resources and adapt to unexpected islanding scenarios. Simulation results in a low-inertia MG with DERs confirm the superiority of the proposed framework. It outperforms conventional methods, including mixed H<inf>2</inf>/H∞ strategy, model predictive control, and convolutional neural network. Through a comprehensive evaluation, the proposed strategy shows superior performance in transient response, voltage stability, power dynamics, statistical analysis, and damping performance.
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An Intelligent Voltage Control With Power Loss Model Integration in Active Distribution Network
(2025-01-01)
Pinthurat, Watcharakorn
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Deanseekeaw, Anurak
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Surinkaew, Tossaporn
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Boonraksa, Terapong
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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
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Pinthurat, Watcharakorn
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Marungsri, Boonruang
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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 overview of reinforcement learning-based approaches for smart home energy management systems with energy storages
(2024-09-01)
Pinthurat, Watcharakorn
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Surinkaew, Tossaporn
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Hredzak, Branislav
The paper's state-of-the-art review focuses on an in-depth evaluation of smart home energy management systems which employ reinforcement learning-based methods to integrate energy storages. In order to optimize energy consumption and improve overall sustainability while maintaining technical and economic constraints, the paper first investigates the multi-faceted aspects of integrating energy storages into smart homes. Second, an overview of a smart home system and a theoretical background of reinforcement learning-based algorithms are given and discussed. Consequently, this study delves into the challenges and benefits of integrating energy storage, specifically looking at ways to lessen the impact of renewable sources’ intermittency, improve grid stability, and streamline efficient energy storage management. Thirdly, the paper highlights the beneficial features of smart home energy storage integration, including reduced costs, increased system resilience, and improved energy efficiency. Therefore, cutting-edge reinforcement learning-based methods utilized in smart home energy management systems that incorporate energy storage are thoroughly examined by evaluating their effectiveness and adaptability, taking into account both multi-agent and single-agent reinforcement learning-based methods. Finally, the study identifies potential research directions, including the development of hybrid reinforcement learning algorithms, integration of demand-side management strategies, and addressing privacy and security concerns in reinforcement learning-based smart home energy management systems. While some research has made use of single-agent reinforcement learning, smart home energy storage systems that use energy storages seldom use multi-agent reinforcement learning techniques. Researchers, practitioners, and policymakers will be able to use this work as a foundation to build smart, sustainable home energy systems.
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Simultaneous Voltage Regulation and Unbalance Compensation in Distribution Systems With an Information-Driven Learning Approach
(2024-04-01)
Pinthurat, Watcharakorn
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Surinkaew, Tossaporn
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Hredzak, Branislav
High penetration and uneven allocation of single-phase and three-phase loads together with photovoltaic (PV) sources in low-voltage distribution systems can result in voltage unbalance and voltage regulation challenges. Previous approaches that treated the voltage unbalance and the voltage regulation as separate problems may have overlooked their interdependencies, thus potentially limiting the effectiveness of those methods. This article proposes a novel framework that can regulate the bus voltages and compensate for the voltage unbalance simultaneously, without requiring any information on phase connection of single-phase PV inverters. The proposed framework is designed based on a Markov game. The bus voltage deviation and the voltage unbalance factor (VUF) are used to define the reward function. Local observation states are obtained from smart meters, and the reactive current of the PV-based voltage source inverter (VSI) is defined as the action within the framework. The framework employs a multiagent deep deterministic policy gradient (known as multiagent deep deterministic policy gradient) algorithm that incorporates centralized training and decentralized execution. It is utilized to solve the framework. To ensure its effectiveness, the framework is trained and tested using real data acquired from smart meters. This data encompasses information on PV generation and load demand. The obtained results demonstrate that the bus voltages are regulated close to their nominal value and the VUF is maintained at less than 2% while the PV-VSIs operate within predefined limits.
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Cyber-Attacks on Power System Frequency Control
(2024-01-01)
Kerdphol, Thongchart
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Teera-Achariyakul, Noppada
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Surinkaew, Tossaporn
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Pinthurat, Watcharakorn
Due to the increasing number of cyber hacking activities, cyber-Attacks become urgent concerns to power system security worldwide. A power system is vulnerable to cyber-Attacks due to the exchanged information in its area. In this study, cyber-Attacks on power system frequency control are studied by corrupting the exchange of information. Considering the frequency measurement of the system, essential attack strategies from hackers' perspectives, which are usually concerned with cyber-Attacks on critical infrastructure, have been modeled concerning the false data injection. Simulation results analyze the strengths and weaknesses of different attacks against frequency control of the system. The exogenous cyber-Attacks on frequency power measurements can cause severe frequency excursions, which lead to system instability and power blackouts. Such attack methods require effective detection from defenders to guarantee system security before hackers further deteriorate the system's stability.
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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
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Kongsuk, Prayad
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Surinkaew, Tossaporn
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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
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Surinkaew, Tossaporn
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Kerdphol, Thongchart
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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.
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Dynamic Assessment of Thailand's Available Transfer Capability for Third-Party Access
(2024-01-01)
Khakkharho, Dhamrongsak
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Sutheerawut, Thanakorn
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Sanjaisue, Junlachat
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Kerdphol, Thongchart
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Teera-Achariyakul, Noppada
Thailand is experiencing significant growth in renewable energy sources, such as solar power and wind power. This growth is leading to an increase in independent electricity trading across public, private, and household sectors, making Thailand the origin of third-party access to electricity in the country. However, the current transmission infrastructure is struggling to keep pace with this development, making it challenging to assess regional transmission capabilities accurately. Furthermore, the rapid urban and industrial growth has also resulted in a significant increase in electricity demand, emphasizing the urgent need for a robust transmission and distribution network. To address these challenges, a paper is currently underway to model the remaining transmission capacity in Thailand's regions using real data and sophisticated electrical engineering software. This analysis primarily concentrates on Available Transfer Capability known as ATC, which draws insights from various methods used in the Indian case study. The goal is to enhance the utilization of the transmission system, support independent electricity trading, and guarantee the stability, security, and reliability of the power system in line with the changing electricity market.

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