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Bilateral privacy-preserving energy sharing in network-constrained models via homomorphic encryption-based distributed optimization
(2026-05-01)
Wang, Hongli
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Yang, Jun
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Li, Gaojunjie
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Wu, Fuzhang
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Xie, Lilong
The proliferation of distributed energy enables prosumers to participate in local trading, yet privacy concerns and network constraints hinder market implementation. This paper proposes a bilateral privacy-preserving energy sharing mechanism that secures sensitive data while enforcing grid operational limits. We develop a prosumer decision model incorporating utility functions and transfer distance factors. To resolve privacy-coordination conflicts, a distributed optimization framework compatible with nonlinear models is designed by integrating gradient descent and dual ascent. This framework guarantees convergence under problem convexity and Lagrangian gradient existence. Furthermore, a homomorphic encryption scheme is integrated to enable dual-blind computations, which prevents plaintext disclosure to either prosumers or the operator while maintaining network safety. Theoretical analysis confirms the scheme's correctness and computational tractability. Numerical simulations on IEEE 14-bus and 33-bus systems validate the mechanism regarding convergence, bilateral privacy protection, and social welfare enhancement. Finally, the algorithm's scalability is demonstrated through penalty-based acceleration, which meets the practical deployment requirements of modern power system.
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Analysis of voltage drop using transformer tap changer and placement of capacitor bank with genetic algorithm
(2025-12-01)
Siregar, Yulianta
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Saragi, Agus Kivander
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Ngamroo, Issarachai
The demand for electrical energy is increasing due to high economic growth and population. The impact is that electrical energy operates excessively to meet the required demand. Unbalanced loads, higher power losses on the line, and voltage drops that are higher than allowed are just a few of the issues that may result from this. Adding tap changers and capacitor banks is one method of improving the voltage profile and power losses. To conduct this study, tap changers and capacitor banks were added to the IEEE 33 bus network system. The value, capacity, and location of the tap changers and capacitor banks in the system were ascertained using the genetic algorithm (GA) approach. According to the simulation results, the voltage profile, which initially had 21 buses outside the IEEE standard limits, may be ideal by installing two tap changers and two capacitor banks. Additionally, reactive power losses decreased from 41.8 kVar to 93.3 kVar, and active power losses decreased from 202.7 kW to 130.7 kW, a decrease of 72 kW.
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A Distributionally Robust Post-Disaster Recovery Method for Distribution Networks Considering Line Repair and Spatiotemporal Dynamic Scheduling of Mobile Energy Storage
(2025-01-01)
Huang, Mengqi
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Li, Yonghui
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Yang, Jun
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Wang, Mengke
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He, Wangang
Extreme disasters often cause large-scale power outages in distribution networks due to damaged lines, significantly impacting system reliability. Current research faces several challenges: traditional methods fail to fully consider the uncertainty of line repair time, existing robust optimization methods encounter difficulties in solving problems involving mobile energy storage (ME), and simple symmetric intervals cannot accurately describe the uncertainty of repair time. To address these challenges, this paper proposes a two-stage distributionally robust post-disaster recovery model that optimizes the connection location of ME in the first stage and adjusts the output of resources such as ME in the second stage to minimize load loss. A Weibull distribution is introduced to fit the repair time of damaged lines, while confidence intervals replace simple symmetric fluctuation intervals to handle the uncertainty of line repair time, improving prediction credibility. The column and constraint generation algorithm is applied to decompose and solve the model. Case studies demonstrate the proposed method's efficacy in maintaining power supply during recovery by simultaneously addressing repair time uncertainty and PV generation variability. At least 26% of the load can be in service even under worst-case scenarios.
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Small-Signal Stability Enhancement Through Integration of Distributed Grid-Forming Loads Considering Multi-Agent Collaboration
(2025-01-01)
Ngamroo, Issarachai
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Surinkaew, Tossaporn
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Mitani, Yasunori
The integration of distributed controllable loads in future islanding microgrids (MGs) is growing. This creates new opportunities to actively shape grid frequency and voltage. As a result, it leads to the development of distributed grid-forming loads (DGFM-Ls). Simultaneously, it is equally crucial to ensure robustness, particularly in preserving small-signal stability amid the multi-agent collaboration. This paper presents a strategy for the small-signal stability enhancement in islanding MGs with DGFM-Ls. The small-signal models of the MG with DGFM-Ls are mathematically developed and analyzed. The multi-agent cooperation is modelled to improve the small-signal stability of the MG with DGFM-Ls. Additionally, uncertainties from multi-agent cooperation, such as partial or complete lack of measured signal observability, are considered. Data quality issues are also taken into account under various conditions. Simulation results are conducted in a MG with a significant penetration of inverter-based resources under various MG operating points and conditions such as topology changes and unavailability of certain agents.
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Improving Inertia Estimation Accuracy Using An Adaptive Framework for Centre of Inertia Selection
(2025-01-01)
Jamraspong, Panupong
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Surinkaew, Tossaporn
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Ngamroo, Issarachai
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Watanabe, Masayuki
The centre of inertia (CoI) is essential for accurate system inertia estimation. Assuming the CoI is static and based on fixed measurements ignores the fact that it changes over time. This can lead to inaccurate estimates since the CoI shifts as system conditions change. To address this issue, this paper presents an adaptive data-driven CoI selection framework that dynamically selects the optimal CoI in a multi-measurement power system. The optimal CoI is used based on selected parameters to estimate the total inertia between any two regions. This process is conducted at each time interval or whenever stamped data from system measurements are received. Simulation results verify that the proposed adaptive CoI selection, compared to a conventional method where the CoI is fixed to a specific pair of measurements, provides more accurate estimation results under varying loading conditions, perturbations, and inertia levels. This paper also explores future developments and identify gaps, while also discussing potential directions for upcoming research works.
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Communication-Driven Learning-Based Harmonic Mitigation Approach for Grid-Forming Converters
(2025-01-01)
Surinkaew, Tossaporn
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Ngamroo, Issarachai
This paper proposes a harmonic mitigation framework for multi-bus microgrids (MGs) that utilizes a communication-driven learning-based approach. A dedicated neural network is used to model additional intelligent control loops. We also develop a global harmonic distortion (GHD) matrix, formulated by the total harmonic distortion at each point of common coupling (PCC). The GHD specifically focuses on harmonic orders that exceed acceptable limits observed from multi-bus systems. The GHD is used as an input signal of the neural network. Based on the system conditions, the developed GHD allows the system to intelligently trigger the control signal whenever dominant harmonic orders are detected and need mitigation. Other input signals include the voltage and current vectors from all PCCs, which contain local harmonics. To train the network, various critical system operating points are collected as time-series data. At this stage, system parameters are not needed; however, secure communication for data transfer is considered instead. Consequently, the collected data are used to train the network using the developed loss functions that focus on harmonic mitigation. After network training, the output signal is sent to the summing points in the direct axis of the inner voltage and current control loops of different models of grid-forming IBRs units. Simulation results are verified in a modified multi-bus MG with IBRs under various operating conditions.
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Intelligence-Driven Grid-Forming Converter Control for Islanding Microgrids
(2025-01-01)
Ngamroo, Issarachai
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Surinkaew, Tossaporn
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Mitani, Yasunori
In modern microgrids (MGs) with high penetration of distributed energy resources (DERs), system reconfiguration occurs more frequently and becomes a significant issue. Fixed-parameter controllers may not handle these tasks effectively, as they lack the ability to adapt to the dynamic conditions in such environments. This paper proposes an intelligence-driven grid-forming (GFM) converter control method for islanding MGs using a robustness-guided neural network (RNN). To enhance the adaptability of the proposed method, traditional proportional-integral controllers in the GFM primary control loops are entirely replaced by the RNN. The RNN is trained by a robustness-guided strategy to replicate their robust behaviors. All the training stages are purely data-driven methods, which means that no system parameters are required for the controller design. Consequently, the proposed method is an intelligence-driven modelless GFM converter control. Compared with traditional methods, the simulation results in all testing scenarios show the clear benefits of the proposed method. The proposed method reduces overshoots by more than 71.24%, which keeps all damping ratios within the stable region and provides faster stabilization. In comparison to traditional methods, at the highest probability, the proposed method improves damping by over 14.7% and reduces the rates of change of frequency and voltage by over 59.97%. Additionally, the proposed method effectively suppresses the interactions between state variables caused by inverter-based resources, with frequencies ranging from 1.0 Hz to 1.422 Hz. Consequently, these frequencies contribute less than 19.79% To the observed transient responses.
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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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Message from the Keynote Speaker
(2025-01-01)
Ngamroo, Issarachai
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Comparative Analysis of The Effect of Epoxy Silicone Rubber Resin with Silica Sand/TiO2 and Silica Sand/MgO Fillers on The Mechanical and Electrical Characteristics of Insulators
(2025-01-01)
Siregar, Yulianta
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Ginting, Rut Daniela
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Ngamroo, Issarachai
Epoxy resin-based insulators are the latest innovation in the development of insulating materials. Epoxy resin insulators made of silicone rubber with fillers can increase the reliability of insulating materials used in high-voltage transmission. Adding fillers such as silica sand and TiO2 and MgO nanoparticles can increase the insulator's electrical strength and mechanical strength. This study shows that the best mixture composition to increase the value of volume resistivity, surface resistivity, dielectric strength, compressive strength, and tensile strength, reviewed in terms of economy, is the S9 test sample. The composition of S9 consists of 30% MPDA resin, 30% DGEBA resin, 20% silica sand, 15% silicone rubber, and 5% MgO. This sample shows a volume resistivity value of 0.313 MΩm, surface resistivity of 74.593 MΩm, dielectric strength of 116 KV/cm, compressive strength of 41 kN, and tensile strength of 5.260 MPa.

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