arXiv subjects

Chenggang Cui

Publications and source records attributed to Chenggang Cui.

10 recordsLinked to original sources

arXiv2026

Model-Free DRL Control for Power Inverters: From Policy Learning to Real-Time Implementation via Knowledge Distillation

In response to the trade-off between control performance and computational burden hindering the deployment of Deep Reinforcement Learning (DRL) in power inverters, this paper presents a novel model-free control framework leveraging policy distillation. To handle the convergence instability and steady-state errors inherent in model-free agents, an error energy-guided hybrid reward mechanism is established to theoretically constrain the exploration space. More specifically, an adaptive importance weighting mechanism is integrated into the distillation architecture to amplify the significance of fluctuation regions, ensuring high-quality transfer of transient control logic by mitigating the observational bias dominated by steady-state data. This approach efficiently compresses the heavy DRL policy into a lightweight neural network, retaining the desired control performance while overcoming the computational bottleneck during deployment. The proposed method is validated through a hardware-based kilowatt-level experimental platform. Experimental comparison results with traditional methods demonstrate that the proposed technique reduces inference time to the microsecond level and achieves superior transient response speed and parameter robustness.

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arXiv2025

Automated Heuristic Design for Unit Commitment Using Large Language Models

The Unit Commitment (UC) problem is a classic challenge in the optimal scheduling of power systems. Years of research and practice have shown that formulating reasonable unit commitment plans can significantly improve the economic efficiency of power systems' operations. In recent years, with the introduction of technologies such as machine learning and the Lagrangian relaxation method, the solution methods for the UC problem have become increasingly diversified, but still face challenges in terms of accuracy and robustness. This paper proposes a Function Space Search (FunSearch) method based on large language models. This method combines pre-trained large language models and evaluators to creatively generate solutions through the program search and evolution process while ensuring their rationality. In simulation experiments, a case of unit commitment with \(10\) units is used mainly. Compared to the genetic algorithm, the results show that FunSearch performs better in terms of sampling time, evaluation time, and total operating cost of the system, demonstrating its great potential as an effective tool for solving the UC problem.

cs.AI↗

arXiv2025

GenControl: Generative AI-Driven Autonomous Design of Control Algorithms

Designing controllers for complex industrial electronic systems is challenging due to nonlinearities and parameter uncertainties, and traditional methods are often slow and costly. To address this, we propose a novel autonomous design framework driven by Large Language Models (LLMs). Our approach employs a bi-level optimization strategy: an LLM intelligently explores and iteratively improves the control algorithm's structure, while a Particle Swarm Optimization (PSO) algorithm efficiently refines the parameters for any given structure. This method achieves end-to-end automated design. Validated through a simulation of a DC-DC Boost converter, our framework successfully evolved a basic controller into a high-performance adaptive version that met all stringent design specifications for fast response, low error, and robustness. This work presents a new paradigm for control design that significantly enhances automation and efficiency.

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arXiv2025

Research on a Two-Layer Demand Response Framework for Electric Vehicle Users and Aggregators Based on LLMs

The widespread adoption of electric vehicles (EVs) has increased the importance of demand response in smart grids. This paper proposes a two-layer demand response optimization framework for EV users and aggregators, leveraging large language models (LLMs) to balance electricity supply and demand and optimize energy utilization during EV charging. The upper-layer model, focusing on the aggregator, aims to maximize profits by adjusting retail electricity prices. The lower-layer model targets EV users, using LLMs to simulate charging demands under varying electricity prices and optimize both costs and user comfort. The study employs a multi-threaded LLM decision generator to dynamically analyze user behavior, charging preferences, and psychological factors. The framework utilizes the PSO method to optimize electricity prices, ensuring user needs are met while increasing aggregator profits. Simulation results show that the proposed model improves EV charging efficiency, alleviates peak power loads, and stabilizes smart grid operations.

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arXiv2025

Dynamic Incentive Strategies for Smart EV Charging Stations: An LLM-Driven User Digital Twin Approach

This paper presents an enhanced electric vehicle demand response system based on large language models, aimed at optimizing the application of vehicle-to-grid technology. By leveraging an large language models-driven multi-agent framework to construct user digital twins integrated with multidimensional user profile features, it enables deep simulation and precise prediction of users' charging and discharging decision-making patterns. Additionally, a data- and knowledge-driven dynamic incentive mechanism is proposed, combining a distributed optimization model under network constraints to optimize the grid-user interaction while ensuring both economic viability and security. Simulation results demonstrate that the approach significantly improves load peak-valley regulation and charging/discharging strategies. Experimental validation highlights the system's substantial advantages in load balancing, user satisfaction and grid stability, providing decision-makers with a scalable V2G management tool that promotes the sustainable, synergistic development of vehicle-grid integration.

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arXiv2024

Electric Vehicle User Charging Behavior Analysis Integrating Psychological and Environmental Factors: A Statistical-Driven LLM based Agent Approach

With the growing adoption of electric vehicles (EVs), understanding user charging behavior has become critical for grid stability and transportation planning. This study investigates the behavioral heterogeneity of EV taxi drivers by analyzing the interaction between psychological traits and situational triggers within dynamic travel contexts. Leveraging large language models (LLMs) as a core simulation tool, a novel framework with statistical enhancement is developed to replicate and analyze the charging behaviors of taxi drivers. LLMs simulate personalized decision-making processes by leveraging natural language reasoning and role-playing capabilities, accounting for factors such as time sensitivity, price awareness, and range anxiety. Simulation results indicate that the framework reliably reproduces real-world charging behaviors across multiple urban environments. his fidelity arises from integrating statistical priors into the reasoning process, allowing the model to anchor its decisions in empirical behavioral patterns. Further analysis highlights the joint influence of environmental and psychological variables on charging decisions and reveals the heterogeneity of different user groups. The findings provide new insights into EV user behavior, offering a foundation for optimizing charging infrastructure, informing energy policy, and advancing the integration of EV behavioral models into smart transportation and energy management systems.

cs.AI↗

arXiv2024

On Physics-Informed Neural Network Control for Power Electronics

Considering the growing necessity for precise modeling of power electronics amidst operational and environmental uncertainties, this paper introduces an innovative methodology that ingeniously combines model-driven and data-driven approaches to enhance the stability of power electronics interacting with grid-forming microgrids. By employing the physics-informed neural network (PINN) as a foundation, this strategy merges robust data-fitting capabilities with fundamental physical principles, thereby constructing an accurate system model. By this means, it significantly enhances the ability to understand and replicate the dynamics of power electronics systems under complex working conditions. Moreover, by incorporating advanced learning-based control methods, the proposed method is enabled to make precise predictions and implement the satisfactory control laws even under serious uncertain conditions. Experimental validation demonstrates the effectiveness and robustness of the proposed approach, highlighting its substantial potential in addressing prevalent uncertainties in controlling modern power electronics systems.

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arXiv2024

Large Language Models based Multi-Agent Framework for Objective Oriented Control Design in Power Electronics

Power electronics, a critical component in modern power systems, face several challenges in control design, including model uncertainties, and lengthy and costly design cycles. This paper is aiming to propose a Large Language Models (LLMs) based multi-agent framework for objective-oriented control design in power electronics. The framework leverages the reasoning capabilities of LLMs and a multi-agent workflow to develop an efficient and autonomous controller design process. The LLM agent is able to understand and respond to high-level instructions in natural language, adapting its behavior based on the task's specific requirements and constraints from a practical implementation point of view. This novel and efficient approach promises a more flexible and adaptable controller design process in power electronics that will largely facilitate the practitioners.

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arXiv2021

Transferring Reinforcement Learning for DC-DC Buck Converter Control via Duty Ratio Mapping: From Simulation to Implementation

Reinforcement learning (RL) control approach with application into power electronics systems has become an emerging topic whilst the sim-to-real issue remains a challenging problem as very few results can be referred to in the literature. Indeed, due to the inevitable mismatch between simulation models and real-life systems, offline trained RL control strategies may sustain unexpected hurdles in practical implementation during transferring procedure. As the main contribution of this paper, a transferring methodology via a delicately designed duty ratio mapping (DRM) is proposed for a DC-DC buck converter. Then, a detailed sim-to-real process is presented to enable the implementation of a model-free deep reinforcement learning (DRL) controller. The feasibility and effectiveness of the proposed methodology are demonstrated by comparative experimental studies.

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arXiv2020

An Intelligent Control Strategy for buck DC-DC Converter via Deep Reinforcement Learning

As a typical switching power supply, the DC-DC converter has been widely applied in DC microgrid. Due to the variation of renewable energy generation, research and design of DC-DC converter control algorithm with outstanding dynamic characteristics has significant theoretical and practical application value. To mitigate the bus voltage stability issue in DC microgrid, an innovative intelligent control strategy for buck DC-DC converter with constant power loads (CPLs) via deep reinforcement learning algorithm is constructed for the first time. In this article, a Markov Decision Process (MDP) model and the deep Q network (DQN) algorithm are defined for DC-DC converter. A model-free based deep reinforcement learning (DRL) control strategy is appropriately designed to adjust the agent-environment interaction through the rewards/penalties mechanism towards achieving converge to nominal voltage. The agent makes approximate decisions by extracting the high-dimensional feature of complex power systems without any prior knowledge. Eventually, the simulation comparison results demonstrate that the proposed controller has stronger self-learning and self-optimization capabilities under the different scenarios.

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