Automatic generation control of is-landed micro-grid using integral reinforcement learning-based adaptive optimal control strategy

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Rasananda Muduli, Debashisha Jena, Tukaram Moger
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引用次数: 0

Abstract

Microgrids serve an essential role in the smart grid infrastructure, facilitating the seamless integration of distributed energy resources and supporting the increased adoption of renewable energy sources to satisfy the growing demand for sustainable energy solutions. This paper presents an application of integral reinforcement learning (IRL) algorithm-based adaptive optimal control strategy for automatic generation control of an is-landed micro-grid. This algorithm is a model-free actor-critic method that learns the critic parameters using the recursive least square method. The actor is straightforward and evaluates the action from the critic directly. The robustness of the proposed control technique is investigated under various uncertainties arising from parameter uncertainty, electric vehicle (EV) aggregator, and renewable energy sources. This study incorporates case studies and comparative analyses to demonstrate the control performance of the proposed control strategy. The effectiveness of the technique is evaluated by comparing it with deep Q-learning (DQN) control techniques and PI controllers. The proposed controller significantly improves performance metrics compared to the DQN and PI controllers. It reduces the peak frequency deviation by 6\(\%\) and 14\(\%\), respectively, compared to the DQN and PI controllers. When subjected to multiple-step load disturbances, the proposed controller reduces the mean square error by 28\(\%\) and 42\(\%\), respectively, while lowering both the integral absolute error and the integral time absolute error by 21\(\%\) and 35\(\%\) compared to the DQN and PI controllers. Additionally, when operating with renewable energy sources, the proposed controller decreases the standard deviation in the frequency deviation by 17\(\%\) compared to the DQN controller and 23\(\%\) compared to the PI controller.

Graphical abstract

Abstract Image

利用基于积分强化学习的自适应优化控制策略实现等陆微型电网的自动发电控制
摘要 微电网在智能电网基础设施中发挥着重要作用,它促进了分布式能源资源的无缝集成,并支持更多采用可再生能源,以满足对可持续能源解决方案日益增长的需求。本文介绍了基于积分强化学习(IRL)算法的自适应最优控制策略在等陆微电网自动发电控制中的应用。该算法是一种无模型行为批判方法,使用递归最小二乘法学习批判参数。行动者直截了当,直接评估批判者的行动。在参数不确定性、电动汽车(EV)聚合器和可再生能源引起的各种不确定性下,对所提出的控制技术的鲁棒性进行了研究。本研究通过案例研究和对比分析,展示了所提控制策略的控制性能。通过与深度 Q 学习(DQN)控制技术和 PI 控制器进行比较,评估了该技术的有效性。与 DQN 和 PI 控制器相比,所提出的控制器大大提高了性能指标。与 DQN 和 PI 控制器相比,它将峰值频率偏差分别降低了 6(\%)和 14(\%)。当受到多级负载扰动时,与 DQN 和 PI 控制器相比,所提出的控制器将均方误差分别降低了 28 (\%)和 42 (\%),同时将积分绝对误差和积分时间绝对误差分别降低了 21 (\%)和 35 (\%)。此外,当与可再生能源一起运行时,与 DQN 控制器相比,提议的控制器将频率偏差的标准偏差降低了 17(\%\),与 PI 控制器相比,降低了 23(\%\)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Electrical Engineering
Electrical Engineering 工程技术-工程:电子与电气
CiteScore
3.60
自引率
16.70%
发文量
0
审稿时长
>12 weeks
期刊介绍: The journal “Electrical Engineering” following the long tradition of Archiv für Elektrotechnik publishes original papers of archival value in electrical engineering with a strong focus on electric power systems, smart grid approaches to power transmission and distribution, power system planning, operation and control, electricity markets, renewable power generation, microgrids, power electronics, electrical machines and drives, electric vehicles, railway electrification systems and electric transportation infrastructures, energy storage in electric power systems and vehicles, high voltage engineering, electromagnetic transients in power networks, lightning protection, electrical safety, electrical insulation systems, apparatus, devices, and components. Manuscripts describing theoretical, computer application and experimental research results are welcomed. Electrical Engineering - Archiv für Elektrotechnik is published in agreement with Verband der Elektrotechnik Elektronik Informationstechnik eV (VDE).
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