Hasan Riyad, Masud Md Shafakat, Haque Nawar, Abdussami Muhammad R
Department of Nuclear Science and Engineering, Military Institute of Science and Technology, Dhaka, Bangladesh.
Department of Electrical and Electronic Engineering, East West University, Dhaka, Bangladesh.
Heliyon. 2022 Nov 21;8(11):e11770. doi: 10.1016/j.heliyon.2022.e11770. eCollection 2022 Nov.
This paper investigates the applications of Proportional-Integrator-Derivative (PID) and Fractional Order PID (FOPID) controllers in Nuclear-Renewable Hybrid Energy Systems (N-R HESs). The N-R HES is a recent technology in the area of decarbonized energy systems. N-R HESs are expected to contribute immensely to providing carbon-free and sustainable energy infrastructure in the upcoming days. It is also anticipated that system resiliency will be the primary concern when nuclear reactors are incorporated with intermittent renewable energy resources. Therefore, in this research, the authors intend to evaluate the compatibility of two classical controllers, PID and FOPID, to ensure the stability of N-R HESs. The N-R HES of this paper consists of different energy sources, such as solar, wind, nuclear, fuel cell systems, Battery Energy Storage Systems (BESS), and Flywheel Energy Storage Systems (FESS). To encounter system performance requirements, the PID and FOPID controller parameters are adjusted using a metaheuristic algorithm, namely Artificial-Bee-Colony (ABC) optimization algorithm. Metaheuristic optimization algorithms always do not guarantee global maxima/minima. Hence, another metaheuristic optimization algorithm, Teaching-Learning-based Optimization (TLBO), is used to validate the results. The results clearly show that the optimal PID and FOPID controllers can handle the system frequency and maintain the stability of the studied N-R HES.
本文研究了比例积分微分（PID）控制器和分数阶PID（FOPID）控制器在核能-可再生能源混合能源系统（N-R HESs）中的应用。N-R HES是脱碳能源系统领域的一项最新技术。预计N-R HESs在未来将为提供无碳和可持续能源基础设施做出巨大贡献。还预计，当核反应堆与间歇性可再生能源相结合时，系统弹性将成为主要关注点。因此，在本研究中，作者打算评估两种经典控制器PID和FOPID的兼容性，以确保N-R HESs的稳定性。本文的N-R HES由不同的能源组成，如太阳能、风能、核能、燃料电池系统、电池储能系统（BESS）和飞轮储能系统（FESS）。为满足系统性能要求，使用一种元启发式算法，即人工蜂群（ABC）优化算法来调整PID和FOPID控制器参数。元启发式优化算法并不总是能保证全局最大值/最小值。因此，使用另一种元启发式优化算法，即基于教学学习的优化（TLBO）来验证结果。结果清楚地表明，最优的PID和FOPID控制器能够处理系统频率并维持所研究的N-R HES的稳定性。
