Smart Energy Hub Frequency Control-Based Machine Learning

doi:10.1109/TETCI.2025.3551991

2025-04-01

PRE

Burak Yıldırım *

Meysam Gheisarnejad

Mohammad Hassan Khooban

DOI：10.1109/TETCI.2025.3551991

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摘要

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The increasing variety of energy conversion units and storage equipment connected to the multi-energy system, along with the uncertain factors posed by distributed wind and photovoltaic power generation, have made the energy flow structure of the system more complex. This complexity has created significant challenges for the frequency regulation of traditional energy hub systems. One of the characteristics of a microgrid (MG) is the use of combined heat and power (CHP) systems to generate both electrical and thermal energy at the same time. This can boost the system's dependability, efficiency, and economic performance. As a CHP's ramping capability makes it a useful tool for monitoring and controlling the MG's frequency, it will be employed in this research to achieve this goal. The complexity of the system's dynamics and set tasks throughout the course of the performance period necessitates advanced control structures for the MG with CHP systems. To address the challenges of controlling in MG with CHP systems, this research introduces a novel control structure based on deep reinforcement learning and single input interval type-2 fuzzy fractional-order proportional integral (SIT2-FFOPI) for this system. The SIT2-FFOPI serves as the main controller, with its fundamental parameters established through the utilization of the Improved Salp Swarm Algorithm (ISSA) optimization technique. An adaptive deep deterministic policy gradient (DDPG)-based actor-critic system has been developed to enhance the main controller's learning potential, thereby enabling it to more effectively address control challenges in the isolated MG. The efficacy of the suggested approach in real-time was evaluated through simulations carried out utilizing an OPAL-RT-based Hardware-in-the-Loop (HiL) configuration. As a result of this study, it was determined that the proposed controller for load disturbance, renewable energy sources (RES) power changes, and contingency circumstances in MG outperforms other controllers in terms of performance.

Keyword:

Cogeneration

Frequency control

Biological system modeling

Load modeling

Wind turbines

Computational modeling

Uncertainty

Generators

Velocity control

Power system stability

CHP systems