Work

Optimal predictive load frequency control with multi-objective PID-based search algorithm

Year: 2025

Type: article

Abstract: Maintaining frequency stability in modern interconnected power systems (IPSs) has become increasingly challenging due to growing system complexity, the integration of variable energy sources, and frequent load fluctuations, particularly in manufacturing and industrial environments where power quality is critical. To address this challenge, we propose an optimal predictive load frequency control (LFC) framework that combines real-time forecasting with multi-objective controller optimization. An online extreme learning machine (ELM) predicts short-term load deviations, enabling proactive regulation. The control structure adopts a cascaded fractional-order design, integrating FOPI and FOPID controllers. These controllers are optimized using a multi-objective PID-based search algorithm (MOPSA), which simultaneously minimizes the integral of time-weighted absolute error (ITAE), enhances the damping ratio, and reduces the cost of energy storage operation. Fast-response energy storage devices (ESDs) are further coordinated to buffer transient imbalances. Simulation results demonstrate that the proposed FOPI–FOPID controllers with ESDs reduce ITAE by 90% (from 464.99 to 44.65) and shorten frequency settling time by 81% (from 83.03 s to 15.49 s), significantly outperforming benchmark methods such as DSA:FOPI–FOPD. These findings confirm the proposed framework’s ability to deliver precise control in dynamic multi-region IPS environments.

Source: Swarm and Evolutionary Computation

DOI: 10.1016/j.swevo.2025.102214

Authors: Yang Yang, Yuchao Gao, Jinran Wu, Shangce Gao

Institutions: Nanjing University of Posts and Telecommunications, The University of Queensland, University of Toyama

Language: English

Topic: Frequency Control in Power Systems

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $3,690