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<p>This paper presents a model predictive control (MPC) approach that utilizes particle swarm optimization (PSO) in conjunction with demand response (DR) and battery energy storage systems (BESS) to address load frequency control issues in microgrids (MGs) within the context of automatic load frequency control (ALFC). The generalized state space analysis is used to model the MPC, incorporating both controllable and uncontrollable generation units. The proposed MPC operates in the microgrid (MG) for frequency regulation as a single-input multi-output system. The MPC is regarded as a parameter-driven controller, with its input parameters optimized using the widely used robust PSO technique to achieve improved frequency control. Moreover, the proposed MPC is integrated with DR, which includes a proportional-integral (PI) controller and a lead compensator for delay compensation, addressing the persistent delay issues in DR. Furthermore, the proposed frequency control scheme evaluates the performance of the MG in conjunction with a state of charge strategy-based BESS in ALFC. So, the input parameter <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(R_{w}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>R</mi> <mi>w</mi> </msub> </math></EquationSource> </InlineEquation> of MPC and PI controller gain parameters of DR are tuned by minimizing the performance index like integral time square error for MPC online application to get smooth frequency regulation for the isolated MG. To evaluate the effectiveness of the proposed scheme of frequency regulation, an isolated MG equipped with a conventional diesel unit, fuel cell, wind energy, solar energy, and battery cell is taken as a test system. The effectiveness of the proposed frequency control scheme is evaluated and compared with a robust fuzzy adaptive MPC and advanced algorithms, including modified genetic algorithm -based MPC. Finally, the results demonstrate the efficacy of the proposed frequency control scheme through various case studies. To practically validate the proposed frequency control scheme, the studied isolated MG is implemented in a real-time platform with the OPAL-RT OP5600 and RT Lab version 19.3.0.228.</p>

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Experimental Assessment of Parameter-Driven MPC for Frequency Regulation in Collaboration with Delay Compensated Demand Response of an Isolated Microgrid

  • 期刊论文

  • 发表日期：

    2025年7月24日

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  • Swetalina Bhuyan,
  • Sunita Halder nee Dey,
  • Subrata Paul

摘要

This paper presents a model predictive control (MPC) approach that utilizes particle swarm optimization (PSO) in conjunction with demand response (DR) and battery energy storage systems (BESS) to address load frequency control issues in microgrids (MGs) within the context of automatic load frequency control (ALFC). The generalized state space analysis is used to model the MPC, incorporating both controllable and uncontrollable generation units. The proposed MPC operates in the microgrid (MG) for frequency regulation as a single-input multi-output system. The MPC is regarded as a parameter-driven controller, with its input parameters optimized using the widely used robust PSO technique to achieve improved frequency control. Moreover, the proposed MPC is integrated with DR, which includes a proportional-integral (PI) controller and a lead compensator for delay compensation, addressing the persistent delay issues in DR. Furthermore, the proposed frequency control scheme evaluates the performance of the MG in conjunction with a state of charge strategy-based BESS in ALFC. So, the input parameter \(R_{w}\) R w of MPC and PI controller gain parameters of DR are tuned by minimizing the performance index like integral time square error for MPC online application to get smooth frequency regulation for the isolated MG. To evaluate the effectiveness of the proposed scheme of frequency regulation, an isolated MG equipped with a conventional diesel unit, fuel cell, wind energy, solar energy, and battery cell is taken as a test system. The effectiveness of the proposed frequency control scheme is evaluated and compared with a robust fuzzy adaptive MPC and advanced algorithms, including modified genetic algorithm -based MPC. Finally, the results demonstrate the efficacy of the proposed frequency control scheme through various case studies. To practically validate the proposed frequency control scheme, the studied isolated MG is implemented in a real-time platform with the OPAL-RT OP5600 and RT Lab version 19.3.0.228.