Optimal Load-Frequency Regulation of Demand Response Supported Isolated Hybrid Microgrid Using Fuzzy PD+I Controller

Barik, Amar Kumar; Tripathy, Debasis; Das, Dulal Chandra; Sahoo, Subash Chandra

doi:10.1007/978-3-030-42363-6_93

Cited by 13 publications

(5 citation statements)

References 11 publications

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“…In the case of collective behavior algorithms, [116] used the PSO to tune into a fuzzy PD+I method for reducing frequency fluctuations to the connection of renewable energy units. The PSO tuned the PD+I gains and fuzzy control parameters.…”

Section: Tuning Algorithmsmentioning

confidence: 99%

Review on Multi-Objective Control Strategies for Distributed Generation on Inverter-Based Microgrids

et al. 2020

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Microgrids have emerged as a solution to address new challenges in power systems with the integration of distributed energy resources (DER). Inverter-based microgrids (IBMG) need to implement proper control systems to avoid stability and reliability issues. Thus, several researchers have introduced multi-objective control strategies for distributed generation on IBMG. This paper presents a review of the different approaches that have been proposed by several authors of multi-objective control. This work describes the main features of the inverter as a key component of microgrids. Details related to accomplishing efficient generation from a control systems’ view have been observed. This study addresses the potential of multi-objective control to overcome conflicting objectives with balanced results. Finally, this paper shows future trends in control objectives and discussion of the different multi-objective approaches.

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Section: Tuning Algorithmsmentioning

confidence: 99%

Review on Multi-Objective Control Strategies for Distributed Generation on Inverter-Based Microgrids

et al. 2020

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“…MGs are generally classified into three types: direct current (DC) MGs, AC MGs, and hybrid [2]. Among these, DC MGs have gained considerable interest due to their distinctive features.…”

Section: Introductionmentioning

confidence: 99%

Enhancing DC microgrid performance through machine learning‐optimized droop control

Saeidinia,

Arabshahi,

Aminirad

et al. 2024

IET Generation Trans & Dist

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A machine learning‐based optimized droop method is suggested here to simultaneously reduce the production cost (PC) and power line losses (PLL) for a class of direct current (DC) microgrids (MGs). Traditionally, a communication‐less technique known as the hybrid droop method has been employed to decrease PC and PLL in DC MGs. However, achieving the desired reduction in either PC or PLL requires arbitrary adjustments of weighting coefficients for each distributed generator in the conventional hybrid droop method. To address this challenge, this paper introduces a systematic approach that capitalizes on the benefits of artificial intelligence to accurately predict both the PC and PLL in a DC MG. Furthermore, an optimization technique relying on the gradient descendent method is employed to independently optimize both PC and PLL for each scenario. The effectiveness of the proposed method is confirmed through a comparative study with classical and hybrid droop coordination schemes under various scenarios such as rapid load changes.

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“…The RES such as WTG, SPV, and linear Fresnel reflector (LFR) type STP units are integrated with waste-to-energy-based BEGS such as BDEG, BGTG, MHTG, and BCHP units in this microgrid [1]. Some works applied battery energy storage (BES) units for ALFC of isolated [20] and interconnected [21,22] microgrids.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the recent works on ALFC of microgrids claimed the efficacy of classical proportional-integral-derivative (PID) controllers over conventional integral (I), proportionalintegral (PI), proportional-derivative (PD), and integral-derivative (ID) controllers [17][18][19][20][21]. Some modern controllers such as model predictive controller [19], fuzzy controller [22], fractional order controllers, and their blends [23,24] were also applied in similar works, however, they were corroborated with PID controllers. The effective tuning of these controllers in ALFC of these complex microgrids is achieved by applying some basic optimization algorithms such as: particle swarm optimization (PSO) [18], grasshopper optimization algorithm (GOA) [20,21], salp swarm algorithm (SSA) [25], selfish-herd optimization (SHO) [26], or their hybrids such as quasi-oppositional selfish-herd optimization (QSHO) [1,17].…”

Section: Introductionmentioning

confidence: 99%

Optimal Voltage–Frequency Regulation in Distributed Sustainable Energy-Based Hybrid Microgrids with Integrated Resource Planning

et al. 2021

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This work is the earliest attempt to propose an integrated resource planning for distributed hybrid microgrids considering virtual-inertia support (VIS) and demand-response support (DRS) systems. Initially, three-distributed sustainable energy-based unequal hybrid microgrids are envisioned with the availability of solar/wind/bioenergy resources. In order to overcome the effects of intermittency in renewable resources and low inertia, each microgrid is incorporated with DRS and VIS units for demand- and supply-side management, respectively. The proposed system is simulated in MATLAB considering real-time recorded solar/wind data with realistic loading for 12 months. A novel quasi-oppositional chaotic selfish-herd optimization (QCSHO) algorithm is proposed by hybridizing quasi-opposition-based learning and chaotic linear search techniques into the selfish-herd optimization, for optimal regulation of voltage and frequency in microgrids. Then, the system responses are compared with 7 algorithms and 5 error functions to tune PID controllers’ gains, which confirmed the superiority of QCSHO over others. Then, the study proceeds to investigate the voltage, frequency, and tie-line power coordination in 5 extreme scenarios of source and load variations in the proposed system without retuning the controllers. Finally, the system responses are analyzed for 10 different possible allocation of VIS and DRS units in different microgrids to find the most suitable combinations, and the results are recorded.

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Optimal Load-Frequency Regulation of Demand Response Supported Isolated Hybrid Microgrid Using Fuzzy PD+I Controller

Cited by 13 publications

References 11 publications

Review on Multi-Objective Control Strategies for Distributed Generation on Inverter-Based Microgrids

Review on Multi-Objective Control Strategies for Distributed Generation on Inverter-Based Microgrids

Enhancing DC microgrid performance through machine learning‐optimized droop control

Optimal Voltage–Frequency Regulation in Distributed Sustainable Energy-Based Hybrid Microgrids with Integrated Resource Planning

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