Microgrid Energy Management System Based on Fuzzy Logic and Monitoring Platform for Data Analysis

Sumarmad, Khaizaran Abdulhussein Al; Sulaiman, Nasri; Wahab, Noor Izzri Abdul; Hizam, Hashim

doi:10.3390/en15114125

Cited by 21 publications

(11 citation statements)

References 39 publications

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“…The basic dynamics of the battery's state of charge (SoC) were modeled using equation ( 14) [31][32][33] while the state of charge constraint is presented in Eq. ( 15) to maintain the battery lifespan.…”

Section: Mathematical Modeling Of Battery Energy Storage System(bess)mentioning

confidence: 99%

Intelligent ANFIS-Based Distributed Generators Energy Control and Power Dispatch of Grid- Connected Microgrids Integrated into Distribution Network

Odonkor

2024

IJEETC

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Power supply management is a critical problem in the operation of a distribution network, considering the causes of large power losses and interruptions in the main grid caused by the unknown connection of loads and DGs that affect power delivery to customers downstream of a distribution network. The above-mentioned problems can be reduced by the integration of microgrids close to load centers and developing a controller using adaptive control techniques to enhance reliable power supply. For this reason, this paper presents an intelligent method for distributed generators' energy control and power dispatch of microgrids integrated into a distribution network employing an Adaptive Neuro- Fuzzy Inference System (ANFIS). The aim is to control distributed generators energy sources, loads, and power dispatch of grid-connected microgrids among multiconnected power sources to maintain a stable power supply without using any optimization techniques. The proposed intelligent ANFIS system is trained for power-sharing purposes and applied to the microgrid controllers. The mathematical modeling of distributed generators, system design, simulation, and testing of the proposed method were done using MATLAB/Simulink software. The results show that the proposed controller is capable of power dispatch and controls the energy harvest of distributed generators. Additionally, it can assign microgrid power source(s) to additional load(s) connected to the active distribution network without interruptions of power flow. The obtained results outperform similar works that used hybrid ANFISPID (Adaptive Neural Fuzzy Inference System-Proportional- Integral-Derivative), PSO-ANFIS (Particle Swarm Optimization-Adaptive Neural Fuzzy Inference System), and GA-ANFIS (Genetic Algorithm-Adaptive Neuro-Fuzzy Inference System) by automatically connecting and controlling distributed generators energy sources with effective power dispatch in mitigating downtime of grid power operations.

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Section: Mathematical Modeling Of Battery Energy Storage System(bess)mentioning

confidence: 99%

Intelligent ANFIS-Based Distributed Generators Energy Control and Power Dispatch of Grid- Connected Microgrids Integrated into Distribution Network

Odonkor

2024

IJEETC

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“…∆P > 0 P load > P pv SOC Batt < SOC Batt min P Batt = ∆P = P load − P pv (14) M5: The generated photovoltaic electricity will be used to supply the load. Excess power is used to charge the SCs.…”

Section: M4mentioning

confidence: 99%

“…Additionally, continuous monitoring of the SOC ensures that the storage systems operate within their optimal range, by ensuring optimized utilization and maximum system efficiency [10][11][12]. Power management of photovoltaic (PV) systems with multi-storage technologies has been the subject of several research papers [13][14][15]. The power management of PV systems with multi-storage technologies involves controlling the power flow between different storages while ensuring that the load is supplied [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Optimized Power Management Approach for Photovoltaic Systems with Hybrid Battery-Supercapacitor Storage

Rekioua,

Kakouche,

Babqi

et al. 2023

Sustainability

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The paper addresses the ongoing and continuous interest in photovoltaic energy systems (PESs). In this context, the study focuses on an isolated photovoltaic system with hybrid battery-supercapacitor storage (HBSS). The integration of supercapacitors (SCs) in this system is particularly important because of their high specific power density. In photovoltaic (PV) systems, multi-storage systems use two or more energy storage technologies to enhance system performance and flexibility. When batteries and supercapacitors are combined in a PV system, their benefits are maximized and offer a more reliable, efficient, cost-effective energy storage option. In addition, effective multi-storage power management in a PV system needs a solid grasp of the energy storage technologies, load power demand profiles, and the whole system architecture. This work establishes a battery-supercapacitor storage system (HBSS) by combining batteries and supercapacitors. The primary objective is to devise a novel management algorithm that effectively controls the different power sources. The algorithm is designed to manage the charge and discharge cycles of the hybrid battery-supercapacitor energy storage system (HBSS), thereby guaranteeing that the state of charge (SOC) for both batteries and supercapacitors is maintained within the specified range. The proposed management algorithm is designed to be simple, efficient, and light on computational resources. It efficiently handles the energy flow within the HBSS, optimizing the usage of both batteries and supercapacitors based on real-time conditions and energy demands. The proposed method ensures their longevity and maximizes their performance by maintaining the SOC of these energy storage components within the specified limits. Simulation results obtained from applying the management strategy are found to be satisfactory. These results show that the proposed algorithm maintains the SOC of batteries and supercapacitors within the desired range, leading to improved energy management and enhanced system efficiency.

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“…In the PID controller, setting Kp, Kd, and kI coefcients is very important. In this project, these coefcients are adjusted by the fuzzy and classical Ziegler-Nichols methods, considering the AC system [3,4].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Intelligent Computing Approach for Energy/Voltage Control of Microgrids

Bouteraa

Kausar

et al. 2023

Journal of Mathematics

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The control and energy management problems of microgrids (MGs) are challenging due to the high level of uncertainties and disturbances such as changes in demands, mechanical powers, and solar energies. So, intelligent computing is needed to be developed for these systems. This paper uses an optimal and robust fuzzy controller for automatic voltage and frequency regulation. The fuzzy logic develops the resistance against uncertainties and disturbances such as irradiation, wind power changes, and load demand variation. The introduced controller uses appropriate and effective criteria that include rising time, settling time, overshoot, and the degree of resistance of the control system to uncertainties and perturbation effects. Through simulations and compassion with conventional regulators, the better accuracy of the suggested approach is demonstrated.

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Microgrid Energy Management System Based on Fuzzy Logic and Monitoring Platform for Data Analysis

Cited by 21 publications

References 39 publications

Intelligent ANFIS-Based Distributed Generators Energy Control and Power Dispatch of Grid- Connected Microgrids Integrated into Distribution Network

Intelligent ANFIS-Based Distributed Generators Energy Control and Power Dispatch of Grid- Connected Microgrids Integrated into Distribution Network

Optimized Power Management Approach for Photovoltaic Systems with Hybrid Battery-Supercapacitor Storage

A Fuzzy Intelligent Computing Approach for Energy/Voltage Control of Microgrids

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