Neuromorphic deep learning frequency regulation in stand-alone microgrids

Yildirim, Burak; Razmi, Peyman; Fathollahi, Arman; Gheisarnejad, Meysam; Khooban, Mohammad‐Hassan

doi:10.1016/j.asoc.2023.110418

Cited by 21 publications

(6 citation statements)

References 32 publications

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“…The selection of the BL_SGLSO model was based on several key criteria, which also offer conceptual advantages over recently published learning-based algorithms [56][57][58].…”

Section: Modeling Formulationmentioning

confidence: 99%

Balancing the Water-Carbon Trade-Off: Development of a Bi-Level Source-Grid-Load Synergistic Optimization Model for Multi-Regional Electric Power System

Liu,

et al. 2024

Electronics

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This study introduces the Bi-Level Source–Grid–Load Synergistic Optimization (BL_SGLSO) model, which effectively balances the competing objectives of water conservation and carbon emission reduction in the power industry. The model aims to establish a clean and low-carbon electric power system by harmoniously reconciling these two critical goals. Through the application of bi-level programming, the BL_SGLSO model adeptly manages the preferences and conflicts of decision makers at various levels while capturing regional interactions and the intricacies of electricity transmission. Key findings reveal that non-fossil energy conversion technologies are poised to become the dominant force in electricity generation, accounting for an impressive 89.34% share by 2050. To mitigate the spatial mismatch between power load and resource allocation, the development of new transmission pathways and the expansion of the “power transmission from west to east” initiative are paramount. Furthermore, the implementation of a carbon-reducing power system offers significant potential for conserving water resources and alleviating water stress. These insights provide invaluable guidance for decision makers seeking to optimize multi-regional electric power systems for both water efficiency and low-carbon outcomes while simultaneously promoting the adoption of renewable energy sources and fostering synergistic development across regions.

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“…The selection of the BL_SGLSO model was based on several key criteria, which also offer conceptual advantages over recently published learning-based algorithms [56][57][58].…”

Section: Modeling Formulationmentioning

confidence: 99%

Balancing the Water-Carbon Trade-Off: Development of a Bi-Level Source-Grid-Load Synergistic Optimization Model for Multi-Regional Electric Power System

Liu,

et al. 2024

Electronics

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“…In tandem with this global shift towards sustainable energy, the power system is encountering new challenges and opportunities. The traditional paradigm of centralized power generation is giving way to a decentralized model, characterized by the widespread adoption of distributed generations and diverse industrial loads [1,2].…”

Section: Introductionmentioning

confidence: 99%

Analyzing Small-Signal Stability in a Multi-Source Single-Area Power System with a Load-Frequency Controller Coordinated with a Photovoltaic System

Shahgholian,

Fathollahi

2024

AppliedMath

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The frequency deviation from the nominal working frequency in power systems is a consequence of the imbalance between total electrical loads and the aggregate power supplied by production units. The sensitivity of energy system frequency to both minor and major load variations underscore the need for effective frequency load control mechanisms. In this paper, frequency load control in single-area power system with multi-source energy is analysed and simulated. Also, the effect of the photovoltaic system on the frequency deviation changes in the energy system is shown. In the single area energy system, the dynamics of thermal turbine with reheat, thermal turbine without reheat and hydro turbine are considered. The simulation results using Simulink/Matlab and model analysis using eigenvalue analysis show the dynamic behaviour of the power system in response to changes in the load.

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“…The self-adaptive nature of Neural Network-based classical controllers allows them to handle uncertainty in dynamic systems. However, the effectiveness of these controllers is contingent upon a properly designed network; otherwise, their performance may degrade [18]. Researchers employ population-based evolutionary computational intelligence approaches such as PSO [19], Fuzzy inference system [20], Manta Ray Foraging [21], and numerous other methods.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Sparrow Search Optimized Fractional Virtual Inertia Control for Frequency Regulation of Multi-Microgrid System

Fadheel,

Wahab,

Manoharan

et al. 2024

IEEE Access

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This paper introduces a robust approach, integrating a Virtual Inertia Controller (VIC) with a modified demand response controller for an islanded Multi-Microgrid (MMG) system, accommodating high levels of Renewable Energy Sources (RESs). In these MGs, the low inertia in the system has an undesirable impact on the stability of MG frequency. As a result, it leads to a weakening of the MGs overall performance. A novel fractional derivative virtual inertia is integrated into the VIC loop to address this issue. This enhancement aims to fortify the MG's stability and robust performance, particularly when facing contingencies. Furthermore, a modified demand response controller has been incorporated into the proposed inertia control technique to mitigate the frequency fluctuations and reduce stress on the energy storage system (ESS). Fractional Order Proportional Integral Derivative (FOPID) controllers have been employed to regulate the active power output of the biodiesel generators and the Geothermal station in the MG. The hybrid sparrow search and mountain gazelle optimizer algorithm (SSAMGO) optimizes the parameters for the three-loop controller. Time-domain simulations assess the effectiveness of proposed controllers in enhancing system frequency stability. SSAMGO's performance was comprehensively evaluated, comparing it to various optimization algorithms in diverse scenarios. The results obtained from the MMG system demonstrate that utilizing the proposed controller technique, optimized with hybrid SSAMGO parameters, yields notable improvements in settling time by 24.68%, 46.20%, 7.52%, and 61.01%, steady-state error values by 72.56%, 98.18%, 98.73%, and 6.67%, undershoot by 105.76%, 144.23%, 19.23%, and 7.69% compared to other stateof-the-art algorithms presented in the literature. Finally, the proposed control technique's effectiveness and robustness are assessed in comparison to conventional inertia control across various system scenarios. These scenarios encompass random load demand fluctuations, real-time changes in RES, and a wide spectrum of system operations, including situations with reduced damping and inertia and high levels of load variation.INDEX TERMS Virtual inertia control, Load frequency control, Renewable energy resources, Hybrid optimization algorithm, Demand response control, Power system stability, Microgrid system.

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Neuromorphic deep learning frequency regulation in stand-alone microgrids

Cited by 21 publications

References 32 publications

Balancing the Water-Carbon Trade-Off: Development of a Bi-Level Source-Grid-Load Synergistic Optimization Model for Multi-Regional Electric Power System

Balancing the Water-Carbon Trade-Off: Development of a Bi-Level Source-Grid-Load Synergistic Optimization Model for Multi-Regional Electric Power System

Analyzing Small-Signal Stability in a Multi-Source Single-Area Power System with a Load-Frequency Controller Coordinated with a Photovoltaic System

A Hybrid Sparrow Search Optimized Fractional Virtual Inertia Control for Frequency Regulation of Multi-Microgrid System

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