Optimizing renewable energy utilization with high gain converters

Tamilselvan, D.; T D, Sudhakar

doi:10.1016/j.rser.2023.114105

Cited by 6 publications

(1 citation statement)

References 42 publications

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“…In Reference 14 , a deep reinforcement learning method is applied to address dynamic and stochastic optimization problems in long-range planning, considering various uncertainties and constraints. Furthermore, Reference 15 suggests a novel control technique for optimizing hybrid renewable energy sources (HRES) through the utilization of a high-gain DC-to-DC converter topology. The current literature on optimizing SAF systems needs to be revised, especially when integrated with PV and wind energy.…”

Section: Introductionmentioning

confidence: 99%

Data-driven assisted real-time optimal control strategy of submerged arc furnace via intelligent energy terminals considering large-scale renewable energy utilization

Zheng,

Pan,

Liu

et al. 2024

Sci Rep

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This study presents a data-driven assisted real-time optimization model which is an innovative approach to address the challenges posed by integrating Submerged Arc Furnace (SAF) systems with renewable energy sources, specifically photovoltaic (PV) and wind power, with modern intelligent energy terminals. Specifically, the proposed method is divided into two stages. The first stage is related to data-driven prediction for addressing local time-varying renewable energy and electricity market prices with predicted information, and the second stage uses an optimization model for real-time SAF dispatch. Connections between intelligent energy terminals, demand-side devices, and load management systems are established to enhance local renewable resource utilization. Additionally, mathematical formulations of the operating resistance in SAF are explored, and deep neuron networks are employed and modified for dynamic uncertainty prediction. The proposed approach is validated through a case study involving an intelligent energy terminal with a 12.5 MVA SAF system and 12 MW capacity renewable generators in an electricity market with fluctuating prices. The findings of this research underscore the efficacy of the proposed optimization model in reducing operational costs and enhancing the utilization of localized renewable energy generation. By integrating four distinct dissatisfaction coefficients into the optimization framework, we demonstrate the model's adaptability and efficiency. The application of the optimization strategy delineated herein results in the SAF system's profitability oscillating between $111 and $416 across various time intervals, contingent upon the coefficient settings. Remarkably, an aggregate daily loss recovery amounting to $1,906.84 can be realized during the optimization period. Such outcomes not only signify considerable economic advantages but also contribute to grid stability and the diminution of renewable energy curtailment, thereby underscoring the dual benefits of economic efficiency and sustainability in energy management practices.

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Section: Introductionmentioning

confidence: 99%

Data-driven assisted real-time optimal control strategy of submerged arc furnace via intelligent energy terminals considering large-scale renewable energy utilization

Zheng,

Pan,

Liu

et al. 2024

Sci Rep

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Mathematical Analysis and Topology Derivation for a Family of Novel Dual Output High‐Voltage Gain Z‐Source Converter for Renewable Energy Application

Gupta,

Joshi

2024

Circuit Theory & Apps

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This paper presents a family of multiport Z‐source converters with minimal number of switches and passive components. The paper focuses on the development of a novel converter and its methodology for selecting the topology of common ground/noncommon ground Z‐source DC–DC single‐input dual‐output configurations, aimed at higher power density, enhancing operational efficiency, size, cost, and centralized power management across multiple ports. In comparison to conventional Z‐source single input single output converters, this design achieves high‐voltage conversion rates while concurrently delivering two distinct outputs with and without common ground, rendering it particularly advantageous for electric vehicle and isolated microgrid applications with renewable energy integration. The proposed converters undergo comprehensive analysis and comparison across various potential configurations. Specifically, configurations yielding equivalent output voltages are prioritized, facilitating their utility in multilevel inverter applications. The assessment encompasses the determination of maximum efficiency; voltage stresses on capacitors, diodes, and switches; inductor currents; and ripples under diverse loads and duty cycles for all configurations. Experimental results are provided to showcase the validity and capabilities of these converters. As research efforts on converter topologies continue to grow, it is expected that this paper will enhance comprehension of multiport Z‐source converter topologies ideas for numerous applications.

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A hybrid approach with MPPT controller for weed cutting based on solar powered lawnmower with minimal intervention of human involvement adopting IoT technology

Suganya,

Mangaiyarkarasi,

Thirugnanam

et al. 2024

Analog Integr Circ Sig Process

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Optimizing renewable energy utilization with high gain converters

Cited by 6 publications

References 42 publications

Data-driven assisted real-time optimal control strategy of submerged arc furnace via intelligent energy terminals considering large-scale renewable energy utilization

Data-driven assisted real-time optimal control strategy of submerged arc furnace via intelligent energy terminals considering large-scale renewable energy utilization

Mathematical Analysis and Topology Derivation for a Family of Novel Dual Output High‐Voltage Gain Z‐Source Converter for Renewable Energy Application

A hybrid approach with MPPT controller for weed cutting based on solar powered lawnmower with minimal intervention of human involvement adopting IoT technology

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