The application of hydrogen and photovoltaic for reactive power optimization

Lin, Rongheng; Ye, Zezhou; Wu, Bo

doi:10.1016/j.ijhydene.2019.08.078

Cited by 17 publications

(9 citation statements)

References 38 publications

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“…Normally, the goal of reactive power optimization is to reduce power loss and improve the power quality of distribution networks [11]. Without loss of generality, the changes of power loss and voltage offset are defined as a comprehensive objective function of reactive power optimization in this paper:…”

Section: Reactive Power Optimization Modelmentioning

confidence: 99%

Ensemble Learning-Based Reactive Power Optimization for Distribution Networks

Zhu

Tang

Wei³

2022

Energies

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Reactive power optimization of distribution networks is of great significance to improve power quality and reduce power loss. However, traditional methods for reactive power optimization of distribution networks either consume a lot of calculation time or have limited accuracy. In this paper, a novel data-driven-based approach is proposed to simultaneously improve the accuracy and reduce calculation time for reactive power optimization using ensemble learning. Specifically, k-fold cross-validation is used to train multiple sub-models, which are merged to obtain high-quality optimization results through the proposed ensemble framework. The simulation results show that the proposed approach outperforms popular baselines, such as light gradient boosting machine, convolutional neural network, case-based reasoning, and multi-layer perceptron. Moreover, the calculation time is much lower than the traditional heuristic methods, such as the genetic algorithm.

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Section: Reactive Power Optimization Modelmentioning

confidence: 99%

Ensemble Learning-Based Reactive Power Optimization for Distribution Networks

Zhu

Tang

Wei³

2022

Energies

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“…Installations designed for hydrogen production based on the electrolysis of water are also often used in energy storage systems that cooperate with solar installations [28,29]. The article [30] presents a system for the storage of the energy generated from PV (photovoltaic) panels.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Operational Performance for an Integrated ‘Power to Synthetic Natural Gas’ System

et al. 2021

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This article presents a power to SNG (synthetic natural gas) system that converts hydrogen into SNG via a methanation process. In our analysis, detailed models for all the elements of the system are built. We assume a direct connection between a wind farm and a hydrogen generator. For the purposes of our calculations, we also assume that the hydrogen generator is powered by the renewable source over a nine-hour period per day (between 21:00 and 06:00), and this corresponds to the off-peak period in energy demand. In addition, a hydrogen tank was introduced to maximize the operating time of the methanation reactor. The cooperation between the main components of the system were simulated using Matlab software. The primary aim of this paper is to assess the influence of various parameters on the operation of the proposed system, and to optimize its yearly operation via a consideration of the most important constraints. The analyses also examine different nominal power values of renewables from 8 to 12 MW and hydrogen generators from 3 to 6 MW. Implementing the proposed configuration, taking into account the direct connection of the hydrogen generator and the methanation reactor, showed that it had a positive effect on the dynamics and the operating times of the individual subsystems within the tested configuration.

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“…DG is directly linked with distribution network [6]; it is a small scale generation [9]. DG is based on the renewable energy sources (RES) which consist of PV, wind turbines, combustion gas turbines, biomass gasification, other small generation units etc.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Based Power Control Strategies of a Grid Integrated Hybrid Distributed Generation System

Kumar

Subrahmanyam

Sydulu

2021

Technol Econ Smart Grids Sustain Energy

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In this manuscript, a genetic algorithm (GA) is proposed for the power management (PM) of a grid integrated hybrid distributed generation (DG) system. The hybrid distribution generation (DG) system incorporates photovoltaic (PV), wind turbine (WT), fuel cell (FC) and battery. The power fluctuations are produced in the distributed generation system, because the hybrid resource utilization and the generation of power is changeable. The major purpose of the proposed control method is "to control the power flow (PF) of active with reactive energy amid the source and grid side. The proposed GA-based power control system is mainly utilized for optimizing active with reactive power flow controllers. By controlling charge with discharge of battery, the proposed system met the energy requirement of the charge and managed the sensitivity of the charge. The proposed method provides an optimal power flow in DG systems. To evaluate the management of PF, the equality with inequality constraints is determined that is used to specify the accessibility of renewable energy sources (RES), the demand for electricity and storage components load level. The security of power system is improved with the help of proposed control system. Moreover, the battery is used to allow the renewable energy system and maintain a stable power output. The proposed method is activated in MATLAB / Simulink work site and the performance is compared with existing methods. The statistical analysis of mean, median and standard deviation (SD) are also analyzed for proposed with existing methods. The proposed technique mean value is 1.5784, median is 1.4892, SD is 0.5883.

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The application of hydrogen and photovoltaic for reactive power optimization

Cited by 17 publications

References 38 publications

Ensemble Learning-Based Reactive Power Optimization for Distribution Networks

Ensemble Learning-Based Reactive Power Optimization for Distribution Networks

Assessment of Operational Performance for an Integrated ‘Power to Synthetic Natural Gas’ System

Genetic Algorithm Based Power Control Strategies of a Grid Integrated Hybrid Distributed Generation System

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