Estimation of Climatic Parameters of a PV System Based on Gradient Method

Gammoudi, Rabiaa; Brahmi, Houda; Dhifaoui, Rachid

doi:10.1155/2019/7385927

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…In this problem, with the consideration of the electric market, we have considered different prices for power produced and reserved from thermal generating units. Nowadays, electricity from solar or wind systems accounts for a high rate from all power sources [38,39]. As such, a consideration of renewable energies can be a good research field in optimizing load dispatch in the electric market.…”

Section: Discussionmentioning

confidence: 99%

Optimal Load Dispatch in Competitive Electricity Market by Using Different Models of Hopfield Lagrange Network

et al. 2019

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In this paper, a Hopfield Lagrange network (HLN) method is applied to solve the optimal load dispatch (OLD) problem under the concern of the competitive electric market. The duty of the HLN is to determine optimal active power output of thermal generating units in the aim of maximizing the benefit of electricity generation from all available units. In addition, the performance of the HLN is also tested by using five different functions consisting of the logistic, hyperbolic tangent, Gompertz, error, and Gudermanian functions for updating outputs of continuous neurons. The five functions are tested on two systems with three units and 10 units considering two revenue models in which the first model considers payment for power delivered and the second model concerns payment for reserve allocated. In order to evaluate the real effectiveness and robustness of the HLN, comparisons with other methods such as particle swarm optimization (PSO), the cuckoo search algorithm (CSA) and differential evolution (DE) are also implemented on the same systems. High benefits and fast execution time from the HLN lead to a conclusion that the HLN should be applied for solving the OLD problem in a competitive electric market. Among the five applied functions, error function is considered to be the most effective one because it can support the HLN to find the highest benefit and reach the fastest convergence with the smallest number of iterations. Thus, it is suggested that error function should be used for updating outputs for continuous neurons of the HLN.

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Section: Discussionmentioning

confidence: 99%

Optimal Load Dispatch in Competitive Electricity Market by Using Different Models of Hopfield Lagrange Network

et al. 2019

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“…Figure 2 presents the refined model of a PV cell [29][30][31][32]. e equivalent electrical circuit is formed by a current source depending on the solar radiation, a diode characterized by a saturation current I 0 and an idealistic factor q, and two resistors modelling the joule losses in the cell R s and R sh , respectively.…”

Section: Pv Array Modellingmentioning

confidence: 99%

A Study of a DC/AC Conversion Structure for Photovoltaic System Connected to the Grid with Active and Reactive Power Control

Brahmi

Dhifaoui

2021

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DC/AC conversion of photovoltaic energy is in great demand for AC applications; the supply of electrical machines and transfer energy to the distribution network is a typical case. This work is realized in this context and presents a new structure for the transfer of photovoltaic energy to the electricity grid. This structure is based on a push-pull converter connected to a three-phase DC/AC inverter. In particular, a great interest is focused on the steady operating conditions of energy transfer. The study also develops the limits and the feasibility of the PV energy transfer to the grid of the proposed structure. Injecting photovoltaic energy into the grid with maximum active power and zero reactive power is also considered for the dynamic regime. Support simulations are carried out to validate the proposed control strategy.

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“…where I ph , I sh , and I d denote the photogenerated current, shunt resistor current, and diode current, respectively. I d and I sh are formulated as follows [5,[45][46][47]:…”

Section: Single Diode Model (Sdm)mentioning

confidence: 99%

Application of Supply‐Demand‐Based Optimization for Parameter Extraction of Solar Photovoltaic Models

et al. 2019

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Modeling solar photovoltaic (PV) systems accurately is based on optimal values of unknown model parameters of PV cells and modules. In recent years, the use of metaheuristics for parameter extraction of PV models gains more and more attentions thanks to their efficacy in solving highly nonlinear multimodal optimization problems. This work addresses a novel application of supply-demand-based optimization (SDO) to extract accurate and reliable parameters for PV models. SDO is a very young and efficient metaheuristic inspired by the supply and demand mechanism in economics. Its exploration and exploitation are balanced well by incorporating different dynamic modes of the cobweb model organically. To validate the feasibility and effectiveness of SDO, four PV models with diverse characteristics including RTC France silicon solar cell, PVM 752 GaAs thin film cell, STM6-40/36 monocrystalline module, and STP6-120/36 polycrystalline module are employed. The experimental results comparing with ten state-of-the-art algorithms demonstrate that SDO performs better or highly competitively in terms of accuracy, robustness, and convergence. In addition, the sensitivity of SDO to variation of population size is empirically investigated. The results indicate that SDO with a relatively small population size can extract accurate and reliable parameters for PV models.

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Estimation of Climatic Parameters of a PV System Based on Gradient Method

Cited by 4 publications

References 12 publications

Optimal Load Dispatch in Competitive Electricity Market by Using Different Models of Hopfield Lagrange Network

Optimal Load Dispatch in Competitive Electricity Market by Using Different Models of Hopfield Lagrange Network

A Study of a DC/AC Conversion Structure for Photovoltaic System Connected to the Grid with Active and Reactive Power Control

Application of Supply‐Demand‐Based Optimization for Parameter Extraction of Solar Photovoltaic Models

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