PV panel model based on datasheet values

Séra, Dezső; Teodorescu, Remus; Rodríguez, Pedro

doi:10.1109/isie.2007.4374981

Cited by 656 publications

(374 citation statements)

References 9 publications

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“…The worldwide photovoltaic (PV) power capacity installation grows in a nearly exponential way, despite their relatively high cost [41]. In this work, we model the solar power generated by solar plants with respect to the varying weather conditions, such as irradiance and temperature, based on a single diode equation [41,36].…”

Section: Solar Power Modelmentioning

confidence: 99%

“…We first model the intermittent generation of renewable energy, i.e., wind power and solar power, with respect to the varying weather conditions in the geographical location of each data center. For example, the available wind power generated from wind turbines is modeled based on the ambient wind speed [35,9], while the available solar power from solar plants is estimated by modeling the maximum power point on irradiance (i.e., solar energy per unit area of the solar panel's face) and temperature [31,41]. Based on the models, we formulate the core objective of GreenWare as a constrained optimization problem, in which the constraints capture the Quality of Service (QoS, e.g., response time) requirements from customers, the intermittent availabilities of renewable energy in different locations, the peak power limit of each data center, and the monthly cost budget of the Internet service operator.…”

Section: Introductionmentioning

confidence: 99%

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GreenWare: Greening Cloud-Scale Data Centers to Maximize the Use of Renewable Energy

Zhang

Wang

2011

Lecture Notes in Computer Science

137

111

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Abstract. To reduce the negative environmental implications (e.g., CO2 emission and global warming) caused by the rapidly increasing energy consumption, many Internet service operators have started taking various initiatives to operate their cloud-scale data centers with renewable energy. Unfortunately, due to the intermittent nature of renewable energy sources such as wind turbines and solar panels, currently renewable energy is often more expensive than brown energy that is produced with conventional fossil-based fuel. As a result, utilizing renewable energy may impose a considerable pressure on the sometimes stringent operation budgets of Internet service operators. Therefore, two key questions faced by many cloud-service operators are 1) how to dynamically distribute service requests among data centers in different geographical locations, based on the local weather conditions, to maximize the use of renewable energy, and 2) how to do that within their allowed operation budgets.In this paper, we propose GreenWare, a novel middleware system that conducts dynamic request dispatching to maximize the percentage of renewable energy used to power a network of distributed data centers, subject to the desired cost budget of the Internet service operator. Our solution first explicitly models the intermittent generation of renewable energy, e.g., wind power and solar power, with respect to varying weather conditions in the geographical location of each data center. We then formulate the core objective of GreenWare as a constrained optimization problem and propose an efficient request dispatching algorithm based on linear-fractional programming (LFP). We evaluate GreenWare with realworld weather, electricity price, and workload traces. Our experimental results show that GreenWare can significantly increase the use of renewable energy in cloud-scale data centers without violating the desired cost budget, despite the intermittent supplies of renewable energy in different locations and time-varying electricity prices and workloads.

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Section: Solar Power Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GreenWare: Greening Cloud-Scale Data Centers to Maximize the Use of Renewable Energy

Zhang

Wang

2011

Lecture Notes in Computer Science

137

111

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“…In the first group, the authors in [17,18,19,20,21] presented analytical methods to calculate the parameters. The identification method proposed in [17] attempts to approximate and solve analytically the equation of the PV as a polynomial of the first order.…”

Section: Introductionmentioning

confidence: 99%

“…Even though a simple parameter estimation method is demonstrated, the method does not provide enough accuracy. The works of [18,19,20,21], however, introduced a system of five equations to resolve the five model parameters. Three of these equations are extracted from three remarkable points [13], i.e., the short-and the open-circuit points coupled with the MPP.…”

Section: Introductionmentioning

confidence: 99%

“…While the forth equation is constructed based on the fact that the derivative of the power at the MPP is zero, the fifth equation is more challenging. The authors in [18] employed an approximation for the temperature coefficient as the fifth equation, however, the others adopt the equation introduced in [19] as a simplified approximation of the shunt resistor. Although these analytical methods are straightforward and only need the nominal values from datasheets, they still need sophisticated numerical or even optimization methods to solve the resulting nonlinear and transcendental system of equations.…”

Section: Introductionmentioning

confidence: 99%

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An accurate method for the PV model identification based on a genetic algorithm and the interior-point method

2014

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Citation: Dizqah, Arash, Maheri, Alireza and Busawon, Krishna (2014) Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) An Accurate Method for the PV Model Identification Based on a GeneticAlgorithm and the Interior-Point Method AbstractDue to the PV module simulation requirements as well as recent applications of model-based controllers, the accurate photovoltaic (PV) model identification method is becoming essential to reduce the PV power losses effectively. The classical PV model identification methods use the manufacturers provided maximum power point (MPP) at the standard test condition (STC). However, the nominal operating cell temperature (NOCT) is the more practical condition and it is shown that the extracted model is not well suited to it. The proposed method in this paper estimates an accurate equivalent electrical circuit for the PV modules using both the STC and NOCT information provided by the manufacturers. A multi-objective global optimization problem is formulated using only the main equation of the PV module at these two conditions that restrains the errors due to employing the experimental temperature coefficients. A novel combination of a genetic algorithm (GA) and the interior-point method (IPM) allows the proposed method to be fast and accurate regardless the PV technology. It is shown that the overall error, which is defined by the sum of the MPP errors of both the STC and the NOCT conditions, is improved by a factor between 5.1% to 31% depending on the PV technology.

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Solar Energy Harvesting: Power Electronics Interfaces and Control

Khaligh

Düşmez

2013

Wiley Encyclopedia of Electrical and Electronics Engineering

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Among renewable energy sources such as biomass, tidal, minihydro, microturbines, and wind, solar energy is one of the most promising sustainable energy resources. Solar energy is converted to electrical energy in photovoltaic (PV) cells through a process called photovoltaic effect. The output voltage polarity of the PV cell is direct current (DC) whose magnitude depends on the connection type of PV cells/modules, while the output current or power mainly varies with solar irradiance. One of the primary tasks in PV systems is to convert the output DC voltage of PV modules to alternate current (AC) to energize general‐purpose AC loads and utility connection. In addition, an interface, which regulates and adjusts the output voltage of the PV, is required because the output voltage of the PV system varies with various factors such as irradiation and temperature. Meanwhile, the power extracted from the PV system must be optimized based on the weather conditions to get the highest efficiency. A PV system is a combination of several components such as PV panels, energy storage system (ESS), and power conditioning units (PCUs). In PV systems, PCU is composed of power electronic converters along with special controls/algorithms, and it is responsible for various tasks including ESS charging, maximum power point tracking (MPPT), and DC–AC conversion. This article aims to describe a complete PV system architecture and design by following design steps: PV cell/module/array modeling, MPPT algorithms and implementation, design and control of DC/DC power electronic converters responsible for MPPT operation, and DC/AC inverter to supply general‐purpose AC loads. In addition to the provided basic concepts, an example of a complete stand‐alone PV system design including the system sizing has been presented. The designed system has been simulated, and detailed results are provided.

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PV panel model based on datasheet values

Cited by 656 publications

References 9 publications

GreenWare: Greening Cloud-Scale Data Centers to Maximize the Use of Renewable Energy

GreenWare: Greening Cloud-Scale Data Centers to Maximize the Use of Renewable Energy

An accurate method for the PV model identification based on a genetic algorithm and the interior-point method

Solar Energy Harvesting: Power Electronics Interfaces and Control

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