New Monitoring System for Photovoltaic Power Plants’ Management

Beránek, Václav; Olšan, T.; Libra, Martin; Poulek, V.; Sedláček, Jan; Dang, Minh-Quan; Tyukhov, I.I.

doi:10.3390/en11102495

Cited by 46 publications

(29 citation statements)

References 32 publications

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“…Except for a very limited number of countries where proactive and generous income policies were implemented at the beginning of the last decade, there has been a more recent mobilization of European governments in this sector, legislating on specific instruments to stimulate production decentralized and small scale power [8]. In the last few years, the solar energy has been gaining importance in the worldwide energy evolution tendency due to a constantly increasing efficiency and lifespan, the decrease of the price of PV modules and by being environmentally friendly [9]. Solar photovoltaic (PV) systems are steadily becoming one of the main three electricity sources in Europe [10].…”

Section: Introductionmentioning

confidence: 99%

Simulation and Comparison of Mathematical Models of PV Cells with Growing Levels of Complexity

Rodrigues¹,

Godina

Marzband

et al. 2018

Energies

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The amount of energy generated from a photovoltaic installation depends mainly on two factors—the temperature and solar irradiance. Numerous maximum power point tracking (MPPT) techniques have been developed for photovoltaic systems. The challenge is what method to employ in order to obtain optimum operating points (voltage and current) automatically at the maximum photovoltaic output power in most conditions. This paper is focused on the structural analysis of mathematical models of PV cells with growing levels of complexity. The main objective is to simulate and compare the characteristic current-voltage (I-V) and power-voltage (P-V) curves of equivalent circuits of the ideal PV cell model and, with one and with two diodes, that is, equivalent circuits with five and seven parameters. The contribution of each parameter is analyzed in the particular context of a given model and then generalized through comparison to a more complex model. In this study the numerical simulation of the models is used intensively and extensively. The approach utilized to model the equivalent circuits permits an adequate simulation of the photovoltaic array systems by considering the compromise between the complexity and accuracy. By utilizing the Newton–Raphson method the studied models are then employed through the use of Matlab/Simulink. Finally, this study concludes with an analysis and comparison of the evolution of maximum power observed in the models.

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Section: Introductionmentioning

confidence: 99%

Simulation and Comparison of Mathematical Models of PV Cells with Growing Levels of Complexity

Rodrigues¹,

Godina

Marzband

et al. 2018

Energies

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“…Further development of the SAM software, and the validation of this software vs. high-frequency data collected on the existing facilities, both for energy production, and weather conditions, is considered a necessary step before further progressing the design of novel concentrating solar power facilities by using this software. The relevance of good data monitoring is also discussed in [45]. The performance of the solar plant may change for any reason, linked to the variability of the resource, for example [46], as well as operation, maintenance, grid conditions, and many other causes, that need to be addressed.…”

Section: Discussionmentioning

confidence: 99%

Validation of SAM Modeling of Concentrated Solar Power Plants

2020

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The paper proposes the validation of the latest System Advisor Model (SAM) vs. the experimental data for concentrated solar power energy facilities. Both parabolic trough, and solar tower, are considered, with and without thermal energy storage. The 250 MW parabolic trough facilities of Genesis, Mojave, and Solana, and the 110 MW solar tower facility of Crescent Dunes, all in the United States South-West, are modeled. The computed monthly average capacity factors for the average weather year are compared with the experimental data measured since the start of the operation of the facilities. While much higher sampling frequencies are needed for proper validation, as monthly averaging dramatically filters out differences between experiments and simulations, computational results are relatively close to measured values for the parabolic trough, and very far from for solar tower systems. The thermal energy storage is also introducing additional inaccuracies. It is concluded that the code needs further development, especially for the solar field and receiver of the solar tower modules, and the thermal energy storage. Validation of models and sub-models vs. high-frequency data collected on existing facilities, for both energy production, power plant parameters, and weather conditions, is a necessary step before using the code for designing novel facilities. National Electricity Market data tell us that even installed capacity of 18.1 GW sometimes is not enough to deliver 0.2 GW to the grid in cases, for example, of low wind after sunset), the coupling of concentrating solar power with molten salt thermal energy storage is extremely attractive, as it may produce fully dispatchable energy.While the concentrating solar power solar tower coupled with thermal energy storage has so far performed badly in the real world, the concentrating solar power parabolic trough with thermal energy storage is already delivering good performances, [8][9][10][11][12], even if still less than the predictions. Solar photovoltaic works with annual average ε about 0.27-0.29, but with much larger than the capacity factor high-frequency standard deviations, for coefficients of variability in excess of unity [15]. While in the virtual reality of model computations concentrating solar power with thermal energy storage may achieve much larger and more uniform ε, with average ε well in excess of 0.5, also addressing the issues of lack of production during night times, and drastically reduced production with clouds, with dramatically reduced standard deviations, the best real-world experience has so far delivered an annual average ε of about 0.36 (Solana). By contrast with photovoltaic, concentrating solar power, especially the solar tower, suffers from cloud coverage, and this may considerably affect the accuracy of the models.As concluded in [8][9][10][11][12], the concentrating solar power parabolic trough has the potential, once a satisfactory design will be industrialized, to deliver the same or better than photovoltaic costs of electricity with...

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“…The development of an innovative solar monitoring system was presented in [19]. The system aimed at measuring the main parameters and characteristics of solar plants; collecting, diagnosing and processing data.…”

Section: Brief Overview Of the Contributions To This Special Issuementioning

confidence: 99%

Special Issue “Intelligent Control in Energy Systems”

Dounis

2019

Energies

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The editor of this special issue on "Intelligent Control in Energy Systems" have made an attempt to publish a book containing original technical articles addressing various elements of intelligent control in energy systems. The response to our call had 60 submissions, of which 27 were published submissions and 33 were rejections. This book contains 27 technical articles and one editorial. All have been written by authors from 15 countries (and Czech Republic), which elaborated several aspects of intelligent control in energy systems. It covers a broad range of topics including fuzzy PID in automotive fuel cell and MPPT tracking, neural network for fuel cell control and dynamic optimization of energy management, adaptive control on power systems, hierarchical Petri Nets in microgrid management, model predictive control for electric vehicle battery and frequency regulation in HVAC systems, deep learning for power consumption forecasting, decision tree for wind systems, risk analysis for demand side management, finite state automata for HVAC control, robust µ-synthesis for microgrid, and neuro-fuzzy systems in energy storage.

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New Monitoring System for Photovoltaic Power Plants’ Management

Cited by 46 publications

References 32 publications

Simulation and Comparison of Mathematical Models of PV Cells with Growing Levels of Complexity

Simulation and Comparison of Mathematical Models of PV Cells with Growing Levels of Complexity

Validation of SAM Modeling of Concentrated Solar Power Plants

Special Issue “Intelligent Control in Energy Systems”

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